In cbrown5/BenthicLatent: Bayesian latent variable model

Appendix S5 Model Evaluation

#library(BenthicLatent)
devtools::load_all('~/Code/BenthicLatent')

num_levels <- 1:4
nlvruns <- length(num_levels)
gamma <- matrix(NA, nrow = nlvruns, ncol = 7)
gammaf <- matrix(NA, nrow = nlvruns, ncol = 7)

for (iruns in 1:nlvruns){
    ilv <- num_levels[iruns]
    savname <- paste0('~/Code/BenthicLatent/data-raw/BLM_numlv',ilv,'_v3.RData')
    load(file = savname)

    gamma[iruns,] <- jagstools::jagsresults(x = mcout3, params = c('gamma'))
    gammaf[iruns,] <- jagstools::jagsresults(x = mcout3, params = c('gammaf'))
}

par(mfrow = c(1,2), mar = c(5,3,1,1))
plot(num_levels, gamma[,5], ylim = c(-1, 1), xlab = "Number of unconstrained LVs", ylab = "Effect size", pch = 16, las = 2, xaxt = "n")
arrows(num_levels, gamma[,3], num_levels, gamma[,7], len = 0)
abline(h=0)
axis(1, at = num_levels)
text(1.1, 1, "(a)")

plot(num_levels, gammaf[,5], ylim = c(-4, 4), xlab = "Number of unconstrained LVs", ylab = "Effect size", pch = 16, las = 2, xaxt = "n")
arrows(num_levels, gammaf[,3], num_levels, gammaf[,7], len = 0)
abline(h=0)
axis(1, at = num_levels)
text(1.1, 4, "(b)")

Figure S1 Estimates for the effect of distance to log ponds (a) and high flow (b) on the constrained latent variable as a function of the number of unconstrained latent variables included in the model. Points show median estimate and bars show the 95% credibility interval.

Table S2 Fit of the two latent variable model to each habitat type. r^2^ values are calculated on observed versus expected abundances. Slope values of 1 indicate now bias, <1 under-predictions and >1 over-prediction.

load('~/Code/BenthicLatent/data-raw/LV mod estimtes of variance and bias.Rdata')
data(lv_input)
df2 <- dplyr::inner_join(est_varbias[[2]], lv_input$habnams_full, by = c('habnams' = "CODE"))
df <- df2[,c(5,4,1,2)]
df <- dplyr::arrange(df, meanabund)
knitr::kable(df, digits = 2, col.names = c('Habitat','Mean abundance','r^2^','Slope of expected on observed'))