plot.sdlrm {sdlrm} | R Documentation |
Diagnostic Plots for the Modified Skew Discrete Laplace Regression
Description
This function provides plots for diagnostic analysis of a modified skew discrete Laplace regression fit.
Usage
## S3 method for class 'sdlrm'
plot(
x,
which = 1:4,
type = c("quantile", "pearson", "response"),
ask = prod(graphics::par("mfcol")) < length(which) && grDevices::dev.interactive(),
pch = "+",
lty = 2,
...
)
Arguments
x |
an object of class |
which |
numeric; if a subset of the plots is required, specify a subset
of the numbers |
type |
character; specifies which residual should be produced in the
envelope plot. The available options are |
ask |
logical; if |
pch , lty , ... |
graphical parameters (see |
Details
The plot
method for "sdlrm"
objects provides six types
of diagnostic plots in the following order:
- Residuals vs fitted values
a plot of the residuals against fitted values.
- Residuals vs observation indices.
an index plot of the residuals against observation indices.
- Normal probability plot
a normal probability plot of the residuals.
- Fitted vs observed frequencies
a bar plot with comparisons of the observed and fitted frequencies.
- Sample autocorrelation plot
sample autocorrelation function plot of the residuals.
- Sample partial autocorrelation plot
sample partial autocorrelation function plot of the residuals.
The which
argument can be used to select a subset of the implemented plots.
Default is which = 1:4
.
Value
plot
method for "sdlrm"
objects returns six types
of diagnostic plots.
Author(s)
Francisco F. de Queiroz <felipeq@ime.usp.br>
Rodrigo M. R. de Medeiros <rodrigo.matheus@ufrn.br>
Examples
## Data set: pss (for description run ?pss)
barplot(table(pss$difference), xlab = "PSS index difference", ylab = "Frequency")
boxplot(pss$difference ~ pss$group, xlab = "Group", ylab = "PSS index difference")
## Fit with a model only for the mean (mode = 1)
fit <- sdlrm(difference ~ group, data = pss, xi = 1)
## Available plots (using the randomized quantile residuals):
# Residuals versus fitted values
plot(fit, which = 1)
# Residuals versus observation indices
plot(fit, which = 2)
# Normal Q-Q plot
plot(fit, which = 3)
# Observed versus fitted frequencies
plot(fit, which = 4)
# Sample autocorelation function of residuals
plot(fit, which = 5)
# Sample partial autocorelation of residuals
plot(fit, which = 6)