## S3 method for class 'glht':
summary(object, test = adjusted(), ...)
## S3 method for class 'glht':
confint(object, parm, level = 0.95, ...)
## S3 method for class 'glht':
coef(object, rhs = FALSE, ...)
## S3 method for class 'glht':
vcov(object, ...)
univariate()
adjusted(type = c("free", "Shaffer", "Westfall", p.adjust.methods),
...)
Ftest()
Chisqtest()glht.rhs = TRUE) of the linear hypothesis
should be returned.adjusted)
to be applied. See below and p.adjust.maxpts,
abseps or releps to
pmvnorm in adjusted or
glht can be used to actually test the global
null hypothesis, each of the partial hypotheses and for
simultaneous confidence intervals for the linear function $K coef and vcov methods compute the linear
function $K The test argument to summary takes a function specifying
the type of test to be applied. Classical Chisq (Wald test) or F statistics
for testing the global hypothesis $H_0$ are implemented in functions
Chisqtest and Ftest. Several approaches to multiplicity adjusted p
values for each of the linear hypotheses are implemented
in function adjusted. The type
argument to adjusted specifies the method to be applied:
"free" implements adjusted p values based on the joint
normal or $t$ distribution of the linear function, and
"Shaffer" and "Westfall" implement logically constraint
multiplicity adjustments (Shaffer, 1986; Westfall, 1997).
In addition, all adjustment methods
implemented in p.adjust are available as well.
Simultaneous confidence intervals for linear functions can be computed
using method confint. Univariate confidence intervals
can be computed by specifying the additional argument adjusted = FALSE
to confint.
All simultaneous inference procedures implemented here control
the family-wise error rate (FWER). Multivariate
normal and $t$ distributions, the latter one only for models of
class lm, are evaluated using the procedures
implemented in package mvtnorm.
summary computes (adjusted) p values for general linear hypotheses,
confint computes (adjusted) confidence intervals.
coef returns estimates of the linear function $K vcov its covariance.Peter H. Westfall (1997), Multiple testing of general contrasts using logical constraints and correlations. Journal of the American Statistical Association, 92, 299--306.
### set up all-pair comparisons for factor `tension' wht <- glht(amod, linfct = mcp(tension = "Tukey"))
### 95% simultaneous confidence intervals plot(print(confint(wht)))
### the same (for balanced designs only) TukeyHSD(amod, "tension")
### corresponding adjusted p values summary(wht)
### confidence bands for a simple linear model, `cars' data plot(cars, xlab = "Speed (mph)", ylab = "Stopping distance (ft)", las = 1)
### fit linear model and add regression line to plot lmod <- lm(dist ~ speed, data = cars) abline(lmod)
### a grid of speeds speeds <- seq(from = min(cars$speed), to = max(cars$speed), length = 10)
### linear hypotheses: 10 selected points on the regression line != 0 K <- cbind(1, speeds)
### set up linear hypotheses cht <- glht(lmod, linfct = K)
### confidence intervals, i.e., confidence bands, and add them plot cci <- confint(cht) lines(speeds, cci$confint[,"lwr"], col = "blue") lines(speeds, cci$confint[,"upr"], col = "blue")
### simultaneous p values for parameters in a Cox model if (require("survival") && require("MASS")) { data("leuk", package = "MASS") leuk.cox <- coxph(Surv(time) ~ ag + log(wbc), data = leuk)
### set up linear hypotheses lht <- glht(leuk.cox, linfct = diag(length(coef(leuk.cox))))
### adjusted p values print(summary(lht)) }