# Code for Chapter 1: Linear Models R code from Chapter 1 of the second edition of `Generalized Additive Models: An Introduction with R' is in the examples section below. ## Author(s) Simon Wood Maintainer: Simon Wood ## References Wood, S.N. (2017) **Generalized Additive Models: An Introduction with R**, CRC ## See Also `mgcv`, `ch1.solutions` ## Examples ```r library(gamair); library(mgcv) ## 1.1.2 data(hubble) hub.mod <- lm(y ~ x - 1, data=hubble) summary(hub.mod) plot(fitted(hub.mod),residuals(hub.mod),xlab="fitted values", ylab="residuals") hub.mod1 <- lm(y ~ x - 1,data=hubble[-c(3,15),]) summary(hub.mod1) plot(fitted(hub.mod1),residuals(hub.mod1), xlab="fitted values",ylab="residuals") hubble.const <- c(coef(hub.mod),coef(hub.mod1))/3.09e19 age <- 1/hubble.const age/(60^2*24*365) ## 1.1.3 cs.hubble <- 163000000 t.stat <- (coef(hub.mod1)-cs.hubble)/vcov(hub.mod1)[1,1]^0.5 pt(t.stat,df=21)*2 # 2 because of |T| in p-value defn. sigb <- summary(hub.mod1)$coefficients[2] h.ci <- coef(hub.mod1)+qt(c(0.025,0.975),df=21)*sigb h.ci h.ci <- h.ci*60^2*24*365.25/3.09e19 # convert to 1/years sort(1/h.ci) ## 1.5.1 data(sperm.comp1) pairs(sperm.comp1[,-1]) sc.mod1 <- lm(count~time.ipc+prop.partner,sperm.comp1) model.matrix(sc.mod1) par(mfrow=c(2,2)) # split the graphics device into 4 panels plot(sc.mod1) # (uses plot.lm as sc.mod1 is class `lm') sperm.comp1[9,] sc.mod1 sc.mod2 <- lm(count~time.ipc+I(prop.partner*time.ipc), sperm.comp1) ## 1.5.2 summary(sc.mod1) ## 1.5.3 sc.mod3 <- lm(count~prop.partner,sperm.comp1) summary(sc.mod3) sc.mod4 <- lm(count~1,sperm.comp1) # null model AIC(sc.mod1,sc.mod3,sc.mod4) ## 1.5.4 data(sperm.comp2) sc2.mod1 <- lm(count~f.age+f.height+f.weight+m.age+m.height+ m.weight+m.vol,sperm.comp2) plot(sc2.mod1) summary(sc2.mod1) sc2.mod2 <- lm(count~f.age+f.height+f.weight+m.height+ m.weight+m.vol,sperm.comp2) summary(sc2.mod2) sc2.mod7 <- lm(count~f.weight,sperm.comp2) summary(sc2.mod7) sc <- sperm.comp2[-19,] sc3.mod1 <- lm(count~f.age+f.height+f.weight+m.age+m.height+ m.weight+m.vol,sc) summary(sc3.mod1) sperm.comp1$m.vol <- sperm.comp2$m.vol[sperm.comp2$pair%in%sperm.comp1$subject] sc1.mod1 <- lm(count~m.vol,sperm.comp1) summary(sc1.mod1) ## 1.5.5 sc.c <- summary(sc1.mod1)$coefficients sc.c # check info extracted from summary sc.c[2,1]+qt(c(.025,.975),6)*sc.c[2,2] ## 1.5.6 df <- data.frame(m.vol=c(10,15,20,25)) predict(sc1.mod1,df,se=TRUE) ## 1.5.7 set.seed(1); n <- 100; x <- runif(n) z <- x + rnorm(n)*.05 y <- 2 + 3 * x + rnorm(n) summary(lm(y~z)) summary(lm(y~x+z)) ## 1.6.4 z <- c(1,1,1,2,2,1,3,3,3,3,4) z z <- as.factor(z) z x <- c("A","A","C","C","C","er","er") x x <- factor(x) x PlantGrowth$group # PlantGrowth$group <- as.factor(PlantGrowth$group) pgm.1 <- lm(weight ~ group,data=PlantGrowth) plot(pgm.1) summary(pgm.1) pgm.0 <- lm(weight~1,data=PlantGrowth) anova(pgm.0,pgm.1) ```

ch1 function