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###-- Linear Models, basic functionality -- weights included.
## From John Maindonald :
roller <- data.frame(
weight = c(1.9, 3.1, 3.3, 4.8, 5.3, 6.1, 6.4, 7.6, 9.8, 12.4),
depression = c( 2, 1, 5, 5, 20, 20, 23, 10, 30, 25))
roller.lmu <- lm(weight~depression, data=roller)
roller.lsfu <- lsfit(roller$depression, roller$weight)
roller.lsf <- lsfit(roller$depression, roller$weight, wt = 1:10)
roller.lsf0 <- lsfit(roller$depression, roller$weight, wt = 0:9)
roller.lm <- lm(weight~depression, data=roller, weights= 1:10)
roller.lm0 <- lm(weight~depression, data=roller, weights= 0:9)
roller.lm9 <- lm(weight~depression, data=roller[-1,],weights= 1:9)
roller.glm <- glm(weight~depression, data=roller, weights= 1:10)
roller.glm0<- glm(weight~depression, data=roller, weights= 0:9)
predict(roller.glm0, type="terms")# failed till 2003-03-31
## FIXME : glm()$residual [1] is NA, lm()'s is ok.
## all.equal(residuals(roller.glm0, type = "partial"),
## residuals(roller.lm0, type = "partial") )
all.equal(deviance(roller.lm),
deviance(roller.glm))
all.equal(weighted.residuals(roller.lm),
residuals (roller.glm))
all.equal(deviance(roller.lm0),
deviance(roller.glm0))
all.equal(weighted.residuals(roller.lm0, drop=FALSE),
residuals (roller.glm0))
(im.lm0 <- influence.measures(roller.lm0))
all.equal(unname(im.lm0 $ infmat),
unname(cbind( dfbetas (roller.lm0)
, dffits (roller.lm0)
, covratio (roller.lm0)
,cooks.distance(roller.lm0)
,lm.influence (roller.lm0)$hat)
))
all.equal(rstandard(roller.lm9),
rstandard(roller.lm0),tol=1e-14)
all.equal(rstudent(roller.lm9),
rstudent(roller.lm0),tol=1e-14)
all.equal(rstudent(roller.lm),
rstudent(roller.glm))
all.equal(cooks.distance(roller.lm),
cooks.distance(roller.glm))
all.equal(summary(roller.lm0)$coefficients,
summary(roller.lm9)$coefficients, tol=1e-14)
all.equal(print(anova(roller.lm0), signif.st=FALSE),
anova(roller.lm9), tol=1e-14)
### more regression tests for lm(), glm(), etc :
## moved from ?influence.measures:
lm.SR <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
(IM <- influence.measures(lm.SR))
summary(IM)
## colnames will differ in the next line
all.equal(dfbetas(lm.SR), IM$infmat[, 1:5], check.attributes = FALSE,
tol = 1e-12)
signif(dfbeta(lm.SR), 3)
covratio (lm.SR)
## predict.lm(.)
all.equal(predict(roller.lm, se.fit=TRUE)$se.fit,
predict(roller.lm, newdata=roller, se.fit=TRUE)$se.fit, tol= 1e-14)
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