Monthly model - Regions
Tanya Rogers
4/22/2022
library(dplyr)
library(lavaan)
library(DiagrammeR)
library(ggplot2)
library(tidyr)
library(lubridate)
source("functions/table_funcs.R")
# For saving SEM diagrams:
library(purrr)
library(DiagrammeRsvg)
library(rsvg)
library(png)
library(grid)
library(ggpubr)
library(patchwork)Import data
combined=read.csv("data/monthly_averages/monthly_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region_monthly.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1995) %>% arrange(Region,Year,Month)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("Far West","West","North","South")
regionorder_pub=c("San Pablo","Suisun","Sacramento","San Joaquin")
focaldata=focaldata%>%
mutate(decyear=year+(month-1)/12)
focaldata = focaldata %>%
mutate(tzoop=hcope+clad+mysid+pcope+rotif_m,
tzoop_e=hcope_e+clad_e+mysid_e+pcope_e+rotif_e,
hzoop=hcope+clad+rotif_m,
hzoop_e=hcope_e+clad_e+rotif_e,
pzoop=mysid+pcope,
pzoop_e=mysid_e+pcope_e,
turbid=-secchi)
fvars=c(fvars,"tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid")
cnames=rbind(cnames,data.frame(Longname = c("Total zooplankton biomass",
"Total zooplankton energy",
"Herbivorous zooplankton biomass",
"Herbivorous zooplankton energy",
"Predatory zooplankton biomass",
"Predatory zooplankton energy",
"Turbidity"),
Shortname=c("tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid"),
Diagramname=c("total zooplankton",
"total zooplankton\nenergy",
"herbivorous\nzooplankton",
"herbivorous\nzooplankton\nenergy",
"predatory\nzooplankton",
"predatory\nzooplankton\nenergy",
"turbidity"),
Datacolumn=NA,Log=c(rep("yes",6),"no"),
Color=c("black","black","#ED7D31","#ED7D31","#7030A0",
"#7030A0","#4472C4"),
Definition = c("summed zooplankton biomass",
"summed zooplankton energy",
"summed herbivorous zooplankton biomass",
"summed herbivorous zooplankton energy",
"summed predatory zooplankton biomass",
"summed predatory zooplankton energy",
"negative secchi depth")))
#focal variables
varnames=c("temp","flow","turbid","din","chla","hzoop","pzoop","potam","corbic","estfish_bsmt","sside","cent","sbass1_bsmt","marfish_bsmt","hcope","clad","amphi_m","pcope","mysid","rotif_m")
#labels for lagged vars
cnameslag=cnames
cnameslag$Shortname=paste0(cnameslag$Shortname,"_1")
cnameslag$Diagramname=paste(cnameslag$Diagramname,"(t-1)")
cnameslag=rbind(cnames,cnameslag)
#labeld for growth rate
cnamesgr=cnames
cnamesgr$Shortname=paste0(cnamesgr$Shortname,"_gr")
cnamesgr$Diagramname=paste(cnamesgr$Diagramname,"(gr)")
cnameslag=rbind(cnameslag,cnamesgr)
source("analysis/semDiagramFunctions.r")Data prep
Log transform, scale.
Within and across regions.
Create set with regional monthly means removed.
#log transform
logvars=fvars[cnames$Log=="yes"]
logtrans=function(x) {
x2=x[which(!is.na(x))]
if(any(x2==0)) {log(x+min(x2[which(x2>0)],na.rm=T))}
else {log(x)}
}
focaldatalog = focaldata %>%
mutate(flow=flow-min(flow,na.rm=T)) %>% #get rid of negative flow values
mutate_at(logvars,logtrans) %>%
group_by(region) %>%
mutate_at(logvars,list("gr"=function(x) {c(NA,diff(x))})) %>%
ungroup()
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:3)]
#scaled within regions
fdr=fdr0 %>%
group_by(region) %>%
#scale
mutate_at(tvars,scale) %>%
#lag
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()
#scaled within regions, remove monthly means
fdr_ds=fdr %>%
group_by(region,month) %>%
mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>%
mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>%
ungroup() %>%
#lag
group_by(region) %>%
mutate_at(tvars,scale) %>%
mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
ungroup() %>%
as.data.frame()
#scaled across regions
# fdr1=fdr0 %>%
# #scale
# mutate_at(tvars,scale) %>%
# #lag
# group_by(region) %>%
# mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
# ungroup() %>%
# as.data.frame()
#scaled across regions, monthly means removed
# fdr1_ds=fdr1 %>%
# group_by(region,month) %>%
# mutate_at(tvars,list("mm"=function(x) {mean(x,na.rm = T)})) %>%
# mutate_at(tvars,function(x) {x-mean(x,na.rm = T)}) %>%
# ungroup() %>%
# #lag
# group_by(region) %>%
# mutate_at(tvars,list("1"=lag,"2"=function(x) {lag(x,2)})) %>%
# ungroup() %>%
# as.data.frame()Data availability
Exclude individual zooplankton plankton groups from zooplankton model if rare (95% of values in a region are less than the across site mean, or more than 10% of values in a region are zeros).
sside and cent have no data in FW and W.
marfish and clams excluded if 95% of values in a region are less than the across site mean, though this results in marfish being excluded from W.
FW: exclude clad, mysid, corbic, sside/cent
W: exclude clad, corbic, marfish, sside/cent
N: exclude clad, potam, marfish
S: exclude mysid, potam, marfish
dataavail=focaldata %>%
gather(var, value, 4:length(fvars)) %>%
group_by(var) %>%
mutate(varmean=mean(value, na.rm=T)) %>% ungroup() %>%
group_by(region, var) %>%
summarize(
propmissing=length(which(is.na(value)))/length(value),
propzeros=length(which(value==0))/length(which(!is.na(value))),
exclude=ifelse(quantile(value,probs = 0.95, na.rm = T)<mean(varmean),T,F)) %>%
as.data.frame()## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.#these variables should not be used (too many zeros)
filter(dataavail,propzeros>0.1 | exclude) %>% filter(var %in% c("mysid","hcope","pcope","rotif_m","clad"))## region var propmissing propzeros exclude
## 1 Far West clad 0.141025641 0.72388060 TRUE
## 2 Far West mysid 0.137820513 0.21933086 TRUE
## 3 North clad 0.012820513 0.11363636 FALSE
## 4 South mysid 0.006410256 0.08709677 TRUE
## 5 West clad 0.009615385 0.20064725 FALSEfilter(dataavail,exclude) %>% filter(var %in% c("marfish_bsmt","potam","corbic"))## region var propmissing propzeros exclude
## 1 Far West corbic 0.1410256 1.0000000 TRUE
## 2 North marfish_bsmt 0.1891026 0.9762846 TRUE
## 3 North potam 0.1378205 0.1486989 TRUE
## 4 South marfish_bsmt 0.1826923 1.0000000 TRUE
## 5 South potam 0.1378205 0.9702602 TRUE
## 6 West corbic 0.1378205 0.8066914 TRUE
## 7 West marfish_bsmt 0.1666667 0.2192308 TRUETime series plots
Other useful plots
Breakdown of total zooplankton biomass.
## Warning: Removed 272 rows containing non-finite values (`stat_align()`).
Correlation between biomass and energy.
for(i in 1:length(regions)) {
dtemp=filter(fdr,region==regions[i])
print(regions[i])
print(cor(dtemp$tzoop,dtemp$tzoop_e,use = "p"))
print(cor(dtemp$hzoop,dtemp$hzoop_e,use = "p"))
print(cor(dtemp$pzoop,dtemp$pzoop_e,use = "p"))
}## [1] "Far West"
## [,1]
## [1,] 0.9941835
## [,1]
## [1,] 0.9945149
## [,1]
## [1,] 0.9996106
## [1] "North"
## [,1]
## [1,] 0.9938912
## [,1]
## [1,] 0.9903659
## [,1]
## [1,] 0.999494
## [1] "South"
## [,1]
## [1,] 0.9955739
## [,1]
## [1,] 0.9952125
## [,1]
## [1,] 0.99925
## [1] "West"
## [,1]
## [1,] 0.9797348
## [,1]
## [1,] 0.9374501
## [,1]
## [1,] 0.9983814Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
Other notes:
Detrended fish indices are NOT correlated in S!
Nitrate and ammonia are positively correlated, max at lag 0 all
regions.
Nitrate and dophos are positively correlated, max at lag 0 all
regions.
Ammonia and dophos are positively correlated, lag 0 for FW and S,
ammonia lags dphos by 3 months in W and N.
Chla nitrate neg correlated, lag 0.
Chla ammonia neg correlated, lag 0.
Chla dophos relationship unclear.
High flow 2-4 month prev = high chla
Hcope lags chla by 1, positive, except FW.
Clad seem to precede chla by 2, positive.
Amphi relationship unclear, prob bc not eating chla in water
column.
In N and W, chla lags potam, negative. The opposite in W.
Mysid and hcope postive, lag 0.
In S and W, hcope lags pcope, negative.
Flow-Turbidity-Estuarine Fishes Relationship
Scatterplots showing relationship between flow and turbidity and fish by region to accompany the discussion regarding flow-fish relationship. This shows how flow transports estuarine fishes downstream at a monthly scale, but turbidity-flow correlation swamps out the positive flow-fish relationship in San Pablo Bay.
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 12 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 12 rows containing missing values (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 12 rows containing non-finite values (`stat_smooth()`).
## Removed 12 rows containing missing values (`geom_point()`).## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 223 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 223 rows containing missing values (`geom_point()`).
## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 223 rows containing non-finite values (`stat_smooth()`).
## Removed 223 rows containing missing values (`geom_point()`).Make a definition table
Make a table similar to table 1 in Mac Nally et al. 2010 in Eco. Apps.
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `across(everything(), .f = mean, na.rm = TRUE)`.
## ℹ In group 1: `year_month = 1995-01-01`.
## Caused by warning:
## ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
## Supply arguments directly to `.fns` through an anonymous function instead.
##
## # Previously
## across(a:b, mean, na.rm = TRUE)
##
## # Now
## across(a:b, \(x) mean(x, na.rm = TRUE))## `summarise()` has grouped output by 'Shortname', 'Longname'. You can override
## using the `.groups` argument.| Variable | Years (missing years) | Definition |
|---|---|---|
| Ammonia | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Amphipods catch | 1997‒2020 (18) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Amphipods mass | 1997‒2020 (18) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Centrarchids DJFMP | 1995‒2020 (3) | year-round - beach seines - biomass |
| Phytoplankton | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Cladocera | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Cladocera catch | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Cladocera energy | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Corbicula | 1997‒2020 (18) | from the Environmental Monitoring Program (EMP) Benthic Survey at DWR - year-round |
| Dissolved Inorganic Nitrogen | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Dissolved Orthophos | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Estuarine fishes BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass of estuarine pelagic forage fishes |
| Estuarine fishes BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass of estuarine pelagic forage fishes |
| Flow | 1995‒2020 (0) | year-round - mean Delta outflow (water leaving the Delta to the Bay) |
| Herbivorous copepods | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Herbivorous copepods catch | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Herbivorous copepods energy | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Herbivorous zooplankton biomass | 1995‒2020 (2) | summed herbivorous zooplankton biomass |
| Herbivorous zooplankton energy | 1995‒2020 (2) | summed herbivorous zooplankton energy |
| Longfin smelt BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass |
| Longfin smelt BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass |
| Marine fishes BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass |
| Marine fishes BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass |
| Mysids | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Mysids catch | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Mysids energy | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Nitrate and Nitrite | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Predatory copepods | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Predatory copepods catch | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Predatory copepods energy | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Potamocorbula | 1997‒2020 (18) | from the Environmental Monitoring Program (EMP) Benthic Survey at DWR - year-round |
| Predatory zooplankton biomass | 1995‒2020 (2) | summed predatory zooplankton biomass |
| Predatory zooplankton energy | 1995‒2020 (2) | summed predatory zooplankton energy |
| Rotifers catch | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Rotifers energy | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Rotifers mass | 1995‒2020 (2) | from 5 different sources - year-round - see Bashevkin et al. 2022 |
| Striped bass age 1+ BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass of age 1+ individuals |
| Striped bass age 1+ BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass of age 1+ individuals |
| Striped bass BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass of age 0 individuals |
| Striped bass BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass of age 0 individuals |
| Secchi | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Delta smelt BSMT | 1995‒2020 (38) | year-round - midwater trawl - biomass |
| Delta smelt BSOT | 1995‒2020 (18) | year-round - otter trawl - biomass |
| Mississippi silverside DJFMP | 1995‒2020 (3) | year-round - beach seines - biomass |
| Temperature | 1995‒2020 (0) | from the Discrete Environmental Monitoring Program (EMP) at DWR - year-round |
| Turbidity | 1995‒2020 (0) | negative secchi depth |
| Total zooplankton biomass | 1995‒2020 (2) | summed zooplankton biomass |
| Total zooplankton energy | 1995‒2020 (2) | summed zooplankton energy |
Exploratory plots
Fish-centered model (upper trophic level aggregates)
modFW='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop~pb1*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*marfish_bsmt_1+ft2*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modW='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modN='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2+ft3^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modS='hzoop~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt~fb1*chla_1+fb2*hzoop_1+fb3*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2+fb3^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2+ft3^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 15 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Used Total
## Number of observations 191 312
##
## Model Test User Model:
##
## Test statistic 7.526
## Degrees of freedom 7
## P-value (Chi-square) 0.376
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop ~
## chla_1 (hb1) -0.002 0.076 -0.022 0.982 -0.002 -0.001
## hzoop_1 (hs1) 0.315 0.065 4.851 0.000 0.315 0.326
## pzoop_1 (ht1) 0.062 0.062 0.991 0.322 0.062 0.065
## potam_1 (ht2) -0.189 0.058 -3.246 0.001 -0.189 -0.212
## estfs__1 (ht3) -0.176 0.069 -2.552 0.011 -0.176 -0.175
## flow (ha1) -0.021 0.075 -0.272 0.785 -0.021 -0.020
## temp (ha2) -0.080 0.072 -1.110 0.267 -0.080 -0.076
## turbid (ha3) 0.040 0.080 0.501 0.616 0.040 0.036
## pzoop ~
## hzoop_1 (pb1) 0.055 0.067 0.811 0.417 0.055 0.054
## pzoop_1 (ps1) 0.344 0.065 5.278 0.000 0.344 0.348
## potam_1 (pt1) -0.102 0.060 -1.697 0.090 -0.102 -0.110
## estfs__1 (pt2) -0.031 0.072 -0.428 0.668 -0.031 -0.029
## flow (pa1) 0.082 0.078 1.050 0.294 0.082 0.076
## temp (pa2) -0.020 0.075 -0.272 0.786 -0.020 -0.019
## turbid (pa3) 0.252 0.084 3.005 0.003 0.252 0.215
## estfish_bsmt ~
## hzoop_1 (fb1) -0.178 0.056 -3.151 0.002 -0.178 -0.192
## pzoop_1 (fb2) 0.116 0.056 2.076 0.038 0.116 0.128
## estfs__1 (fs1) 0.343 0.064 5.369 0.000 0.343 0.355
## flow (fa1) 0.092 0.068 1.349 0.177 0.092 0.092
## temp (fa2) -0.032 0.065 -0.497 0.619 -0.032 -0.032
## turbid (fa3) 0.246 0.073 3.390 0.001 0.246 0.230
## mrfsh__1 (ft1) 0.001 0.064 0.019 0.985 0.001 0.001
## sbss1__1 (ft2) -0.018 0.065 -0.285 0.776 -0.018 -0.019
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .pzoop -0.034 0.058 -0.586 0.558 -0.034 -0.042
## .estfish_bsmt -0.045 0.050 -0.905 0.365 -0.045 -0.066
## .pzoop ~~
## .estfish_bsmt -0.088 0.052 -1.684 0.092 -0.088 -0.123
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop 0.773 0.079 9.772 0.000 0.773 0.754
## .pzoop 0.842 0.086 9.772 0.000 0.842 0.750
## .estfish_bsmt 0.606 0.062 9.772 0.000 0.606 0.641
##
## R-Square:
## Estimate
## hzoop 0.246
## pzoop 0.250
## estfish_bsmt 0.359
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.002 0.076 0.022 0.982 0.002 0.001
## hs 0.315 0.065 4.851 0.000 0.315 0.326
## ht 0.266 0.058 4.617 0.000 0.266 0.283
## ha 0.092 0.072 1.283 0.199 0.092 0.086
## pb 0.055 0.067 0.811 0.417 0.055 0.054
## ps 0.344 0.065 5.278 0.000 0.344 0.348
## pt 0.107 0.058 1.837 0.066 0.107 0.113
## pa 0.265 0.073 3.627 0.000 0.265 0.229
## fb 0.212 0.059 3.618 0.000 0.212 0.231
## fs 0.343 0.064 5.369 0.000 0.343 0.355
## ft 0.018 0.064 0.286 0.775 0.018 0.019
## fa 0.265 0.062 4.263 0.000 0.265 0.249summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Used Total
## Number of observations 210 312
##
## Model Test User Model:
##
## Test statistic 2.513
## Degrees of freedom 4
## P-value (Chi-square) 0.642
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop ~
## chla_1 (hb1) 0.075 0.063 1.187 0.235 0.075 0.073
## hzoop_1 (hs1) 0.360 0.072 5.019 0.000 0.360 0.349
## pzoop_1 (ht1) 0.034 0.064 0.529 0.597 0.034 0.033
## potam_1 (ht2) -0.232 0.069 -3.369 0.001 -0.232 -0.216
## estfs__1 (ht3) -0.018 0.063 -0.287 0.774 -0.018 -0.017
## flow (ha1) 0.197 0.070 2.833 0.005 0.197 0.185
## temp (ha2) 0.025 0.061 0.414 0.679 0.025 0.025
## turbid (ha3) -0.202 0.068 -2.974 0.003 -0.202 -0.187
## pzoop ~
## chla_1 (pb1) 0.185 0.060 3.065 0.002 0.185 0.186
## hzoop_1 (pb2) 0.112 0.069 1.617 0.106 0.112 0.112
## pzoop_1 (ps1) 0.414 0.062 6.675 0.000 0.414 0.411
## potam_1 (pt1) -0.089 0.066 -1.347 0.178 -0.089 -0.085
## estfs__1 (pt2) 0.034 0.061 0.569 0.570 0.034 0.034
## flow (pa1) -0.106 0.067 -1.577 0.115 -0.106 -0.103
## temp (pa2) 0.187 0.059 3.179 0.001 0.187 0.190
## turbid (pa3) 0.037 0.066 0.561 0.575 0.037 0.035
## estfish_bsmt ~
## hzoop_1 (fb1) 0.135 0.071 1.896 0.058 0.135 0.132
## pzoop_1 (fb2) -0.079 0.069 -1.147 0.251 -0.079 -0.077
## estfs__1 (fs1) 0.175 0.072 2.430 0.015 0.175 0.169
## flow (fa1) -0.222 0.074 -2.985 0.003 -0.222 -0.210
## temp (fa2) 0.017 0.064 0.269 0.788 0.017 0.017
## turbid (fa3) 0.248 0.071 3.503 0.000 0.248 0.232
## sbss1__1 (ft1) 0.233 0.069 3.382 0.001 0.233 0.224
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .pzoop 0.179 0.049 3.660 0.000 0.179 0.261
## .estfish_bsmt -0.078 0.053 -1.483 0.138 -0.078 -0.103
## .pzoop ~~
## .estfish_bsmt 0.136 0.052 2.633 0.008 0.136 0.185
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop 0.708 0.069 10.247 0.000 0.708 0.669
## .pzoop 0.664 0.065 10.247 0.000 0.664 0.666
## .estfish_bsmt 0.813 0.079 10.247 0.000 0.813 0.787
##
## R-Square:
## Estimate
## hzoop 0.331
## pzoop 0.334
## estfish_bsmt 0.213
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.075 0.063 1.187 0.235 0.075 0.073
## hs 0.360 0.072 5.019 0.000 0.360 0.349
## ht 0.235 0.069 3.401 0.001 0.235 0.219
## ha 0.284 0.076 3.748 0.000 0.284 0.264
## pb 0.216 0.054 4.041 0.000 0.216 0.217
## ps 0.414 0.062 6.675 0.000 0.414 0.411
## pt 0.095 0.063 1.518 0.129 0.095 0.092
## pa 0.218 0.063 3.477 0.001 0.218 0.219
## fb 0.156 0.080 1.956 0.050 0.156 0.153
## fs 0.175 0.072 2.430 0.015 0.175 0.169
## ft 0.233 0.069 3.382 0.001 0.233 0.224
## fa 0.333 0.082 4.058 0.000 0.333 0.314summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 13 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 31
##
## Used Total
## Number of observations 193 312
##
## Model Test User Model:
##
## Test statistic 8.253
## Degrees of freedom 8
## P-value (Chi-square) 0.409
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop ~
## chla_1 (hb1) 0.037 0.066 0.556 0.578 0.037 0.037
## hzoop_1 (hs1) 0.216 0.070 3.067 0.002 0.216 0.212
## pzoop_1 (ht1) 0.040 0.073 0.544 0.586 0.040 0.039
## corbic_1 (ht2) 0.005 0.068 0.071 0.944 0.005 0.005
## estfs__1 (ht3) -0.079 0.068 -1.160 0.246 -0.079 -0.084
## flow (ha1) 0.242 0.075 3.200 0.001 0.242 0.241
## temp (ha2) 0.107 0.066 1.624 0.104 0.107 0.111
## turbid (ha3) 0.189 0.070 2.720 0.007 0.189 0.185
## pzoop ~
## chla_1 (pb1) 0.247 0.059 4.195 0.000 0.247 0.248
## hzoop_1 (pb2) 0.176 0.063 2.812 0.005 0.176 0.172
## pzoop_1 (ps1) 0.232 0.065 3.585 0.000 0.232 0.229
## corbic_1 (pt1) 0.031 0.060 0.516 0.606 0.031 0.030
## estfs__1 (pt2) -0.060 0.060 -0.990 0.322 -0.060 -0.064
## flow (pa1) -0.436 0.067 -6.515 0.000 -0.436 -0.436
## temp (pa2) 0.079 0.058 1.349 0.177 0.079 0.082
## turbid (pa3) 0.004 0.062 0.063 0.950 0.004 0.004
## estfish_bsmt ~
## hzoop_1 (fb1) 0.152 0.074 2.057 0.040 0.152 0.139
## pzoop_1 (fb2) 0.012 0.075 0.162 0.871 0.012 0.011
## estfs__1 (fs1) 0.130 0.071 1.824 0.068 0.130 0.129
## flow (fa1) -0.468 0.078 -6.002 0.000 -0.468 -0.435
## temp (fa2) -0.016 0.067 -0.238 0.812 -0.016 -0.015
## turbid (fa3) 0.066 0.073 0.911 0.362 0.066 0.060
## sside_1 (ft1) -0.018 0.068 -0.263 0.793 -0.018 -0.017
## cent_1 (ft2) -0.132 0.069 -1.914 0.056 -0.132 -0.125
## sbss1__1 (ft3) 0.095 0.071 1.332 0.183 0.095 0.091
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .pzoop 0.177 0.053 3.347 0.001 0.177 0.248
## .estfish_bsmt -0.059 0.059 -1.002 0.317 -0.059 -0.072
## .pzoop ~~
## .estfish_bsmt -0.002 0.052 -0.046 0.964 -0.002 -0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop 0.803 0.082 9.823 0.000 0.803 0.816
## .pzoop 0.632 0.064 9.823 0.000 0.632 0.642
## .estfish_bsmt 0.830 0.085 9.823 0.000 0.830 0.731
##
## R-Square:
## Estimate
## hzoop 0.184
## pzoop 0.358
## estfish_bsmt 0.269
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.037 0.066 0.556 0.578 0.037 0.037
## hs 0.216 0.070 3.067 0.002 0.216 0.212
## ht 0.088 0.075 1.171 0.241 0.088 0.093
## ha 0.325 0.071 4.605 0.000 0.325 0.323
## pb 0.303 0.058 5.211 0.000 0.303 0.302
## ps 0.232 0.065 3.585 0.000 0.232 0.229
## pt 0.067 0.063 1.074 0.283 0.067 0.071
## pa 0.443 0.065 6.848 0.000 0.443 0.443
## fb 0.152 0.072 2.107 0.035 0.152 0.139
## fs 0.130 0.071 1.824 0.068 0.130 0.129
## ft 0.163 0.063 2.587 0.010 0.163 0.156
## fa 0.473 0.080 5.935 0.000 0.473 0.440summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 14 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
##
## Used Total
## Number of observations 199 312
##
## Model Test User Model:
##
## Test statistic 5.565
## Degrees of freedom 7
## P-value (Chi-square) 0.591
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop ~
## chla_1 (hb1) 0.258 0.072 3.575 0.000 0.258 0.241
## hzoop_1 (hs1) 0.217 0.069 3.140 0.002 0.217 0.208
## pzoop_1 (ht1) -0.030 0.076 -0.391 0.695 -0.030 -0.027
## corbic_1 (ht2) 0.073 0.070 1.046 0.295 0.073 0.068
## estfs__1 (ht3) 0.003 0.069 0.048 0.961 0.003 0.003
## flow (ha1) 0.213 0.071 2.979 0.003 0.213 0.195
## temp (ha2) 0.171 0.066 2.579 0.010 0.171 0.169
## turbid (ha3) -0.042 0.068 -0.624 0.533 -0.042 -0.041
## pzoop ~
## chla_1 (pb1) 0.289 0.061 4.755 0.000 0.289 0.290
## hzoop_1 (pb2) 0.151 0.058 2.598 0.009 0.151 0.156
## pzoop_1 (ps1) 0.320 0.064 5.004 0.000 0.320 0.317
## corbic_1 (pt1) -0.062 0.058 -1.065 0.287 -0.062 -0.062
## estfs__1 (pt2) 0.016 0.058 0.274 0.784 0.016 0.017
## flow (pa1) -0.134 0.060 -2.216 0.027 -0.134 -0.131
## temp (pa2) -0.035 0.056 -0.627 0.531 -0.035 -0.037
## turbid (pa3) 0.095 0.057 1.666 0.096 0.095 0.099
## estfish_bsmt ~
## chla_1 (fb1) 0.144 0.071 2.034 0.042 0.144 0.139
## hzoop_1 (fb2) 0.144 0.068 2.103 0.035 0.144 0.142
## pzoop_1 (fb3) -0.052 0.074 -0.709 0.479 -0.052 -0.050
## estfs__1 (fs1) 0.190 0.068 2.785 0.005 0.190 0.194
## flow (fa1) -0.096 0.071 -1.359 0.174 -0.096 -0.091
## temp (fa2) -0.027 0.065 -0.422 0.673 -0.027 -0.028
## turbid (fa3) 0.210 0.071 2.935 0.003 0.210 0.211
## sside_1 (ft1) 0.055 0.065 0.859 0.390 0.055 0.056
## cent_1 (ft2) -0.073 0.067 -1.085 0.278 -0.073 -0.076
## sbss1__1 (ft3) 0.067 0.069 0.983 0.325 0.067 0.068
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop ~~
## .pzoop 0.117 0.051 2.300 0.021 0.117 0.165
## .estfish_bsmt 0.042 0.058 0.730 0.465 0.042 0.052
## .pzoop ~~
## .estfish_bsmt 0.131 0.049 2.647 0.008 0.131 0.191
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop 0.837 0.084 9.975 0.000 0.837 0.810
## .pzoop 0.597 0.060 9.975 0.000 0.597 0.664
## .estfish_bsmt 0.785 0.079 9.975 0.000 0.785 0.810
##
## R-Square:
## Estimate
## hzoop 0.190
## pzoop 0.336
## estfish_bsmt 0.190
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.258 0.072 3.575 0.000 0.258 0.241
## hs 0.217 0.069 3.140 0.002 0.217 0.208
## ht 0.079 0.072 1.095 0.274 0.079 0.074
## ha 0.276 0.071 3.914 0.000 0.276 0.261
## pb 0.326 0.057 5.686 0.000 0.326 0.329
## ps 0.320 0.064 5.004 0.000 0.320 0.317
## pt 0.064 0.057 1.110 0.267 0.064 0.064
## pa 0.168 0.062 2.706 0.007 0.168 0.169
## fb 0.210 0.070 2.974 0.003 0.210 0.205
## fs 0.190 0.068 2.785 0.005 0.190 0.194
## ft 0.114 0.065 1.750 0.080 0.114 0.116
## fa 0.232 0.076 3.045 0.002 0.232 0.231#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitS)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Upper trophic level aggregates")
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
upper_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="San Pablo",
manual_port_settings=TRUE)
upper_plot_far_west#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
upper_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="Suisun",
manual_port_settings=TRUE)
upper_plot_west#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
upper_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="Sacramento",
manual_port_settings=TRUE)
upper_plot_north#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
upper_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="San Joaquin",
manual_port_settings=TRUE)
upper_plot_southSave updated SEM diagrams
Total effects
ssFW=standardizedsolution(modfitFW) %>% mutate(region="San Pablo")
ssW=standardizedsolution(modfitW) %>% mutate(region="Suisun")
ssN=standardizedsolution(modfitN) %>% mutate(region="Sacramento")
ssS=standardizedsolution(modfitS) %>% mutate(region="San Joaquin")
ssut=rbind(ssFW,ssW,ssN,ssS) %>% filter(op==":=") %>% select(region,lhs,est.std:ci.upper) %>%
separate(lhs,c("variable","influence"), sep=1) %>%
mutate(variable=case_when(variable=="h" ~ "herbivorous\nzooplankton",
variable=="p" ~ "predatory\nzooplankton",
variable=="f" ~ "estuarine\nfishes"),
influence=case_when(influence=="b" ~ "bottom-up",
influence=="t" ~ "top-down",
influence=="s" ~ "self-regulation",
influence=="a" ~ "environmental"),
region=factor(region, levels=regionorder_pub),
influence=factor(influence, levels=c("self-regulation","bottom-up","top-down","environmental")),
variable=factor(variable,levels=c("estuarine\nfishes","predatory\nzooplankton","herbivorous\nzooplankton")),
sig=ifelse(pvalue<0.05,"*",""))
uteffects<-ggplot(ssut,aes(x=influence,y=est.std)) +
facet_grid(variable~region) +
geom_errorbar(aes(ymin=ci.lower, ymax=ci.upper),width=0.5) +
geom_point() +
geom_text(aes(y=ci.upper+0.05, label=sig)) +
geom_hline(yintercept = 0) +
ggtitle("Monthly regional models: upper trophic level")+
theme_bw() +
theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1), plot.title = element_text(hjust = 0.5)) +
labs(y="total effect (standardized)")Phytoplankton-centered model (lower trophic level aggregates)
modFW='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
potam~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modW='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
potam~lb1*din_1+lb2*chla_1+lb3*hzoop_1+lb4*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2+lb4^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modN='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ct3*pzoop_1+ca1*flow+ca2*temp+ca3*turbid
corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2+ct3^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modS='din~ns1*din_1+nt1*chla+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
chla~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ca1*flow+ca2*temp+ca3*turbid
corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 14 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 234 312
##
## Model Test User Model:
##
## Test statistic 5.455
## Degrees of freedom 4
## P-value (Chi-square) 0.244
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din ~
## din_1 (ns1) 0.399 0.055 7.254 0.000 0.399 0.401
## chla (nt1) -0.217 0.060 -3.650 0.000 -0.217 -0.199
## hzoop_1 (nn1) 0.048 0.049 0.982 0.326 0.048 0.055
## pzoop_1 (nn2) -0.082 0.048 -1.690 0.091 -0.082 -0.095
## potam_1 (nn3) -0.015 0.047 -0.317 0.751 -0.015 -0.018
## flow (na1) -0.163 0.056 -2.890 0.004 -0.163 -0.176
## temp (na2) -0.110 0.053 -2.086 0.037 -0.110 -0.122
## turbid (na3) 0.121 0.056 2.141 0.032 0.121 0.130
## chla ~
## din_1 (cb1) 0.012 0.061 0.199 0.842 0.012 0.013
## chla_1 (cs1) 0.255 0.064 3.977 0.000 0.255 0.260
## hzoop_1 (ct1) -0.021 0.052 -0.405 0.686 -0.021 -0.026
## potam_1 (ct2) -0.021 0.050 -0.413 0.680 -0.021 -0.027
## flow (ca1) 0.059 0.058 1.015 0.310 0.059 0.070
## temp (ca2) 0.127 0.056 2.263 0.024 0.127 0.153
## turbid (ca3) -0.045 0.059 -0.752 0.452 -0.045 -0.053
## potam ~
## chla_1 (lb1) 0.073 0.060 1.207 0.228 0.073 0.057
## hzoop_1 (lb2) -0.075 0.051 -1.472 0.141 -0.075 -0.073
## pzoop_1 (lb3) -0.162 0.050 -3.242 0.001 -0.162 -0.160
## potam_1 (ls1) 0.629 0.048 13.015 0.000 0.629 0.629
## flow (la1) 0.113 0.057 1.973 0.049 0.113 0.103
## temp (la2) -0.042 0.054 -0.774 0.439 -0.042 -0.039
## turbid (la3) 0.037 0.058 0.634 0.526 0.037 0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din ~~
## .potam -0.053 0.036 -1.460 0.144 -0.053 -0.096
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din 0.532 0.049 10.817 0.000 0.532 0.680
## .chla 0.596 0.055 10.817 0.000 0.596 0.911
## .potam 0.568 0.053 10.817 0.000 0.568 0.517
##
## R-Square:
## Estimate
## din 0.320
## chla 0.089
## potam 0.483
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.399 0.055 7.254 0.000 0.399 0.401
## nt 0.217 0.060 3.650 0.000 0.217 0.199
## nn 0.096 0.050 1.909 0.056 0.096 0.112
## na 0.231 0.061 3.770 0.000 0.231 0.250
## cb 0.012 0.061 0.199 0.842 0.012 0.013
## cs 0.255 0.064 3.977 0.000 0.255 0.260
## ct 0.029 0.056 0.526 0.599 0.029 0.038
## ca 0.147 0.058 2.532 0.011 0.147 0.176
## lb 0.193 0.050 3.881 0.000 0.193 0.185
## ls 0.629 0.048 13.015 0.000 0.629 0.629
## la 0.126 0.049 2.567 0.010 0.126 0.115summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 12 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Used Total
## Number of observations 257 312
##
## Model Test User Model:
##
## Test statistic 5.119
## Degrees of freedom 3
## P-value (Chi-square) 0.163
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din ~
## din_1 (ns1) 0.444 0.046 9.626 0.000 0.444 0.443
## chla (nt1) -0.133 0.041 -3.223 0.001 -0.133 -0.129
## hzoop_1 (nn1) -0.004 0.044 -0.098 0.922 -0.004 -0.004
## pzoop_1 (nn2) 0.033 0.041 0.824 0.410 0.033 0.035
## potam_1 (nn3) 0.037 0.043 0.857 0.391 0.037 0.038
## flow (na1) -0.426 0.046 -9.197 0.000 -0.426 -0.425
## temp (na2) 0.036 0.039 0.932 0.351 0.036 0.037
## turbid (na3) 0.116 0.042 2.759 0.006 0.116 0.119
## chla ~
## din_1 (cb1) -0.123 0.070 -1.754 0.080 -0.123 -0.126
## chla_1 (cs1) 0.149 0.065 2.282 0.022 0.149 0.150
## hzoop_1 (ct1) 0.065 0.063 1.024 0.306 0.065 0.070
## potam_1 (ct2) 0.013 0.065 0.200 0.842 0.013 0.014
## flow (ca1) 0.076 0.069 1.099 0.272 0.076 0.077
## temp (ca2) -0.060 0.058 -1.031 0.303 -0.060 -0.064
## turbid (ca3) -0.024 0.063 -0.371 0.710 -0.024 -0.025
## potam ~
## din_1 (lb1) 0.014 0.047 0.301 0.763 0.014 0.014
## chla_1 (lb2) -0.028 0.044 -0.648 0.517 -0.028 -0.027
## hzoop_1 (lb3) -0.006 0.045 -0.134 0.893 -0.006 -0.006
## pzoop_1 (lb4) 0.073 0.041 1.785 0.074 0.073 0.072
## potam_1 (ls1) 0.724 0.044 16.557 0.000 0.724 0.719
## flow (la1) -0.128 0.046 -2.778 0.005 -0.128 -0.124
## temp (la2) -0.007 0.039 -0.190 0.849 -0.007 -0.007
## turbid (la3) -0.077 0.042 -1.829 0.067 -0.077 -0.076
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din ~~
## .potam 0.010 0.023 0.441 0.660 0.010 0.027
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din 0.362 0.032 11.336 0.000 0.362 0.380
## .chla 0.815 0.072 11.336 0.000 0.815 0.902
## .potam 0.363 0.032 11.336 0.000 0.363 0.355
##
## R-Square:
## Estimate
## din 0.620
## chla 0.098
## potam 0.645
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.444 0.046 9.626 0.000 0.444 0.443
## nt 0.133 0.041 3.223 0.001 0.133 0.129
## nn 0.050 0.044 1.150 0.250 0.050 0.052
## na 0.443 0.048 9.175 0.000 0.443 0.443
## cb 0.123 0.070 1.754 0.080 0.123 0.126
## cs 0.149 0.065 2.282 0.022 0.149 0.150
## ct 0.066 0.066 0.998 0.318 0.066 0.071
## ca 0.099 0.067 1.478 0.139 0.099 0.103
## lb 0.080 0.042 1.902 0.057 0.080 0.079
## ls 0.724 0.044 16.557 0.000 0.724 0.719
## la 0.150 0.041 3.645 0.000 0.150 0.146summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 9 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Used Total
## Number of observations 255 312
##
## Model Test User Model:
##
## Test statistic 6.897
## Degrees of freedom 3
## P-value (Chi-square) 0.075
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din ~
## din_1 (ns1) 0.408 0.045 9.160 0.000 0.408 0.412
## chla (nt1) -0.160 0.036 -4.480 0.000 -0.160 -0.171
## hzoop_1 (nn1) 0.070 0.038 1.830 0.067 0.070 0.071
## pzoop_1 (nn2) 0.060 0.041 1.476 0.140 0.060 0.060
## corbic_1 (nn3) -0.035 0.038 -0.929 0.353 -0.035 -0.035
## flow (na1) -0.460 0.044 -10.513 0.000 -0.460 -0.458
## temp (na2) 0.042 0.038 1.106 0.269 0.042 0.043
## turbid (na3) 0.091 0.037 2.488 0.013 0.091 0.095
## chla ~
## din_1 (cb1) -0.169 0.079 -2.133 0.033 -0.169 -0.160
## chla_1 (cs1) 0.187 0.063 2.953 0.003 0.187 0.186
## hzoop_1 (ct1) -0.093 0.066 -1.407 0.160 -0.093 -0.088
## corbic_1 (ct2) -0.018 0.065 -0.278 0.781 -0.018 -0.017
## pzoop_1 (ct3) -0.102 0.071 -1.437 0.151 -0.102 -0.096
## flow (ca1) -0.072 0.075 -0.961 0.336 -0.072 -0.068
## temp (ca2) 0.141 0.064 2.208 0.027 0.141 0.136
## turbid (ca3) 0.131 0.063 2.087 0.037 0.131 0.128
## corbic ~
## chla_1 (lb1) 0.042 0.052 0.813 0.416 0.042 0.044
## hzoop_1 (lb2) 0.010 0.056 0.175 0.861 0.010 0.010
## pzoop_1 (lb3) -0.015 0.058 -0.265 0.791 -0.015 -0.015
## corbic_1 (ls1) 0.466 0.056 8.366 0.000 0.466 0.463
## flow (la1) 0.090 0.060 1.509 0.131 0.090 0.089
## temp (la2) -0.033 0.055 -0.591 0.555 -0.033 -0.033
## turbid (la3) -0.120 0.053 -2.271 0.023 -0.120 -0.124
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din ~~
## .corbic 0.000 0.030 0.008 0.994 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din 0.325 0.029 11.292 0.000 0.325 0.341
## .chla 0.962 0.085 11.292 0.000 0.962 0.888
## .corbic 0.713 0.063 11.292 0.000 0.713 0.738
##
## R-Square:
## Estimate
## din 0.659
## chla 0.112
## corbic 0.262
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.408 0.045 9.160 0.000 0.408 0.412
## nt 0.160 0.036 4.480 0.000 0.160 0.171
## nn 0.099 0.034 2.880 0.004 0.099 0.100
## na 0.470 0.044 10.660 0.000 0.470 0.469
## cb 0.169 0.079 2.133 0.033 0.169 0.160
## cs 0.187 0.063 2.953 0.003 0.187 0.186
## ct 0.139 0.061 2.277 0.023 0.139 0.131
## ca 0.206 0.065 3.153 0.002 0.206 0.198
## lb 0.046 0.055 0.831 0.406 0.046 0.048
## ls 0.466 0.056 8.366 0.000 0.466 0.463
## la 0.154 0.057 2.707 0.007 0.154 0.156summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 9 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 312
##
## Model Test User Model:
##
## Test statistic 23.734
## Degrees of freedom 4
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din ~
## din_1 (ns1) 0.406 0.056 7.270 0.000 0.406 0.407
## chla (nt1) -0.144 0.052 -2.768 0.006 -0.144 -0.143
## hzoop_1 (nn1) -0.016 0.056 -0.284 0.776 -0.016 -0.015
## pzoop_1 (nn2) 0.126 0.057 2.210 0.027 0.126 0.116
## corbic_1 (nn3) 0.048 0.053 0.901 0.368 0.048 0.047
## flow (na1) -0.052 0.054 -0.956 0.339 -0.052 -0.050
## temp (na2) 0.054 0.053 1.029 0.303 0.054 0.053
## turbid (na3) 0.252 0.058 4.329 0.000 0.252 0.248
## chla ~
## din_1 (cb1) 0.102 0.064 1.598 0.110 0.102 0.103
## chla_1 (cs1) 0.332 0.060 5.520 0.000 0.332 0.331
## hzoop_1 (ct1) 0.057 0.063 0.907 0.364 0.057 0.054
## corbic_1 (ct2) 0.098 0.060 1.632 0.103 0.098 0.097
## flow (ca1) -0.103 0.061 -1.686 0.092 -0.103 -0.100
## temp (ca2) 0.018 0.059 0.309 0.757 0.018 0.018
## turbid (ca3) -0.026 0.067 -0.394 0.694 -0.026 -0.026
## corbic ~
## chla_1 (lb1) 0.061 0.058 1.041 0.298 0.061 0.061
## hzoop_1 (lb2) -0.025 0.061 -0.403 0.687 -0.025 -0.024
## pzoop_1 (lb3) -0.044 0.063 -0.696 0.487 -0.044 -0.041
## corbic_1 (ls1) 0.315 0.058 5.473 0.000 0.315 0.318
## flow (la1) 0.082 0.058 1.407 0.160 0.082 0.081
## temp (la2) -0.078 0.058 -1.355 0.176 -0.078 -0.078
## turbid (la3) 0.184 0.058 3.184 0.001 0.184 0.185
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din ~~
## .corbic -0.019 0.046 -0.402 0.688 -0.019 -0.025
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din 0.680 0.060 11.314 0.000 0.680 0.650
## .chla 0.880 0.078 11.314 0.000 0.880 0.852
## .corbic 0.811 0.072 11.314 0.000 0.811 0.808
##
## R-Square:
## Estimate
## din 0.350
## chla 0.148
## corbic 0.192
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.406 0.056 7.270 0.000 0.406 0.407
## nt 0.144 0.052 2.768 0.006 0.144 0.143
## nn 0.136 0.058 2.338 0.019 0.136 0.126
## na 0.263 0.060 4.414 0.000 0.263 0.258
## cb 0.102 0.064 1.598 0.110 0.102 0.103
## cs 0.332 0.060 5.520 0.000 0.332 0.331
## ct 0.113 0.059 1.931 0.053 0.113 0.111
## ca 0.108 0.059 1.821 0.069 0.108 0.105
## lb 0.079 0.065 1.206 0.228 0.079 0.078
## ls 0.315 0.058 5.473 0.000 0.315 0.318
## la 0.216 0.055 3.911 0.000 0.216 0.216#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-bind_rows(coef_table, coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Lower trophic level aggregates"))
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
lower_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="San Pablo",
manual_port_settings=TRUE)
lower_plot_far_west#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
lower_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="Suisun",
manual_port_settings=TRUE)
lower_plot_west#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
lower_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="Sacramento",
manual_port_settings=TRUE)
lower_plot_north#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
lower_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="San Joaquin",
manual_port_settings=TRUE)
lower_plot_southSave updated SEM diagrams
Combine upper and lower SEM diagrams
monthly_theme <- theme(plot.margin=unit(c(0,0.1,0.5,0), "lines"))
## Convert the grobs to ggplots, then add extra spacing between the two rows:
upper_ggplots <- lapply(upper_grobs, ggarrange)
upper_ggplots[[1]] <- upper_ggplots[[1]] + monthly_theme
upper_ggplots[[2]] <- upper_ggplots[[2]] + monthly_theme
upper_figure_1 <- ggarrange(plotlist=upper_ggplots, labels=c("(a)","(b)","(c)","(d)"),
font.label=list(size=11)) %>%
annotate_figure(top = text_grob("Monthly regional models: upper trophic level",
color = "black",
face = "bold",
size = 11)) +
theme(plot.margin=unit(c(0,0.1,1,0), "lines"))
lower_ggplots <- lapply(lower_grobs, ggarrange)
lower_ggplots[[1]] <- lower_ggplots[[1]] + monthly_theme
lower_ggplots[[2]] <- lower_ggplots[[2]] + monthly_theme
lower_figure_1 <- ggarrange(plotlist=lower_ggplots, labels=c("(e)","(f)","(g)","(h)"),
font.label=list(size=11)) %>%
annotate_figure(top = text_grob("Monthly regional models: lower trophic level",
color = "black",
face = "bold",
size = 11))
combined <- ggarrange(plotlist=list(upper_figure_1, lower_figure_1), ncol=1)
combined
ggsave('./fig_output/sem_upper_and_lower.png', combined,
width=15, height=22, dpi=300, bg="white", units="cm")Total effects
ssFW=standardizedsolution(modfitFW) %>% mutate(region="San Pablo")
ssW=standardizedsolution(modfitW) %>% mutate(region="Suisun")
ssN=standardizedsolution(modfitN) %>% mutate(region="Sacramento")
ssS=standardizedsolution(modfitS) %>% mutate(region="San Joaquin")
sslt=rbind(ssFW,ssW,ssN,ssS) %>% filter(op==":=") %>% select(region,lhs,est.std:ci.upper) %>%
separate(lhs,c("variable","influence"), sep=1) %>%
mutate(variable=case_when(variable=="n" ~ "DIN",
variable=="c" ~ "phytoplankton",
variable=="l" ~ "clams"),
influence=case_when(influence=="b" ~ "bottom-up",
influence=="t" ~ "top-down",
influence=="s" ~ "self-regulation",
influence=="a" ~ "environmental",
influence=="n" ~ "nutrient cycling"),
region=factor(region, levels=regionorder_pub),
influence=factor(influence, levels=c("self-regulation","bottom-up","top-down","environmental","nutrient cycling")),
variable=factor(variable,levels=c("clams","phytoplankton","DIN")),
sig=ifelse(pvalue<0.05,"*",""))
lteffects<-ggplot(sslt,aes(x=influence,y=est.std)) +
facet_grid(variable~region) +
geom_errorbar(aes(ymin=ci.lower, ymax=ci.upper),width=0.5) +
geom_point() +
geom_text(aes(y=ci.upper+0.05, label=sig)) +
geom_hline(yintercept = 0) +
ggtitle("Monthly regional models: lower trophic level")+
theme_bw() +
theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1), plot.title = element_text(hjust = 0.5)) +
labs(y="total effect (standardized)")
teffects<-uteffects/lteffects + plot_annotation(tag_levels="a", tag_prefix="(", tag_suffix=")")
ggsave(plot=teffects, filename="./fig_output/teffects.png",width = 6,height=10)Zooplankton-centered model (individual groups)
#1
# modFW='chla~chla_1+hcope_1+amphi_m_1+potam_1+flow+turbid+temp
# hcope~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
# amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
# pcope~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
# '
# modW='chla~chla_1+hcope_1+amphi_m_1+potam_1+flow+turbid+temp+mysid_1
# hcope~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1
# amphi_m~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
# pcope~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1
# mysid~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
# '
# modN='chla~chla_1+hcope_1+amphi_m_1+corbic_1+flow+turbid+temp
# hcope~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
# amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
# pcope~hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
# mysid~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
# '
# modS='chla~chla_1+hcope_1+clad_1+corbic_1+flow+turbid+temp
# hcope~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
# clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
# amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
# pcope~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
# '
#2
modFW='chla~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp
hcope~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
'
modW='chla~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp+mysid_1
hcope~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
amphi_m~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
mysid~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
'
modN='chla~chla_1+hcope_1+clad_1+amphi_m_1+rotif_m_1+corbic_1+flow+turbid+temp
hcope~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope~hcope_1+pcope_1+clad_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1+chla_1
mysid~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
'
modS='chla~chla_1+hcope_1+clad_1+rotif_m_1+corbic_1+flow+turbid+temp
hcope~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
clad~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
amphi_m~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 50
##
## Used Total
## Number of observations 192 312
##
## Model Test User Model:
##
## Test statistic 17.700
## Degrees of freedom 15
## P-value (Chi-square) 0.279
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.200 0.068 2.934 0.003 0.200 0.204
## hcope_1 0.059 0.055 1.082 0.279 0.059 0.076
## amphi_m_1 0.023 0.067 0.345 0.730 0.023 0.029
## rotif_m_1 -0.067 0.063 -1.066 0.286 -0.067 -0.078
## potam_1 -0.005 0.050 -0.091 0.928 -0.005 -0.006
## flow 0.119 0.072 1.641 0.101 0.119 0.141
## turbid -0.079 0.069 -1.150 0.250 -0.079 -0.090
## temp 0.135 0.062 2.195 0.028 0.135 0.158
## hcope ~
## chla_1 0.090 0.082 1.090 0.276 0.090 0.072
## hcope_1 0.235 0.071 3.328 0.001 0.235 0.236
## pcope_1 0.069 0.072 0.950 0.342 0.069 0.066
## potam_1 -0.145 0.063 -2.283 0.022 -0.145 -0.157
## flow -0.072 0.083 -0.867 0.386 -0.072 -0.067
## turbid -0.018 0.084 -0.214 0.830 -0.018 -0.016
## temp -0.055 0.078 -0.713 0.476 -0.055 -0.050
## estfish_bsmt_1 -0.133 0.076 -1.747 0.081 -0.133 -0.130
## amphi_m ~
## chla_1 0.054 0.040 1.378 0.168 0.054 0.044
## amphi_m_1 0.729 0.038 19.393 0.000 0.729 0.744
## flow -0.262 0.042 -6.206 0.000 -0.262 -0.249
## turbid -0.012 0.040 -0.296 0.767 -0.012 -0.011
## temp 0.014 0.036 0.380 0.704 0.014 0.013
## estfish_bsmt_1 0.042 0.034 1.223 0.221 0.042 0.042
## rotif_m ~
## chla_1 -0.199 0.075 -2.633 0.008 -0.199 -0.172
## rotif_m_1 0.380 0.066 5.738 0.000 0.380 0.377
## flow 0.102 0.074 1.375 0.169 0.102 0.103
## turbid 0.009 0.076 0.116 0.908 0.009 0.008
## temp 0.055 0.069 0.803 0.422 0.055 0.055
## estfish_bsmt_1 0.008 0.065 0.124 0.901 0.008 0.009
## pcope ~
## hcope_1 0.044 0.065 0.670 0.503 0.044 0.046
## pcope_1 0.298 0.067 4.444 0.000 0.298 0.301
## potam_1 -0.092 0.059 -1.578 0.114 -0.092 -0.105
## flow 0.206 0.076 2.708 0.007 0.206 0.201
## turbid 0.087 0.078 1.114 0.265 0.087 0.081
## temp 0.159 0.072 2.214 0.027 0.159 0.152
## estfish_bsmt_1 -0.007 0.070 -0.105 0.916 -0.007 -0.008
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.042 0.054 0.785 0.432 0.042 0.057
## .amphi_m 0.018 0.025 0.705 0.481 0.018 0.051
## .rotif_m 0.069 0.048 1.425 0.154 0.069 0.103
## .pcope -0.013 0.050 -0.263 0.793 -0.013 -0.019
## .hcope ~~
## .amphi_m -0.011 0.031 -0.363 0.716 -0.011 -0.026
## .rotif_m 0.033 0.060 0.559 0.576 0.033 0.040
## .pcope -0.204 0.064 -3.215 0.001 -0.204 -0.239
## .amphi_m ~~
## .rotif_m -0.031 0.028 -1.122 0.262 -0.031 -0.081
## .pcope -0.009 0.029 -0.298 0.766 -0.009 -0.022
## .rotif_m ~~
## .pcope 0.052 0.055 0.936 0.349 0.052 0.068
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.596 0.061 9.798 0.000 0.596 0.899
## .hcope 0.926 0.095 9.798 0.000 0.926 0.852
## .amphi_m 0.201 0.021 9.798 0.000 0.201 0.193
## .rotif_m 0.742 0.076 9.798 0.000 0.742 0.807
## .pcope 0.792 0.081 9.798 0.000 0.792 0.802
##
## R-Square:
## Estimate
## chla 0.101
## hcope 0.148
## amphi_m 0.807
## rotif_m 0.193
## pcope 0.198summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 71
##
## Used Total
## Number of observations 215 312
##
## Model Test User Model:
##
## Test statistic 27.661
## Degrees of freedom 16
## P-value (Chi-square) 0.035
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.145 0.068 2.126 0.034 0.145 0.147
## hcope_1 0.071 0.070 1.016 0.310 0.071 0.071
## amphi_m_1 0.129 0.066 1.958 0.050 0.129 0.138
## rotif_m_1 0.101 0.062 1.621 0.105 0.101 0.111
## potam_1 -0.049 0.071 -0.684 0.494 -0.049 -0.051
## flow 0.084 0.073 1.145 0.252 0.084 0.083
## turbid 0.069 0.074 0.935 0.350 0.069 0.069
## temp -0.101 0.064 -1.588 0.112 -0.101 -0.105
## mysid_1 -0.167 0.070 -2.392 0.017 -0.167 -0.169
## hcope ~
## chla_1 0.040 0.065 0.621 0.534 0.040 0.041
## hcope_1 0.307 0.068 4.485 0.000 0.307 0.308
## pcope_1 -0.098 0.060 -1.626 0.104 -0.098 -0.105
## mysid_1 0.049 0.070 0.696 0.486 0.049 0.049
## potam_1 -0.165 0.065 -2.516 0.012 -0.165 -0.171
## flow -0.137 0.071 -1.928 0.054 -0.137 -0.135
## turbid -0.119 0.071 -1.670 0.095 -0.119 -0.119
## temp 0.063 0.062 1.011 0.312 0.063 0.065
## estfish_bsmt_1 0.048 0.060 0.792 0.428 0.048 0.050
## rotif_m_1 0.175 0.059 2.964 0.003 0.175 0.192
## amphi_m ~
## chla_1 -0.045 0.038 -1.197 0.231 -0.045 -0.043
## amphi_m_1 0.783 0.038 20.645 0.000 0.783 0.787
## mysid_1 0.081 0.039 2.085 0.037 0.081 0.077
## flow 0.008 0.040 0.206 0.837 0.008 0.008
## turbid -0.110 0.042 -2.646 0.008 -0.110 -0.103
## temp -0.103 0.037 -2.800 0.005 -0.103 -0.100
## estfish_bsmt_1 -0.177 0.037 -4.775 0.000 -0.177 -0.176
## rotif_m ~
## chla_1 0.021 0.067 0.311 0.755 0.021 0.019
## rotif_m_1 0.336 0.063 5.333 0.000 0.336 0.335
## flow 0.241 0.072 3.329 0.001 0.241 0.216
## turbid -0.106 0.070 -1.501 0.133 -0.106 -0.096
## temp 0.029 0.065 0.451 0.652 0.029 0.028
## estfish_bsmt_1 -0.188 0.062 -3.051 0.002 -0.188 -0.181
## pcope ~
## hcope_1 -0.130 0.060 -2.172 0.030 -0.130 -0.125
## pcope_1 0.393 0.055 7.136 0.000 0.393 0.404
## mysid_1 0.118 0.063 1.866 0.062 0.118 0.115
## potam_1 0.046 0.061 0.760 0.447 0.046 0.046
## flow 0.138 0.064 2.163 0.031 0.138 0.131
## turbid -0.144 0.065 -2.223 0.026 -0.144 -0.138
## temp 0.229 0.055 4.137 0.000 0.229 0.228
## estfish_bsmt_1 0.026 0.056 0.465 0.642 0.026 0.026
## rotif_m_1 0.161 0.054 2.990 0.003 0.161 0.169
## mysid ~
## chla_1 0.179 0.058 3.099 0.002 0.179 0.180
## hcope_1 0.070 0.060 1.166 0.243 0.070 0.069
## pcope_1 0.107 0.053 2.012 0.044 0.107 0.114
## amphi_m_1 -0.124 0.056 -2.224 0.026 -0.124 -0.132
## mysid_1 0.372 0.063 5.941 0.000 0.372 0.372
## flow -0.171 0.060 -2.840 0.005 -0.171 -0.168
## turbid 0.260 0.064 4.076 0.000 0.260 0.258
## temp 0.100 0.055 1.799 0.072 0.100 0.102
## estfish_bsmt_1 -0.025 0.055 -0.456 0.649 -0.025 -0.026
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.168 0.054 3.118 0.002 0.168 0.218
## .amphi_m -0.007 0.031 -0.219 0.827 -0.007 -0.015
## .rotif_m 0.150 0.057 2.644 0.008 0.150 0.183
## .pcope 0.015 0.048 0.323 0.747 0.015 0.022
## .mysid 0.091 0.047 1.928 0.054 0.091 0.133
## .hcope ~~
## .amphi_m -0.036 0.030 -1.195 0.232 -0.036 -0.082
## .rotif_m -0.014 0.054 -0.258 0.796 -0.014 -0.018
## .pcope 0.080 0.046 1.716 0.086 0.080 0.118
## .mysid 0.204 0.048 4.299 0.000 0.204 0.307
## .amphi_m ~~
## .rotif_m 0.042 0.032 1.313 0.189 0.042 0.090
## .pcope 0.068 0.028 2.444 0.015 0.068 0.169
## .mysid -0.038 0.027 -1.405 0.160 -0.038 -0.096
## .rotif_m ~~
## .pcope 0.068 0.049 1.385 0.166 0.068 0.095
## .mysid 0.031 0.048 0.647 0.518 0.031 0.044
## .pcope ~~
## .mysid 0.055 0.041 1.336 0.182 0.055 0.091
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.799 0.077 10.368 0.000 0.799 0.852
## .hcope 0.747 0.072 10.368 0.000 0.747 0.791
## .amphi_m 0.266 0.026 10.368 0.000 0.266 0.249
## .rotif_m 0.838 0.081 10.368 0.000 0.838 0.736
## .pcope 0.613 0.059 10.368 0.000 0.613 0.599
## .mysid 0.595 0.057 10.368 0.000 0.595 0.622
##
## R-Square:
## Estimate
## chla 0.148
## hcope 0.209
## amphi_m 0.751
## rotif_m 0.264
## pcope 0.401
## mysid 0.378summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 83
##
## Used Total
## Number of observations 205 312
##
## Model Test User Model:
##
## Test statistic 35.521
## Degrees of freedom 29
## P-value (Chi-square) 0.188
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.162 0.074 2.173 0.030 0.162 0.149
## hcope_1 -0.033 0.101 -0.326 0.744 -0.033 -0.024
## clad_1 0.207 0.085 2.437 0.015 0.207 0.186
## amphi_m_1 0.065 0.079 0.819 0.413 0.065 0.056
## rotif_m_1 -0.047 0.077 -0.610 0.542 -0.047 -0.044
## corbic_1 -0.028 0.078 -0.357 0.721 -0.028 -0.024
## flow -0.033 0.086 -0.387 0.699 -0.033 -0.030
## turbid 0.109 0.078 1.399 0.162 0.109 0.099
## temp 0.134 0.076 1.763 0.078 0.134 0.125
## hcope ~
## chla_1 -0.009 0.039 -0.232 0.816 -0.009 -0.012
## hcope_1 0.121 0.068 1.778 0.075 0.121 0.128
## pcope_1 -0.096 0.043 -2.201 0.028 -0.096 -0.131
## mysid_1 0.046 0.060 0.766 0.443 0.046 0.058
## corbic_1 0.101 0.042 2.431 0.015 0.101 0.125
## flow -0.394 0.049 -8.050 0.000 -0.394 -0.498
## turbid 0.089 0.047 1.891 0.059 0.089 0.115
## temp 0.097 0.043 2.224 0.026 0.097 0.127
## estfish_bsmt_1 0.007 0.044 0.161 0.872 0.007 0.010
## clad ~
## chla_1 -0.036 0.057 -0.631 0.528 -0.036 -0.035
## clad_1 0.377 0.063 6.026 0.000 0.377 0.356
## pcope_1 0.015 0.057 0.272 0.786 0.015 0.016
## flow 0.457 0.067 6.796 0.000 0.457 0.428
## turbid 0.004 0.062 0.061 0.951 0.004 0.004
## temp 0.051 0.058 0.888 0.374 0.051 0.050
## estfish_bsmt_1 0.019 0.058 0.322 0.747 0.019 0.019
## amphi_m ~
## chla_1 0.052 0.054 0.974 0.330 0.052 0.058
## amphi_m_1 0.499 0.057 8.740 0.000 0.499 0.514
## flow 0.052 0.061 0.858 0.391 0.052 0.056
## turbid -0.019 0.056 -0.341 0.733 -0.019 -0.021
## temp -0.039 0.055 -0.716 0.474 -0.039 -0.044
## estfish_bsmt_1 -0.068 0.055 -1.231 0.218 -0.068 -0.079
## rotif_m ~
## chla_1 -0.083 0.064 -1.304 0.192 -0.083 -0.081
## rotif_m_1 0.155 0.064 2.422 0.015 0.155 0.151
## flow 0.405 0.073 5.537 0.000 0.405 0.382
## turbid -0.052 0.067 -0.776 0.438 -0.052 -0.050
## temp -0.075 0.065 -1.158 0.247 -0.075 -0.073
## estfish_bsmt_1 -0.015 0.065 -0.230 0.818 -0.015 -0.015
## pcope ~
## hcope_1 -0.144 0.102 -1.418 0.156 -0.144 -0.114
## pcope_1 0.251 0.065 3.896 0.000 0.251 0.257
## clad_1 -0.135 0.070 -1.920 0.055 -0.135 -0.128
## mysid_1 0.063 0.090 0.705 0.481 0.063 0.059
## corbic_1 0.064 0.066 0.980 0.327 0.064 0.059
## flow -0.193 0.076 -2.554 0.011 -0.193 -0.182
## turbid -0.245 0.071 -3.446 0.001 -0.245 -0.235
## temp 0.072 0.066 1.087 0.277 0.072 0.070
## estfish_bsmt_1 -0.063 0.067 -0.952 0.341 -0.063 -0.065
## chla_1 0.145 0.064 2.262 0.024 0.145 0.141
## mysid ~
## hcope_1 -0.021 0.084 -0.253 0.800 -0.021 -0.018
## pcope_1 -0.061 0.054 -1.132 0.258 -0.061 -0.067
## mysid_1 0.173 0.073 2.361 0.018 0.173 0.175
## amphi_m_1 -0.082 0.052 -1.567 0.117 -0.082 -0.080
## flow -0.420 0.062 -6.787 0.000 -0.420 -0.428
## turbid 0.228 0.060 3.810 0.000 0.228 0.236
## temp 0.080 0.054 1.480 0.139 0.080 0.085
## estfish_bsmt_1 0.056 0.056 1.007 0.314 0.056 0.062
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.021 0.044 0.485 0.628 0.021 0.034
## .clad 0.150 0.061 2.474 0.013 0.150 0.175
## .amphi_m 0.021 0.057 0.365 0.715 0.021 0.025
## .rotif_m 0.104 0.068 1.536 0.125 0.104 0.108
## .pcope -0.062 0.066 -0.940 0.347 -0.062 -0.066
## .mysid 0.092 0.056 1.640 0.101 0.092 0.115
## .hcope ~~
## .clad -0.059 0.034 -1.722 0.085 -0.059 -0.121
## .amphi_m 0.039 0.033 1.186 0.236 0.039 0.083
## .rotif_m -0.007 0.038 -0.191 0.848 -0.007 -0.013
## .pcope 0.060 0.038 1.564 0.118 0.060 0.110
## .mysid 0.180 0.034 5.259 0.000 0.180 0.395
## .clad ~~
## .amphi_m 0.004 0.044 0.088 0.930 0.004 0.006
## .rotif_m 0.107 0.053 2.013 0.044 0.107 0.142
## .pcope -0.051 0.052 -0.981 0.326 -0.051 -0.069
## .mysid -0.083 0.044 -1.895 0.058 -0.083 -0.134
## .amphi_m ~~
## .rotif_m -0.128 0.051 -2.530 0.011 -0.128 -0.180
## .pcope -0.088 0.049 -1.778 0.075 -0.088 -0.125
## .mysid 0.088 0.042 2.108 0.035 0.088 0.149
## .rotif_m ~~
## .pcope 0.151 0.059 2.562 0.010 0.151 0.182
## .mysid 0.002 0.049 0.048 0.962 0.002 0.003
## .pcope ~~
## .mysid 0.134 0.049 2.750 0.006 0.134 0.196
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 1.100 0.109 10.124 0.000 1.100 0.907
## .hcope 0.360 0.036 10.124 0.000 0.360 0.595
## .clad 0.669 0.066 10.124 0.000 0.669 0.608
## .amphi_m 0.603 0.060 10.124 0.000 0.603 0.713
## .rotif_m 0.842 0.083 10.124 0.000 0.842 0.773
## .pcope 0.818 0.081 10.124 0.000 0.818 0.754
## .mysid 0.577 0.057 10.124 0.000 0.577 0.619
##
## R-Square:
## Estimate
## chla 0.093
## hcope 0.405
## clad 0.392
## amphi_m 0.287
## rotif_m 0.227
## pcope 0.246
## mysid 0.381summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 20 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 65
##
## Used Total
## Number of observations 210 312
##
## Model Test User Model:
##
## Test statistic 25.378
## Degrees of freedom 22
## P-value (Chi-square) 0.279
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## chla_1 0.276 0.069 3.996 0.000 0.276 0.267
## hcope_1 0.121 0.068 1.771 0.077 0.121 0.113
## clad_1 0.157 0.067 2.342 0.019 0.157 0.158
## rotif_m_1 -0.106 0.068 -1.563 0.118 -0.106 -0.098
## corbic_1 0.013 0.065 0.204 0.838 0.013 0.013
## flow -0.065 0.074 -0.876 0.381 -0.065 -0.059
## turbid 0.011 0.067 0.170 0.865 0.011 0.011
## temp 0.082 0.065 1.255 0.210 0.082 0.080
## hcope ~
## chla_1 0.177 0.057 3.113 0.002 0.177 0.183
## hcope_1 0.353 0.061 5.796 0.000 0.353 0.352
## pcope_1 -0.000 0.055 -0.006 0.995 -0.000 -0.000
## corbic_1 0.116 0.058 2.019 0.043 0.116 0.120
## flow -0.144 0.062 -2.306 0.021 -0.144 -0.140
## turbid -0.095 0.058 -1.648 0.099 -0.095 -0.100
## temp 0.178 0.057 3.125 0.002 0.178 0.186
## estfish_bsmt_1 -0.185 0.058 -3.198 0.001 -0.185 -0.192
## clad ~
## chla_1 0.126 0.058 2.166 0.030 0.126 0.121
## clad_1 0.524 0.058 9.073 0.000 0.524 0.526
## pcope_1 -0.038 0.054 -0.702 0.483 -0.038 -0.038
## flow 0.197 0.061 3.237 0.001 0.197 0.178
## turbid -0.049 0.057 -0.852 0.394 -0.049 -0.047
## temp 0.112 0.056 1.979 0.048 0.112 0.109
## estfish_bsmt_1 -0.094 0.055 -1.698 0.090 -0.094 -0.091
## amphi_m ~
## chla_1 -0.033 0.070 -0.469 0.639 -0.033 -0.031
## amphi_m_1 0.218 0.068 3.202 0.001 0.218 0.214
## flow -0.006 0.076 -0.079 0.937 -0.006 -0.005
## turbid 0.167 0.072 2.337 0.019 0.167 0.160
## temp 0.089 0.070 1.265 0.206 0.089 0.085
## estfish_bsmt_1 0.051 0.072 0.715 0.474 0.051 0.049
## rotif_m ~
## chla_1 0.034 0.065 0.525 0.599 0.034 0.034
## rotif_m_1 0.119 0.066 1.793 0.073 0.119 0.114
## flow 0.322 0.070 4.610 0.000 0.322 0.304
## turbid -0.071 0.064 -1.101 0.271 -0.071 -0.072
## temp 0.001 0.065 0.022 0.982 0.001 0.001
## estfish_bsmt_1 0.171 0.065 2.609 0.009 0.171 0.173
## pcope ~
## chla_1 0.211 0.065 3.265 0.001 0.211 0.202
## hcope_1 -0.058 0.065 -0.903 0.367 -0.058 -0.054
## clad_1 0.082 0.063 1.303 0.193 0.082 0.082
## pcope_1 0.445 0.061 7.359 0.000 0.445 0.446
## corbic_1 -0.055 0.061 -0.903 0.367 -0.055 -0.052
## flow 0.029 0.069 0.415 0.678 0.029 0.026
## turbid -0.076 0.064 -1.192 0.233 -0.076 -0.074
## temp 0.011 0.062 0.183 0.855 0.011 0.011
## estfish_bsmt_1 -0.048 0.063 -0.755 0.450 -0.048 -0.046
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla ~~
## .hcope 0.081 0.052 1.561 0.118 0.081 0.108
## .clad 0.203 0.053 3.831 0.000 0.203 0.274
## .amphi_m -0.008 0.064 -0.123 0.902 -0.008 -0.009
## .rotif_m 0.113 0.060 1.891 0.059 0.113 0.132
## .pcope -0.044 0.056 -0.772 0.440 -0.044 -0.053
## .hcope ~~
## .clad 0.046 0.044 1.063 0.288 0.046 0.074
## .amphi_m 0.023 0.055 0.422 0.673 0.023 0.029
## .rotif_m 0.014 0.050 0.281 0.778 0.014 0.019
## .pcope 0.026 0.048 0.550 0.582 0.026 0.038
## .clad ~~
## .amphi_m -0.098 0.055 -1.783 0.075 -0.098 -0.124
## .rotif_m 0.032 0.050 0.641 0.521 0.032 0.044
## .pcope 0.088 0.048 1.822 0.069 0.088 0.127
## .amphi_m ~~
## .rotif_m 0.058 0.063 0.926 0.354 0.058 0.064
## .pcope 0.030 0.060 0.506 0.613 0.030 0.035
## .rotif_m ~~
## .pcope 0.200 0.057 3.512 0.000 0.200 0.250
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla 0.875 0.085 10.247 0.000 0.875 0.840
## .hcope 0.636 0.062 10.247 0.000 0.636 0.696
## .clad 0.628 0.061 10.247 0.000 0.628 0.596
## .amphi_m 0.991 0.097 10.247 0.000 0.991 0.904
## .rotif_m 0.839 0.082 10.247 0.000 0.839 0.866
## .pcope 0.763 0.074 10.247 0.000 0.763 0.718
##
## R-Square:
## Estimate
## chla 0.160
## hcope 0.304
## clad 0.404
## amphi_m 0.096
## rotif_m 0.134
## pcope 0.282#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#Extract model coefficients for table
coef_table<-bind_rows(coef_table, coef_tabler(modfitFW, modfitW, modfitN, modfitS, name="Individual zooplankton groups"))%>%
mutate(Model=case_when(
Model == "Individual zooplankton groups" ~ "Zooplankton",
Model == "Lower trophic level aggregates" ~ "Lower trophic",
Model == "Upper trophic level aggregates" ~ "Upper trophic"),
across(c(Response, Predictor),
~recode(.x, amphi_m="amphi", amphi_m_1="amphi_1", rotif_m="rotif", rotif_m_1="rotif_1")))
write.csv(coef_table, "fig_output/monthly coefficients.csv", row.names = FALSE)
#FAR WEST
# myLavaanPlot(model=modfitFW, labels=labelsfarwest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
zoop_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_zoop",
region="Far West",
title="San Pablo",
manual_port_settings=TRUE,
font_size=12)
zoop_plot_far_west#WEST
# myLavaanPlot(model=modfitW, labels=labelswest,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
zoop_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_zoop",
region="West",
title="Suisun",
manual_port_settings=TRUE,
font_size=12)
zoop_plot_west#NORTH
# myLavaanPlot(model=modfitN, labels=labelsnorth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
zoop_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_zoop",
region="North",
title="Sacramento",
manual_port_settings=TRUE,
font_size=12)
zoop_plot_north#SOUTH
# myLavaanPlot(model=modfitS, labels=labelssouth,
# node_options=list(shape="box", fontname="Helvetica"),
# coefs=TRUE, stand=TRUE, covs=FALSE, sig=0.05,
# width=c("regress","latent"),
# color=c("regress","latent"))
zoop_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_zoop",
region="South",
title="San Joaquin",
manual_port_settings=TRUE,
font_size=12)
zoop_plot_southSave updated SEM diagrams
zoop_grobs <- map(list(zoop_plot_far_west,
zoop_plot_north,
zoop_plot_west,
zoop_plot_south), ~convert_html_to_grob(.x, 2000))
zoop_figure <- ggarrange(plotlist=zoop_grobs, labels=c("(a)", "(b)", "(c)", "(d)"),
font.label=list(size=11)) %>%
annotate_figure(top = text_grob("Monthly Regional Models (zooplankton groups)",
color = "black",
face = "bold",
size = 11))
ggsave('./fig_output/sem_zoop.png',zoop_figure, width=8, height=7, dpi=300, bg = "white")Total effects
Haven’t done yet.
Growth rates
Upper trophic
modFW='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop_gr~pb1*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*marfish_bsmt_1+ft2*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modW='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*potam_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*potam_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modN='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt_gr~fb1*hzoop_1+fb2*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2+ft3^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modS='hzoop_gr~hb1*chla_1+hs1*hzoop_1+ht1*pzoop_1+ht2*corbic_1+ht3*estfish_bsmt_1+ha1*flow+ha2*temp+ha3*turbid
pzoop_gr~pb1*chla_1+pb2*hzoop_1+ps1*pzoop_1+pt1*corbic_1+pt2*estfish_bsmt_1+pa1*flow+pa2*temp+pa3*turbid
estfish_bsmt_gr~fb1*chla_1+fb2*hzoop_1+fb3*pzoop_1+fs1*estfish_bsmt_1+fa1*flow+fa2*temp+fa3*turbid+ft1*sside_1+ft2*cent_1+ft3*sbass1_bsmt_1
hb:=sqrt(hb1^2)
hs:=sqrt(hs1^2)
ht:=sqrt(ht1^2+ht2^2+ht3^2)
ha:=sqrt(ha1^2+ha2^2+ha3^2)
pb:=sqrt(pb1^2+pb2^2)
ps:=sqrt(ps1^2)
pt:=sqrt(pt1^2+pt2^2)
pa:=sqrt(pa1^2+pa2^2+pa3^2)
fb:=sqrt(fb1^2+fb2^2+fb3^2)
fs:=sqrt(fs1^2)
ft:=sqrt(ft1^2+ft2^2+ft3^2)
fa:=sqrt(fa1^2+fa2^2+fa3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 25 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Used Total
## Number of observations 191 312
##
## Model Test User Model:
##
## Test statistic 6.836
## Degrees of freedom 7
## P-value (Chi-square) 0.446
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop_gr ~
## chla_1 (hb1) 0.023 0.058 0.396 0.692 0.023 0.023
## hzoop_1 (hs1) -0.500 0.050 -10.079 0.000 -0.500 -0.618
## pzoop_1 (ht1) 0.064 0.048 1.351 0.177 0.064 0.081
## potam_1 (ht2) -0.134 0.044 -3.008 0.003 -0.134 -0.179
## estfs__1 (ht3) -0.127 0.053 -2.405 0.016 -0.127 -0.151
## flow (ha1) -0.039 0.058 -0.670 0.503 -0.039 -0.044
## temp (ha2) -0.048 0.055 -0.868 0.385 -0.048 -0.054
## turbid (ha3) 0.045 0.061 0.736 0.462 0.045 0.048
## pzoop_gr ~
## hzoop_1 (pb1) 0.072 0.103 0.701 0.484 0.072 0.045
## pzoop_1 (ps1) -0.898 0.100 -9.018 0.000 -0.898 -0.568
## potam_1 (pt1) -0.180 0.092 -1.966 0.049 -0.180 -0.121
## estfs__1 (pt2) 0.017 0.110 0.150 0.881 0.017 0.010
## flow (pa1) 0.118 0.120 0.984 0.325 0.118 0.068
## temp (pa2) -0.015 0.115 -0.134 0.894 -0.015 -0.009
## turbid (pa3) 0.329 0.128 2.571 0.010 0.329 0.176
## estfish_bsmt_gr ~
## hzoop_1 (fb1) -0.409 0.133 -3.073 0.002 -0.409 -0.187
## pzoop_1 (fb2) 0.240 0.132 1.822 0.068 0.240 0.112
## estfs__1 (fs1) -1.365 0.151 -9.059 0.000 -1.365 -0.599
## flow (fa1) 0.176 0.161 1.093 0.274 0.176 0.075
## temp (fa2) -0.027 0.153 -0.178 0.858 -0.027 -0.011
## turbid (fa3) 0.475 0.172 2.765 0.006 0.475 0.188
## mrfsh__1 (ft1) -0.033 0.150 -0.218 0.828 -0.033 -0.013
## sbss1__1 (ft2) -0.043 0.152 -0.281 0.779 -0.043 -0.018
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr ~~
## .pzoop_gr -0.080 0.069 -1.164 0.244 -0.080 -0.085
## .estfsh_bsmt_gr -0.048 0.089 -0.541 0.589 -0.048 -0.039
## .pzoop_gr ~~
## .estfsh_bsmt_gr -0.401 0.189 -2.120 0.034 -0.401 -0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr 0.451 0.046 9.772 0.000 0.451 0.626
## .pzoop_gr 1.973 0.202 9.772 0.000 1.973 0.685
## .estfsh_bsmt_gr 3.377 0.346 9.772 0.000 3.377 0.643
##
## R-Square:
## Estimate
## hzoop_gr 0.374
## pzoop_gr 0.315
## estfsh_bsmt_gr 0.357
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.023 0.058 0.396 0.692 0.023 0.023
## hs 0.500 0.050 10.079 0.000 0.500 0.618
## ht 0.195 0.044 4.445 0.000 0.195 0.248
## ha 0.076 0.061 1.251 0.211 0.076 0.085
## pb 0.072 0.103 0.701 0.484 0.072 0.045
## ps 0.898 0.100 9.018 0.000 0.898 0.568
## pt 0.181 0.093 1.938 0.053 0.181 0.121
## pa 0.350 0.112 3.139 0.002 0.350 0.189
## fb 0.475 0.138 3.432 0.001 0.475 0.218
## fs 1.365 0.151 9.059 0.000 1.365 0.599
## ft 0.054 0.156 0.344 0.731 0.054 0.023
## fa 0.507 0.149 3.410 0.001 0.507 0.202summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 29
##
## Used Total
## Number of observations 210 312
##
## Model Test User Model:
##
## Test statistic 2.096
## Degrees of freedom 4
## P-value (Chi-square) 0.718
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop_gr ~
## chla_1 (hb1) 0.043 0.034 1.272 0.204 0.043 0.081
## hzoop_1 (hs1) -0.299 0.039 -7.714 0.000 -0.299 -0.565
## pzoop_1 (ht1) 0.024 0.035 0.690 0.490 0.024 0.045
## potam_1 (ht2) -0.120 0.037 -3.275 0.001 -0.120 -0.218
## estfs__1 (ht3) -0.011 0.034 -0.324 0.746 -0.011 -0.020
## flow (ha1) 0.076 0.038 2.008 0.045 0.076 0.138
## temp (ha2) -0.001 0.033 -0.017 0.987 -0.001 -0.001
## turbid (ha3) -0.110 0.037 -2.978 0.003 -0.110 -0.198
## pzoop_gr ~
## chla_1 (pb1) 0.162 0.050 3.224 0.001 0.162 0.207
## hzoop_1 (pb2) 0.075 0.057 1.323 0.186 0.075 0.096
## pzoop_1 (ps1) -0.418 0.051 -8.226 0.000 -0.418 -0.527
## potam_1 (pt1) -0.072 0.055 -1.323 0.186 -0.072 -0.089
## estfs__1 (pt2) 0.022 0.050 0.441 0.659 0.022 0.027
## flow (pa1) -0.087 0.055 -1.568 0.117 -0.087 -0.106
## temp (pa2) 0.143 0.048 2.978 0.003 0.143 0.185
## turbid (pa3) 0.071 0.054 1.314 0.189 0.071 0.086
## estfish_bsmt_gr ~
## hzoop_1 (fb1) 0.134 0.105 1.275 0.202 0.134 0.082
## pzoop_1 (fb2) -0.120 0.102 -1.185 0.236 -0.120 -0.073
## estfs__1 (fs1) -1.007 0.107 -9.450 0.000 -1.007 -0.606
## flow (fa1) -0.244 0.110 -2.225 0.026 -0.244 -0.144
## temp (fa2) 0.052 0.095 0.542 0.588 0.052 0.032
## turbid (fa3) 0.310 0.105 2.964 0.003 0.310 0.181
## sbss1__1 (ft1) 0.262 0.103 2.538 0.011 0.262 0.156
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr ~~
## .pzoop_gr 0.077 0.022 3.560 0.000 0.077 0.253
## .estfsh_bsmt_gr -0.119 0.043 -2.797 0.005 -0.119 -0.197
## .pzoop_gr ~~
## .estfsh_bsmt_gr -0.038 0.061 -0.622 0.534 -0.038 -0.043
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr 0.207 0.020 10.247 0.000 0.207 0.745
## .pzoop_gr 0.445 0.043 10.247 0.000 0.445 0.723
## .estfsh_bsmt_gr 1.777 0.173 10.247 0.000 1.777 0.667
##
## R-Square:
## Estimate
## hzoop_gr 0.255
## pzoop_gr 0.277
## estfsh_bsmt_gr 0.333
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.043 0.034 1.272 0.204 0.043 0.081
## hs 0.299 0.039 7.714 0.000 0.299 0.565
## ht 0.123 0.037 3.337 0.001 0.123 0.224
## ha 0.133 0.041 3.255 0.001 0.133 0.241
## pb 0.179 0.045 3.994 0.000 0.179 0.228
## ps 0.418 0.051 8.226 0.000 0.418 0.527
## pt 0.076 0.053 1.437 0.151 0.076 0.093
## pa 0.182 0.055 3.310 0.001 0.182 0.230
## fb 0.180 0.119 1.510 0.131 0.180 0.110
## fs 1.007 0.107 9.450 0.000 1.007 0.606
## ft 0.262 0.103 2.538 0.011 0.262 0.156
## fa 0.398 0.121 3.286 0.001 0.398 0.233summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 31
##
## Used Total
## Number of observations 193 312
##
## Model Test User Model:
##
## Test statistic 8.379
## Degrees of freedom 8
## P-value (Chi-square) 0.397
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop_gr ~
## chla_1 (hb1) 0.029 0.049 0.598 0.550 0.029 0.034
## hzoop_1 (hs1) -0.536 0.053 -10.202 0.000 -0.536 -0.610
## pzoop_1 (ht1) 0.003 0.054 0.058 0.954 0.003 0.004
## corbic_1 (ht2) 0.027 0.050 0.540 0.589 0.027 0.031
## estfs__1 (ht3) -0.058 0.051 -1.138 0.255 -0.058 -0.072
## flow (ha1) 0.135 0.056 2.393 0.017 0.135 0.156
## temp (ha2) 0.034 0.049 0.690 0.490 0.034 0.041
## turbid (ha3) 0.149 0.052 2.865 0.004 0.149 0.168
## pzoop_gr ~
## chla_1 (pb1) 0.338 0.092 3.683 0.000 0.338 0.210
## hzoop_1 (pb2) 0.196 0.097 2.017 0.044 0.196 0.119
## pzoop_1 (ps1) -1.032 0.101 -10.240 0.000 -1.032 -0.631
## corbic_1 (pt1) 0.087 0.094 0.930 0.353 0.087 0.053
## estfs__1 (pt2) -0.093 0.094 -0.991 0.322 -0.093 -0.062
## flow (pa1) -0.543 0.104 -5.214 0.000 -0.543 -0.337
## temp (pa2) 0.059 0.091 0.651 0.515 0.059 0.038
## turbid (pa3) 0.105 0.096 1.095 0.274 0.105 0.064
## estfish_bsmt_gr ~
## hzoop_1 (fb1) 0.324 0.178 1.825 0.068 0.324 0.122
## pzoop_1 (fb2) 0.084 0.179 0.469 0.639 0.084 0.032
## estfs__1 (fs1) -1.431 0.171 -8.373 0.000 -1.431 -0.587
## flow (fa1) -0.646 0.188 -3.443 0.001 -0.646 -0.247
## temp (fa2) 0.048 0.162 0.298 0.766 0.048 0.019
## turbid (fa3) 0.107 0.175 0.612 0.541 0.107 0.040
## sside_1 (ft1) -0.024 0.162 -0.147 0.883 -0.024 -0.009
## cent_1 (ft2) -0.270 0.165 -1.642 0.101 -0.270 -0.106
## sbss1__1 (ft3) 0.193 0.170 1.139 0.255 0.193 0.076
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr ~~
## .pzoop_gr 0.266 0.063 4.251 0.000 0.266 0.321
## .estfsh_bsmt_gr -0.200 0.107 -1.876 0.061 -0.200 -0.136
## .pzoop_gr ~~
## .estfsh_bsmt_gr 0.064 0.195 0.329 0.742 0.064 0.024
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr 0.447 0.045 9.823 0.000 0.447 0.610
## .pzoop_gr 1.530 0.156 9.823 0.000 1.530 0.598
## .estfsh_bsmt_gr 4.814 0.490 9.823 0.000 4.814 0.717
##
## R-Square:
## Estimate
## hzoop_gr 0.390
## pzoop_gr 0.402
## estfsh_bsmt_gr 0.283
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.029 0.049 0.598 0.550 0.029 0.034
## hs 0.536 0.053 10.202 0.000 0.536 0.610
## ht 0.064 0.053 1.204 0.229 0.064 0.078
## ha 0.203 0.051 4.018 0.000 0.203 0.233
## pb 0.391 0.090 4.323 0.000 0.391 0.242
## ps 1.032 0.101 10.240 0.000 1.032 0.631
## pt 0.127 0.098 1.298 0.194 0.127 0.081
## pa 0.557 0.104 5.327 0.000 0.557 0.345
## fb 0.335 0.166 2.016 0.044 0.335 0.126
## fs 1.431 0.171 8.373 0.000 1.431 0.587
## ft 0.333 0.151 2.204 0.028 0.333 0.130
## fa 0.657 0.189 3.469 0.001 0.657 0.251summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 33 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 32
##
## Used Total
## Number of observations 199 312
##
## Model Test User Model:
##
## Test statistic 3.419
## Degrees of freedom 7
## P-value (Chi-square) 0.844
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hzoop_gr ~
## chla_1 (hb1) 0.139 0.046 2.990 0.003 0.139 0.185
## hzoop_1 (hs1) -0.402 0.044 -9.058 0.000 -0.402 -0.551
## pzoop_1 (ht1) -0.030 0.049 -0.613 0.540 -0.030 -0.039
## corbic_1 (ht2) 0.030 0.045 0.679 0.497 0.030 0.041
## estfs__1 (ht3) 0.005 0.044 0.105 0.916 0.005 0.007
## flow (ha1) 0.103 0.046 2.237 0.025 0.103 0.134
## temp (ha2) 0.049 0.043 1.151 0.250 0.049 0.069
## turbid (ha3) -0.044 0.043 -1.002 0.316 -0.044 -0.061
## pzoop_gr ~
## chla_1 (pb1) 0.327 0.077 4.258 0.000 0.327 0.259
## hzoop_1 (pb2) 0.133 0.073 1.806 0.071 0.133 0.108
## pzoop_1 (ps1) -0.775 0.081 -9.594 0.000 -0.775 -0.606
## corbic_1 (pt1) -0.019 0.073 -0.262 0.793 -0.019 -0.015
## estfs__1 (pt2) 0.031 0.073 0.425 0.671 0.031 0.026
## flow (pa1) -0.142 0.076 -1.869 0.062 -0.142 -0.110
## temp (pa2) -0.043 0.071 -0.608 0.543 -0.043 -0.036
## turbid (pa3) 0.106 0.072 1.471 0.141 0.106 0.087
## estfish_bsmt_gr ~
## chla_1 (fb1) 0.239 0.184 1.299 0.194 0.239 0.081
## hzoop_1 (fb2) 0.340 0.178 1.917 0.055 0.340 0.118
## pzoop_1 (fb3) -0.016 0.192 -0.083 0.934 -0.016 -0.005
## estfs__1 (fs1) -1.578 0.178 -8.887 0.000 -1.578 -0.562
## flow (fa1) -0.152 0.184 -0.828 0.408 -0.152 -0.050
## temp (fa2) -0.052 0.168 -0.311 0.756 -0.052 -0.019
## turbid (fa3) 0.466 0.186 2.514 0.012 0.466 0.164
## sside_1 (ft1) -0.028 0.166 -0.171 0.864 -0.028 -0.010
## cent_1 (ft2) -0.233 0.173 -1.344 0.179 -0.233 -0.084
## sbss1__1 (ft3) 0.143 0.176 0.811 0.417 0.143 0.051
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr ~~
## .pzoop_gr 0.182 0.043 4.256 0.000 0.182 0.316
## .estfsh_bsmt_gr -0.163 0.097 -1.684 0.092 -0.163 -0.120
## .pzoop_gr ~~
## .estfsh_bsmt_gr 0.379 0.162 2.345 0.019 0.379 0.169
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .hzoop_gr 0.347 0.035 9.975 0.000 0.347 0.685
## .pzoop_gr 0.951 0.095 9.975 0.000 0.951 0.659
## .estfsh_bsmt_gr 5.321 0.533 9.975 0.000 5.321 0.671
##
## R-Square:
## Estimate
## hzoop_gr 0.315
## pzoop_gr 0.341
## estfsh_bsmt_gr 0.329
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hb 0.139 0.046 2.990 0.003 0.139 0.185
## hs 0.402 0.044 9.058 0.000 0.402 0.551
## ht 0.043 0.048 0.884 0.377 0.043 0.057
## ha 0.122 0.047 2.616 0.009 0.122 0.163
## pb 0.353 0.073 4.816 0.000 0.353 0.281
## ps 0.775 0.081 9.594 0.000 0.775 0.606
## pt 0.037 0.072 0.508 0.611 0.037 0.030
## pa 0.182 0.078 2.338 0.019 0.182 0.145
## fb 0.416 0.170 2.443 0.015 0.416 0.143
## fs 1.578 0.178 8.887 0.000 1.578 0.562
## ft 0.275 0.184 1.493 0.136 0.275 0.099
## fa 0.493 0.195 2.525 0.012 0.493 0.173#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitS)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
upper_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="Far West",
manual_port_settings=TRUE)
upper_plot_far_west#WEST
upper_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="West",
manual_port_settings=TRUE)
upper_plot_west#NORTH
upper_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="North",
manual_port_settings=TRUE)
upper_plot_north#SOUTH
upper_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_upper_trophic",
title="South",
manual_port_settings=TRUE)
upper_plot_southLower trophic
modFW='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
potam~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modW='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*potam_1+na1*flow+na2*temp+na3*turbid
chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*potam_1+ca1*flow+ca2*temp+ca3*turbid
potam~lb1*din_1+lb2*chla_1+lb3*hzoop_1+lb4*pzoop_1+ls1*potam_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2+lb4^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modN='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ct3*pzoop_1+ca1*flow+ca2*temp+ca3*turbid
corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2+ct3^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modS='din_gr~ns1*din_1+nt1*chla_gr+nn1*hzoop_1+nn2*pzoop_1+nn3*corbic_1+na1*flow+na2*temp+na3*turbid
chla_gr~cb1*din_1+cs1*chla_1+ct1*hzoop_1+ct2*corbic_1+ca1*flow+ca2*temp+ca3*turbid
corbic~lb1*chla_1+lb2*hzoop_1+lb3*pzoop_1+ls1*corbic_1+la1*flow+la2*temp+la3*turbid
ns:=sqrt(ns1^2)
nt:=sqrt(nt1^2)
nn:=sqrt(nn1^2+nn2^2+nn3^2)
na:=sqrt(na1^2+na2^2+na3^2)
cb:=sqrt(cb1^2)
cs:=sqrt(cs1^2)
ct:=sqrt(ct1^2+ct2^2)
ca:=sqrt(ca1^2+ca2^2+ca3^2)
lb:=sqrt(lb1^2+lb2^2+lb3^2)
ls:=sqrt(ls1^2)
la:=sqrt(la1^2+la2^2+la3^2)
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 234 312
##
## Model Test User Model:
##
## Test statistic 6.797
## Degrees of freedom 4
## P-value (Chi-square) 0.147
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din_gr ~
## din_1 (ns1) -0.145 0.017 -8.372 0.000 -0.145 -0.462
## chla_gr (nt1) -0.163 0.030 -5.358 0.000 -0.163 -0.291
## hzoop_1 (nn1) 0.001 0.015 0.070 0.944 0.001 0.004
## pzoop_1 (nn2) -0.021 0.015 -1.420 0.156 -0.021 -0.079
## potam_1 (nn3) -0.003 0.015 -0.216 0.829 -0.003 -0.012
## flow (na1) -0.039 0.017 -2.224 0.026 -0.039 -0.133
## temp (na2) -0.016 0.017 -0.996 0.319 -0.016 -0.058
## turbid (na3) 0.034 0.017 1.954 0.051 0.034 0.117
## chla_gr ~
## din_1 (cb1) 0.005 0.032 0.163 0.871 0.005 0.009
## chla_1 (cs1) -0.326 0.034 -9.482 0.000 -0.326 -0.539
## hzoop_1 (ct1) -0.006 0.028 -0.221 0.825 -0.006 -0.013
## potam_1 (ct2) -0.015 0.027 -0.575 0.565 -0.015 -0.032
## flow (ca1) 0.014 0.031 0.450 0.653 0.014 0.027
## temp (ca2) 0.045 0.030 1.510 0.131 0.045 0.089
## turbid (ca3) -0.024 0.032 -0.745 0.456 -0.024 -0.046
## potam ~
## chla_1 (lb1) 0.054 0.060 0.886 0.376 0.054 0.042
## hzoop_1 (lb2) -0.073 0.051 -1.432 0.152 -0.073 -0.071
## pzoop_1 (lb3) -0.163 0.050 -3.263 0.001 -0.163 -0.161
## potam_1 (ls1) 0.630 0.048 13.023 0.000 0.630 0.630
## flow (la1) 0.113 0.057 1.980 0.048 0.113 0.103
## temp (la2) -0.044 0.054 -0.807 0.420 -0.044 -0.041
## turbid (la3) 0.036 0.058 0.616 0.538 0.036 0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr ~~
## .potam -0.014 0.011 -1.214 0.225 -0.014 -0.080
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr 0.051 0.005 10.817 0.000 0.051 0.661
## .chla_gr 0.170 0.016 10.817 0.000 0.170 0.688
## .potam 0.568 0.053 10.817 0.000 0.568 0.518
##
## R-Square:
## Estimate
## din_gr 0.339
## chla_gr 0.312
## potam 0.482
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.145 0.017 8.372 0.000 0.145 0.462
## nt 0.163 0.030 5.358 0.000 0.163 0.291
## nn 0.022 0.015 1.424 0.154 0.022 0.080
## na 0.054 0.020 2.773 0.006 0.054 0.187
## cb 0.005 0.032 0.163 0.871 0.005 0.009
## cs 0.326 0.034 9.482 0.000 0.326 0.539
## ct 0.016 0.029 0.574 0.566 0.016 0.035
## ca 0.053 0.030 1.778 0.075 0.053 0.103
## lb 0.187 0.049 3.815 0.000 0.187 0.181
## ls 0.630 0.048 13.023 0.000 0.630 0.630
## la 0.126 0.049 2.574 0.010 0.126 0.116summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Used Total
## Number of observations 257 312
##
## Model Test User Model:
##
## Test statistic 15.957
## Degrees of freedom 3
## P-value (Chi-square) 0.001
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din_gr ~
## din_1 (ns1) -0.163 0.017 -9.891 0.000 -0.163 -0.582
## chla_gr (nt1) -0.127 0.026 -4.880 0.000 -0.127 -0.244
## hzoop_1 (nn1) -0.016 0.016 -1.032 0.302 -0.016 -0.061
## pzoop_1 (nn2) 0.004 0.015 0.293 0.770 0.004 0.016
## potam_1 (nn3) 0.005 0.016 0.317 0.751 0.005 0.018
## flow (na1) -0.129 0.017 -7.730 0.000 -0.129 -0.460
## temp (na2) 0.021 0.014 1.477 0.140 0.021 0.076
## turbid (na3) 0.035 0.015 2.318 0.020 0.035 0.129
## chla_gr ~
## din_1 (cb1) -0.053 0.034 -1.569 0.117 -0.053 -0.098
## chla_1 (cs1) -0.336 0.032 -10.635 0.000 -0.336 -0.607
## hzoop_1 (ct1) 0.033 0.031 1.087 0.277 0.033 0.065
## potam_1 (ct2) 0.001 0.032 0.033 0.973 0.001 0.002
## flow (ca1) 0.012 0.033 0.348 0.728 0.012 0.021
## temp (ca2) -0.040 0.028 -1.409 0.159 -0.040 -0.076
## turbid (ca3) -0.010 0.031 -0.342 0.732 -0.010 -0.020
## potam ~
## din_1 (lb1) 0.015 0.047 0.317 0.751 0.015 0.014
## chla_1 (lb2) -0.025 0.044 -0.576 0.565 -0.025 -0.024
## hzoop_1 (lb3) -0.007 0.045 -0.151 0.880 -0.007 -0.007
## pzoop_1 (lb4) 0.073 0.041 1.779 0.075 0.073 0.072
## potam_1 (ls1) 0.724 0.044 16.550 0.000 0.724 0.719
## flow (la1) -0.128 0.046 -2.777 0.005 -0.128 -0.124
## temp (la2) -0.007 0.039 -0.181 0.856 -0.007 -0.007
## turbid (la3) -0.077 0.042 -1.826 0.068 -0.077 -0.076
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr ~~
## .potam 0.003 0.008 0.347 0.728 0.003 0.022
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr 0.047 0.004 11.336 0.000 0.047 0.633
## .chla_gr 0.190 0.017 11.336 0.000 0.190 0.685
## .potam 0.363 0.032 11.336 0.000 0.363 0.355
##
## R-Square:
## Estimate
## din_gr 0.367
## chla_gr 0.315
## potam 0.645
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.163 0.017 9.891 0.000 0.163 0.582
## nt 0.127 0.026 4.880 0.000 0.127 0.244
## nn 0.018 0.016 1.083 0.279 0.018 0.066
## na 0.135 0.017 7.804 0.000 0.135 0.484
## cb 0.053 0.034 1.569 0.117 0.053 0.098
## cs 0.336 0.032 10.635 0.000 0.336 0.607
## ct 0.033 0.031 1.080 0.280 0.033 0.065
## ca 0.043 0.030 1.422 0.155 0.043 0.081
## lb 0.079 0.042 1.877 0.061 0.079 0.078
## ls 0.724 0.044 16.550 0.000 0.724 0.719
## la 0.150 0.041 3.644 0.000 0.150 0.145summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 23 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 27
##
## Used Total
## Number of observations 255 312
##
## Model Test User Model:
##
## Test statistic 24.097
## Degrees of freedom 3
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din_gr ~
## din_1 (ns1) -0.189 0.019 -9.727 0.000 -0.189 -0.583
## chla_gr (nt1) -0.121 0.023 -5.193 0.000 -0.121 -0.263
## hzoop_1 (nn1) 0.017 0.017 1.027 0.305 0.017 0.054
## pzoop_1 (nn2) 0.006 0.018 0.343 0.731 0.006 0.019
## corbic_1 (nn3) -0.009 0.017 -0.523 0.601 -0.009 -0.027
## flow (na1) -0.151 0.019 -7.798 0.000 -0.151 -0.459
## temp (na2) 0.025 0.017 1.511 0.131 0.025 0.078
## turbid (na3) 0.025 0.016 1.557 0.119 0.025 0.079
## chla_gr ~
## din_1 (cb1) -0.089 0.045 -1.985 0.047 -0.089 -0.126
## chla_1 (cs1) -0.407 0.036 -11.404 0.000 -0.407 -0.607
## hzoop_1 (ct1) -0.034 0.037 -0.928 0.353 -0.034 -0.049
## corbic_1 (ct2) -0.005 0.037 -0.140 0.889 -0.005 -0.007
## pzoop_1 (ct3) -0.043 0.040 -1.081 0.280 -0.043 -0.061
## flow (ca1) -0.057 0.042 -1.334 0.182 -0.057 -0.079
## temp (ca2) 0.054 0.036 1.492 0.136 0.054 0.077
## turbid (ca3) 0.068 0.035 1.932 0.053 0.068 0.100
## corbic ~
## chla_1 (lb1) 0.041 0.052 0.796 0.426 0.041 0.043
## hzoop_1 (lb2) 0.010 0.056 0.175 0.861 0.010 0.010
## pzoop_1 (lb3) -0.015 0.058 -0.263 0.793 -0.015 -0.015
## corbic_1 (ls1) 0.465 0.056 8.364 0.000 0.465 0.463
## flow (la1) 0.090 0.060 1.510 0.131 0.090 0.089
## temp (la2) -0.033 0.055 -0.591 0.554 -0.033 -0.033
## turbid (la3) -0.120 0.053 -2.270 0.023 -0.120 -0.124
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr ~~
## .corbic -0.001 0.013 -0.082 0.935 -0.001 -0.005
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr 0.064 0.006 11.292 0.000 0.064 0.624
## .chla_gr 0.307 0.027 11.292 0.000 0.307 0.632
## .corbic 0.713 0.063 11.292 0.000 0.713 0.738
##
## R-Square:
## Estimate
## din_gr 0.376
## chla_gr 0.368
## corbic 0.262
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.189 0.019 9.727 0.000 0.189 0.583
## nt 0.121 0.023 5.193 0.000 0.121 0.263
## nn 0.020 0.016 1.299 0.194 0.020 0.063
## na 0.155 0.019 8.064 0.000 0.155 0.472
## cb 0.089 0.045 1.985 0.047 0.089 0.126
## cs 0.407 0.036 11.404 0.000 0.407 0.607
## ct 0.056 0.035 1.604 0.109 0.056 0.078
## ca 0.104 0.039 2.696 0.007 0.104 0.149
## lb 0.045 0.055 0.815 0.415 0.045 0.047
## ls 0.465 0.056 8.364 0.000 0.465 0.463
## la 0.154 0.057 2.707 0.007 0.154 0.156summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 17 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 312
##
## Model Test User Model:
##
## Test statistic 2.092
## Degrees of freedom 4
## P-value (Chi-square) 0.719
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## din_gr ~
## din_1 (ns1) -0.156 0.023 -6.782 0.000 -0.156 -0.393
## chla_gr (nt1) -0.213 0.029 -7.324 0.000 -0.213 -0.386
## hzoop_1 (nn1) -0.023 0.023 -1.037 0.300 -0.023 -0.055
## pzoop_1 (nn2) 0.010 0.023 0.412 0.680 0.010 0.022
## corbic_1 (nn3) 0.028 0.022 1.298 0.194 0.028 0.069
## flow (na1) -0.013 0.022 -0.583 0.560 -0.013 -0.031
## temp (na2) 0.022 0.022 1.030 0.303 0.022 0.054
## turbid (na3) 0.058 0.024 2.429 0.015 0.058 0.143
## chla_gr ~
## din_1 (cb1) 0.052 0.042 1.243 0.214 0.052 0.073
## chla_1 (cs1) -0.390 0.040 -9.827 0.000 -0.390 -0.537
## hzoop_1 (ct1) 0.045 0.042 1.071 0.284 0.045 0.058
## corbic_1 (ct2) 0.053 0.039 1.348 0.178 0.053 0.073
## flow (ca1) -0.063 0.040 -1.556 0.120 -0.063 -0.084
## temp (ca2) 0.005 0.039 0.133 0.894 0.005 0.007
## turbid (ca3) -0.014 0.044 -0.314 0.754 -0.014 -0.019
## corbic ~
## chla_1 (lb1) 0.054 0.058 0.928 0.354 0.054 0.055
## hzoop_1 (lb2) -0.024 0.061 -0.384 0.701 -0.024 -0.023
## pzoop_1 (lb3) -0.043 0.063 -0.675 0.500 -0.043 -0.040
## corbic_1 (ls1) 0.315 0.058 5.471 0.000 0.315 0.318
## flow (la1) 0.082 0.058 1.396 0.163 0.082 0.080
## temp (la2) -0.078 0.058 -1.351 0.177 -0.078 -0.078
## turbid (la3) 0.185 0.058 3.194 0.001 0.185 0.185
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr ~~
## .corbic -0.001 0.019 -0.047 0.962 -0.001 -0.003
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .din_gr 0.113 0.010 11.314 0.000 0.113 0.682
## .chla_gr 0.382 0.034 11.314 0.000 0.382 0.705
## .corbic 0.811 0.072 11.314 0.000 0.811 0.808
##
## R-Square:
## Estimate
## din_gr 0.318
## chla_gr 0.295
## corbic 0.192
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ns 0.156 0.023 6.782 0.000 0.156 0.393
## nt 0.213 0.029 7.324 0.000 0.213 0.386
## nn 0.038 0.024 1.604 0.109 0.038 0.092
## na 0.063 0.024 2.603 0.009 0.063 0.156
## cb 0.052 0.042 1.243 0.214 0.052 0.073
## cs 0.390 0.040 9.827 0.000 0.390 0.537
## ct 0.069 0.039 1.787 0.074 0.069 0.094
## ca 0.064 0.039 1.640 0.101 0.064 0.087
## lb 0.073 0.066 1.111 0.267 0.073 0.072
## ls 0.315 0.058 5.471 0.000 0.315 0.318
## la 0.216 0.055 3.913 0.000 0.216 0.216#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
lower_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="Far West",
manual_port_settings=TRUE)
lower_plot_far_west#WEST
lower_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="West",
manual_port_settings=TRUE)
lower_plot_west#NORTH
lower_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="North",
manual_port_settings=TRUE)
lower_plot_north#SOUTH
lower_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_lower_trophic",
title="South",
manual_port_settings=TRUE)
lower_plot_southZooplankton
modFW='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp
hcope_gr~chla_1+hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope_gr~hcope_1+pcope_1+potam_1+flow+turbid+temp+estfish_bsmt_1
estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+flow+turbid+temp
'
modW='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+potam_1+flow+turbid+temp+mysid_1
hcope_gr~chla_1+hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
amphi_m_gr~chla_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope_gr~hcope_1+pcope_1+mysid_1+potam_1+flow+turbid+temp+estfish_bsmt_1+rotif_m_1
mysid_gr~chla_1+hcope_1+pcope_1+amphi_m_1+mysid_1+flow+turbid+temp+estfish_bsmt_1
estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+mysid_1+flow+turbid+temp
'
modN='chla_gr~chla_1+hcope_1+amphi_m_1+rotif_m_1+corbic_1+flow+turbid+temp
hcope_gr~chla_1+hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope_gr~hcope_1+pcope_1+mysid_1+corbic_1+flow+turbid+temp+estfish_bsmt_1+chla_1
mysid_gr~hcope_1+pcope_1+mysid_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+mysid_1+flow+turbid+temp
'
modS='chla_gr~chla_1+hcope_1+clad_1+rotif_m_1+corbic_1+flow+turbid+temp
hcope_gr~chla_1+hcope_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
clad_gr~chla_1+clad_1+pcope_1+flow+turbid+temp+estfish_bsmt_1
amphi_m_gr~chla_1+amphi_m_1+flow+turbid+temp+estfish_bsmt_1
rotif_m_gr~chla_1+rotif_m_1+flow+turbid+temp+estfish_bsmt_1
pcope_gr~chla_1+hcope_1+clad_1+pcope_1+corbic_1+flow+turbid+temp+estfish_bsmt_1
estfish_bsmt_gr~estfish_bsmt_1+hcope_1+pcope_1+amphi_m_1+rotif_m_1+clad_1+flow+turbid+temp
'
modfitFW=sem(modFW, data=filter(fdr_ds,region=="Far West"))
modfitW=sem(modW, data=filter(fdr_ds,region=="West"))
modfitN=sem(modN, data=filter(fdr_ds,region=="North"))
modfitS=sem(modS, data=filter(fdr_ds,region=="South"))
summary(modfitFW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Used Total
## Number of observations 183 312
##
## Model Test User Model:
##
## Test statistic 22.070
## Degrees of freedom 17
## P-value (Chi-square) 0.182
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla_gr ~
## chla_1 -0.346 0.037 -9.334 0.000 -0.346 -0.573
## hcope_1 0.042 0.030 1.423 0.155 0.042 0.088
## amphi_m_1 0.010 0.036 0.283 0.777 0.010 0.021
## rotif_m_1 -0.028 0.035 -0.798 0.425 -0.028 -0.052
## potam_1 -0.006 0.027 -0.222 0.824 -0.006 -0.014
## flow 0.032 0.040 0.797 0.425 0.032 0.061
## turbid -0.037 0.039 -0.953 0.341 -0.037 -0.067
## temp 0.028 0.034 0.808 0.419 0.028 0.052
## hcope_gr ~
## chla_1 0.133 0.080 1.656 0.098 0.133 0.093
## hcope_1 -0.720 0.070 -10.314 0.000 -0.720 -0.626
## pcope_1 0.070 0.072 0.975 0.329 0.070 0.058
## potam_1 -0.111 0.062 -1.803 0.071 -0.111 -0.106
## flow -0.074 0.081 -0.906 0.365 -0.074 -0.060
## turbid -0.050 0.086 -0.585 0.559 -0.050 -0.038
## temp -0.047 0.077 -0.615 0.539 -0.047 -0.038
## estfish_bsmt_1 -0.137 0.077 -1.782 0.075 -0.137 -0.114
## amphi_m_gr ~
## chla_1 0.074 0.083 0.895 0.371 0.074 0.058
## amphi_m_1 -0.534 0.080 -6.705 0.000 -0.534 -0.523
## flow -0.511 0.089 -5.752 0.000 -0.511 -0.468
## turbid 0.003 0.086 0.034 0.973 0.003 0.002
## temp 0.063 0.077 0.818 0.413 0.063 0.056
## estfish_bsmt_1 0.096 0.074 1.296 0.195 0.096 0.090
## rotif_m_gr ~
## chla_1 -0.390 0.164 -2.381 0.017 -0.390 -0.146
## rotif_m_1 -1.317 0.147 -8.970 0.000 -1.317 -0.551
## flow 0.148 0.161 0.918 0.359 0.148 0.065
## turbid 0.086 0.170 0.505 0.614 0.086 0.035
## temp 0.072 0.152 0.471 0.637 0.072 0.031
## estfish_bsmt_1 -0.007 0.144 -0.050 0.960 -0.007 -0.003
## pcope_gr ~
## hcope_1 0.131 0.134 0.979 0.328 0.131 0.062
## pcope_1 -1.298 0.138 -9.379 0.000 -1.298 -0.583
## potam_1 -0.197 0.117 -1.682 0.093 -0.197 -0.102
## flow 0.359 0.156 2.308 0.021 0.359 0.158
## turbid 0.206 0.164 1.254 0.210 0.206 0.084
## temp 0.239 0.148 1.616 0.106 0.239 0.103
## estfish_bsmt_1 -0.031 0.147 -0.209 0.835 -0.031 -0.014
## estfish_bsmt_gr ~
## estfish_bsmt_1 -1.319 0.150 -8.784 0.000 -1.319 -0.587
## hcope_1 -0.234 0.136 -1.726 0.084 -0.234 -0.109
## pcope_1 0.314 0.141 2.223 0.026 0.314 0.140
## amphi_m_1 -0.138 0.160 -0.862 0.389 -0.138 -0.064
## rotif_m_1 -0.320 0.150 -2.137 0.033 -0.320 -0.133
## flow 0.155 0.172 0.898 0.369 0.155 0.067
## turbid 0.448 0.169 2.654 0.008 0.448 0.182
## temp -0.078 0.151 -0.514 0.607 -0.078 -0.033
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr ~~
## .hcope_gr 0.023 0.029 0.779 0.436 0.023 0.058
## .amphi_m_gr -0.001 0.029 -0.030 0.976 -0.001 -0.002
## .rotif_m_gr 0.140 0.058 2.391 0.017 0.140 0.180
## .pcope_gr 0.038 0.056 0.687 0.492 0.038 0.051
## .estfsh_bsmt_gr -0.057 0.056 -1.003 0.316 -0.057 -0.074
## .hcope_gr ~~
## .amphi_m_gr -0.032 0.065 -0.491 0.624 -0.032 -0.036
## .rotif_m_gr 0.143 0.129 1.107 0.268 0.143 0.082
## .pcope_gr -0.376 0.128 -2.945 0.003 -0.376 -0.223
## .estfsh_bsmt_gr -0.107 0.126 -0.846 0.398 -0.107 -0.063
## .amphi_m_gr ~~
## .rotif_m_gr -0.233 0.130 -1.798 0.072 -0.233 -0.134
## .pcope_gr 0.016 0.125 0.128 0.898 0.016 0.009
## .estfsh_bsmt_gr 0.004 0.126 0.035 0.972 0.004 0.003
## .rotif_m_gr ~~
## .pcope_gr -0.184 0.248 -0.743 0.458 -0.184 -0.055
## .estfsh_bsmt_gr -0.117 0.250 -0.467 0.640 -0.117 -0.035
## .pcope_gr ~~
## .estfsh_bsmt_gr -0.513 0.245 -2.092 0.036 -0.513 -0.157
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr 0.175 0.018 9.566 0.000 0.175 0.671
## .hcope_gr 0.877 0.092 9.566 0.000 0.877 0.599
## .amphi_m_gr 0.875 0.091 9.566 0.000 0.875 0.754
## .rotif_m_gr 3.454 0.361 9.566 0.000 3.454 0.678
## .pcope_gr 3.242 0.339 9.566 0.000 3.242 0.642
## .estfsh_bsmt_gr 3.312 0.346 9.566 0.000 3.312 0.644
##
## R-Square:
## Estimate
## chla_gr 0.329
## hcope_gr 0.401
## amphi_m_gr 0.246
## rotif_m_gr 0.322
## pcope_gr 0.358
## estfsh_bsmt_gr 0.356summary(modfitW, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 87
##
## Used Total
## Number of observations 202 312
##
## Model Test User Model:
##
## Test statistic 24.272
## Degrees of freedom 18
## P-value (Chi-square) 0.146
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla_gr ~
## chla_1 -0.330 0.035 -9.561 0.000 -0.330 -0.611
## hcope_1 0.065 0.036 1.815 0.070 0.065 0.115
## amphi_m_1 0.047 0.033 1.424 0.154 0.047 0.088
## rotif_m_1 0.066 0.032 2.056 0.040 0.066 0.129
## potam_1 -0.010 0.036 -0.288 0.774 -0.010 -0.019
## flow -0.005 0.037 -0.140 0.888 -0.005 -0.009
## turbid 0.011 0.038 0.296 0.767 0.011 0.020
## temp -0.062 0.032 -1.909 0.056 -0.062 -0.116
## mysid_1 -0.051 0.036 -1.449 0.147 -0.051 -0.092
## hcope_gr ~
## chla_1 0.033 0.037 0.895 0.371 0.033 0.055
## hcope_1 -0.352 0.040 -8.798 0.000 -0.352 -0.559
## pcope_1 -0.052 0.035 -1.472 0.141 -0.052 -0.088
## mysid_1 0.036 0.041 0.869 0.385 0.036 0.057
## potam_1 -0.088 0.037 -2.359 0.018 -0.088 -0.142
## flow -0.085 0.041 -2.095 0.036 -0.085 -0.138
## turbid -0.044 0.042 -1.061 0.289 -0.044 -0.071
## temp 0.034 0.036 0.945 0.345 0.034 0.057
## estfish_bsmt_1 0.013 0.036 0.375 0.708 0.013 0.022
## rotif_m_1 0.103 0.034 3.039 0.002 0.103 0.182
## amphi_m_gr ~
## chla_1 -0.072 0.059 -1.215 0.224 -0.072 -0.081
## amphi_m_1 -0.304 0.061 -4.959 0.000 -0.304 -0.348
## mysid_1 0.153 0.062 2.478 0.013 0.153 0.167
## flow 0.003 0.063 0.055 0.956 0.003 0.004
## turbid -0.210 0.066 -3.187 0.001 -0.210 -0.228
## temp -0.162 0.058 -2.801 0.005 -0.162 -0.186
## estfish_bsmt_1 -0.251 0.061 -4.084 0.000 -0.251 -0.281
## rotif_m_gr ~
## chla_1 0.008 0.101 0.077 0.938 0.008 0.005
## rotif_m_1 -0.884 0.097 -9.143 0.000 -0.884 -0.572
## flow 0.309 0.110 2.803 0.005 0.309 0.183
## turbid -0.245 0.109 -2.252 0.024 -0.245 -0.144
## temp -0.005 0.098 -0.056 0.956 -0.005 -0.003
## estfish_bsmt_1 -0.215 0.097 -2.217 0.027 -0.215 -0.130
## pcope_gr ~
## hcope_1 -0.136 0.061 -2.225 0.026 -0.136 -0.142
## pcope_1 -0.494 0.057 -8.656 0.000 -0.494 -0.554
## mysid_1 0.123 0.064 1.931 0.053 0.123 0.131
## potam_1 0.032 0.063 0.506 0.613 0.032 0.034
## flow 0.116 0.063 1.845 0.065 0.116 0.124
## turbid -0.073 0.065 -1.132 0.258 -0.073 -0.077
## temp 0.184 0.055 3.360 0.001 0.184 0.206
## estfish_bsmt_1 0.001 0.057 0.018 0.986 0.001 0.001
## rotif_m_1 0.141 0.055 2.558 0.011 0.141 0.164
## mysid_gr ~
## chla_1 0.253 0.078 3.254 0.001 0.253 0.209
## hcope_1 0.076 0.082 0.920 0.357 0.076 0.060
## pcope_1 0.130 0.073 1.764 0.078 0.130 0.109
## amphi_m_1 -0.119 0.076 -1.582 0.114 -0.119 -0.100
## mysid_1 -0.697 0.086 -8.135 0.000 -0.697 -0.556
## flow -0.212 0.081 -2.602 0.009 -0.212 -0.169
## turbid 0.332 0.087 3.806 0.000 0.332 0.263
## temp 0.118 0.075 1.563 0.118 0.118 0.099
## estfish_bsmt_1 0.006 0.078 0.076 0.940 0.006 0.005
## estfish_bsmt_gr ~
## estfish_bsmt_1 -0.964 0.104 -9.304 0.000 -0.964 -0.583
## hcope_1 0.104 0.106 0.982 0.326 0.104 0.061
## pcope_1 -0.027 0.101 -0.269 0.788 -0.027 -0.017
## amphi_m_1 -0.269 0.104 -2.594 0.009 -0.269 -0.166
## rotif_m_1 0.011 0.098 0.112 0.911 0.011 0.007
## mysid_1 -0.164 0.112 -1.464 0.143 -0.164 -0.097
## flow -0.224 0.108 -2.071 0.038 -0.224 -0.133
## turbid 0.259 0.114 2.265 0.023 0.259 0.152
## temp 0.073 0.097 0.756 0.449 0.073 0.045
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr ~~
## .hcope_gr 0.055 0.016 3.501 0.000 0.055 0.254
## .amphi_m_gr 0.004 0.025 0.157 0.875 0.004 0.011
## .rotif_m_gr 0.134 0.044 3.049 0.002 0.134 0.220
## .pcope_gr -0.012 0.024 -0.488 0.625 -0.012 -0.034
## .mysid_gr 0.078 0.033 2.397 0.017 0.078 0.171
## .estfsh_bsmt_gr -0.016 0.042 -0.391 0.696 -0.016 -0.028
## .hcope_gr ~~
## .amphi_m_gr -0.047 0.028 -1.686 0.092 -0.047 -0.119
## .rotif_m_gr -0.026 0.047 -0.557 0.577 -0.026 -0.039
## .pcope_gr 0.027 0.026 1.026 0.305 0.027 0.072
## .mysid_gr 0.185 0.038 4.905 0.000 0.185 0.368
## .estfsh_bsmt_gr -0.122 0.047 -2.586 0.010 -0.122 -0.185
## .amphi_m_gr ~~
## .rotif_m_gr 0.127 0.077 1.649 0.099 0.127 0.117
## .pcope_gr 0.010 0.043 0.246 0.806 0.010 0.017
## .mysid_gr -0.112 0.058 -1.929 0.054 -0.112 -0.137
## .estfsh_bsmt_gr 0.046 0.075 0.612 0.541 0.046 0.043
## .rotif_m_gr ~~
## .pcope_gr 0.077 0.073 1.054 0.292 0.077 0.074
## .mysid_gr -0.100 0.099 -1.012 0.312 -0.100 -0.071
## .estfsh_bsmt_gr 0.173 0.130 1.339 0.181 0.173 0.095
## .pcope_gr ~~
## .mysid_gr -0.035 0.055 -0.638 0.524 -0.035 -0.045
## .estfsh_bsmt_gr -0.016 0.072 -0.218 0.827 -0.016 -0.015
## .mysid_gr ~~
## .estfsh_bsmt_gr -0.076 0.097 -0.778 0.436 -0.076 -0.055
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr 0.199 0.020 10.050 0.000 0.199 0.676
## .hcope_gr 0.239 0.024 10.050 0.000 0.239 0.662
## .amphi_m_gr 0.634 0.063 10.050 0.000 0.634 0.802
## .rotif_m_gr 1.862 0.185 10.050 0.000 1.862 0.689
## .pcope_gr 0.577 0.057 10.050 0.000 0.577 0.693
## .mysid_gr 1.057 0.105 10.050 0.000 1.057 0.713
## .estfsh_bsmt_gr 1.804 0.180 10.050 0.000 1.804 0.666
##
## R-Square:
## Estimate
## chla_gr 0.324
## hcope_gr 0.338
## amphi_m_gr 0.198
## rotif_m_gr 0.311
## pcope_gr 0.307
## mysid_gr 0.287
## estfsh_bsmt_gr 0.334summary(modfitN, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 83
##
## Used Total
## Number of observations 186 312
##
## Model Test User Model:
##
## Test statistic 27.444
## Degrees of freedom 22
## P-value (Chi-square) 0.195
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla_gr ~
## chla_1 -0.379 0.042 -8.961 0.000 -0.379 -0.553
## hcope_1 0.034 0.058 0.582 0.560 0.034 0.039
## amphi_m_1 0.026 0.044 0.586 0.558 0.026 0.035
## rotif_m_1 0.002 0.042 0.052 0.959 0.002 0.003
## corbic_1 -0.017 0.044 -0.392 0.695 -0.017 -0.025
## flow -0.013 0.047 -0.286 0.775 -0.013 -0.020
## turbid 0.029 0.044 0.652 0.514 0.029 0.042
## temp 0.046 0.043 1.060 0.289 0.046 0.068
## hcope_gr ~
## chla_1 -0.020 0.057 -0.362 0.718 -0.020 -0.019
## hcope_1 -0.799 0.102 -7.869 0.000 -0.799 -0.602
## pcope_1 -0.143 0.063 -2.273 0.023 -0.143 -0.141
## mysid_1 0.072 0.085 0.854 0.393 0.072 0.066
## corbic_1 0.154 0.057 2.685 0.007 0.154 0.142
## flow -0.387 0.072 -5.353 0.000 -0.387 -0.364
## turbid 0.123 0.069 1.777 0.076 0.123 0.117
## temp 0.018 0.065 0.284 0.776 0.018 0.018
## estfish_bsmt_1 -0.003 0.065 -0.048 0.961 -0.003 -0.003
## amphi_m_gr ~
## chla_1 0.052 0.066 0.790 0.430 0.052 0.051
## amphi_m_1 -0.503 0.070 -7.197 0.000 -0.503 -0.453
## flow 0.006 0.073 0.082 0.935 0.006 0.006
## turbid -0.059 0.068 -0.872 0.383 -0.059 -0.059
## temp -0.034 0.066 -0.518 0.605 -0.034 -0.035
## estfish_bsmt_1 -0.059 0.067 -0.882 0.378 -0.059 -0.062
## rotif_m_gr ~
## chla_1 -0.157 0.127 -1.240 0.215 -0.157 -0.070
## rotif_m_1 -1.440 0.128 -11.263 0.000 -1.440 -0.649
## flow 0.663 0.144 4.616 0.000 0.663 0.294
## turbid -0.149 0.133 -1.123 0.261 -0.149 -0.067
## temp -0.124 0.129 -0.964 0.335 -0.124 -0.056
## estfish_bsmt_1 -0.068 0.130 -0.528 0.598 -0.068 -0.033
## pcope_gr ~
## hcope_1 -0.052 0.169 -0.305 0.760 -0.052 -0.023
## pcope_1 -1.041 0.106 -9.787 0.000 -1.041 -0.603
## mysid_1 0.197 0.143 1.380 0.168 0.197 0.106
## corbic_1 0.069 0.102 0.678 0.498 0.069 0.037
## flow -0.271 0.121 -2.240 0.025 -0.271 -0.149
## turbid -0.294 0.116 -2.527 0.012 -0.294 -0.164
## temp 0.038 0.109 0.347 0.729 0.038 0.021
## estfish_bsmt_1 -0.181 0.110 -1.641 0.101 -0.181 -0.107
## chla_1 0.249 0.104 2.392 0.017 0.249 0.137
## mysid_gr ~
## hcope_1 0.092 0.306 0.302 0.763 0.092 0.022
## pcope_1 -0.181 0.193 -0.939 0.348 -0.181 -0.057
## mysid_1 -2.365 0.257 -9.188 0.000 -2.365 -0.692
## amphi_m_1 -0.309 0.182 -1.697 0.090 -0.309 -0.085
## flow -1.147 0.216 -5.315 0.000 -1.147 -0.344
## turbid 0.808 0.208 3.891 0.000 0.808 0.246
## temp 0.146 0.191 0.764 0.445 0.146 0.045
## estfish_bsmt_1 0.095 0.199 0.478 0.633 0.095 0.031
## estfish_bsmt_gr ~
## estfish_bsmt_1 -1.399 0.176 -7.968 0.000 -1.399 -0.570
## hcope_1 0.191 0.275 0.695 0.487 0.191 0.058
## pcope_1 -0.186 0.176 -1.057 0.291 -0.186 -0.074
## amphi_m_1 -0.067 0.183 -0.369 0.712 -0.067 -0.023
## rotif_m_1 0.083 0.175 0.473 0.636 0.083 0.032
## mysid_1 0.316 0.230 1.375 0.169 0.316 0.117
## flow -0.485 0.194 -2.502 0.012 -0.485 -0.184
## turbid 0.101 0.184 0.548 0.583 0.101 0.039
## temp -0.026 0.169 -0.154 0.878 -0.026 -0.010
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr ~~
## .hcope_gr 0.103 0.036 2.828 0.005 0.103 0.212
## .amphi_m_gr 0.044 0.037 1.186 0.236 0.044 0.087
## .rotif_m_gr 0.074 0.073 1.026 0.305 0.074 0.075
## .pcope_gr -0.116 0.060 -1.923 0.055 -0.116 -0.142
## .mysid_gr 0.075 0.106 0.706 0.480 0.075 0.052
## .estfsh_bsmt_gr -0.095 0.094 -1.010 0.312 -0.095 -0.074
## .hcope_gr ~~
## .amphi_m_gr 0.046 0.056 0.811 0.417 0.046 0.060
## .rotif_m_gr -0.331 0.112 -2.962 0.003 -0.331 -0.222
## .pcope_gr 0.343 0.094 3.670 0.000 0.343 0.279
## .mysid_gr 1.061 0.178 5.952 0.000 1.061 0.485
## .estfsh_bsmt_gr -0.163 0.142 -1.148 0.251 -0.163 -0.084
## .amphi_m_gr ~~
## .rotif_m_gr -0.178 0.115 -1.555 0.120 -0.178 -0.115
## .pcope_gr -0.337 0.097 -3.459 0.001 -0.337 -0.262
## .mysid_gr 0.161 0.168 0.956 0.339 0.161 0.070
## .estfsh_bsmt_gr -0.213 0.149 -1.435 0.151 -0.213 -0.106
## .rotif_m_gr ~~
## .pcope_gr 0.133 0.183 0.730 0.465 0.133 0.054
## .mysid_gr -0.837 0.331 -2.529 0.011 -0.837 -0.189
## .estfsh_bsmt_gr 0.123 0.287 0.430 0.667 0.123 0.032
## .pcope_gr ~~
## .mysid_gr 0.914 0.277 3.301 0.001 0.914 0.249
## .estfsh_bsmt_gr 0.424 0.239 1.776 0.076 0.424 0.131
## .mysid_gr ~~
## .estfsh_bsmt_gr -0.069 0.421 -0.165 0.869 -0.069 -0.012
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr 0.323 0.033 9.644 0.000 0.323 0.686
## .hcope_gr 0.734 0.076 9.644 0.000 0.734 0.652
## .amphi_m_gr 0.802 0.083 9.644 0.000 0.802 0.773
## .rotif_m_gr 3.014 0.313 9.644 0.000 3.014 0.594
## .pcope_gr 2.057 0.213 9.644 0.000 2.057 0.626
## .mysid_gr 6.521 0.676 9.644 0.000 6.521 0.589
## .estfsh_bsmt_gr 5.066 0.525 9.644 0.000 5.066 0.730
##
## R-Square:
## Estimate
## chla_gr 0.314
## hcope_gr 0.348
## amphi_m_gr 0.227
## rotif_m_gr 0.406
## pcope_gr 0.374
## mysid_gr 0.411
## estfsh_bsmt_gr 0.270summary(modfitS, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 81
##
## Used Total
## Number of observations 192 312
##
## Model Test User Model:
##
## Test statistic 23.259
## Degrees of freedom 24
## P-value (Chi-square) 0.505
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla_gr ~
## chla_1 -0.437 0.048 -9.175 0.000 -0.437 -0.573
## hcope_1 0.065 0.044 1.479 0.139 0.065 0.087
## clad_1 0.099 0.042 2.357 0.018 0.099 0.140
## rotif_m_1 -0.064 0.044 -1.433 0.152 -0.064 -0.083
## corbic_1 -0.004 0.043 -0.097 0.923 -0.004 -0.006
## flow -0.069 0.049 -1.403 0.161 -0.069 -0.088
## turbid -0.003 0.045 -0.071 0.943 -0.003 -0.004
## temp 0.020 0.044 0.452 0.651 0.020 0.027
## hcope_gr ~
## chla_1 0.146 0.058 2.504 0.012 0.146 0.156
## hcope_1 -0.460 0.057 -8.041 0.000 -0.460 -0.495
## pcope_1 -0.001 0.054 -0.009 0.993 -0.001 -0.001
## corbic_1 0.100 0.055 1.809 0.070 0.100 0.108
## flow -0.119 0.062 -1.922 0.055 -0.119 -0.125
## turbid -0.088 0.058 -1.531 0.126 -0.088 -0.098
## temp 0.060 0.057 1.056 0.291 0.060 0.067
## estfish_bsmt_1 -0.138 0.056 -2.474 0.013 -0.138 -0.155
## clad_gr ~
## chla_1 0.202 0.083 2.438 0.015 0.202 0.163
## clad_1 -0.552 0.077 -7.164 0.000 -0.552 -0.480
## pcope_1 0.006 0.077 0.081 0.936 0.006 0.005
## flow 0.220 0.084 2.627 0.009 0.220 0.174
## turbid -0.100 0.079 -1.263 0.206 -0.100 -0.084
## temp 0.019 0.078 0.246 0.806 0.019 0.016
## estfish_bsmt_1 -0.110 0.076 -1.452 0.146 -0.110 -0.093
## amphi_m_gr ~
## chla_1 -0.041 0.094 -0.432 0.666 -0.041 -0.025
## amphi_m_1 -0.885 0.089 -9.919 0.000 -0.885 -0.594
## flow -0.074 0.100 -0.743 0.457 -0.074 -0.045
## turbid 0.198 0.096 2.063 0.039 0.198 0.126
## temp 0.081 0.093 0.867 0.386 0.081 0.052
## estfish_bsmt_1 0.066 0.094 0.700 0.484 0.066 0.042
## rotif_m_gr ~
## chla_1 0.005 0.101 0.053 0.957 0.005 0.003
## rotif_m_1 -1.059 0.096 -10.992 0.000 -1.059 -0.604
## flow 0.365 0.105 3.477 0.001 0.365 0.204
## turbid -0.088 0.098 -0.902 0.367 -0.088 -0.052
## temp -0.035 0.098 -0.360 0.719 -0.035 -0.021
## estfish_bsmt_1 0.164 0.096 1.702 0.089 0.164 0.098
## pcope_gr ~
## chla_1 0.342 0.100 3.411 0.001 0.342 0.212
## hcope_1 -0.116 0.099 -1.175 0.240 -0.116 -0.072
## clad_1 0.125 0.095 1.325 0.185 0.125 0.084
## pcope_1 -0.759 0.096 -7.893 0.000 -0.759 -0.490
## corbic_1 0.044 0.094 0.469 0.639 0.044 0.028
## flow -0.003 0.106 -0.029 0.977 -0.003 -0.002
## turbid -0.090 0.099 -0.913 0.361 -0.090 -0.058
## temp -0.002 0.096 -0.019 0.985 -0.002 -0.001
## estfish_bsmt_1 -0.056 0.097 -0.571 0.568 -0.056 -0.036
## estfish_bsmt_gr ~
## estfish_bsmt_1 -1.450 0.174 -8.314 0.000 -1.450 -0.522
## hcope_1 0.008 0.175 0.047 0.962 0.008 0.003
## pcope_1 -0.194 0.176 -1.105 0.269 -0.194 -0.069
## amphi_m_1 0.232 0.158 1.465 0.143 0.232 0.087
## rotif_m_1 -0.017 0.173 -0.098 0.922 -0.017 -0.006
## clad_1 0.252 0.167 1.510 0.131 0.252 0.093
## flow -0.154 0.191 -0.806 0.421 -0.154 -0.052
## turbid 0.516 0.178 2.901 0.004 0.516 0.185
## temp -0.033 0.172 -0.191 0.848 -0.033 -0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr ~~
## .hcope_gr 0.115 0.035 3.337 0.001 0.115 0.248
## .clad_gr 0.183 0.048 3.815 0.000 0.183 0.286
## .amphi_m_gr 0.001 0.055 0.024 0.981 0.001 0.002
## .rotif_m_gr 0.186 0.060 3.131 0.002 0.186 0.232
## .pcope_gr -0.012 0.057 -0.208 0.835 -0.012 -0.015
## .estfsh_bsmt_gr -0.025 0.102 -0.245 0.807 -0.025 -0.018
## .hcope_gr ~~
## .clad_gr 0.185 0.060 3.063 0.002 0.185 0.227
## .amphi_m_gr 0.104 0.071 1.465 0.143 0.104 0.106
## .rotif_m_gr -0.189 0.075 -2.506 0.012 -0.189 -0.184
## .pcope_gr 0.098 0.073 1.347 0.178 0.098 0.098
## .estfsh_bsmt_gr -0.301 0.132 -2.289 0.022 -0.301 -0.167
## .clad_gr ~~
## .amphi_m_gr 0.067 0.097 0.692 0.489 0.067 0.050
## .rotif_m_gr 0.171 0.102 1.674 0.094 0.171 0.122
## .pcope_gr 0.080 0.099 0.800 0.423 0.080 0.058
## .estfsh_bsmt_gr -0.296 0.179 -1.653 0.098 -0.296 -0.120
## .amphi_m_gr ~~
## .rotif_m_gr -0.089 0.123 -0.725 0.468 -0.089 -0.052
## .pcope_gr -0.154 0.120 -1.283 0.199 -0.154 -0.093
## .estfsh_bsmt_gr -0.666 0.220 -3.031 0.002 -0.666 -0.224
## .rotif_m_gr ~~
## .pcope_gr 0.418 0.129 3.246 0.001 0.418 0.241
## .estfsh_bsmt_gr 0.438 0.227 1.933 0.053 0.438 0.141
## .pcope_gr ~~
## .estfsh_bsmt_gr 0.742 0.225 3.290 0.001 0.742 0.244
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .chla_gr 0.364 0.037 9.798 0.000 0.364 0.677
## .hcope_gr 0.594 0.061 9.798 0.000 0.594 0.736
## .clad_gr 1.117 0.114 9.798 0.000 1.117 0.785
## .amphi_m_gr 1.621 0.165 9.798 0.000 1.621 0.658
## .rotif_m_gr 1.776 0.181 9.798 0.000 1.776 0.625
## .pcope_gr 1.693 0.173 9.798 0.000 1.693 0.703
## .estfsh_bsmt_gr 5.440 0.555 9.798 0.000 5.440 0.690
##
## R-Square:
## Estimate
## chla_gr 0.323
## hcope_gr 0.264
## clad_gr 0.215
## amphi_m_gr 0.342
## rotif_m_gr 0.375
## pcope_gr 0.297
## estfsh_bsmt_gr 0.310#modificationindices(modfitW, sort=T, maximum.number=20)
#residuals(modfitW)
labelsfarwest=createLabels(modfitFW, cnameslag)
labelswest=createLabels(modfitW, cnameslag)
labelsnorth=createLabels(modfitN, cnameslag)
labelssouth=createLabels(modfitS, cnameslag)
#FAR WEST
zoop_plot_far_west <- createGraph(fit=modfitFW,
reference_df=cnameslag,
model_type="monthly_zoop",
region="Far West",
title="Far West",
manual_port_settings=TRUE)
zoop_plot_far_west#WEST
zoop_plot_west <- createGraph(fit=modfitW,
reference_df=cnameslag,
model_type="monthly_zoop",
region="West",
title="West",
manual_port_settings=TRUE)
zoop_plot_west#NORTH
zoop_plot_north <- createGraph(fit=modfitN,
reference_df=cnameslag,
model_type="monthly_zoop",
region="North",
title="North",
manual_port_settings=TRUE)
zoop_plot_north#SOUTH
zoop_plot_south <- createGraph(fit=modfitS,
reference_df=cnameslag,
model_type="monthly_zoop",
region="South",
title="South",
manual_port_settings=TRUE)
zoop_plot_south