Chapter11 {DanielBiostatistics10th}

Chapter 11: Regression Analysis: Some Additional Techniques

Description

Examples in Chapter 11, Regression Analysis: Some Additional Techniques.

Value

No function defined for Chapter 11.

Examples

library(DanielBiostatistics10th)
library(car) # ?car::scatterplot
library(DescTools)

# Example 11.1.1; Page 540, 
head(EXA_C11_S01_01)
head(log(EXA_C11_S01_01$conc, base = 10))
head(EXA_C11_S01_01$logConc)

# Example 11.1.2; Page 542, 
head(EXA_C11_S01_02)
cor.test(~ sbp + weight, data = EXA_C11_S01_02)
cor.test(~ sbp + bmi, data = EXA_C11_S01_02) 

# Example 11.2.1; Page 545, 
head(EXA_C11_S02_01)
d1121 = within(EXA_C11_S02_01, expr = {
  SMOKE = as.logical(SMOKE)
})
xlab1121 = 'Length of gestation (weeks)'; ylab1121 = 'Birth weight (grams)'
scatterplot(GRAMS ~ WEEKS | SMOKE, data = d1121, regLine = FALSE, smooth = FALSE,
            xlab = xlab1121, ylab = ylab1121, main = 'Figure 11.2.1')
summary(m1121_main <- lm(GRAMS ~ WEEKS + SMOKE, data = d1121)) # Figure 11.2.2
confint(m1121_main)
scatterplot(GRAMS ~ WEEKS | SMOKE, data = d1121, regLine = FALSE, smooth = FALSE,
            xlab = xlab1121, ylab = ylab1121, main = 'Figure 11.2.3')
(cf_main = m1121_main$coefficients)
abline(a = cf_main[1L], b = cf_main[2L], col = 'blue') # regression line for non-smoking mothers
abline(a = cf_main[1L] + cf_main[3L], b = cf_main[2L], col = 'magenta') 

# Example 11.2.3; Page 551, 
d1123 = within(EXA_C11_S02_03, expr = {
  METHOD = factor(METHOD, levels = c('C', 'A', 'B')) # textbook designated 'C' as reference level
})
summary(mod_1123 <- lm(EFFECT ~ AGE * METHOD, data = d1123)) # Figure 11.2.5
confint(mod_1123)
scatterplot(EFFECT ~ AGE | METHOD, data = d1123, smooth = FALSE,
            xlab = 'Age', ylab = 'Treatment effectiveness', main = 'Figure 11.2.6')

# Example 11.3.1; Page 561, 
head(EXA_C11_S03_01)
names(EXA_C11_S03_01) = c('JOBPER', 'ASRV', 'ENTH', 'AMB', 'COMM', 'PROB', 'INIT')
summary(mod_1131_raw <- lm(JOBPER ~ ASRV + ENTH + AMB + COMM + PROB + INIT, data = EXA_C11_S03_01))
# summary(mod_1131 <- MASS::stepAIC(mod_1131_raw, direction = 'backward'))
# the stepwise selection criterion used in MINITAB is not necessarily AIC

# Example 11.4.1; Page 572, 
addmargins(d1141 <- array(c(92L, 21L, 15L, 20L), dim = c(2L, 2L), dimnames = list(
  OCAD = c('Present', 'Absent'), Sex = c('Male', 'Female')))) # Table 11.4.2
(d1141a = within(as.data.frame(as.table(d1141)), expr = {
  OCAD = (OCAD == 'Present')
  Sex = factor(Sex, levels = c('Female', 'Male'))
}))
summary(m1141 <- glm(OCAD ~ Sex, family = binomial, weights = Freq, data = d1141a)) # Figure 11.4.1
exp(m1141$coefficients[2L]) # exp(beta_M)
exp(confint(m1141)) # confidence interval of exp(beta)
predict(m1141, newdata = data.frame(Sex = setNames(nm = c('Male', 'Female'))), type = 'response')

# Example 11.4.2; Page 573, 
head(EXA_C11_S04_02)
summary(mod_1142 <- glm(ATT ~ AGE, family = binomial, data = EXA_C11_S04_02)) # Figure 11.4.2
exp(mod_1142$coefficients[2L])
exp(confint(mod_1142))
car::Anova(mod_1142) # Optional

# Example 11.4.3; Page 576, 
head(REV_C11_24)
summary(glm(ONSET ~ HIAA + TRYPT, family = binomial, data = REV_C11_24)) # Figure 11.4.4
# Predictor TRYPT should be removed from model due to p-value \approx 1 
summary(glm(ONSET ~ HIAA, family = binomial, data = REV_C11_24)) 

# Example 11.4.4-11.4.5; Page 578-579
DescTools::PseudoR2(mod_1142, which = 'CoxSnell')
DescTools::PseudoR2(mod_1142, which = 'Nagelkerke')