ggplot_inla_residuals: Plot residuals of observed vs predicted values for INLA model...

In timcdlucas/INLAutils: Utility Functions for 'INLA'

Plot residuals of observed vs predicted values for INLA model using ggplot2

Description

Plot residuals of observed vs predicted values for INLA model using ggplot2

Usage

ggplot_inla_residuals(inla.model, observed, CI = FALSE, binwidth = NULL)

Arguments

inla.model	An inla object
observed	The observed values
CI	Add credible intervals to the fitted values?
binwidth	The size of the bins used for the histogram. If NULL ggplot guesses for you.

Examples

## Not run: 
 library(INLA)
 data(Epil)
 observed <- Epil[1:30, 'y']
 Epil <- rbind(Epil, Epil[1:30, ])
 Epil[1:30, 'y'] <- NA
 ## make centered covariates
 formula = y ~ Trt + Age + V4 +
          f(Ind, model="iid") + f(rand,model="iid")
 result = inla(formula, family="poisson", data = Epil, 
               control.predictor = list(compute = TRUE, link = 1))
 p <- ggplot_inla_residuals(result, observed)
 
 # Subplots can be altered afterwards
 p[[1]] <- p[[1]] + theme_grey()
 cowplot::plot_grid(plotlist = p)

 data(Seeds)
 l <- nrow(Seeds)
 Seeds <- rbind(Seeds, Seeds)
 Seeds$r[1:l] <- NA


 formula = r ~ x1 * x2 + f(plate, model = "iid")
 mod.seeds = inla(formula, data=Seeds, family = "binomial", Ntrials = n, 
                  control.predictor = list(compute = TRUE, link = 1))
 ggplot_inla_residuals(mod.seeds, na.omit(Seeds$r / Seeds$n))

## End(Not run)

timcdlucas/INLAutils documentation built on Nov. 29, 2022, 5:41 a.m.