MCglmnet {MCglmnet} | R Documentation |
Monte Carlo Resampling Enhanced Regularized Generalized Linear Models and Cox Proportional Hazard Models
MCglmnet(d.train, xCol, yCol, d.test = NULL, xCol.test, yCol.test = NULL, idCol.test, RespType = c("B", "M", "S", "C"), CV.iter = 10, n.fold = 5, n.glmnet.fold = 5, seed.iter = 50, Strat = TRUE, perf.glmnet = "auc", lambda.method = c("opt", "1se"), resample.method = c("CV", "bootstrap"), SigGenesList = "SigList.csv", BoxPlotName = "boxplot.pdf", CoefPlotName = "coefplot.pdf", ofile = TRUE, ofilename = "results.csv", RobustGenesList = "robust.genes.list.csv", Path = "./", Ext.Val = FALSE, Output.Ext.Raw = FALSE, results.ext.RAW = "ext.raw.preds.csv", Results.Ext = "external.results.csv", seed = 263, XParm = 4, YParm = 4, pre.filter = NULL, time.cut = NULL, ...)
d.train |
training data |
xCol |
the start column number in the training data. All columns (include this column) on its right hand side are the predictors in training data |
yCol |
the column number for the response variable in the training data. |
d.test |
test data. |
xCol.test |
the start column number for predictors in the test data. |
yCol.test |
the column number for the response variable in the test data. |
idCol.test |
id column in the test set. (ATTENTION: May not need this in final package.) |
RespType |
the response variable type. “B” means binary, “M” means multiple categories, “C” means continuous, and "S" means survival; |
CV.iter |
number of iterations for n-fold outside cross validation. Default is 10. |
n.fold |
number of folds in the n-fold outside cross validation. Default is 5. |
n.glmnet.fold |
number of folds in the inside cross validation for tuning parameter selection. Default is 5. |
seed.iter |
number of MC iterations. Default is 50. |
Strat |
indicator (TRUE or FALSE) for whether statification is used in n-fold cross validation folds generation. Default value is TRUE. |
perf.glmnet |
the performance measure used in the inside cross validation for tuning parameter selection. Default is "auc" for Binary Response. |
lambda.method |
the method used for selecting tuning parameter lambda in inside cross validation. "opt" or "1se". Default is "opt". |
resample.method |
resampling method used in MC iterations. "CV" (cross validation) or "bootstrap". Default is "CV". |
SigGenesList |
output file for signature markers. Default is "SigList.csv". |
BoxPlotName |
output file for boxplots for signature markers. Default is "BoxPlots-SigList.pdf". |
CoefPlotName |
output file for coefficient plot for signature markers. Default is "coef.plot-SigList.pdf". |
ofile |
indicator for whether to save to the log file. TRUE or FALSE. Default is TRUE. |
ofilename |
log file name. Default is "results.csv". |
RobustGenesList |
output file for Robust markers. The frequencies of each markers was selected in the cross validation are recorded. Default is "robust.gene.list.csv". |
Path |
The path for output files. Default is currenty directory "./". |
Ext.Val |
indictor for whether conducting external validation on test data. TRUE or FALSE. Default is FALSE. |
Output.Ext.Raw |
indicator for whether prediction results on test data is saved. TRUE or FALSE. Default is FALSE. |
results.ext.RAW |
output file for prediction results on test data. Default is "ext.raw.preds.csv". |
Results.Ext |
output file for predictive performance for external validation on the test data. Default is "external.results.csv". |
seed |
the seed for random number generation that is set before running MCglmnet. Default is 263. |
XParm |
the number of rows in the layout of the Boxplots in the boxplot output file. |
YParm |
the number of columns in the layout of the Boxplots in the boxplot output file. |
pre.filter |
number of prefitlered markers. |
time.cut |
time cutoff values for evaulating performance for time-to-event response. |
... |
extra parameters that are passed to "glmnet" inside the MCglmnent algorithm. e.g., the elasticnet mixing parameter "alpha" can be passed. |
The MCglmnet function does not return an object. All the results are saved in the output files.
Feng Hong, Lu Tian, Viswanath Devanarayan
Feng Hong, Lu Tian, and Viswanath Devanarayan (In Review) Improving the robustness of variable selection and predictive performance of regularized generalized linear models and cox proportional hazard models.
## Not run: MCglmnet(dat.train, xCol=2, yCol=1, RespType="B", d.test=dat.test, xCol.test=2, yCol.test=1, Ext.Val=TRUE, lambda.method="1se", CV.iter=10, n.fold=10, seed.iter=50, Path="./", ofile=TRUE, alpha=1, pre.filter=10); ## End(Not run)