rm(list = ls())
fit <- lm(weight ~ height, women)
summary(fit)
women$weight
fitted(fit)
confint(fit)
residuals(fit)
with(women,plot(height,weight,
xlab = "height (in inches)",
ylab = "weight (in pounds)"))
abline(fit)
fit2 <- lm(weight ~ height + I(height^2), women)
summary(fit2)
with(women,plot(height,weight,
xlab = "height (in inches)",
ylab = "weight (in pounds)"))
lines(women$height,fitted(fit2))
library(car)
scatterplot(weight ~ height, data = women,
spread = FALSE, smoother.agrs=list(lty=2),pch = 20,
main = "women age 30 - 39",
xlab = "height (inches)",
ylab = "weight (lbs.)")
states <- as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
cor(states)
library(car)
scatterplotMatrix(states)
fit <- lm(Murder~Population+Illiteracy+Income+Frost,states)
summary(fit)
coefficients(fit)
confint(fit)
fit <- lm(mpg~hp+wt+hp:wt,mtcars)
summary(fit)
# interpretation about interaction
library(effects)
plot(effect("hp:wt",fit,,list(wt=c(2.2,3.2,4.2))),multiline=T)
fit <- lm(weight ~ height, women)
par(mfrow=c(2,2))
plot(fit)
fit <- lm(weight ~ height + I(height^2), women)
par(mfrow=c(2,2))
plot(fit)
newfit <- lm(weight~ height + I(height^2), data= women[-c(13,15),])
par(mfrow=c(2,2))
plot(newfit)
states <- as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
fit <- lm(Murder~Population+Illiteracy+Income+Frost,states)
par(mfrow=c(2,2))
plot(fit)
par(mfrow=c(1,1))
qqPlot(fit,lables=row.names(states),id.method= "identify",
simulate=T, main = "Q-Q plot")
states["Nevada",]
fitted(fit)["Nevada"]
residuals(fit)['Nevada']
rstudent(fit)["Nevada"]
residualplot <- function(fit, nbreaks= 10){
z <- rstudent(fit)
hist(z, breaks= nbreaks, freq = F,
xlab = "Studentized residual",
main= "Distribution of errors")
rug(jitter(z),col="brown")
curve(dnorm(x,mean=mean(z),sd=sd(z)),
add=T, col="blue",lwd= 2)
lines(density
(z)$x,density(z)$y,
col= "red",lwd= 2,lty= 2)
legend("topright",
legend = c("normal curve","kernel density curve"),
lty= 1:2,col=c("blue","red"),cex=.7)
}
residualplot(fit)
library(car)
durbinWatsonTest(fit)
crPlots(fit)
ncvTest(fit)
spreadLevelPlot(fit)
library(gvlma)
gvmodel <- gvlma(fit)
summary(gvmodel)
vif(fit)
library(car)
outlierTest(fit)
n <- 50
hat.plot <- function(fit){
p <- length(coefficients(fit))
n <- length(fitted(fit))
plot(hatvalues(fit),main= "Index Plot of hat values")
abline(h=c(2,3)*p/n,col= "red", lty= 2)
identify(1:n, hatvalues(fit), names(hatvalues(fit)))
}
hat.plot(fit)
cutoff <- 4/(nrow(states)-length(fit$coefficients)-2)
plot(fit,which = 4,cook.levels = cutoff)
abline(h=cutoff, lty=2,col="red")
library(car)
avPlots(fit,ask= FALSE, id.method ='identify')
ls("package:car")
influencePlot(fit,id.method="identify", main="Influence plot",
sub="circle size is proportional to cook's distance")
states <- as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
fit1 <- lm(Murder~Population+Illiteracy+Income+Frost,states)
fit2 <- lm(Murder~ Population+ Illiteracy +Income , data= states)
anova(fit2,fit1)
AIC(fit1,fit2)
library(MASS)
states <- as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
fit <- lm(Murder~Population+Illiteracy+Income+Frost,states)
stepAIC(fit,direction = "backward")
library(leaps)
states <- as.data.frame(state.x77[,c("Murder","Population","Illiteracy","Income","Frost")])
leaps <- regsubsets(Murder ~ Population + Illiteracy +Income + Frost, states,nbest = 4)
plot(leaps, scale = 'adjr2')
library(car)
subsets(leaps,statistic = "cp",
main = "Cp plot for all subsets regression")
abline(1,1,lty=2,col="red")
