Description Usage Arguments Value Author(s) References See Also Examples
This function shows the cross-validated prediction performance of models with sequentially reduced number of predictors (ranked by variable importance) via a nested cross-validation procedure.
1 2 3 |
trainx.autosome |
A matrix of autosomal markers with each column corresponding to a SNP coded as count of a particular allele (i.e. 0,1 or 2), and each row corresponding to a sample/individual. |
trainx.xchrom |
A matrix of X chromosome markers, each marker coded as two adjacent columns, alleles of a marker are coded as 0 or 1 for carrying a particular allele. Although males only have one X-chromosome, their markers are coded as 2 columns as well, the second column being a duplicate of the first. Each row of this matrix corresponds to a sample/individual. This data must be phased in chromosomal order. |
trainx.covar |
A matrix of covariates, each column being a different covariate, and each row, a sample/individual. |
trainy |
vector of response, must be a factor and have length equal to the number
of rows in |
cv.fold |
number of folds in the cross-validation |
scale |
if |
step |
if |
mtry |
a function of number of remaining predictor variables to
use as the |
recursive |
whether variable importance is (re-)assessed at each step of variable reduction |
... |
other arguments passed on to |
A list with the following components:
list(n.var=n.var, error.cv=error.cv, predicted=cv.pred)
n.var |
vector of number of variables used at each step |
error.cv |
corresponding vector of error rates or MSEs at each step |
predicted |
list of |
Andy Liaw, with slight modifications by Greg Jenkins
Svetnik, V., Liaw, A., Tong, C. and Wang, T., “Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules”, MCS 2004, Roli, F. and Windeatt, T. (Eds.) pp. 334-343.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | set.seed(647)
data(snpRFexample)
result <- snpRFcv(trainx.autosome=autosome.snps,trainx.xchrom=xchrom.snps,
trainx.covar=covariates, trainy=phenotype)
with(result, plot(n.var, error.cv, log="x", type="o", lwd=2))
## The following can take a while to run, so if you really want to try
## it, copy and paste the code into R.
## Not run:
result <- replicate(5,snpRFcv(trainx.autosome=autosome.snps,
trainx.xchrom=xchrom.snps,
trainx.covar=covariates, trainy=phenotype),
simplify=FALSE)
error.cv <- sapply(result, "[[", "error.cv")
matplot(result[[1]]$n.var, cbind(rowMeans(error.cv), error.cv), type="l",
lwd=c(2, rep(1, ncol(error.cv))), col=1, lty=1, log="x",
xlab="Number of variables", ylab="CV Error")
## End(Not run)
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