mgcViz (version 0.2.0)

check1D: Checking GAM residuals or responses along one covariate

Description

This function extracts the residuals of a fitted GAM model, and orders them according to the value of a single covariate. Then several visual residuals diagnostics can be plotted by adding layers.

Usage

check1D(
  o,
  x,
  type = "auto",
  maxpo = 10000,
  na.rm = TRUE,
  trans = NULL,
  useSim = TRUE
)

Value

The function will return an object of class c("plotSmooth", "gg"), unless argument x is a list. In that case the function will return an object of class c("plotGam", "gg") containing a checking plot for each variable.

Arguments

o

an object of class gamViz.

x

it can be either a) a single character, b) a numeric vector or c) a list of characters. In case a) it should be the name of one of the variables in the dataframe used to fit o. In case b) its length should be equal to the length of o$y. In case c) it should be a list of names variables in the dataframe used to fit o.

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 must return a vector of the same length.

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.

Examples

### Example 1: diagnosing heteroscedasticity
library(mgcViz);
set.seed(4124)
n <- 1e4
x <- rnorm(n); y <- rnorm(n);

# Residuals are heteroscedastic w.r.t. x
ob <- (x)^2 + (y)^2 + (0.2*abs(x) + 1)  * rnorm(n)
b <- bam(ob ~ s(x,k=30) + s(y, k=30), discrete = TRUE)
b <- getViz(b)

# Look at residuals along "x"
ck <- check1D(b, "x", type = "tnormal")

# Can't see that much
ck + l_dens(type = "cond", alpha = 0.8) + l_points() + l_rug(alpha = 0.2)

# Some evidence of heteroscedasticity
ck + l_densCheck()

# Compare observed residuals std dev with that of simulated data,
# heteroscedasticity is clearly visible
b <- getViz(b, nsim = 50)
check1D(b, "x") + l_gridCheck1D(gridFun = sd, showReps = TRUE)

# This also works with factor or logical data
fac <- sample(letters, n, replace = TRUE)
logi <- sample(c(TRUE, FALSE), n, replace = TRUE)
b <- bam(ob ~ s(x,k=30) + s(y, k=30) + fac + logi, discrete = TRUE)
b <- getViz(b, nsim = 50)

# Look along "fac"
ck <- check1D(b, "fac") 
ck + l_points() + l_rug() 
ck + l_gridCheck1D(gridFun = sd)

# Look along "logi"
ck <- check1D(b, "logi") 
ck + l_points() + l_rug() 
ck + l_gridCheck1D(gridFun = sd)