## ----include = FALSE, warning=FALSE, message=FALSE----------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
set.seed(101)
start_time = Sys.time()
# Install locally
#  devtools::install_local( R'(C:\Users\James.Thorson\Desktop\Git\dsem)', force=TRUE )
# Build
#  setwd(R'(C:\Users\James.Thorson\Desktop\Git\dsem)'); devtools::build_rmd("vignettes/vignette.Rmd"); rmarkdown::render( "vignettes/vignette.Rmd", rmarkdown::pdf_document())

## ----setup, echo=TRUE, message=FALSE, warning=FALSE---------------------------
library(dsem)
library(dynlm)
library(ggplot2)
library(reshape)
library(gridExtra)
library(phylopath)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# simulate normal distribution
x = rnorm(100)
y = 1 + 0.5 * x + rnorm(100)
data = data.frame(x=x, y=y)

# Fit as linear model
Lm = lm( y ~ x, data=data )

# Fit as DSEM
fit = dsem( 
  sem = "x -> y, 0, beta",
  tsdata = ts(data),
  control = dsem_control(quiet=TRUE) 
)

# Display output
m1 = rbind(
  "lm" = summary(Lm)$coef[2,1:2],
  "dsem" = summary(fit)[1,9:10]
)
knitr::kable( m1, digits=3)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# sample-based quantile residuals
samples = loo_residuals(fit, what="samples", track_progress=FALSE)
which_use = which(!is.na(data))
fitResp = loo_residuals( fit, what="loo", track_progress=FALSE)[,'est']
simResp = apply(samples, MARGIN=3, FUN=as.vector)[which_use,]

# Build and display DHARMa object
res = DHARMa::createDHARMa(
  simulatedResponse = simResp,
  observedResponse = unlist(data)[which_use],
  fittedPredictedResponse = fitResp )
plot(res)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# Get DSEM Loo residuals and LM working residuals
res = loo_residuals(fit, what="quantiles", track_progress=FALSE)
res0 = resid(Lm,"working")

# Plot comparison
plot( x=res0, y=qnorm(res[,2]),
      xlab="linear model residuals", ylab="dsem residuals" )
abline(a=0,b=1)

## -----------------------------------------------------------------------------
# Simulate a log-linked Poisson GLM
x = rnorm(100)
p = 1 + 0.8 * x
y = rpois( length(x), lambda = exp(p) )
data = data.frame(x=x, y=y)

# Fit as generalized linear model
Glm = glm( y ~ x, data=data, family = poisson() )

# Define zero process errors (only process errors) in response y
sem = "
  x -> y, 0, beta
  y <-> y, 0, NA, 0
"

# Fit as DSEM
fit = dsem( 
  sem = sem,
  tsdata = ts(data),
  family = list( x = fixed(), y = poisson("log") ),
  control = dsem_control(quiet=TRUE) 
)

# Display output
m1 = rbind(
  "glm" = summary(Glm)$coef[2,1:2],
  "dsem" = summary(fit)[1,9:10]
)
knitr::kable( m1, digits=3)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
data(KleinI, package="AER")
TS = ts(data.frame(KleinI, "time"=time(KleinI) - 1931))

# Specify by declaring each arrow and lag
sem = "
  # Link, lag, param_name
  cprofits -> consumption, 0, a1
  cprofits -> consumption, 1, a2
  pwage -> consumption, 0, a3
  gwage -> consumption, 0, a3

  cprofits -> invest, 0, b1
  cprofits -> invest, 1, b2
  capital -> invest, 0, b3

  gnp -> pwage, 0, c2
  gnp -> pwage, 1, c3
  time -> pwage, 0, c1
"
tsdata = TS[,c("time","gnp","pwage","cprofits",'consumption',
               "gwage","invest","capital")]
fit = dsem( 
  sem = sem,
  tsdata = tsdata,
  estimate_delta0 = TRUE,
  control = dsem_control(
    quiet = TRUE,
    newton_loops = 0
  ) 
)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# Specify using equations
equations = "
  consumption = a1*cprofits + a2*lag[cprofits,1]+ a3*pwage + a3*gwage
  invest = b1*cprofits + b2*lag[cprofits,1] + b3*capital
  pwage = c1*time + c2*gnp + c3*lag[gnp,1]
"

# Convert and run
sem_equations = convert_equations(equations)
fit = dsem( 
  sem = sem_equations,
  tsdata = tsdata,
  estimate_delta0 = TRUE,
  control = dsem_control(
    quiet = TRUE,
    newton_loops = 0
  ) 
)

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# dynlm
fm_cons <- dynlm(consumption ~ cprofits + L(cprofits) + I(pwage + gwage), data = TS)
fm_inv <- dynlm(invest ~ cprofits + L(cprofits) + capital, data = TS)
fm_pwage <- dynlm(pwage ~ gnp + L(gnp) + time, data = TS)

# Compile results
m1 = rbind( summary(fm_cons)$coef[-1,],
            summary(fm_inv)$coef[-1,],
            summary(fm_pwage)$coef[-1,] )[,1:2]
m2 = summary(fit$sdrep)[1:9,]
m = rbind(
  data.frame("var"=rownames(m1),m1,"method"="OLS","eq"=rep(c("C","I","Wp"),each=3)),
  data.frame("var"=rownames(m1),m2,"method"="GMRF","eq"=rep(c("C","I","Wp"),each=3))
)
m = cbind(m, "lower"=m$Estimate-m$Std..Error, "upper"=m$Estimate+m$Std..Error )

# ggplot display of estimates
longform = melt( as.data.frame(KleinI) )
  longform$year = rep( time(KleinI), 9 )
p1 = ggplot( data=longform, aes(x=year, y=value) ) +
  facet_grid( rows=vars(variable), scales="free" ) +
  geom_line( )

p2 = ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) +
  geom_point( position=position_dodge(0.9) ) +
  geom_errorbar( aes(ymax=as.numeric(upper),ymin=as.numeric(lower)),
                 width=0.25, position=position_dodge(0.9))  #

p3 = plot( as_fitted_DAG(fit) ) +
     expand_limits(x = c(-0.2,1) )
p4 = plot( as_fitted_DAG(fit, lag=1), text_size=4 ) +
     expand_limits(x = c(-0.2,1), y = c(-0.2,0) )

p1
p2
grid.arrange( arrangeGrob(p3, p4, nrow=2) )

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=5, eval=FALSE----------
# library(tmbstan)
# 
# # MCMC for both fixed and random effects
# mcmc = tmbstan( fit$obj, init="last.par.best" )
# summary_mcmc = summary(mcmc)

## ----echo=FALSE, message=FALSE, fig.width=7, fig.height=5, eval=FALSE---------
# saveRDS( summary_mcmc, file=file.path(R'(C:\Users\James.Thorson\Desktop\Git\dsem\inst\tmbstan)',"summary_mcmc.RDS") )

## ----echo=FALSE, message=FALSE, fig.width=6, fig.height=4, out.width = "100%", eval=TRUE----
summary_mcmc = readRDS( file.path(system.file("tmbstan",package="dsem"),"summary_mcmc.RDS") )

## ----echo=TRUE, message=FALSE, fig.width=6, fig.height=4, out.width = "100%", eval=TRUE----
# long-form data frame
m1 = summary_mcmc$summary[1:17,c('mean','sd')]
rownames(m1) = paste0( "b", seq_len(nrow(m1)) )
m2 = summary(fit$sdrep)[1:17,c('Estimate','Std. Error')]
m = rbind(
  data.frame('mean'=m1[,1], 'sd'=m1[,2], 'par'=rownames(m1), "method"="MCMC"),
  data.frame('mean'=m2[,1], 'sd'=m2[,2], 'par'=rownames(m1), "method"="LA")
)
m$lower = m$mean - m$sd
m$upper = m$mean + m$sd

# plot
ggplot(data=m, aes(x=par, y=mean, col=method)) +
  geom_point( position=position_dodge(0.9) ) +
  geom_errorbar( aes(ymax=as.numeric(upper),ymin=as.numeric(lower)),
                 width=0.25, position=position_dodge(0.9))  #

## ----echo=TRUE, message=FALSE, fig.width=5, fig.height=7, warning=FALSE-------
data(isle_royale)
data = ts( log(isle_royale[,2:3]), start=1959)

sem = "
  # Link, lag, param_name
  wolves -> wolves, 1, arW
  moose -> wolves, 1, MtoW
  wolves -> moose, 1, WtoM
  moose -> moose, 1, arM
"
# initial first without delta0 (to improve starting values)
fit0 = dsem( 
  sem = sem,
  tsdata = data,
  estimate_delta0 = FALSE,
  control = dsem_control(
    quiet = FALSE,
    getsd = FALSE
  ) 
)

#
parameters = fit0$obj$env$parList()
  parameters$delta0_j = rep( 0, ncol(data) )

# Refit with delta0
fit = dsem( 
  sem = sem,
  tsdata = data,
  estimate_delta0 = TRUE,
  control = dsem_control( 
    quiet=TRUE,
    parameters = parameters 
  ) 
)

# dynlm
fm_wolf = dynlm( wolves ~ 1 + L(wolves) + L(moose), data=data )   #
fm_moose = dynlm( moose ~ 1 + L(wolves) + L(moose), data=data )   #

# MARSS
library(MARSS)
z.royale.dat <- t(scale(data.frame(data),center=TRUE,scale=FALSE))
royale.model.1 <- list(
  Z = "identity",
  B = "unconstrained",
  Q = "diagonal and unequal",
  R = "zero",
  U = "zero"
)
kem.1 <- MARSS(z.royale.dat, model = royale.model.1)
SE <- MARSSparamCIs( kem.1 )

# Using VAR package
library(vars)
var = VAR( data, type="const" )

### Compile
m1 = rbind( summary(fm_wolf)$coef[-1,], summary(fm_moose)$coef[-1,] )[,1:2]
m2 = summary(fit$sdrep)[1:4,]
#m2 = cbind( "Estimate"=fit$opt$par, "Std. Error"=fit$sdrep$par.fixed )[1:4,]
m3 = cbind( SE$parMean[c(1,3,2,4)], SE$par.se$B[c(1,3,2,4)] )
colnames(m3) = colnames(m2)
m4 = rbind( summary(var$varresult$wolves)$coef[-3,], summary(var$varresult$moose)$coef[-3,] )[,1:2]

# Bundle
m = rbind(
  data.frame("var"=rownames(m1), m1, "method"="dynlm", "eq"=rep(c("Wolf","Moose"),each=2)),
  data.frame("var"=rownames(m1), m2, "method"="dsem", "eq"=rep(c("Wolf","Moose"),each=2)),
  data.frame("var"=rownames(m1), m3, "method"="MARSS", "eq"=rep(c("Wolf","Moose"),each=2)),
  data.frame("var"=rownames(m1), m4, "method"="vars", "eq"=rep(c("Wolf","Moose"),each=2))
)
#knitr::kable( m1, digits=3)
#knitr::kable( m2, digits=3)

m = cbind(m, "lower"=m$Estimate-m$Std..Error, "upper"=m$Estimate+m$Std..Error )

# ggplot estimates ... interaction(x,y) causes an error sometimes
library(ggplot2)
library(ggpubr)
library(ggraph)
longform = reshape( isle_royale, idvar = "year", direction="long", varying=list(2:3), v.names="abundance", timevar="species", times=c("wolves","moose") )
p1 = ggplot( data=longform, aes(x=year, y=abundance) ) +
  facet_grid( rows=vars(species), scales="free" ) +
  geom_point( )

p2 = ggplot(data=m, aes(x=interaction(var,eq), y=Estimate, color=method)) +
  geom_point( position=position_dodge(0.9) ) +
  geom_errorbar( aes(ymax=as.numeric(upper),ymin=as.numeric(lower)),
                 width=0.25, position=position_dodge(0.9))  #
p3 = plot( as_fitted_DAG(fit, lag=1), rotation=0 ) +
     geom_edge_loop( aes( label=round(weight,2), direction=0)) + #arrow=arrow(), , angle_calc="along", label_dodge=grid::unit(10,"points") )
     expand_limits(x = c(-0.1,0) )

ggarrange( p1, p2, p3,
           labels = c("Time-series data", "Estimated effects", "Fitted path digram"),
           ncol = 1, nrow = 3)

## ----echo=TRUE, message=FALSE, fig.width=5, fig.height=7, warning=FALSE-------
# Calculate total effects
effect = total_effect( fit )

# Plot total effect
ggplot( effect) + 
  geom_bar( aes(lag, total_effect, fill=lag), stat='identity', col='black', position='dodge' ) +
  facet_grid( from ~ to  )

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
data(bering_sea)
Z = ts( bering_sea )
family = Map(function(.) fixed(), colnames(Z))

# Specify model
sem = "
  # Link, lag, param_name
  log_seaice -> log_CP, 0, seaice_to_CP
  log_CP -> log_Cfall, 0, CP_to_Cfall
  log_CP -> log_Esummer, 0, CP_to_E
  log_PercentEuph -> log_RperS, 0, Seuph_to_RperS
  log_PercentCop -> log_RperS, 0, Scop_to_RperS
  log_Esummer -> log_PercentEuph, 0, Esummer_to_Suph
  log_Cfall -> log_PercentCop, 0, Cfall_to_Scop
  SSB -> log_RperS, 0, SSB_to_RperS

  log_seaice -> log_seaice, 1, AR1, 0.001
  log_CP -> log_CP, 1,  AR2, 0.001
  log_Cfall -> log_Cfall, 1, AR4, 0.001
  log_Esummer -> log_Esummer, 1, AR5, 0.001
  SSB -> SSB, 1, AR6, 0.001
  log_RperS ->  log_RperS, 1, AR7, 0.001
  log_PercentEuph -> log_PercentEuph, 1, AR8, 0.001
  log_PercentCop -> log_PercentCop, 1, AR9, 0.001
"

# Fit
fit = dsem( 
  sem = sem,
  tsdata = Z,
  family = family,
  control = dsem_control(
    use_REML=FALSE, 
    quiet=TRUE
  ) 
)
ParHat = fit$obj$env$parList()
# summary( fit )

## ----echo=TRUE, message=FALSE, fig.width=7, fig.height=7----------------------
# Timeseries plot
oldpar <- par(no.readonly = TRUE)
par( mfcol=c(3,3), mar=c(2,2,2,0), mgp=c(2,0.5,0), tck=-0.02 )
for(i in 1:ncol(bering_sea)){
  tmp = bering_sea[,i,drop=FALSE]
  tmp = cbind( tmp, "pred"=ParHat$x_tj[,i] )
  SD = as.list(fit$sdrep,what="Std.")$x_tj[,i]
  tmp = cbind( tmp, "lower"=tmp[,2] - ifelse(is.na(SD),0,SD),
                    "upper"=tmp[,2] + ifelse(is.na(SD),0,SD) )
  #
  plot( x=rownames(bering_sea), y=tmp[,1], ylim=range(tmp,na.rm=TRUE),
        type="p", main=colnames(bering_sea)[i], pch=20, cex=2 )
  lines( x=rownames(bering_sea), y=tmp[,2], type="l", lwd=2,
         col="blue", lty="solid" )
  polygon( x=c(rownames(bering_sea),rev(rownames(bering_sea))),
           y=c(tmp[,3],rev(tmp[,4])), col=rgb(0,0,1,0.2), border=NA )
}
par(oldpar)

## ----echo=TRUE, message=FALSE, fig.width=8, fig.height=8----------------------
#
library(phylopath)
library(ggplot2)
library(ggpubr)
library(reshape)
library(gridExtra)
longform = melt( bering_sea )
  longform$year = rep( 1963:2023, ncol(bering_sea) )
p0 = ggplot( data=longform, aes(x=year, y=value) ) +
  facet_grid( rows=vars(variable), scales="free" ) +
  geom_point( )

p1 = plot( (as_fitted_DAG(fit)), edge.width=1, type="width",
           text_size=4, show.legend=FALSE,
           arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) +
     scale_x_continuous(expand = c(0.4, 0.1))
p1$layers[[1]]$mapping$edge_width = 1
p2 = plot( (as_fitted_DAG(fit, what="p_value")), edge.width=1, type="width",
           text_size=4, show.legend=FALSE, colors=c('black', 'black'),
           arrow = grid::arrow(type='closed', 18, grid::unit(10,'points')) ) +
     scale_x_continuous(expand = c(0.4, 0.1))
p2$layers[[1]]$mapping$edge_width = 0.5

#grid.arrange( arrangeGrob( p0+ggtitle("timeseries"),
#              arrangeGrob( p1+ggtitle("Estimated path diagram"),
#                           p2+ggtitle("Estimated p-values"), nrow=2), ncol=2 ) )
ggarrange(p1, p2, labels = c("Simultaneous effects", "Two-sided p-value"),
                    ncol = 1, nrow = 2)

## ----echo=TRUE, message=FALSE, fig.width=5, fig.height=7, warning=FALSE-------
data(sea_otter)
Z = ts( sea_otter[,-1] )

# Specify model
sem = "
  Pycno_CANNERY_DC -> log_Urchins_CANNERY_DC, 0, x2
  log_Urchins_CANNERY_DC -> log_Kelp_CANNERY_DC, 0, x3
  log_Otter_Count_CANNERY_DC -> log_Kelp_CANNERY_DC, 0, x4

  Pycno_CANNERY_UC -> log_Urchins_CANNERY_UC, 0, x2
  log_Urchins_CANNERY_UC -> log_Kelp_CANNERY_UC, 0, x3
  log_Otter_Count_CANNERY_UC -> log_Kelp_CANNERY_UC, 0, x4

  Pycno_HOPKINS_DC -> log_Urchins_HOPKINS_DC, 0, x2
  log_Urchins_HOPKINS_DC -> log_Kelp_HOPKINS_DC, 0, x3
  log_Otter_Count_HOPKINS_DC -> log_Kelp_HOPKINS_DC, 0, x4

  Pycno_HOPKINS_UC -> log_Urchins_HOPKINS_UC, 0, x2
  log_Urchins_HOPKINS_UC -> log_Kelp_HOPKINS_UC, 0, x3
  log_Otter_Count_HOPKINS_UC -> log_Kelp_HOPKINS_UC, 0, x4

  Pycno_LOVERS_DC -> log_Urchins_LOVERS_DC, 0, x2
  log_Urchins_LOVERS_DC -> log_Kelp_LOVERS_DC, 0, x3
  log_Otter_Count_LOVERS_DC -> log_Kelp_LOVERS_DC, 0, x4

  Pycno_LOVERS_UC -> log_Urchins_LOVERS_UC, 0, x2
  log_Urchins_LOVERS_UC -> log_Kelp_LOVERS_UC, 0, x3
  log_Otter_Count_LOVERS_UC -> log_Kelp_LOVERS_UC, 0, x4

  Pycno_MACABEE_DC -> log_Urchins_MACABEE_DC, 0, x2
  log_Urchins_MACABEE_DC -> log_Kelp_MACABEE_DC, 0, x3
  log_Otter_Count_MACABEE_DC -> log_Kelp_MACABEE_DC, 0, x4

  Pycno_MACABEE_UC -> log_Urchins_MACABEE_UC, 0, x2
  log_Urchins_MACABEE_UC -> log_Kelp_MACABEE_UC, 0, x3
  log_Otter_Count_MACABEE_UC -> log_Kelp_MACABEE_UC, 0, x4

  Pycno_OTTER_PT_DC -> log_Urchins_OTTER_PT_DC, 0, x2
  log_Urchins_OTTER_PT_DC -> log_Kelp_OTTER_PT_DC, 0, x3
  log_Otter_Count_OTTER_PT_DC -> log_Kelp_OTTER_PT_DC, 0, x4

  Pycno_OTTER_PT_UC -> log_Urchins_OTTER_PT_UC, 0, x2
  log_Urchins_OTTER_PT_UC -> log_Kelp_OTTER_PT_UC, 0, x3
  log_Otter_Count_OTTER_PT_UC -> log_Kelp_OTTER_PT_UC, 0, x4

  Pycno_PINOS_CEN -> log_Urchins_PINOS_CEN, 0, x2
  log_Urchins_PINOS_CEN -> log_Kelp_PINOS_CEN, 0, x3
  log_Otter_Count_PINOS_CEN -> log_Kelp_PINOS_CEN, 0, x4

  Pycno_SIREN_CEN -> log_Urchins_SIREN_CEN, 0, x2
  log_Urchins_SIREN_CEN -> log_Kelp_SIREN_CEN, 0, x3
  log_Otter_Count_SIREN_CEN -> log_Kelp_SIREN_CEN, 0, x4

  # AR1
  Pycno_CANNERY_DC -> Pycno_CANNERY_DC, 1, ar1
  log_Urchins_CANNERY_DC -> log_Urchins_CANNERY_DC, 1, ar2
  log_Otter_Count_CANNERY_DC -> log_Otter_Count_CANNERY_DC, 1, ar3
  log_Kelp_CANNERY_DC -> log_Kelp_CANNERY_DC, 1, ar4

  Pycno_CANNERY_UC -> Pycno_CANNERY_UC, 1, ar1
  log_Urchins_CANNERY_UC -> log_Urchins_CANNERY_UC, 1, ar2
  log_Otter_Count_CANNERY_UC -> log_Otter_Count_CANNERY_UC, 1, ar3
  log_Kelp_CANNERY_UC -> log_Kelp_CANNERY_UC, 1, ar4

  Pycno_HOPKINS_DC -> Pycno_HOPKINS_DC, 1, ar1
  log_Urchins_HOPKINS_DC -> log_Urchins_HOPKINS_DC, 1, ar2
  log_Otter_Count_HOPKINS_DC -> log_Otter_Count_HOPKINS_DC, 1, ar3
  log_Kelp_HOPKINS_DC -> log_Kelp_HOPKINS_DC, 1, ar4

  Pycno_HOPKINS_UC -> Pycno_HOPKINS_UC, 1, ar1
  log_Urchins_HOPKINS_UC -> log_Urchins_HOPKINS_UC, 1, ar2
  log_Otter_Count_HOPKINS_UC -> log_Otter_Count_HOPKINS_UC, 1, ar3
  log_Kelp_HOPKINS_UC -> log_Kelp_HOPKINS_UC, 1, ar4

  Pycno_LOVERS_DC -> Pycno_LOVERS_DC, 1, ar1
  log_Urchins_LOVERS_DC -> log_Urchins_LOVERS_DC, 1, ar2
  log_Otter_Count_LOVERS_DC -> log_Otter_Count_LOVERS_DC, 1, ar3
  log_Kelp_LOVERS_DC -> log_Kelp_LOVERS_DC, 1, ar4

  Pycno_LOVERS_UC -> Pycno_LOVERS_UC, 1, ar1
  log_Urchins_LOVERS_UC -> log_Urchins_LOVERS_UC, 1, ar2
  log_Otter_Count_LOVERS_UC -> log_Otter_Count_LOVERS_UC, 1, ar3
  log_Kelp_LOVERS_UC -> log_Kelp_LOVERS_UC, 1, ar4

  Pycno_MACABEE_DC -> Pycno_MACABEE_DC, 1, ar1
  log_Urchins_MACABEE_DC -> log_Urchins_MACABEE_DC, 1, ar2
  log_Otter_Count_MACABEE_DC -> log_Otter_Count_MACABEE_DC, 1, ar3
  log_Kelp_MACABEE_DC -> log_Kelp_MACABEE_DC, 1, ar4

  Pycno_MACABEE_UC -> Pycno_MACABEE_UC, 1, ar1
  log_Urchins_MACABEE_UC -> log_Urchins_MACABEE_UC, 1, ar2
  log_Otter_Count_MACABEE_UC -> log_Otter_Count_MACABEE_UC, 1, ar3
  log_Kelp_MACABEE_UC -> log_Kelp_MACABEE_UC, 1, ar4

  Pycno_OTTER_PT_DC -> Pycno_OTTER_PT_DC, 1, ar1
  log_Urchins_OTTER_PT_DC -> log_Urchins_OTTER_PT_DC, 1, ar2
  log_Otter_Count_OTTER_PT_DC -> log_Otter_Count_OTTER_PT_DC, 1, ar3
  log_Kelp_OTTER_PT_DC -> log_Kelp_OTTER_PT_DC, 1, ar4

  Pycno_OTTER_PT_UC -> Pycno_OTTER_PT_UC, 1, ar1
  log_Urchins_OTTER_PT_UC -> log_Urchins_OTTER_PT_UC, 1, ar2
  log_Otter_Count_OTTER_PT_UC -> log_Otter_Count_OTTER_PT_UC, 1, ar3
  log_Kelp_OTTER_PT_UC -> log_Kelp_OTTER_PT_UC, 1, ar4

  Pycno_PINOS_CEN -> Pycno_PINOS_CEN, 1, ar1
  log_Urchins_PINOS_CEN -> log_Urchins_PINOS_CEN, 1, ar2
  log_Otter_Count_PINOS_CEN -> log_Otter_Count_PINOS_CEN, 1, ar3
  log_Kelp_PINOS_CEN -> log_Kelp_PINOS_CEN, 1, ar4

  Pycno_SIREN_CEN -> Pycno_SIREN_CEN, 1, ar1
  log_Urchins_SIREN_CEN -> log_Urchins_SIREN_CEN, 1, ar2
  log_Otter_Count_SIREN_CEN -> log_Otter_Count_SIREN_CEN, 1, ar3
  log_Kelp_SIREN_CEN -> log_Kelp_SIREN_CEN, 1, ar4
"

# Fit model
fit = dsem( 
  sem = sem,
  tsdata = Z,
  control = dsem_control(
    use_REML=FALSE, 
    quiet=TRUE
  ) 
)

#
library(phylopath)
library(ggplot2)
library(ggpubr)
get_part = function(x){
  vars = c("log_Kelp","log_Otter","log_Urchin","Pycno")
  index = sapply( vars, FUN=\(y) grep(y,rownames(x$coef))[1] )
  x$coef = x$coef[index,index]
  dimnames(x$coef) = list( vars, vars )
  return(x)
}
p1 = plot( get_part(as_fitted_DAG(fit)), type="width", show.legend=FALSE)
p1$layers[[1]]$mapping$edge_width = 0.5
p2 = plot( get_part(as_fitted_DAG(fit, what="p_value" )), type="width",
           show.legend=FALSE, colors=c('black', 'black'))
p2$layers[[1]]$mapping$edge_width = 0.1

longform = melt( sea_otter[,-1], as.is=TRUE )
longform$X1 = 1999:2019[longform$X1]
longform$Site = gsub( "log_Kelp_", "",
                gsub( "log_Urchins_", "",
                gsub( "Pycno_", "",
                gsub( "log_Otter_Count_", "", longform$X2))))
longform$Species = sapply( seq_len(nrow(longform)), FUN=\(i)gsub(longform$Site[i],"",longform$X2[i]) )
p3 = ggplot( data=longform, aes(x=X1, y=value, col=Species) ) +
  facet_grid( rows=vars(Site), scales="free" ) +
  geom_line( )

ggarrange(p1 + scale_x_continuous(expand = c(0.3, 0)),
                    p2 + scale_x_continuous(expand = c(0.3, 0)),
                    labels = c("Simultaneous effects", "Two-sided p-value"),
                    ncol = 1, nrow = 2)

## ----include = FALSE, warning=FALSE, message=FALSE----------------------------
run_time = Sys.time() - start_time

