Draw coefficients/odds ratio/hazard ratio plot

Usage

modelPlot(
  fit,
  widths = NULL,
  change.pointsize = TRUE,
  show.OR = TRUE,
  show.ref = TRUE,
  bw = TRUE,
  legend.position = "top",
  ...
)

Arguments

fit: An object of class glm
widths: Numeric vector
change.pointsize: logical Whether or not change point size
show.OR: logical Whether or not show odds ratio
show.ref: logical Whether or not show reference
bw: logical If true, use grey scale
legend.position: legend position default value is 'top'
...: Further arguments to be passed to autoReg()

Value

modelPlot returns an object of class "modelPlot" An object of class modelPlot is a list containing at least of the following components:

tab1: The first table containing names
tab2: The 2nd table containing levels
tab3: The 3rd table containing coefficients or odds ratio or hazards ratio
p: A ggplot
widths: the widths of the tables and the ggplot

Examples

fit=lm(mpg~wt*hp+am,data=mtcars)
modelPlot(fit,widths=c(1,0,2,3))

modelPlot(fit,uni=TRUE,threshold=1,widths=c(1,0,2,3))

fit=lm(Sepal.Width~Sepal.Length*Species,data=iris)
modelPlot(fit)

modelPlot(fit,uni=TRUE,change.pointsize=FALSE)

# \donttest{
data(cancer,package="survival")
fit=glm(status~rx+age+sex+nodes+obstruct+perfor,data=colon,family="binomial")
modelPlot(fit)

modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1)

modelPlot(fit,multi=TRUE,imputed=TRUE,change.pointsize=FALSE)
#> Warning: Number of logged events: 1

data(colon_s,package="finalfit")
fit=glm(mort_5yr~age.factor+sex.factor+obstruct.factor+perfor.factor,data=colon_s,family="binomial")
modelPlot(fit)

modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1)

modelPlot(fit,uni=TRUE,multi=TRUE)

modelPlot(fit,uni=TRUE,multi=TRUE,threshold=1,show.ref=FALSE)

library(survival)
fit=coxph(Surv(time,status)~rx+age+sex+obstruct+perfor,data=colon)
modelPlot(fit)

modelPlot(fit,uni=TRUE,threshold=1)

modelPlot(fit,multi=FALSE,final=TRUE,threshold=1)

fit=coxph(Surv(time,status)~age.factor+sex.factor+obstruct.factor+perfor.factor,data=colon_s)
modelPlot(fit)

modelPlot(fit,uni=TRUE,threshold=1)

modelPlot(fit,uni=TRUE,threshold=1,show.ref=FALSE)

modelPlot(fit,imputed=TRUE)

# }