Description Usage Arguments Value Examples
View source: R/diagnostic.plot.R
A set of visualization tools for the diagnostic of the fitted model in
the partial association analysis. It can provides a plot matrix including QQ plots,
residualvsfitted plots, residualvscovariate plots of all the fitted models.
This function also support the direct diagnostic of the cumulative link regression model
in the class of clm
, glm
, lrm
,
orm
, polr
. Currently, vglm
is not supported.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56  diagnostic.plot(object, ...)
## Default S3 method:
diagnostic.plot(object, ...)
## S3 method for class 'resid'
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
## S3 method for class 'PAsso'
diagnostic.plot(
object,
output = c("qq", "fitted", "covariate"),
model_id = NULL,
x_name = NULL,
...
)
## S3 method for class 'glm'
diagnostic.plot(
object,
output = c("qq", "fitted", "covariate"),
x = NULL,
fit = NULL,
distribution = qnorm,
ncol = NULL,
alpha = 1,
xlab = NULL,
color = "#444444",
shape = 19,
size = 2,
qqpoint.color = "#444444",
qqpoint.shape = 19,
qqpoint.size = 2,
qqline.color = "#888888",
qqline.linetype = "dashed",
qqline.size = 1,
smooth = TRUE,
smooth.color = "red",
smooth.linetype = 1,
smooth.size = 1,
fill = NULL,
resp_name = NULL,
...
)
## S3 method for class 'clm'
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
## S3 method for class 'lrm'
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
## S3 method for class 'orm'
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
## S3 method for class 'polr'
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)

object 
The object in the support classes (This function is mainly designed
for 
... 
Additional optional arguments can be passed onto 
output 
A character string specifying what type of output to plot. Default is

model_id 
A number refers to the index of the model that needs to be diagnosed. If NULL, all models will be diagnosed. 
x_name 
A string refers to the covariate name that needs to be diagnosed. If NULL, all adjustments will be diagnosed. 
x 
A vector giving the covariate values to use for residualby
covariate plots (i.e., when 
fit 
The fitted model from which the residuals were extracted. (Only
required if 
distribution 
Function that computes the quantiles for the reference
distribution to use in the quantilequantile plot. Default is 
ncol 
Integer specifying the number of columns to use for the plot
layout (if requesting multiple plots). Default is 
alpha 
A single values in the interval [0, 1] controlling the opacity
alpha of the plotted points. Only used when 
xlab 
Character string giving the text to use for the xaxis label in
residualbycovariate plots. Default is 
color 
Character string or integer specifying what color to use for the
points in the residual vs fitted value/covariate plot.
Default is 
shape 
Integer or single character specifying a symbol to be used for plotting the points in the residual vs fitted value/covariate plot. 
size 
Numeric value specifying the size to use for the points in the residual vs fitted value/covariate plot. 
qqpoint.color 
Character string or integer specifying what color to use for the points in the quantilequantile plot. 
qqpoint.shape 
Integer or single character specifying a symbol to be used for plotting the points in the quantilequantile plot. 
qqpoint.size 
Numeric value specifying the size to use for the points in the quantilequantile plot. 
qqline.color 
Character string or integer specifying what color to use for the points in the quantilequantile plot. 
qqline.linetype 
Integer or character string (e.g., 
qqline.size 
Numeric value specifying the thickness of the line in the quantilequantile plot. 
smooth 
Logical indicating whether or not too add a nonparametric
smooth to certain plots. Default is 
smooth.color 
Character string or integer specifying what color to use for the nonparametric smooth. 
smooth.linetype 
Integer or character string (e.g., 
smooth.size 
Numeric value specifying the thickness of the line for the nonparametric smooth. 
fill 
Character string or integer specifying the color to use to fill
the boxplots for residualbycovariate plots when 
resp_name 
Character string to specify the response name that will be displayed in the figure. 
A "ggplot"
object for supported models. For class "PAsso", it returns a plot in
"gtable"
object that combines diagnostic plots of all responses.
A "ggplot" object based on the input residuals.
A "ggplot" object based on the input residuals.
A plot in "gtable" object that combines diagnostic plots of all responses.
A "ggplot" object based on the residuals generated from glm object.
A "ggplot" object based on the residuals generated from clm object.
A "ggplot" object based on the residuals generated from lrm object.
A "ggplot" object based on the residuals generated from orm object.
A "ggplot" object based on the residuals generated from polr object.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # Import data for partial association analysis
data("ANES2016")
ANES2016$PreVote.num < as.factor(ANES2016$PreVote.num)
PAsso_3v < PAsso(responses = c("PreVote.num", "PID", "selfLR"),
adjustments = c("income.num", "age", "edu.year"),
data = ANES2016, uni.model = "probit",
method = c("kendall"),
resids.type = "surrogate", jitter = "latent")
diag_p1 < diagnostic.plot(object = PAsso_3v, output = "qq")
diag_p2 < diagnostic.plot(object = PAsso_3v, output = "fitted")
diag_p3 < diagnostic.plot(object = PAsso_3v, output = "covariate")
# Simply diagnose a model
# Fit cumulative link models
fit1 < ordinal::clm(PreVote.num ~ income.num + age + edu.year, data = ANES2016, link = "logit")
# diagnostic.plot
plot_qq_1 < diagnostic.plot(object = fit1, output = "qq")
plot_fit_1 < diagnostic.plot(object = fit1, output = "fitted")
plot_cov_1 < diagnostic.plot(object = fit1, output = "covariate")

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