1 ## Infection Example
   2 
   3 load("infection.rdata")
   4 summary(infection)
   5 
   6 m.inf <- glm(infected~age*sex,family=binomial,
   7                                data=infection)
   8 
   9 summary(m.inf)
  10 
  11 ## coefficients
  12 ### intercepts
  13 invlogit(coef(m.inf)[1])
  14 invlogit(coef(m.inf)[1] + coef(m.inf)[3])
  15 
  16 ### slopes
  17 exp(coef(m.inf)[2])
  18 exp(coef(m.inf)[2] + coef(m.inf)[4])
  19 
  20 exp(30 * coef(m.inf)[2])
  21 exp(30 * (coef(m.inf)[2] + coef(m.inf)[4]))
  22 
  23 solve(0.015657,3.000513)
  24 solve(0.02670685,2.883849)
  25 
  26 ## all effects
  27 
  28 allEffects(m.inf)
  29 
  30 allEffects(m.inf,
  31            xlevels = list(age = seq(0,200,by = 50)))
  32 
  33 
  34 
  35 
  36 ## different link functions
  37 ggplot(births,aes(x = gestwks, y = as.numeric(lowbw)-1)) +
  38     geom_smooth(method = "glm", family = "binomial",se = T,size = 2) +
  39     geom_smooth(method = "glm", family = binomial(link = "probit"),se = T,size = 2, colour = "red") +
  40     geom_smooth(method = "glm", family = binomial(link = "cloglog"),se = T,size = 2, colour = "green") +
  41     geom_point(shape="|")
  42 
  43 
  44 ## multiple linear regression
  45 sc.race <- read.table("ScotsRaces.dat",header = T)
  46 m1 <- lm(time ~ climb + distance, data = sc.race)
  47 m1
  48 
  49 
  50 
  51 resid(m1)
  52 
  53 m.climb <- lm(time ~ distance, data = sc.race)
  54 m.dist <- lm(climb ~ distance, data = sc.race)
  55 
  56 m.res <- lm(resid(m.climb) ~ resid(m.dist))
  57 
  58 
  59 ## av.plots()
  60 require(car)
  61 avPlots(m1)
  62 
  63 avPlot(m1,"climb")
  64 
  65 ##
  66 head(cbind(resid(m.dist),
  67            avPlot(m1,"climb"),
  68            resid(m.climb)))
  69       
  70 ## summarizing the fit of a linaer model
  71 require(psych)
  72 describe(sc.race[,-1])
  73 
  74 M <- cor(sc.race[,-1])
  75 M
  76 
  77 pairs(sc.race[,-1])
  78 
  79 pairs(sc.race[,-1],panel = panel.smooth)
  80 
  81 require(corrplot)
  82 corrplot.mixed(M)
  83 
  84 ### partial correlation
  85 require(ppcor)
  86 pcor.test(sc.race$distance,sc.race$climb,sc.race$time)
  87