Checking GAM simulated residuals or responses

This function extracts the residuals or responses of a fitted GAM model, then it compares their distribution with that of model-based simulations.

check0D(
  o,
  type = "auto",
  maxpo = 10000,
  na.rm = TRUE,
  trans = NULL,
  useSim = TRUE
)

Arguments

o	an object of class gamViz.
type	the type of residuals to be used. See residuals.gamViz. If "type == y" then the raw observations will be used.
maxpo	maximum number of residuals points that will be used by layers such as l_rug(). If number of datapoints > maxpo, then a subsample of maxpo points will be taken.
na.rm	if TRUE missing cases in x or y will be dropped out.
trans	function used to transform the observed and simulated residuals or responses. It must take a vector of as input, and it must either a vector of the same length or a scalar.
useSim	if FALSE then the simulated responses contained in object o will not be used by this function or by any of the layers that can be used with its output.

Value

An object of class c("plotSmooth", "gg").

Examples

# The variance of the response distribution changes along x2 
library(mgcViz)
n  <- 400
x1 <- runif(n, -1, 1)
x2 <- runif(n, -1, 1)
dat <- data.frame("x1" = x1, "x2" = x2,
                  "y" = sin(3*x1) + 0.5 * x2^2 + pmax(x2, 0.2) * rnorm(n))

# Fit model with constant variance and perform posterior simulations (post = TRUE) 
# which take into account smoothing parameter uncertainty (unconditional = TRUE)
b <- gamV(y ~ s(x1)+s(x2), data = dat,
          aViz = list(nsim = 50, post = TRUE, unconditional = TRUE))

# Histogram of simulated vs observed residuals: the latter are fat tailed
check0D(b) + l_hist() + l_rug()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# Histogram of simulated 4th central moment (~ kurtosis) of simulated residuals.
# The vertical line is the 4th moment of the observed residuals
check0D(b, trans = function(.y) mean((.y - mean(.y))^4)) + l_dens1D() + l_vline() + l_rug()
# Residuals look very fat tails, but the real problem here is the heteroscedasticity 
# which can be diagnosted using check1D(b, "x2") + l_gridCheck1D(sd)