FRESA.CAD (version 2.0.2)

uniRankVar: Univariate analysis of features (additional values returned)

Description

This function reports the mean and standard deviation for each feature in a model, and ranks them according to a user-specified score. Additionally, it does a Kolmogorov-Smirnov (KS) test on the raw and z-standardized data. It also reports the raw and z-standardized t-test score, the p-value of the Wilcoxon rank-sum test, the integrated discrimination improvement (IDI), the net reclassification improvement (NRI), the net residual improvement (NeRI), and the area under the ROC curve (AUC). Furthermore, it reports the z-value of the variable significance on the fitted model. Besides reporting an ordered data frame, this function returns all arguments as values, so that the results can be updates with the update.uniRankVar if needed.

Usage

uniRankVar(variableList,
	           formula,
	           Outcome,
	           data,
	           categorizationType = c("Raw",
	                                   "Categorical",
	                                   "ZCategorical",
	                                   "RawZCategorical",
	                                   "RawTail",
	                                   "RawZTail"),
	           type = c("LOGIT", "LM", "COX"),
	           rankingTest = c("zIDI",
	                           "zNRI",
	                           "IDI",
	                           "NRI",
	                           "NeRI",
	                           "Ztest",
	                           "AUC",
	                           "CStat",
	                           "Kendall"),
	            cateGroups = c(0.1, 0.9),
	            raw.dataFrame = NULL,
	            description = ".",
	            uniType = c("Binary", "Regression"),
	            fullAnalysis=TRUE)

Arguments

variableList

A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables

formula

An object of class formula with the formula to be fitted

Outcome

The name of the column in data that stores an optional binary outcome that may be used to show the stratified analysis

data

A data frame where all variables are stored in different columns

categorizationType

How variables will be analysed : As given in data ("Raw"); broken into the p-value categories given by cateGroups ("Categorical"); broken into the p-value categories given by cateGroups, and weighte

type

Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")

rankingTest

Variables will be ranked based on: The z-score of the IDI ("zIDI"), the z-score of the NRI ("zNRI"), the IDI ("IDI"), the NRI ("NRI"), the NeRI ("NeRI"), the z-score of the model fit ("Ztest"), the AUC ("AUC"), the Somers' rank

cateGroups

A vector of percentiles to be used for the categorization procedure

raw.dataFrame

A data frame similar to data, but with unadjusted data, used to get the means and variances of the unadjusted data

description

The name of the column in variableList that stores the variable description

uniType

Type of univariate analysis: Binary classification ("Binary") or regression ("Regression")

fullAnalysis

If FALSE it will only order the features according to its z-statistics of the linear model

Value

• orderframe
  A sorted list of model variables stored in a data frame
• variableList
  The argument variableList
• formula
  The argument formula
• Outcome
  The argument Outcome
• data
  The argument data
• categorizationType
  The argument categorizationType
• type
  The argument type
• rankingTest
  The argument rankingTest
• cateGroups
  The argument cateGroups
• raw.dataFrame
  The argument raw.dataFrame
• description
  The argument description
• uniType
  The argument uniType

Details

This function will create valid dummy categorical variables if, and only if, data has been z-standardized. The p-values provided in cateGroups will be converted to its corresponding z-score, which will then be used to create the categories. If non z-standardized data were to be used, the categorization analysis would return wrong results.

References

Pencina, M. J., D'Agostino, R. B., & Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine 27(2), 157-172.

See Also

update.uniRankVar, univariateRankVariables

Examples

# Start the graphics device driver to save all plots in a pdf format
	pdf(file = "Example.pdf")
	# Get the stage C prostate cancer data from the rpart package
	library(rpart)
	data(stagec)
	# Split the stages into several columns
	dataCancer <- cbind(stagec[,c(1:3,5:6)],
	                    gleason4 = 1*(stagec[,7] == 4),
	                    gleason5 = 1*(stagec[,7] == 5),
	                    gleason6 = 1*(stagec[,7] == 6),
	                    gleason7 = 1*(stagec[,7] == 7),
	                    gleason8 = 1*(stagec[,7] == 8),
	                    gleason910 = 1*(stagec[,7] >= 9),
	                    eet = 1*(stagec[,4] == 2),
	                    diploid = 1*(stagec[,8] == "diploid"),
	                    tetraploid = 1*(stagec[,8] == "tetraploid"),
	                    notAneuploid = 1-1*(stagec[,8] == "aneuploid"))
	# Remove the incomplete cases
	dataCancer <- dataCancer[complete.cases(dataCancer),]
	# Load a pre-stablished data frame with the names and descriptions of all variables
	data(cancerVarNames)
	# Rank the variables:
	# - Analyzing the raw data
	# - According to the zIDI
	rankedDataCancer <- uniRankVar(variableList = cancerVarNames,
	                               formula = "Surv(pgtime, pgstat) ~ 1",
	                               Outcome = "pgstat",
	                               data = dataCancer, 
	                               categorizationType = "Raw", 
	                               type = "COX", 
	                               rankingTest = "zIDI",
	                               description = "Description")
	# Shut down the graphics device driver
	dev.off()