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R package predict3d

R package predict3d aims to draw predicts plot for various regression models. The main two functions are ggPredict() for 2-dimensional plot and predict3d() for 3-dimensional plot.

Package Installation

You can install predict3d package from CRAN.

install.packages("predict3d")

You can install the developmental version of predict3d package from github.

if(!require(devtools)) install.packages("devtools")
devtools::install_github("cardiomoon/predict3d"")

Linear Regression Models

You can draw linear regression models. First model has one categorical and one continuous explanatory variables.

require(predict3d)
require(rgl)
fit1=lm(Sepal.Length~Sepal.Width*Species,data=iris)
fit1

Call:
lm(formula = Sepal.Length ~ Sepal.Width * Species, data = iris)

Coefficients:
                  (Intercept)                    Sepal.Width  
                       2.6390                         0.6905  
            Speciesversicolor               Speciesvirginica  
                       0.9007                         1.2678  
Sepal.Width:Speciesversicolor   Sepal.Width:Speciesvirginica  
                       0.1746                         0.2110  

You can draw plot for this model. ggPredict() function draws a scatterplot with regression line and shows regression equations parallel to the regression lines.

ggPredict(fit1,digits=1)

predict3d(fit1,radius=0.05)
rglwidget(elementId = "1st")

You can see the live 3d plot at http://rpubs.com/cardiomoon/471347.

The second model has two continuous variables as explanatory variables. You can change the labels and the relative x position and the y position.

fit2=lm(mpg~wt*hp,data=mtcars)
ggPredict(fit2,labels=paste0("label",1:3),xpos=c(0.3,0.4,0.3))

predict3d(fit2)
rglwidget(elementId = "2nd")

Generalized Linear Models

You can draw generalized linear models.

require(TH.data)
fit3=glm(cens~pnodes*age*horTh,data=GBSG2,family=binomial)
ggPredict(fit3,se=TRUE,show.text = FALSE)

predict3d(fit3,radius=0.5)
rglwidget(elementId = "3rd")

Local Polynomial Regression Fitting

You can draw the loess model.

fit=loess(mpg~hp*wt,data=mtcars)
ggPredict(fit)

predict3d(fit,radius=2)
rglwidget(elementId = "4th")

Play with predict3d()

Once you have create a model with predict3d(), you can move your object with your mouse or R codes. For example, You can rotate you object with this R codes.

start <- proc.time()[3]
while ((i <- 36*(proc.time()[3] - start)) < 360) {
     view3d(i, i/4); 
}
play3d(spin3d(axis = c(1, 0, 0), rpm = 30), duration = 2)

You can save your 3d plot as a figure file or pdf file.

rgl.bringtotop()
rgl.snapshot("fig1.png")
rgl.postscript("fig2.pdf","pdf")

For more information about package rgl, please read the package vignette at: https://CRAN.R-project.org/package=rgl/vignettes/rgl.html

You can see the following vignettes published on rpubs.com

Drawing the multiple regression models(Part I)

Part II. Transforming variables

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.