R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.2.0  (2005-10-06 r35749)
ISBN 3-900051-07-0

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> 
> ### Regression tests for fixed bugs
> 
> set.seed(290875)
> library(coin)
Loading required package: survival
Loading required package: splines
Loading required package: mvtnorm
Loading required package: modeltools
> isequal <- coin:::isequal
> 
> ### I() returns objects of class AsIs which caused an error in `trafo'
> df <- data.frame(x1 = rnorm(100), x2 = rnorm(100), x3 = gl(2, 50))
> independence_test(I(x1 / x2) ~ x3, data = df)

	Asymptotic General Independence Test

data:  I(x1/x2) by groups 1, 2 
T = -0.4491, p-value = 0.6534
alternative hypothesis: two.sided 

> independence_test(I(x1 < 0) ~ x3, data = df)

	Asymptotic General Independence Test

data:  I(x1 < 0) by groups 1, 2 
T = 0.7966, p-value = 0.4257
alternative hypothesis: two.sided 

> 
> ### expectation was wrong when varonly = TRUE in case both
> ### xtrafo and ytrafo were multivariate 
> if (require(multcomp)) {
+     df <- data.frame(x = runif(30), y = runif(30), z = gl(3, 10))
+     a <- independence_test(x + y ~ z, data = df,
+          distribution = approximate(B = 19999),
+          xtrafo = function(data) trafo(data, factor_trafo = function(x)
+              model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))))
+     b <- independence_test(x + y ~ z, data = df,
+          xtrafo = function(data) trafo(data, factor_trafo = function(x)
+              model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))))
+     isequal(expectation(a), expectation(b))
+ }
Loading required package: multcomp
[1] TRUE
> 
> 
> ### `statistic' for linear and standardized statistics was wrong in case of 
> ### scores
> data("jobsatisfaction")
> stopifnot(unique(dim(statistic(lbl_test(jobsatisfaction), "linear"))) == 1)
> stopifnot(unique(dim(statistic(lbl_test(jobsatisfaction), "standardized"))) == 1)
> 
> 
> ### support() failed in most cases
> df <- data.frame(x = runif(20), y = runif(20), z = gl(2, 10))
> support(independence_test(x ~ z, data = df))
[1] -4.264891  4.264891
> support(independence_test(x ~ z, data = df, teststat = "quad"))
[1]  0.00000 19.51142
> ite <- independence_test(I(round(x, 1)) ~ z, data = df, dist = exact())
> ae <- support(ite)
> de <- sapply(ae, function(x) dperm(ite, x))
> sum(de)
[1] 1
> ita <- independence_test(I(round(x, 1)) ~ z, data = df, 
+                          dist = approximate(B = 100000))
> aa <- support(ita)
> da <- sapply(aa, function(x) dperm(ita, x))
> sum(da)
[1] 1
> mean(round(ae, 10) %in% aa)
[1] 1
> 
> plot(aa, da, type = "s", lty = 1)
> lines(ae, de, type = "s", lty = 2)
> itas <- independence_test(I(round(x, 1)) ~ z, data = df)
> lines(ae[-1], diff(sapply(ae, function(x) pperm(itas, x))), lty = 3)
> legend("topleft", lty = 1:3, legend = c("approx", "exact", "asympt"), bty = "n")
> 
> ### check correct handling of multiple censoring indicators (in modeltools)
> ### was never wrong, just in case...
> data("photocar", package = "coin")
> i1 <- independence_test(Surv(time, event) + Surv(dmin, tumor) + ntumor ~ group,
+                   data = photocar)
> i2 <- independence_test(Surv(time, event) ~ group, data = photocar)
> i3 <- independence_test(Surv(dmin, tumor) ~ group, data = photocar)
> 
> stopifnot(max(abs(statistic(i1, "standardized")[,1] - 
+                   statistic(i2, "stand"))) < sqrt(.Machine$double.eps))
> stopifnot(max(abs(statistic(i1, "standardized")[,2] - 
+                   statistic(i3, "stand"))) < sqrt(.Machine$double.eps))
> 
> ### check new var_trafo argument
> x <- rnorm(20)
> y <- gl(2, 10)
> a <- trafo(data.frame(x = x, y = y), numeric_trafo = normal_trafo)
> b <- trafo(data.frame(x = x, y = y), var_trafo = list(x = normal_trafo))
> stopifnot(all.equal(a, b))
> 
> ### check for multiple ordered factors
> mydf <- data.frame(x = ordered(gl(4, 5)), y = ordered(gl(5, 4)), 
+                    z = rnorm(20))
> try(independence_test(x + y ~ z , data = mydf))
Error in .local(.Object, ...) : handling of multiple ordered factors currently not implemented
> try(independence_test(x + z ~ y , data = mydf))
Error in .local(.Object, ...) : handling of multiple ordered factors currently not implemented
> try(independence_test(z ~ x + y , data = mydf))
Error in .local(.Object, ...) : handling of multiple ordered factors currently not implemented
> 
> ### NA's and weights
> mydf <- data.frame(x = 1:10, y = gl(2, 5), w = rep(2, 10))
> s <- statistic(independence_test(x ~ y, data = mydf, weights = ~ w), "linear")
> stopifnot(s == 30)
> mydf$x[1] <- NA
> s <- statistic(independence_test(x ~ y, data = mydf, weights = ~ w), "linear")
> stopifnot(s == 28)
> 
> ### two observations only
> mydf <- data.frame(x = 1:10, y = gl(2, 5))
> independence_test(y ~ x, data = mydf, subset = c(1, 6))

	Asymptotic General Independence Test

data:  y by x 
T = -1, p-value = 0.3173
alternative hypothesis: two.sided 

> independence_test(y ~ x, data = mydf, subset = c(1, 2))

	Asymptotic General Independence Test

data:  y by x 
T = NaN, p-value = NA
alternative hypothesis: two.sided 

Warning message:
The conditional covariance matrix has zero diagonal elements in: .local(.Object, ...) 
> try(independence_test(y ~ x, data = mydf, subset = 1))
Error in validObject(.Object) : invalid class "IndependenceProblem" object: FALSE
> 
> ### names of expectation and covariance
> YOY <- data.frame(length = c(46, 28, 46, 37, 32, 41, 42, 45, 38, 44,
+                              42, 60, 32, 42, 45, 58, 27, 51, 42, 52,
+                              38, 33, 26, 25, 28, 28, 26, 27, 27, 27,
+                              31, 30, 27, 29, 30, 25, 25, 24, 27, 30),
+                   site = factor(c(rep("I", 10), rep("II", 10),
+                                   rep("III", 10), rep("IV", 10))))
> 
> it <- independence_test(length ~ site, data = YOY,
+     ytrafo = function(data) trafo(data, numeric_trafo = rank),
+     teststat = "quadtype")
> expectation(it)
  I  II III  IV 
205 205 205 205 
> covariance(it)
            I        II       III        IV
I   1019.0385 -339.6795 -339.6795 -339.6795
II  -339.6795 1019.0385 -339.6795 -339.6795
III -339.6795 -339.6795 1019.0385 -339.6795
IV  -339.6795 -339.6795 -339.6795 1019.0385
> 
> mydf <- data.frame(x = rnorm(10), y = rnorm(10), z = gl(2, 5))
> it <- independence_test(x + y ~ z, data = mydf)
> statistic(it, "linear")
          x         y
1 -2.066068 0.3292961
> expectation(it)
       1:x        1:y 
-1.6340021 -0.9750075 
> covariance(it)
           1:x        1:y
1:x  0.4417981 -0.8139286
1:y -0.8139286  2.5231787
>