Linear regression
lm(y ~ x1 + x2 + x3) # multiple linear regression
lm(log(y) ~ x) # log transformed
lm(sqrt(y) ~ x) # sqrt transformed
lm( y ~ log(x)) # fields transformed
llm(log(y) ~ log(x)) # everything is transformed
lm(y ~ .) # use all fields for regression model
lm(y ~ x + 0) # forced zero intercept
lm(y ~ x*k) # interaction of two variables
lm(y ~ x + k + x:k) # product of xkl but without interaction
lm(y ~ (x + k + ... + l)^2) # all first order interactions
lm(y ~ I(x1 + x2)) # sum of variables
lm(y ~ I(x1^2)) # product of variables (not interation)
lm(y ~ x + I(x^2) + I(x^3)) # polynomial regression
lm(y ~ poly(x,3)) # same as previous
# Forward/backward stepwise regression
# improve model
fit <- lm(y ~ x1 + x2)
bwd.fit <- step(fit, direction = 'backward')
fwd.fit <- step(fit, direction = 'forward', scope( ~ x1 + x2))
Test linear moedel
plot(m) # plot residuals
car::outlier.test(m)
dwtest(m) # Durbin-Watson Test of the model residuals
One-way ANOVA
oneway.test(x ~ f)
aov(x ~ f)
anova(m1, m2) # compair two models
