define Better from ordinal Improved
Arthritis$Better <- Arthritis$Improved > 'None'
simple linear logistic regression
arth.mod0 <- glm(Better ~ Age, data=Arthritis, family='binomial')
anova(arth.mod0)
## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: Better
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev
## NULL 83 116
## Age 1 7.29 82 109
plot, with +-1 SE
plot(Better ~ Age, data=Arthritis, ylab="Prob (Better)")
pred <- predict(arth.mod0, type="response", se.fit=TRUE)
ord <-order(Arthritis$Age)
lines(Arthritis$Age[ord], pred$fit[ord], lwd=3)
upper <- pred$fit + pred$se.fit
lower <- pred$fit - pred$se.fit
lines(Arthritis$Age[ord], upper[ord], lty=2, col="blue")
lines(Arthritis$Age[ord], lower[ord], lty=2, col="blue")
# smoothed non-parametric curve
lines(lowess(Arthritis$Age[ord], Arthritis$Better[ord]), lwd=2, col="red")
![](data:image/png;base64,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)
main effects model
arth.mod1 <- glm(Better ~ Age + Sex + Treatment , data=Arthritis, family='binomial')
Anova(arth.mod1)
## Analysis of Deviance Table (Type II tests)
##
## Response: Better
## LR Chisq Df Pr(>Chisq)
## Age 6.16 1 0.01308 *
## Sex 6.98 1 0.00823 **
## Treatment 12.20 1 0.00048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
same, using update()
arth.mod1 <- update(arth.mod0, . ~ . + Sex + Treatment)
Anova(arth.mod1)
## Analysis of Deviance Table (Type II tests)
##
## Response: Better
## LR Chisq Df Pr(>Chisq)
## Age 6.16 1 0.01308 *
## Sex 6.98 1 0.00823 **
## Treatment 12.20 1 0.00048 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plots, using effects package
library(effects)
arth.eff <- allEffects(arth.mod1, xlevels=list(Age=seq(25,75,5)))
plot(arth.eff, ylab="Prob(Better)")
![](data:image/png;base64,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)
forward selection from the main effects model
step(arth.mod1, direction="forward", scope=.~ (Age+Sex+Treatment)^2 + Age^2)
## Start: AIC=100
## Better ~ Age + Sex + Treatment
##
## Df Deviance AIC
## + Age:Sex 1 88.6 98.6
## <none> 92.1 100.1
## + Age:Treatment 1 91.5 101.5
## + Sex:Treatment 1 91.6 101.6
##
## Step: AIC=98.6
## Better ~ Age + Sex + Treatment + Age:Sex
##
## Df Deviance AIC
## <none> 88.6 98.6
## + Sex:Treatment 1 88.4 100.4
## + Age:Treatment 1 88.6 100.6
##
## Call: glm(formula = Better ~ Age + Sex + Treatment + Age:Sex, family = "binomial",
## data = Arthritis)
##
## Coefficients:
## (Intercept) Age SexMale TreatmentTreated Age:SexMale
## -4.5548 0.0773 2.7507 1.7970 -0.0794
##
## Degrees of Freedom: 83 Total (i.e. Null); 79 Residual
## Null Deviance: 116
## Residual Deviance: 88.6 AIC: 98.6
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