```
mod.davis <- lm(weight ~ repwt, data=Davis)
## the following are equivalent:
linearHypothesis(mod.davis, diag(2), c(0,1))
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"))
linearHypothesis(mod.davis, c("(Intercept)", "repwt"), c(0,1))
linearHypothesis(mod.davis, c("(Intercept)", "repwt = 1"))
## use asymptotic Chi-squared statistic
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), test = "Chisq")
## the following are equivalent:
## use HC3 standard errors via white.adjust option
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
white.adjust = TRUE)
## covariance matrix *function* (where type = "hc3" is the default)
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), vcov. = hccm)
## covariance matrix *estimate*, specifying type explicitly
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = hccm(mod.davis, type = "hc3"))
## specifying additional type argument to covariance matrix function
linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"),
vcov = function(m) hccm(m, type = "hc3"))
mod.duncan <- lm(prestige ~ income + education, data=Duncan)
## the following are all equivalent:
linearHypothesis(mod.duncan, "1*income - 1*education = 0")
linearHypothesis(mod.duncan, "income = education")
linearHypothesis(mod.duncan, "income - education")
linearHypothesis(mod.duncan, "1income - 1education = 0")
linearHypothesis(mod.duncan, "0 = 1*income - 1*education")
linearHypothesis(mod.duncan, "income-education=0")
linearHypothesis(mod.duncan, "1*income - 1*education + 1 = 1")
linearHypothesis(mod.duncan, "2income = 2*education")
mod.duncan.2 <- lm(prestige ~ type*(income + education), data=Duncan)
coefs <- names(coef(mod.duncan.2))
## test against the null model (i.e., only the intercept is not set to 0)
linearHypothesis(mod.duncan.2, coefs[-1])
## test all interaction coefficients equal to 0
linearHypothesis(mod.duncan.2, coefs[grep(":", coefs)], verbose=TRUE)
linearHypothesis(mod.duncan.2, matchCoefs(mod.duncan.2, ":"), verbose=TRUE) # equivalent
lh <- linearHypothesis(mod.duncan.2, coefs[grep(":", coefs)])
attr(lh, "value") # value of linear function
attr(lh, "vcov") # covariance matrix of linear function
## a multivariate linear model for repeated-measures data
## see ?OBrienKaiser for a description of the data set used in this example.
mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~ treatment*gender,
data=OBrienKaiser)
coef(mod.ok)
## specify the model for the repeated measures:
phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
levels=c("pretest", "posttest", "followup"))
hour <- ordered(rep(1:5, 3))
idata <- data.frame(phase, hour)
idata
## test the four-way interaction among the between-subject factors
## treatment and gender, and the intra-subject factors
## phase and hour
linearHypothesis(mod.ok, c("treatment1:gender1", "treatment2:gender1"),
title="treatment:gender:phase:hour", idata=idata, idesign=~phase*hour,
iterms="phase:hour")
## mixed-effects models examples:
if (FALSE) # loads nlme package
library(nlme)
example(lme)
linearHypothesis(fm2, "age = 0")
if (FALSE) # loads lme4 package
library(lme4)
example(glmer)
linearHypothesis(gm1, matchCoefs(gm1, "period"))
if (require(nnet)){
print(m <- multinom(partic ~ hincome + children, data=Womenlf))
print(coefs <- as.vector(outer(c("not.work.", "parttime."),
c("hincome", "childrenpresent"),
paste0)))
linearHypothesis(m, coefs) # ominbus Wald test
}
```

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