Simple Linear Regression in R - Generic Functions (Part 3)

[anish-arya]

Part 2 link:

Simple Linear Regression in R - Introduction Part 2

Simple Linear Regression Model Fitting Part 1 link: The fat data frame contains 252 observations (individuals) on 19 variables. Here we don’t need all the variables, so let’s create a smaller dataset to use. fatdata<-fat[,c(1,2,5:11)] summary(fatdata[,-1]) # do you remember what the negative index (-1) here means? [image]Suppose we are interested in the relationship between body percent fat and neck circumference. lm1 ← lm(pctfat.brozek ~ neck, data = fatdata) plot(pctfat.brozek …

Generic functions for fitted (linear) model objects

Function	Description
print()	simple printed display
summary()	standard regression output
coef()	(or coefficients()) extracting the regression coefficients
residuals()	(or resid()) extracting residuals
fitted()	(or fitted.values()) extracting fitted values
anova()	comparison of nested models
predict()	predictions for new data
plot()	diagnostic plots
confint()	confidence intervals for the regression coefficients
deviance()	residual sum of squares
vcov()	(estimated) variance-covariance matrix
logLik()	log-likelihood (assuming normally distribted errors)
AIC	information criteria including AIC, BIC/SBC (assuming normally distributed errors)