PAsso (version 0.1.8)

diagnostic.plot: Residual-based diagnostic plots

Description

A set of visualization tools for the diagnostic of the fitted model in the partial association analysis. It can provides a plot matrix including Q-Q plots, residual-vs-fitted plots, residual-vs-covariate 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.

Usage

diagnostic.plot(object, ...)
# S3 method for default
diagnostic.plot(object, ...)
# S3 method for resid
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
# S3 method for PAsso
diagnostic.plot(
  object,
  output = c("qq", "fitted", "covariate"),
  model_id = NULL,
  x_name = NULL,
  ...
)
# S3 method for 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 clm
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
# S3 method for lrm
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
# S3 method for orm
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)
# S3 method for polr
diagnostic.plot(object, output = c("qq", "fitted", "covariate"), ...)

Arguments

object

The object in the support classes (This function is mainly designed for PAsso).

...

Additional optional arguments can be passed onto ggplot for drawing various plots.

output

A character string specifying what type of output to plot. Default is "qq" which produces a plot matrix with quantile-quantile plots of the residuals. "fitted" produces a plot matrix between residuals and all corresponding fitted responses. "covariates" produces a plot matrix between residuals and corresponding covariate.

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 residual-by- covariate plots (i.e., when output = "covariate").

fit

The fitted model from which the residuals were extracted. (Only required if output = "fitted" and object inherits from class "resid".)

distribution

Function that computes the quantiles for the reference distribution to use in the quantile-quantile plot. Default is qnorm which is only appropriate for models using a probit link function. When jitter.scale = "probability", the reference distribution is always U(-0.5, 0.5). (Only required if object inherits from class "resid".)

ncol

Integer specifying the number of columns to use for the plot layout (if requesting multiple plots). Default is NULL.

alpha

A single values in the interval [0, 1] controlling the opacity alpha of the plotted points. Only used when nsim > 1.

xlab

Character string giving the text to use for the x-axis label in residual-by-covariate plots. Default is NULL.

color

Character string or integer specifying what color to use for the points in the residual vs fitted value/covariate plot. Default is "black".

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 quantile-quantile plot.

qqpoint.shape

Integer or single character specifying a symbol to be used for plotting the points in the quantile-quantile plot.

qqpoint.size

Numeric value specifying the size to use for the points in the quantile-quantile plot.

qqline.color

Character string or integer specifying what color to use for the points in the quantile-quantile plot.

qqline.linetype

Integer or character string (e.g., "dashed") specifying the type of line to use in the quantile-quantile plot.

qqline.size

Numeric value specifying the thickness of the line in the quantile-quantile plot.

smooth

Logical indicating whether or not too add a nonparametric smooth to certain plots. Default is TRUE.

smooth.color

Character string or integer specifying what color to use for the nonparametric smooth.

smooth.linetype

Integer or character string (e.g., "dashed") specifying the type of line to use for the nonparametric smooth.

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 residual-by-covariate plots when x is of class "factor". Default is NULL which colors the boxplots according to the factor levels.

resp_name

Character string to specify the response name that will be displayed in the figure.

Value

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.

Examples

# NOT RUN {
# 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")

# }