Displaying 4 results from an estimated 4 matches for "xtrafo".
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trafo
2012 Jan 09
2
Unexpected results using the oneway_test in the coin package
...1)
### Kruskal-Wallis test
kruskal_test(breeding ~ habitat, data = mydata, distribution = approximate(B = 9999))
### Nemenyi-Damico-Wolfe-Dunn test (joint ranking)
NDWD <- oneway_test(breeding ~ habitat, data = mydata,
ytrafo = function(data) trafo(data, numeric_trafo = rank),
xtrafo = function(data) trafo(data, factor_trafo = function(x)
model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))),
teststat = "max", distribution = approximate(B = 900000))
### global p-value
print(pvalue(NDWD))
### sites-by-site p values at alpha = 0.01 (pa...
2010 Apr 22
2
Jonckheere-Terpstra test using coin package?
Is it possible to implement the Jonckheere-Terpstra test for ordered
alternatives using the coin package: Conditional Inference Procedures
in a Permutation Test Framework?
I found jonckheere.test{clinfun}, but it uses a normal approximation
when ties are present in the data. To make this concrete, I've
include
a small dataset. Thanks. --Dale
Hollander and Wolfe, 1999 Table 6.6, pg 205
2012 Aug 23
0
party package: ctree - survival data - extracting statistics/predictors
..."party" package.
I came up with this command:
test <- ctree(Surv(time, event)~.,
data =data.test,
controls=ctree_control(teststat="max", testtype="Bonferroni", mincriterion=0.95,savesplitstats = TRUE),
ytrafo = function(data)trafo(data, numeric_trafo = rank),
xtrafo=function(data)trafo(data, surv_trafo=logrank_trafo(data, ties.method = "logrank"))
)
which works well but as I am not a statistician it is quite confusing and i might not run it properly.
My technical problem is that I would like to extract the statistics output from my "test"...
2012 Mar 26
0
Different result with "kruskal.test" and post-hoc analysis with Nemenyi-Damico-Wolfe-Dunn test implemented in the help page for oneway_test in the coin package that uses multcomp
...tackoverflow.com/questions/2478272/kruskal-wallis-test-with-details-on-pairwise-comparisons
class <- m.class.length.lf
var <- m.class.l
dft <- data.frame(class,var)
NDWD <- oneway_test(var ~ class, data = dft,
ytrafo = function(data) trafo(data, numeric_trafo = rank),
xtrafo = function(data) trafo(data, factor_trafo = function(x)
model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))),
teststat = "max", distribution = approximate(B=1000))
### global p-value
print(pvalue(NDWD))
[1] 0.074
99 percent confidence interval:
0....