cvdmngroup {DirichletMultinomial} | R Documentation |
Run cross-validation on Dirichlet-Multinomial generative classifiers.
cvdmngroup(ncv, count, k, z, ..., verbose = FALSE, .lapply = parallel::mclapply)
ncv |
|
count |
|
k |
named |
z |
True group assignment. |
... |
Additional arguments, passed to |
verbose |
|
.lapply |
A function used to perform the outer cross-vaildation
loop, e.g., |
A data.frame
summarizing classifications of test samples in
cross-validation groups. Columns are:
group |
The cross-validation group in which the indivdual was used for testing. |
additional columns |
Named after classification groups, giving the posterior probability of assignment. |
Martin Morgan mailto:mtmorgan@fhcrc.org
dmn
, DirichletMultinomial-package,
vignette("DirichletMultinomial")
data(xval) ## result of following commands head(xval) ## Not run: ## count matrix fl <- system.file(package="DirichletMultinomial", "extdata", "Twins.csv") count <- t(as.matrix(read.csv(fl, row.names=1))) ## phenotype fl <- system.file(package="DirichletMultinomial", "extdata", "TwinStudy.t") pheno0 <- scan(fl) lvls <- c("Lean", "Obese", "Overwt") pheno <- factor(lvls[pheno0 + 1], levels=lvls) names(pheno) <- rownames(count) ## subset keep <- c("Lean", "Obese") count <- count[pheno pheno <- factor(pheno[pheno ## cross-validation, single Dirichlet component for Lean, 3 for Obese xval <- cvdmngroup(nrow(count), count, c(Lean=1, Obese=3), pheno, verbose=TRUE, mc.preschedule=FALSE) ## End(Not run)