View source: R/global.significance.R
global.significance  R Documentation 
This function runs a permutation test to evaluates the global effect of subjectrelated covariates (Z). Returns an estimated pvalue.
global.significance( X, Y, Z, ntree = 200, mtry = NULL, nperm = 500, nodesize = NULL, nodedepth = NULL, nsplit = 10, Xcenter = TRUE, Ycenter = TRUE )
X 
The first multivariate data set which has n observations and px variables. A data.frame of numeric values. 
Y 
The second multivariate data set which has n observations and py variables. A data.frame of numeric values. 
Z 
The set of subjectrelated covariates which has n observations and pz variables. Used in random forest growing. A data.frame with numeric values and factors. 
ntree 
Number of trees. 
mtry 
Number of zvariables randomly selected as candidates for splitting a node. The default is pz/3 where pz is the number of z variables. Values are always rounded up. 
nperm 
Number of permutations. 
nodesize 
Forest average number of unique data points in a terminal node. The default is the 3 * (px+py) where px and py are the number of x and y variables, respectively. 
nodedepth 
Maximum depth to which a tree should be grown. In the default, this parameter is ignored. 
nsplit 
Nonnegative integer value for the number of random splits to
consider for each candidate splitting variable. When zero or 
Xcenter 
Should the columns of X be centered? The default is

Ycenter 
Should the columns of Y be centered? The default is

An object of class (rfcca,globalsignificance)
which is a list
with the following components:
call 
The original call to 
pvalue 
pvalue, see below for details. 
n 
Sample size of the data ( 
ntree 
Number of trees grown. 
nperm 
Number of permutations. 
mtry 
Number of variables randomly selected for splitting at each node. 
nodesize 
Minimum forest average number of unique data points in a terminal node. 
nodedepth 
Maximum depth to which a tree is allowed to be grown. 
nsplit 
Number of randomly selected split points. 
xvar 
Data frame of xvariables. 
xvar.names 
A character vector of the xvariable names. 
yvar 
Data frame of yvariables. 
yvar.names 
A character vector of the yvariable names. 
zvar 
Data frame of zvariables. 
zvar.names 
A character vector of the zvariable names. 
predicted.oob 
OOB predicted canonical correlations for training observations based on the selected final canonical correlation estimation method. 
predicted.perm 
Predicted canonical correlations for the permutations. A matrix of predictions with observations on the ows and permutations on the columns. 
We perform a hypothesis test to evaluate the global effect of the
subjectrelated covariates on distinguishing between canonical correlations.
Define the unconditional canonical correlation between X and
Y as ρ_{CCA}(X,Y) which is found by computing CCA with
all X and Y, and the conditional canonical correlation between
X and Y given Z as ρ(X,Y  Z) which is found by
rfcca()
. If there is a global effect of Z on correlations
between X and Y, ρ(X,Y  Z) should be significantly
different from ρ_{CCA}(X,Y). We conduct a permutation test
for the null hypothesis
H_0 : ρ(X,Y  Z) = ρ_{CCA}(X,Y)
We estimate a pvalue with the permutation test. If the pvalue is less than the prespecified significance level α, we reject the null hypothesis.
rfcca
predict.rfcca
print.rfcca
## load generated example data data(data, package = "RFCCA") set.seed(2345) global.significance(X = data$X, Y = data$Y, Z = data$Z, ntree = 40, nperm = 5)
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