mgcViz: l_gridQCheck2D – R documentation

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l_gridQCheck2D

Binning and checking QGAM residuals

Description

This layer bins the residuals, r, according to the value of the corresponding covariates, x1 and x2. Then we calculate the proportion of negative residuals in each bin, which should not deviate too much from the theoretical proportion (eg 0.5 if we fit the median). Mainly useful in conjuction with check2D.

Usage

l_gridQCheck2D(qu = NULL, bw = c(NA, NA), stand = TRUE, binFun = NULL, ...)

Arguments

`qu`	the quantile of interest. Should be in (0, 1).
`bw`	numeric vector giving bin width in the vertical and horizontal directions. See the `binwidth` arguments in `?ggplot2::stat_summary_hex`. If left to `NA`, it will be set to 1/20 of the ranges of x1 and x2.
`stand`	if left to `TRUE` then the observed proportion of negative residuals `p_hat` in the i-th cell is normalized using the standard error `se = sqrt(qu(1-qu)/n)`, where `n` is the number of observation in that cell. That is, if `stand=TRUE` we plot `(p_hat-qu)/se` rather than simply `p_hat`.
`binFun`	the `ggplot2` function used to perform the binning. By default it is either ggplot2::stat_summary_2d or ggplot2::stat_summary_hex, depending on the class of the covariates x1 and x2.
`...`	graphical arguments to be passed to `ggplot2::stat_summary_hex`.

Value

An object of class gamLayer

Examples

library(mgcViz);
set.seed(4124)
n <- 4e2 
dat <- data.frame(x = rnorm(n), y = rnorm(n))

# Simulate some data, residuals are heteroscedastic w.r.t. x
dat$ob <- (dat$x)^2 + (dat$y)^2 + (0.2*abs(dat$x) + 1)  * rnorm(n)
b <- qgamV(ob ~ x + s(y), qu = 0.3, data = dat)

# We have a residual pattern along x (increase n above to 
# see the problem more clearly) 
check2D(b, "x", "y") + l_gridQCheck2D(qu = 0.3, bw = c(0.4, 0.4))

# We need a smooth wrt x to make the pattern disappear
## Not run: 
b1 <- qgamV(ob ~ s(x) + s(y), qu = 0.3, data = dat)
 
check2D(b1, "x", "y") + l_gridQCheck2D(qu = 0.3, bw = c(0.4, 0.4))

## End(Not run)

mgcViz

Visualisations for Generalized Additive Models

v0.1.6

GPL-3

Authors

Matteo Fasiolo [aut, cre], Raphael Nedellec [aut], Yannig Goude [ctb], Christian Capezza [ctb], Simon N. Wood [ctb]

Initial release

2020-03-04