mlclassKap {highMLR} | R Documentation |
Applications of machine learning in survival analysis by prognostic classification of genes by Kaplan-Meier estimator.
Description
Applications of machine learning in survival analysis by prognostic classification of genes by Kaplan-Meier estimator.
Usage
mlclassKap(m, n, idSurv, idEvent, Time, s_ID, per = 20, fold = 3, data)
Arguments
m |
Starting column number from where high dimensional variates to be selected. |
n |
Ending column number till where high dimensional variates to be selected. |
idSurv |
"Column/Variable name" consisting duration of survival. |
idEvent |
"Column/Variable name" consisting survival event. |
Time |
"Column/Variable name" consisting timepoints of repeated observations. |
s_ID |
"Column/Variable name" consisting unique identification for each subject. |
per |
Percentage value for ordering, default=20. |
fold |
Number of fold for resampling, default=3. |
data |
High dimensional data containing survival observations and high dimensional covariates. |
Value
A list of genes as per their classifications
- GeneClassification
List of genes classified using Cox proportional hazard model
- GeneClassification$Positive_Gene
Sublist of genes classified as positive genes
- GeneClassification$Negative_Gene
Sublist of genes classified as negative genes
- GeneClassification$Volatile_Gene
Sublist of genes classified as volatile genes
- Result
A dataframe consisting threshold values with corresponding coefficients and p-values.
Examples
## Not run:
##
mlclassKap(m=50,n=59,idSurv="OS",idEvent="event",Time="Visit",s_ID="ID",per=20,fold=3,data=srdata)
##
## End(Not run)