R/carstm_posterior_predictive_check.R

In jae0/carstm: Conditional autoregressive spatiotemporal models with a focus upon fisheries stock assessment

carstm_posterior_predictive_check = function( p, M )  {

    vn = p$variabletomodel
    iobs = which(M$tag == "observations")
 
    fit = carstm_model( p=p, DS="modelled_fit") #,  sppoly = sppoly )

    setDT(M)
    pld = data.table(
      observed = M[iobs , ..vn] , 
      fitted = fit$summary.fitted.values[["mean"]] [iobs]
    )

    names(pld) = c("observed", "fitted")

    if (exists("family", p)) {
      if (p$family=="lognormal") {
        pld$fitted = exp( pld$fitted)
      }
    }

    if (exists( "data_transformation", p )) {
      if (exists("backward", p$data_transformation)) {
        pld$fitted = p$data_transformation$backward( pld$fitted )
      }
    }

    anno1 = paste( "Pearson correlation: ", round( cor( pld$fitted, pld$observed, use="pairwise.complete.obs" ), 3))
    # cor( fitted, observed, use="pairwise.complete.obs", "spearman" )

    out = ggplot(pld, aes(x =  observed, y = fitted )) +
      geom_abline(slope=1, intercept=0, color="darkgray", lwd=1.4 ) +
      geom_point(color="slategray", alpha=0.2) +
      labs(caption=anno1, color="slateblue") +
      theme( plot.caption = element_text(hjust = 0, size=12 ) )# move caption to the left 
   
    outputdir = file.path( p$modeldir, p$carstm_model_label )
    fn = file.path(outputdir, paste(vn, "posterior_predictive_check.png", sep="_" ) )
    ggsave(filename=fn, plot=out, device="png", width=12, height = 8)
    print(out)  
    return(fn)
}