here is the dataset that i used
Vehicle,Speed,FrictionVehPed,FrictionPedGround,Restitution,ThrowDisiance,Percentile,Gender,CGHeight,Stature,Weight
ToyotaCamry,5,0.2,1.2,0.3,4.7,95,Male,40.1,62.65,221
ToyotaCamry,10,0.25,1.25,0.35,4.52,95,Male,40.6,63.15,221.5
ToyotaCamry,15,0.3,1.3,0.4,11.16,95,Male,41.1,63.65,222
ToyotaCamry,20,0.35,1.35,0.45,20.52,95,Male,41.6,64.15,222.5
ToyotaCamry,25,0.4,1.4,0.5,30.48,95,Male,40.1,62.65,221
ToyotaCamry,30,0.45,1.45,0.55,42.75,95,Male,40.1,62.65,221
ToyotaCamry,35,0.2,0.2,0.2,49.95,95,Male,40.6,63.15,221.5
ToyotaCamry,40,0.2,0.2,0.2,65.18,95,Male,41.1,63.65,222
ToyotaCamry,45,0.2,0.2,0.2,88.72,95,Male,41.6,64.15,222.5
ToyotaCamry,50,0.25,0.25,0.25,110.16,95,Female,42.1,64.65,223
ToyotaCamry,55,0.3,0.3,0.3,132.22,95,Female,42.6,65.15,223.5
ToyotaCamry,60,0.35,0.35,0.35,152.28,95,Female,41.85,64.4,222.75
ToyotaCamry,65,0.4,0.4,0.4,194.59,95,Female,41.1,63.65,222
ToyotaCamry,70,0.2,0.2,0.2,211.65,95,Female,40.35,62.9,221.25
ChevroletSuburban,5,0.2,0.2,0.2,4.78,95,Female,39.6,62.15,220.5
ChevroletSuburban,10,0.25,0.25,0.25,4.73,95,Female,40.1,62.65,221
ChevroletSuburban,15,0.3,0.3,0.3,15.86,95,Female,40.6,63.15,221.5
ChevroletSuburban,20,0.35,0.35,0.35,30.07,95,Female,39.85,62.4,220.75
here is the code for Anova output
#upload dataset (select R community attached)
mydata <- read.csv(file.choose(),header = T,sep = ',', stringsAsFactors=FALSE);
#define intercept-only model
intercept_only <- lm(ThrowDisiance ~ 1, data=mydata)
#define model with all predictors
all <- lm(ThrowDisiance ~ ., data=mydata)
#perform forward stepwise regression
forward <- step(intercept_only, direction='forward', scope=formula(all), trace=0)
#view results of forward stepwise regression
xx <- forward$anova
aa<- as.list(rownames(xx))
n<-length(aa)
#variable 1
x <- as.list( aa[1:n])
#Variable 2
l<-c()
i=1
while(i<=n) {
if (i==1){
b<-"relative"
}
else{
b<-"relative"
}
l<-c(l,b)
i=i+1
}
wfmeasure <- as.list(l)
#Variable 3
l <-c()
i=1
totResidDev <- round(xx$"Resid. Dev"[1],3)
while(i<=n) {
if (i==1){
b<-paste(as.character(sprintf(round(xx$"Resid. Dev"[1]/totResidDev100,3), fmt="%#.2f")),"%")
}
else{
b<-paste(as.character(sprintf(round(xx$Deviance[i]-1/totResidDev*100,3), fmt="%#.2f")),"%")
}
l<-c(l,b)
i=i+1
}
text <- as.list(l)
text <- as.list(as.character(round(xx$coefficients[2:n,1],3)))
#Variable 4
l <-c()
i=1
while(i<=n) {
if (i==1){
b<-sprintf(round(xx$"Resid. Dev"[1],3), fmt="%#.3f")
}
else{
b<-sprintf(round(xx$Deviance[i]*-1,3), fmt="%#.3f")
}
l<-c(l,b)
i=i+1
}
y <- as.list(l)
datawaterfall = data.frame(x=factor(x,levels=x),wfmeasure,text,y)
f <- list( #plotly chart axis names /naming: where you got: Axes Labels | R | Plotly
family = "Courier New, monospace",
size = 15,
color = "#000000"
)
xname <- list(
title = "Predictors",
titlefont = f
)
yname <- list(
title = "% Deviance from Intercept",
titlefont = f
)
plot_ly(
datawaterfall, name = "20", type = "waterfall", measure = ~wfmeasure,
x = ~x, textposition = "outside", y= ~y, text =~text,
connector = list(line = list(color= "rgb(63, 63, 63)")))%>%
layout(xaxis = xname, yaxis = yname)%>%
layout(xaxis = list(tickfont = list(size = 15)),
yaxis = list(tickfont = list(size = 5)))
