FUNDAMENTALS OF R
# This file is intended to get you started with R.
# First read in the data and examine it.
Example <- read.table("C:/users/wood/Documents/My Teaching/Maximum Likelihood/Data/EXAMPLE.txt", header=TRUE)
attach(Example)
Example
# Now summarize the data for all variables.
summary(Example)
# Now compute the mean, variance, and standard deviation for a single variable.
mean(X1)
var(X1)
sd(X1)
median(X1)
max(X1)
min(X1)
# Now compute the skewness and kurtosis using the e1071 library
library(e1071)
skewness(X1)
kurtosis(X1)
# Now create a new variable and add it to the active data file.
Example$NEWVAR <- Y+X1+X2+X3
detach(Example)
attach(Example)
# Now do a correlation matrix among the original variables.
cor(Example[,c("X1","X2","X3","Y")], use="complete.obs")
# Now do a scatterplot between two variables with a superimposed regression line,
# a parametric regression line, and box plots for each variable.
library(car)
scatterplot(Y~X1, reg.line=lm, boxplots='xy', smooth=TRUE, span =0.5, data=Example)
# Now create two variables X1*X2 and X3 squared and add to the active data.
Example$X1X2 <- X1 * X2
Example$X3sqr <- X3^2
detach(Example)
attach(Example)
# Get descriptive statistics on the new variables
summary(X1X2)
var(X1X2)
sd(X1X2)
summary(X3sqr)
var(X3sqr)
sd(X3sqr)
# Look at the entire dataset.
Example
# Now regress Y on X1, X2, X1X2, and X3sqr and look at the output object.
regress.model <- lm(Y ~ X1 + X2 + X1X2 + X3sqr)
summary(regress.model)
# Now add the residuals and predicted values to the dataset.
Example$residuals <- residuals(regress.model)
Example$fitted <- fitted(regress.model)
detach(Example)
attach(Example)
# Now list and plot the residuals and predicted values
residuals
plot(residuals)
fitted
plot(fitted)
# Now get the analysis of variance table for the regression.
anova(regress.model)
# Now construct a CUSUM plot for model stability.
library(strucchange)
plot(efp(Y ~ X1 + X2 + X1X2 + X3sqr, type = "Rec-CUSUM"))