rfc | R Documentation |
Wrapper for the GenerateLearningsets
classification
functions.
rfc(data, status, k = 5, method = "CV", ...)
data |
An (n, m) matrix or data.frame with no outcome attribute. |
status |
A vector of length n, containing the outcome. |
k |
number of cross-validation iterations (default = 5). |
method |
One of the |
... |
Currently ignored. |
A list of objects of class "cloutput" and "clvarseloutput", respectively.
Fernando Palluzzi fernando.palluzzi@gmail.com
Slawski M, Daumer M, Boulesteix AL. CMA - a comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9, 439 (2008). <https://doi.org/10.1186/1471-2105-9-439>
See us.predict
to launch all
morphonode modules at once.
See also topsim
for a simple similarity
search.
# Extract a subset of 300 subjects and an outcome vector of length 30 # from the default simulated dataset x <- mosaic::sample(mpm.us, 300, replace = FALSE, prob = NULL) y <- x$y x <- x[, 2:15] dim(x) length(y) # Build a 5-fold cross-validation object CV <- rfc(x, status = y) # Performances of the first of five predictors CV1 <- CV[[1]] P <- performance(obs = CV1@y, pred = CV1@yhat) print(P)
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