uniRankVar: Univariate analysis of features (additional values returned)

Description Usage Arguments Details Value Author(s) References See Also

View source: R/uniRankVar.R

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

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	uniRankVar(variableList,
	           formula,
	           Outcome,
	           data,
	           categorizationType = c("Raw",
	                                   "Categorical",
	                                   "ZCategorical",
	                                   "RawZCategorical",
	                                   "RawTail",
	                                   "RawZTail",
	                                   "Tail",
	                                   "RawRaw"),
	           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,
	            acovariates = NULL, 
	            timeOutcome = NULL)

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 analyzed : 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 weighted by the z-score ("ZCategorical"); broken into the p-value categories given by cateGroups, weighted by the z-score, plus the raw values ("RawZCategorical"); raw values, plus the tails ("RawTail"); or raw values, weighted by the z-score, plus the tails ("RawZTail")

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 correlation ("Cstat"), or the Kendall rank correlation ("Kendall")

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

acovariates

the list of covariates

timeOutcome

the name of the Time to event feature

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.

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

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

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


FRESA.CAD documentation built on Jan. 13, 2021, 3:39 p.m.