library(nlme)
Orthodont2 <- Orthodont
Orthodont2$newAge <- Orthodont$age - 11
fm1Orth.lme2 <- lme(distance ~ Sex * newAge,
data = Orthodont2,
random = ~ newAge | Subject
)
summary(fm1Orth.lme2)
contrast(fm1Orth.lme2,
a = list(Sex = levels(Orthodont2$Sex), newAge = 8 - 11),
b = list(Sex = levels(Orthodont2$Sex), newAge = 10 - 11)
)
# ---------------------------------------------------------------------------
anova_model <- lm(expression ~ diet * group, data = two_factor_crossed)
anova(anova_model)
library(ggplot2)
theme_set(theme_bw() + theme(legend.position = "top"))
ggplot(two_factor_crossed) +
aes(x = diet, y = expression, col = group, shape = group) +
geom_point() +
geom_smooth(aes(group = group), method = lm, se = FALSE)
int_model <- lm(expression ~ diet * group, data = two_factor_crossed)
main_effects <- lm(expression ~ diet + group, data = two_factor_crossed)
# Interaction effect is probably real:
anova(main_effects, int_model)
# Test treatment in low fat diet:
veh_group <- list(diet = "low fat", group = "vehicle")
trt_group <- list(diet = "low fat", group = "treatment")
contrast(int_model, veh_group, trt_group)
# ---------------------------------------------------------------------------
car_mod <- lm(mpg ~ am + wt, data = mtcars)
print(summary(car_mod), digits = 5)
mean_wt <- mean(mtcars$wt)
manual_trans <- list(am = 0, wt = mean_wt)
auto_trans <- list(am = 1, wt = mean_wt)
print(contrast(car_mod, manual_trans, auto_trans), digits = 5)
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