Displaying 4 results from an estimated 4 matches for "factor_trafo".
2013 Jan 11
0
Error with looping through a list of strings as variables
Dear R users:
I have been trying to figure out how to include string variables in a for
loop to run multiple random forests with little success. The current code
returns the following error:
Error in trafo(data = data, numeric_trafo = numeric_trafo, factor_trafo =
factor_trafo, :
data class character is not supported
In addition: Warning message:
In storage.mode(RET@predict_trafo) <- "double" : NAs introduced by coercion
The code runs fine with the data before I add the > for (h in varlist){ loop.
Loops i, k work without issue as long...
2012 Jan 09
2
Unexpected results using the oneway_test in the coin package
..._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 (page 244)
print(pvalue(NDWD, method = "...
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 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
...ruskal-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:
0.05425181 0.09791886
### sites (I = II...