Annual model - Regions
Tanya Rogers
4/22/2022
library(dplyr)
library(lavaan)
library(DiagrammeR)
library(tidyr)
library(ggplot2)
source("functions/table_funcs.R")
# For saving SEM diagrams:
library(purrr)
library(DiagrammeRsvg)
library(rsvg)
library(png)
library(grid)
library(ggpubr)Import data
combined=read.csv("data/annual_averages/annual_data_compiled_regions.csv",stringsAsFactors = F)
cnames=read.csv("analysis/column_names_region.csv", stringsAsFactors = F)
dsub=filter(combined, Year>=1975) %>% arrange(Region,Year)
focaldata=dsub[,cnames$Datacolumn]
fvars=cnames$Shortname
colnames(focaldata)=fvars
regions=unique(focaldata$region)
regionorder=c("West","North","South")
regionorder_pub=c("Whole estuary", "Suisun","Sacramento","San Joaquin")
years=1975:2021
focaldata = focaldata %>%
mutate(tzoop=hcope+clad+mysid+pcope+rotif_m,
tzoop_e=hcope_e+clad_e+mysid_e+pcope_e+rotif_e,
hzoop=hcope+clad+rotif_m,
hzoop_e=hcope_e+clad_e+rotif_e,
pzoop=mysid+pcope,
pzoop_e=mysid_e+pcope_e,
turbid=-secchi)
fvars=c(fvars,"tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid")
cnames=rbind(cnames,data.frame(Longname=NA,Shortname=c("tzoop","tzoop_e",
"hzoop","hzoop_e",
"pzoop","pzoop_e","turbid"),
Diagramname=c("total zooplankton",
"total zooplankton\nenergy",
"herbivorous\nzooplankton",
"herbivorous\nzooplankton\nenergy",
"predatory\nzooplankton",
"predatory\nzooplankton\nenergy",
"turbidity"),
Datacolumn=NA,Log=c(rep("yes",6),"no"),
Color=c("black","black","#ED7D31","#ED7D31","#7030A0",
"#7030A0","#4472C4")))
#focal variables
varnames=c("temp","flow","turbid","chla","hzoop","pzoop","potam","corbic","estfish","estfish_bsmt","estfish_stn")
source("analysis/semDiagramFunctions.r")Data prep
Log transform, scale
#log transform
logvars=fvars[cnames$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)
#scale data
fdr0=focaldatalog
tvars=fvars[-(1:2)]
fdr=fdr0 %>% group_by(region) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame()
#detrended data
fdr_dtr=fdr0 %>% group_by(region) %>%
#detrend
mutate_at(tvars,function(x) {
x<<-x
if(!all(is.na(x))) {
if((length(which(x==0))/length(x))<0.5) {
x2<<-x
x2[x2==0]=NA
res<<-residuals(lm(x2~years))
out=x
out[which(!is.na(x2))]=res
return(out)
} else {return(x)}
} else {return(x)}
}) %>%
#lag
#mutate_at(tvars,list("1"=lag)) %>%
#scale
mutate_at(-(1:2),scale) %>%
ungroup() %>%
as.data.frame() Time series plots
Other useful plots
Breakdown of total zooplankton biomass.
## Warning: Removed 15 rows containing non-finite values (`stat_align()`).
Similarity of fish indices.
## Warning: Removed 7 rows containing missing values (`geom_line()`).
## Warning: Removed 7 rows containing missing values (`geom_line()`).
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] "North"
## [,1]
## [1,] 0.9959517
## [,1]
## [1,] 0.9870616
## [,1]
## [1,] 0.9990414
## [1] "South"
## [,1]
## [1,] 0.9941208
## [,1]
## [1,] 0.9860441
## [,1]
## [1,] 0.9993711
## [1] "West"
## [,1]
## [1,] 0.995754
## [,1]
## [1,] 0.9851301
## [,1]
## [1,] 0.9994171Cross-correlation matrices
(only sig correlations shown… no correction for multiple comparisons)
## Warning: `expand_scale()` was deprecated in ggplot2 3.3.0.
## ℹ Please use `expansion()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Note correlation of fish indices, or lack thereof.
SEM model
With and without detrending.
West
#1
# modwest='zoop=~hcope+mysid
# fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# fish~zoop+flow
# '
#2
# modwest='chla~potam+flow
# tzoop~chla+potam+flow
# estfish_bsmt~tzoop+flow
# estfish_bsot~tzoop+flow
# '
#3
# modwest='chla~potam+flow+temp+turbid
# tzoop~chla+potam+flow+temp+turbid
# amphi~chla+potam+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow
# #estfish_bsot~tzoop+amphi+flow+temp+turbid
# amphi~~tzoop
# '
#4
# modwest='chla~potam+flow+temp+turbid
# hzoop~chla+potam+flow+temp+turbid
# pzoop~chla+potam+flow+temp+turbid+hzoop
# amphi~chla+potam+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid
# amphi~~hzoop+pzoop
# '
#5
modwest='chla~potam+flow+temp+turbid
hzoop~chla+potam+flow+temp+turbid
pzoop~chla+potam+flow+temp+turbid+hzoop
fish~hzoop+pzoop+potam+flow+temp+turbid
fish=~estfish+estfish_stn+estfish_bsmt
'
modfitwest=sem(modwest, data=filter(fdr,region=="West"))
modfitwest_dtr=sem(modwest, data=filter(fdr_dtr,region=="West"))
summary(modfitwest, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 20.568
## Degrees of freedom 15
## P-value (Chi-square) 0.151
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.871 0.875
## estfish_stn 0.925 0.116 7.972 0.000 0.806 0.885
## estfish_bsmt 0.856 0.144 5.952 0.000 0.745 0.755
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam -0.285 0.169 -1.688 0.091 -0.285 -0.285
## flow 0.281 0.182 1.540 0.124 0.281 0.280
## temp -0.281 0.150 -1.870 0.062 -0.281 -0.278
## turbid -0.221 0.199 -1.112 0.266 -0.221 -0.213
## hzoop ~
## chla 0.542 0.100 5.404 0.000 0.542 0.625
## potam -0.306 0.111 -2.757 0.006 -0.306 -0.353
## flow 0.093 0.119 0.777 0.437 0.093 0.106
## temp 0.042 0.099 0.421 0.674 0.042 0.048
## turbid -0.255 0.128 -1.993 0.046 -0.255 -0.284
## pzoop ~
## chla 0.375 0.086 4.367 0.000 0.375 0.430
## potam -0.012 0.079 -0.151 0.880 -0.012 -0.014
## flow -0.319 0.078 -4.082 0.000 -0.319 -0.364
## temp -0.016 0.065 -0.240 0.810 -0.016 -0.018
## turbid 0.134 0.087 1.532 0.126 0.134 0.148
## hzoop 0.610 0.103 5.926 0.000 0.610 0.606
## fish ~
## hzoop 0.248 0.159 1.565 0.118 0.285 0.247
## pzoop 0.267 0.147 1.821 0.069 0.307 0.267
## potam -0.285 0.090 -3.165 0.002 -0.328 -0.327
## flow 0.194 0.097 1.995 0.046 0.223 0.222
## temp 0.005 0.071 0.076 0.939 0.006 0.006
## turbid 0.237 0.102 2.327 0.020 0.272 0.262
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.232 0.065 3.577 0.000 0.232 0.234
## .estfish_stn 0.180 0.052 3.465 0.001 0.180 0.217
## .estfish_bsmt 0.419 0.101 4.149 0.000 0.419 0.430
## .chla 0.718 0.161 4.472 0.000 0.718 0.719
## .hzoop 0.289 0.065 4.472 0.000 0.289 0.385
## .pzoop 0.122 0.027 4.472 0.000 0.122 0.161
## .fish 0.061 0.039 1.557 0.119 0.080 0.080
##
## R-Square:
## Estimate
## estfish 0.766
## estfish_stn 0.783
## estfish_bsmt 0.570
## chla 0.281
## hzoop 0.615
## pzoop 0.839
## fish 0.920summary(modfitwest_dtr, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 35 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 16.070
## Degrees of freedom 15
## P-value (Chi-square) 0.377
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.644 0.698
## estfish_stn 1.202 0.252 4.767 0.000 0.774 0.786
## estfish_bsmt 0.996 0.251 3.964 0.000 0.641 0.650
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## potam 0.175 0.157 1.119 0.263 0.175 0.178
## flow 0.632 0.196 3.228 0.001 0.632 0.629
## temp -0.330 0.144 -2.295 0.022 -0.330 -0.325
## turbid -0.422 0.183 -2.307 0.021 -0.422 -0.418
## hzoop ~
## chla 0.493 0.130 3.802 0.000 0.493 0.493
## potam -0.035 0.130 -0.269 0.788 -0.035 -0.036
## flow 0.421 0.180 2.338 0.019 0.421 0.420
## temp -0.006 0.125 -0.045 0.964 -0.006 -0.006
## turbid -0.517 0.160 -3.232 0.001 -0.517 -0.511
## pzoop ~
## chla 0.414 0.097 4.263 0.000 0.414 0.491
## potam 0.029 0.084 0.343 0.732 0.029 0.035
## flow -0.255 0.123 -2.065 0.039 -0.255 -0.301
## temp -0.041 0.080 -0.509 0.611 -0.041 -0.048
## turbid 0.025 0.115 0.216 0.829 0.025 0.029
## hzoop 0.428 0.101 4.216 0.000 0.428 0.508
## fish ~
## hzoop 0.055 0.107 0.518 0.605 0.086 0.086
## pzoop 0.213 0.118 1.801 0.072 0.331 0.278
## potam -0.083 0.074 -1.126 0.260 -0.129 -0.130
## flow 0.514 0.129 3.974 0.000 0.798 0.792
## temp -0.042 0.069 -0.600 0.548 -0.065 -0.064
## turbid -0.008 0.101 -0.081 0.936 -0.013 -0.012
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.436 0.107 4.089 0.000 0.436 0.513
## .estfish_stn 0.371 0.102 3.645 0.000 0.371 0.382
## .estfish_bsmt 0.564 0.134 4.209 0.000 0.564 0.578
## .chla 0.698 0.156 4.472 0.000 0.698 0.703
## .hzoop 0.470 0.105 4.472 0.000 0.470 0.473
## .pzoop 0.193 0.043 4.472 0.000 0.193 0.274
## .fish 0.022 0.040 0.545 0.586 0.053 0.053
##
## R-Square:
## Estimate
## estfish 0.487
## estfish_stn 0.618
## estfish_bsmt 0.422
## chla 0.297
## hzoop 0.527
## pzoop 0.726
## fish 0.947labelswest <- createLabels(modfitwest, cnames)
# residuals(modfitwest)
# modificationindices(modfitwest)North
#1
# modnorth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+potam+flow
# chla~potam+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
# modnorth='zoop=~clad
# zoop~chla+corbic+potam+flow
# chla~corbic+potam+flow
# estfish_bsmt~zoop+flow+sside+chla
# estfish_bsot~zoop+flow+sside+chla
# '
#2
# modnorth='chla~corbic+potam+flow
# tzoop~chla+corbic+potam+flow
# estfish_bsmt~tzoop+flow+chla
# estfish_bsot~tzoop+flow
# '
#3
# modnorth='chla~corbic+potam+flow+temp+turbid
# tzoop~chla+corbic+potam+flow+temp+turbid
# amphi~chla+corbic+potam+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow+temp+turbid+chla+sside
# #estfish_bsot~tzoop+amphi+flow+temp+turbid+sside
# amphi~~tzoop
# '
#4
# modnorth='chla~corbic+flow+temp+turbid
# hzoop~chla+corbic+flow+temp+turbid
# pzoop~chla+corbic+flow+temp+turbid+hzoop
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid+chla+sside
# amphi~~hzoop+pzoop
# '
#5
modnorth='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~chla+hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish+estfish_stn #+estfish_bsmt
estfish_stn~~chla
'
modfitnorth=sem(modnorth, data=filter(fdr,region=="North"))
modfitnorth_dtr=sem(modnorth, data=filter(fdr_dtr,region=="North"))
summary(modfitnorth, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 4.572
## Degrees of freedom 5
## P-value (Chi-square) 0.470
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.828 0.808
## estfish_stn 0.901 0.169 5.324 0.000 0.747 0.843
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.449 0.126 3.547 0.000 0.449 0.450
## flow 0.210 0.146 1.437 0.151 0.210 0.201
## temp 0.182 0.131 1.392 0.164 0.182 0.178
## turbid 0.108 0.133 0.813 0.416 0.108 0.106
## hzoop ~
## chla 0.138 0.116 1.182 0.237 0.138 0.168
## corbic 0.314 0.117 2.697 0.007 0.314 0.384
## flow -0.136 0.123 -1.110 0.267 -0.136 -0.159
## temp 0.073 0.110 0.663 0.508 0.073 0.086
## turbid 0.377 0.105 3.595 0.000 0.377 0.448
## pzoop ~
## chla 0.580 0.090 6.471 0.000 0.580 0.602
## corbic 0.347 0.096 3.625 0.000 0.347 0.361
## flow -0.483 0.094 -5.119 0.000 -0.483 -0.479
## temp -0.004 0.084 -0.044 0.965 -0.004 -0.004
## turbid 0.127 0.091 1.397 0.162 0.127 0.129
## hzoop 0.157 0.118 1.328 0.184 0.157 0.134
## fish ~
## chla -0.561 0.183 -3.058 0.002 -0.677 -0.686
## hzoop 0.521 0.159 3.281 0.001 0.629 0.524
## pzoop 0.517 0.200 2.587 0.010 0.624 0.610
## corbic -0.282 0.150 -1.885 0.059 -0.340 -0.346
## flow 0.274 0.156 1.748 0.080 0.330 0.320
## temp 0.077 0.109 0.708 0.479 0.093 0.092
## turbid 0.273 0.121 2.266 0.023 0.330 0.326
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.281 0.114 2.474 0.013 0.281 0.595
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.364 0.113 3.219 0.001 0.364 0.346
## .estfish_stn 0.317 0.108 2.940 0.003 0.317 0.404
## .chla 0.705 0.156 4.528 0.000 0.705 0.686
## .hzoop 0.392 0.087 4.528 0.000 0.392 0.565
## .pzoop 0.225 0.050 4.528 0.000 0.225 0.235
## .fish 0.188 0.090 2.100 0.036 0.274 0.274
##
## R-Square:
## Estimate
## estfish 0.654
## estfish_stn 0.596
## chla 0.314
## hzoop 0.435
## pzoop 0.765
## fish 0.726summary(modfitnorth_dtr, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 5.442
## Degrees of freedom 5
## P-value (Chi-square) 0.364
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.611 0.616
## estfish_stn 1.188 0.422 2.814 0.005 0.725 0.726
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.517 0.158 3.271 0.001 0.517 0.486
## flow 0.151 0.160 0.945 0.344 0.151 0.143
## temp 0.173 0.134 1.291 0.197 0.173 0.167
## turbid 0.200 0.141 1.412 0.158 0.200 0.193
## hzoop ~
## chla 0.365 0.157 2.324 0.020 0.365 0.393
## corbic -0.062 0.186 -0.336 0.737 -0.062 -0.063
## flow 0.026 0.179 0.148 0.883 0.026 0.027
## temp 0.160 0.151 1.062 0.288 0.160 0.166
## turbid -0.069 0.161 -0.431 0.667 -0.069 -0.072
## pzoop ~
## chla 0.751 0.109 6.910 0.000 0.751 0.730
## corbic 0.292 0.121 2.413 0.016 0.292 0.267
## flow -0.539 0.117 -4.623 0.000 -0.539 -0.497
## temp 0.020 0.099 0.197 0.843 0.020 0.018
## turbid 0.007 0.105 0.066 0.947 0.007 0.007
## hzoop 0.020 0.102 0.193 0.847 0.020 0.018
## fish ~
## chla -0.425 0.217 -1.961 0.050 -0.696 -0.713
## hzoop 0.098 0.108 0.911 0.362 0.161 0.153
## pzoop 0.332 0.182 1.826 0.068 0.544 0.573
## corbic -0.411 0.189 -2.179 0.029 -0.673 -0.648
## flow 0.339 0.177 1.911 0.056 0.555 0.538
## temp 0.137 0.118 1.163 0.245 0.225 0.222
## turbid 0.052 0.123 0.425 0.671 0.086 0.085
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish_stn ~~
## .chla 0.460 0.184 2.496 0.013 0.460 0.676
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.611 0.169 3.607 0.000 0.611 0.621
## .estfish_stn 0.623 0.240 2.593 0.010 0.623 0.624
## .chla 0.743 0.164 4.528 0.000 0.743 0.708
## .hzoop 0.750 0.166 4.528 0.000 0.750 0.830
## .pzoop 0.318 0.070 4.528 0.000 0.318 0.286
## .fish 0.166 0.109 1.524 0.128 0.444 0.444
##
## R-Square:
## Estimate
## estfish 0.379
## estfish_stn 0.376
## chla 0.292
## hzoop 0.170
## pzoop 0.714
## fish 0.556labelsnorth <- createLabels(modfitnorth, cnames)
# residuals(modfitnorth)
# modificationindices(modfitnorth)South
#1
# modsouth='zoop=~hcope+mysid
# #fish=~estfish_bsmt+estfish_bsot
# zoop~chla+corbic+flow
# chla~corbic+flow
# estfish_bsmt~zoop+flow
# estfish_bsot~zoop+flow
# '
#2
# modsouth='chla~corbic+flow
# tzoop~chla+corbic+flow
# estfish_bsmt~tzoop+flow+corbic+sside
# estfish_bsot~tzoop+flow+corbic+sside
# '
#3
# modsouth='chla~corbic+flow+temp+turbid
# tzoop~chla+corbic+flow+temp+turbid
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~tzoop+amphi+flow+temp+turbid+corbic+sside
# #estfish_bsot~tzoop+amphi+flow+temp+turbid+corbic+sside
# amphi~~tzoop
# '
#4
# modsouth='chla~corbic+flow+temp+turbid
# hzoop~chla+corbic+flow+temp+turbid
# pzoop~chla+corbic+flow+temp+turbid+hzoop
# amphi~chla+corbic+flow+temp+turbid
# estfish_bsmt~hzoop+pzoop+amphi+flow+temp+turbid+corbic+sside
# amphi~~hzoop+pzoop
# '
#5
modsouth='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish+estfish_stn+estfish_bsmt
'
modsouth_dtr='chla~corbic+flow+temp+turbid
hzoop~chla+corbic+flow+temp+turbid
pzoop~chla+corbic+flow+temp+turbid+hzoop
fish~hzoop+pzoop+corbic+flow+temp+turbid
fish=~estfish #+estfish_stn+estfish_bsmt
'
modfitsouth=sem(modsouth, data=filter(fdr,region=="South"))
modfitsouth_dtr=sem(modsouth_dtr, data=filter(fdr_dtr,region=="South"))
summary(modfitsouth, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Used Total
## Number of observations 40 47
##
## Model Test User Model:
##
## Test statistic 17.817
## Degrees of freedom 15
## P-value (Chi-square) 0.272
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.913 0.921
## estfish_stn 0.776 0.124 6.275 0.000 0.709 0.764
## estfish_bsmt 0.630 0.152 4.139 0.000 0.575 0.583
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.284 0.149 1.906 0.057 0.284 0.283
## flow -0.124 0.137 -0.906 0.365 -0.124 -0.128
## temp 0.081 0.140 0.578 0.563 0.081 0.081
## turbid 0.392 0.158 2.478 0.013 0.392 0.373
## hzoop ~
## chla 0.449 0.107 4.213 0.000 0.449 0.523
## corbic 0.326 0.105 3.104 0.002 0.326 0.378
## flow -0.052 0.093 -0.562 0.574 -0.052 -0.063
## temp -0.022 0.095 -0.229 0.819 -0.022 -0.025
## turbid -0.021 0.115 -0.182 0.855 -0.021 -0.023
## pzoop ~
## chla 0.493 0.127 3.894 0.000 0.493 0.516
## corbic -0.037 0.116 -0.323 0.747 -0.037 -0.039
## flow -0.066 0.092 -0.711 0.477 -0.066 -0.071
## temp 0.047 0.094 0.503 0.615 0.047 0.049
## turbid 0.179 0.113 1.579 0.114 0.179 0.178
## hzoop 0.311 0.156 1.989 0.047 0.311 0.279
## fish ~
## hzoop 0.271 0.131 2.065 0.039 0.297 0.255
## pzoop -0.116 0.113 -1.027 0.304 -0.127 -0.121
## corbic 0.160 0.097 1.652 0.099 0.175 0.175
## flow -0.070 0.078 -0.904 0.366 -0.077 -0.080
## temp 0.016 0.079 0.198 0.843 0.017 0.017
## turbid 0.763 0.101 7.585 0.000 0.835 0.794
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.149 0.068 2.186 0.029 0.149 0.152
## .estfish_stn 0.359 0.090 4.001 0.000 0.359 0.417
## .estfish_bsmt 0.644 0.150 4.304 0.000 0.644 0.661
## .chla 0.723 0.162 4.472 0.000 0.723 0.724
## .hzoop 0.329 0.074 4.472 0.000 0.329 0.446
## .pzoop 0.322 0.072 4.472 0.000 0.322 0.352
## .fish 0.114 0.065 1.756 0.079 0.136 0.136
##
## R-Square:
## Estimate
## estfish 0.848
## estfish_stn 0.583
## estfish_bsmt 0.339
## chla 0.276
## hzoop 0.554
## pzoop 0.648
## fish 0.864summary(modfitsouth_dtr, standardized=T, rsq=T)## lavaan 0.6.17 ended normally after 28 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 25
##
## Used Total
## Number of observations 41 47
##
## Model Test User Model:
##
## Test statistic 0.334
## Degrees of freedom 1
## P-value (Chi-square) 0.564
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## fish =~
## estfish 1.000 0.909 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## chla ~
## corbic 0.120 0.157 0.764 0.445 0.120 0.120
## flow -0.033 0.162 -0.202 0.840 -0.033 -0.034
## temp 0.176 0.158 1.110 0.267 0.176 0.176
## turbid -0.048 0.163 -0.291 0.771 -0.048 -0.048
## hzoop ~
## chla 0.447 0.123 3.629 0.000 0.447 0.458
## corbic 0.274 0.125 2.199 0.028 0.274 0.281
## flow -0.008 0.128 -0.062 0.951 -0.008 -0.008
## temp 0.036 0.127 0.283 0.777 0.036 0.037
## turbid -0.179 0.129 -1.390 0.164 -0.179 -0.184
## pzoop ~
## chla 0.536 0.138 3.897 0.000 0.536 0.516
## corbic -0.123 0.128 -0.960 0.337 -0.123 -0.118
## flow -0.013 0.124 -0.101 0.920 -0.013 -0.012
## temp 0.150 0.123 1.222 0.222 0.150 0.145
## turbid -0.094 0.128 -0.732 0.464 -0.094 -0.090
## hzoop 0.232 0.152 1.528 0.126 0.232 0.218
## fish ~
## hzoop 0.241 0.159 1.518 0.129 0.265 0.255
## pzoop -0.267 0.142 -1.883 0.060 -0.293 -0.301
## corbic 0.104 0.137 0.753 0.451 0.114 0.113
## flow 0.005 0.132 0.034 0.973 0.005 0.005
## temp 0.108 0.133 0.814 0.416 0.119 0.118
## turbid 0.357 0.136 2.624 0.009 0.393 0.388
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .estfish 0.000 0.000 0.000
## .chla 0.927 0.205 4.528 0.000 0.927 0.950
## .hzoop 0.575 0.127 4.528 0.000 0.575 0.621
## .pzoop 0.544 0.120 4.528 0.000 0.544 0.517
## .fish 0.613 0.135 4.528 0.000 0.743 0.743
##
## R-Square:
## Estimate
## estfish 1.000
## chla 0.050
## hzoop 0.379
## pzoop 0.483
## fish 0.257labelssouth <- createLabels(modfitsouth, cnames)
# residuals(modfitsouth)
# modificationindices(modfitsouth)Nice plots
Original units
West
North
South
Detrended
West
North
South
Save updated SEM diagrams
plot_grobs <- map(list(figW, figN, figS), ~convert_html_to_grob(.x, 2000))
combined_figure <- ggarrange(plotlist=plot_grobs, labels="auto",
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEM (regional)",
color = "black",
face = "bold",
size = 9))# +
#theme(plot.margin = unit(c(1,0,0,0), "lines"))
ggsave('./fig_output/sem_annual_regions.png', combined_figure, width=6, height=6,
dpi=300, bg = "white")Combine and save annual no region model and annual regional models
load('./fig_output/sem_annual_noregions.RData')
plot_grobs <- map(list(sem_annual_noregions, figN, figW, figS),
~convert_html_to_grob(.x, 2000))
combined_figure <- ggarrange(plotlist=plot_grobs, labels=c("(a)", "(b)", "(c)", "(d)"),
font.label=list(size=12)) %>%
annotate_figure(top = text_grob("Annual SEMs",
color = "black",
face = "bold",
size = 14))
ggsave('./fig_output/sem_annual.tiff', combined_figure, width=7, height=7, dpi=600,
bg = "white")
#detrended
plot_grobs <- map(list(sem_annual_noregions_dtr, figNdt, figWdt, figSdt),
~convert_html_to_grob(.x, 2000))
combined_figure_dtr <- ggarrange(plotlist=plot_grobs, labels=c("(a)", "(b)", "(c)", "(d)"),
font.label=list(size=9)) %>%
annotate_figure(top = text_grob("Annual SEMs, Detrended",
color = "black",
face = "bold",
size = 11))
ggsave('./fig_output/sem_annual_detrend.png', combined_figure_dtr, width=8, height=7,
dpi=300, bg = "white")Save model coefficients for manuscript table
load("./fig_output/coeftable_we.Rdata")
bind_rows(
coeftable_we,
coef_tabler(modfitwest, modfitnorth, modfitsouth, name="Original"))%>%
arrange(Model, Region)%>%
write.csv("fig_output/annual coefficients.csv", row.names = FALSE)