ggPredict: Visualize predictions from the multiple regression models.

In predict3d: Draw Three Dimensional Predict Plot Using Package 'rgl'

Visualize predictions from the multiple regression models.

Description

Visualize predictions from the multiple regression models.

Usage

ggPredict(
  fit,
  pred = NULL,
  modx = NULL,
  mod2 = NULL,
  modx.values = NULL,
  mod2.values = NULL,
  dep = NULL,
  mode = 1,
  colorn = 3,
  maxylev = 6,
  show.point = getOption("ggPredict.show.point", TRUE),
  show.error = FALSE,
  error.color = "red",
  jitter = NULL,
  se = FALSE,
  alpha = 0.1,
  show.text = TRUE,
  add.modx.values = TRUE,
  add.loess = FALSE,
  labels = NULL,
  angle = NULL,
  xpos = NULL,
  vjust = NULL,
  digits = 2,
  facet.modx = FALSE,
  facetbycol = TRUE,
  plot = TRUE,
  summarymode = 1,
  ...
)

Arguments

fit	An object of class "lm" or "glm"
pred	The name of predictor variable
modx	Optional. The name of moderator variable
mod2	Optional. The name of second moderator variable
modx.values	For which values of the moderator should lines be plotted? Default is NULL. If NULL, then the customary +/- 1 standard deviation from the mean as well as the mean itself are used for continuous moderators. If the moderator is a factor variable and modx.values is NULL, each level of the factor is included.
mod2.values	For which values of the second moderator should lines be plotted? Default is NULL. If NULL, then the customary +/- 1 standard deviation from the mean as well as the mean itself are used for continuous moderators. If the moderator is a factor variable and modx.values is NULL, each level of the factor is included.
dep	Optional. The name of dependent variable
mode	A numeric. Useful when the variables are numeric. If 1, c(-1,0,1)*sd + mean is used. If 2, the 14th, 50th, 86th percentile values used. If 3 sequence over a the range of a vector used
colorn	The number of regression lines when the modifier variable(s) are numeric.
maxylev	An integer indicating the maximum number of levels of numeric variable be treated as a categorical variable
show.point	Logical. Whether or not add points
show.error	Logical. Whether or not show error
error.color	color of error. dafault value is "red"
jitter	logical Whether or not use geom_jitter
se	Logical. Whether or not add confidence interval
alpha	A numeric. Transparency
show.text	Logical. Whether or not add regression equation as label
add.modx.values	Logical. Whether or not add moderator values to regression equation
add.loess	Logical. Whether or not add loess line
labels	labels on regression lines
angle	angle of text
xpos	x axis position of label
vjust	vertical alignment of labels
digits	integer indicating the number of decimal places
facet.modx	Create separate panels for each level of the moderator? Default is FALSE
facetbycol	Logical.
plot	Logical. Should a plot of the results be printed? Default is TRUE.
summarymode	An integer indicating method of extracting typical value of variables. If 1, typical() is used.If 2, mean() is used.
...	additional arguments to be passed to geom_text

Examples

fit=loess(mpg~hp*wt*am,data=mtcars)
ggPredict(fit)
ggPredict(fit,hp)
## Not run: 
ggPredict(fit,hp,wt)
fit=lm(mpg~wt*hp-1,data=mtcars)
ggPredict(fit,xpos=0.7)
fit=lm(mpg~hp*wt,data=mtcars)
ggPredict(fit)
ggPredict(fit,labels=paste0("label",1:3),xpos=c(0.3,0.6,0.4))
ggPredict(fit,se=TRUE)
ggPredict(fit,mode=3,colorn=40,show.text=FALSE)
fit=lm(log(mpg)~hp*wt,data=mtcars)
ggPredict(fit,dep=mpg)
fit=lm(mpg~hp*wt*cyl,data=mtcars)
ggPredict(fit,modx=wt,modx.values=c(2,3,4,5),mod2=cyl,show.text=FALSE)
ggPredict(fit,hp,wt,show.point=FALSE,se=TRUE,xpos=0.5)
ggPredict(fit,modx=wt,xpos=0.3)
ggPredict(fit)
mtcars$engine=ifelse(mtcars$vs==0,"V-shaped","straight")
fit=lm(mpg~wt*engine,data=mtcars)
ggPredict(fit)
require(TH.data)
fit=glm(cens~pnodes*horTh,data=GBSG2,family=binomial)
ggPredict(fit,pnodes,horTh,se=TRUE,xpos=c(0.6,0.3),angle=c(40,60),vjust=c(2,-0.5))
fit1=glm(cens~pnodes,data=GBSG2,family=binomial)
ggPredict(fit1,vjust=1.5,angle=45)
fit3=glm(cens~pnodes*age,data=GBSG2,family=binomial)
ggPredict(fit3,pred=pnodes,modx=age,mode=3,colorn=10,show.text=FALSE)
fit2=glm(cens~pnodes*age*horTh,data=GBSG2,family=binomial)
ggPredict(fit2,pred=pnodes,modx=age,mod2=horTh,mode=3,colorn=10,show.text=FALSE)
fit=lm(mpg~log(hp)*wt,data=mtcars)
ggPredict(fit,hp,wt)
fit=lm(mpg~hp*wt+disp+gear+carb+am,data=mtcars)
ggPredict(fit,disp,gear,am)
library(moonBook)
fit=lm(weight~I(height^3)+I(height^2)+height+sex,data=radial)
ggPredict(fit)
predict3d(fit)

## End(Not run)

predict3d documentation built on April 14, 2023, 12:26 a.m.