# NOT RUN {
## example 1:
# the data consist of ages (in months) at which an
# infant starts to walk alone.
# prepare data
DATA1 <- subset(ZelazoKolb1972, Group != "Control")
# fit unrestricted linear model
fit1.lm <- lm(Age ~ -1 + Group, data = DATA1)
# the variable names can be used to impose constraints on
# the corresponding regression parameters.
coef(fit1.lm)
# constraint syntax: assuming that the walking
# exercises would not have a negative effect of increasing the
# mean age at which a child starts to walk.
myConstraints1 <- ' GroupActive < GroupPassive;
GroupPassive < GroupNo '
conTest(fit1.lm, myConstraints1)
# another way is to first fit the restricted model
fit.restr1 <- restriktor(fit1.lm, constraints = myConstraints1)
conTest(fit.restr1)
# }
# NOT RUN {
# Or in matrix notation.
Amat1 <- rbind(c(-1, 0, 1),
c( 0, 1, -1))
myRhs1 <- rep(0L, nrow(Amat1))
myNeq1 <- 0
conTest(fit1.lm, constraints = Amat1,
rhs = myRhs1, neq = myNeq1)
# }
# NOT RUN {
#########################
## Artificial examples ##
#########################
# generate data
n <- 10
means <- c(1,2,1,3)
nm <- length(means)
group <- as.factor(rep(1:nm, each = n))
y <- rnorm(n * nm, rep(means, each = n))
DATA2 <- data.frame(y, group)
# fit unrestricted linear model
fit2.lm <- lm(y ~ -1 + group, data = DATA2)
coef(fit2.lm)
## example 2: increasing means
myConstraints2 <- ' group1 < group2
group2 < group3
group3 < group4 '
# compute F-test for hypothesis test Type A and compute the tail
# probability based on the parametric bootstrap. We only generate 9
# bootstrap samples in this example; in practice you may wish to
# use a much higher number.
conTest(fit2.lm, constraints = myConstraints2, type = "A",
boot = "parametric", R = 9)
# or fit restricted linear model
fit2.con <- restriktor(fit2.lm, constraints = myConstraints2)
conTest(fit2.con)
# }
# NOT RUN {
# increasing means in matrix notation.
Amat2 <- rbind(c(-1, 1, 0, 0),
c( 0,-1, 1, 0),
c( 0, 0,-1, 1))
myRhs2 <- rep(0L, nrow(Amat2))
myNeq2 <- 0
conTest(fit2.con, constraints = Amat2, rhs = myRhs2, neq = myNeq2,
type = "A", boot = "parametric", R = 9)
# }
# NOT RUN {
## example 3:
# combination of equality and inequality constraints.
myConstraints3 <- ' group1 == group2
group3 < group4 '
conTest(fit2.lm, constraints = myConstraints3, type = "B", neq.alt = 1)
# fit resticted model and compute model-based bootstrapped
# standard errors. We only generate 9 bootstrap samples in this
# example; in practice you may wish to use a much higher number.
# Note that, a warning message may be thrown because the number of
# bootstrap samples is too low.
fit3.con <- restriktor(fit2.lm, constraints = myConstraints3,
se = "boot.model.based", B = 9)
conTest(fit3.con, type = "B", neq.alt = 1)
## example 4:
# restriktor can also be used to define effects using the := operator
# and impose constraints on them. For example, is the
# average effect (AVE) larger than zero?
# generate data
n <- 30
b0 <- 10; b1 = 0.5; b2 = 1; b3 = 1.5
X <- c(rep(c(0), n/2), rep(c(1), n/2))
set.seed(90)
Z <- rnorm(n, 16, 5)
y <- b0 + b1*X + b2*Z + b3*X*Z + rnorm(n, 0, sd = 10)
DATA3 = data.frame(cbind(y, X, Z))
# fit linear model with interaction
fit4.lm <- lm(y ~ X*Z, data = DATA3)
# constraint syntax
myConstraints4 <- ' AVE := X + 16.86137*X.Z;
AVE > 0 '
conTest(fit4.lm, constraints = myConstraints4)
# or
fit4.con <- restriktor(fit4.lm, constraints = ' AVE := X + 16.86137*X.Z;
AVE > 0 ')
conTest(fit4.con)
# }
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