Permutation Transformation Tests
P-values for a parameter in a linear transformation model and corresponding confidence intervals obtained from by the permutation principle
perm_test(object, ...)
## S3 method for class 'tram'
perm_test(object, parm = names(coef(object)),
statistic = c("Score", "Likelihood", "Wald"),
alternative = c("two.sided", "less", "greater"),
nullvalue = 0, confint = TRUE, level = .95,
Taylor = FALSE, block_permutation = TRUE, maxsteps = 25, ...)object |
an object of class |
parm |
a vector of names of parameters to be tested.
These parameters must be present in |
statistic |
a character string specifying the statistic to be
permuted. The default |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
nullvalue |
a number specifying an optional parameter used to form the null hypothesis. |
confint |
a logical indicating whether a confidence interval should be
computed. Score confidence intervals are computed by default. A
1st order Taylor approximation to the Score statistc is used with
|
level |
the confidence level. |
block_permutation |
a logical indicating wheather stratifying variables shall be interpreted as blocks defining admissible permutations. |
Taylor |
a logical requesting the use of a 1st order Taylor approximation when inverting the score statistic. |
maxsteps |
number of function evaluations when inverting the score statistic for computing confidence intervals. |
... |
additional arguments to |
Permutation test for one single parameters in the linear
predictor of object is computed. This parameters must be present
in object. This is somewhat experimental and not recommended for
serious practical use (yet!).
An object of class htest or a list thereof. See Coxph
for an example.
## Tritiated Water Diffusion Across Human Chorioamnion
## Hollander and Wolfe (1999, p. 110, Tab. 4.1)
diffusion <- data.frame(
pd = c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46,
1.15, 0.88, 0.90, 0.74, 1.21),
age = factor(rep(c("At term", "12-26 Weeks"), c(10, 5)))
)
### plot the two quantile functions
boxplot(pd ~ age, data = diffusion)
### the Wilcoxon rank sum test, with a confidence interval
### for a median shift
wilcox.test(pd ~ age, data = diffusion, conf.int = TRUE, exact = TRUE)
### a corresponding parametric transformation model with a log-odds ratio
### difference parameter, ie a difference on the log-odds scale
md <- Colr(pd ~ age, data = diffusion)
### assess model fit by plotting estimated distribution fcts
agef <- sort(unique(diffusion$age))
col <- c("black", "darkred")
plot(as.mlt(md), newdata = data.frame(age = agef),
type = "distribution", col = col)
legend("bottomright", col = col, lty = 1, legend = levels(agef),
bty = "n", pch = 19)
## compare with ECDFs: not too bad (but not good, either)
npfit <- with(diffusion, tapply(pd, age, ecdf))
lines(npfit[[1]], col = col[1])
lines(npfit[[2]], col = col[2])
### Wald confidence interval
confint(md)
### Likelihood confidence interval
confint(profile(md))
### Score confidence interval
confint(score_test(md))
confint(score_test(md, Taylor = TRUE))
### exact permutation score test
(pt <- perm_test(md, confint = TRUE, distribution = "exact"))
(pt <- perm_test(md, confint = TRUE, distribution = "exact",
Taylor = TRUE))
### compare with probabilistic indices obtained from asht::wmwTest
if (require("asht", warn.conflicts = FALSE)) {
print(wt2 <- wmwTest(pd ~ I(relevel(age, "At term")),
data = diffusion, method = "exact.ce"))
### as log-odds ratios
print(PI(prob = wt2$conf.int))
print(PI(prob = wt2$estimate))
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.