View source: R/SelectFeaturesRW.R
SelectFeaturesRW | R Documentation |
Finds the important variables presenting a coordinated response in all the replicate-blocks
SelectFeaturesRW(
RW = RW,
results = results,
ndim = NULL,
blocks = NULL,
threshold_cor = 1,
threshold_cov = 1,
mean.RW = T,
plots = "NO"
)
RW |
The object used as input in the ComDim analysis. |
results |
The output object obtained in the ComDim analysis. |
ndim |
The number of the component for which the most important variables are to be calculated. |
blocks |
A vector with the indices or the names for the replicate blocks of the same data type. |
threshold_cor |
The "times" parameter used to calculate the threshold in the following formula: cor(variable) > times * sd(cor(variables)). Minimal value that can be assigned to threshold_cor is 1. |
threshold_cov |
The "times" parameter used to calculate the threshold in the following formula: cov(variable) > times * sd(cov(variables)). Minimal value that can be assigned to threshold_cor is 1. |
mean.RW |
Logical value to indicate whether the RW data must be mean-centered (TRUE) or not (FALSE). |
plots |
Parameter to indicate whether a plot must be produced. Possible values are "NO" for no plots, "separated" for plotting each plot individually, and "together" to plot all the plots in the same grid. |
An object with 2 lists. The first list contains the important variables presenting a positive relationship with the scores, while the second list contains the most important variables presenting a negative relationship.
b1 = matrix(rnorm(500),10,50)
batch_b1 = rep(1,10)
b2 = matrix(rnorm(800),30,80)
batch_b2 = c(rep(1,10),rep(2,10),rep(3,10))
mb <- BuildMultiBlock(b1, batches = batch_b1)
mb <- BuildMultiBlock(b2, growingMB = mb, batches = batch_b2, equalSampleNumber = FALSE)
rw <- SplitRW(mb)
results<-ComDim_PCA(rw, 2) # In this analysis, we used 2 components.
features <- SelectFeaturesRW(RW = rw, results = results, ndim = 1, blocks = c(2,3,4))
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