LP.QDC: The main function for two-sample quantile and distribution...

In QDComparison: Modern Nonparametric Tools for Two-Sample Quantile and Distribution Comparisons

Description

This function runs the entire quantile and distribution comparison, giving LP comoments, LP coefficients, LPINFOR test statistic, p-value, estimated comparison density with null-band, and intervals where the comparison density is above or below the null band

Usage

LP.QDC(x,y,m=6,smooth="TRUE",method="BIC",alpha=0.05,
       B=1000,spar=NA,plot="TRUE",inset=-0.2)

Arguments

x	Indicator variable denoting group membership
y	Response variable with measured values
m	Number of LP comoments and LP coefficients to be calculated, default: 6
smooth	If smoothing should be applied, default: TRUE
method	Smoothing method as AIC or BIC, default: BIC
alpha	Alpha-level for confidence bands, default: 0.05
B	Number of permutations of the x labels, default: 1000
spar	"spar" in "smooth.spline" of upper and lower bounds of confidence bands, default: NA, let smooth.splines function figure it out
plot	Should plotting be performed, default: TRUE
inset	Graphical parameter that controls where the color legend is plotted below x-axis, default: -0.2

Value

A list containing:

band	y-values of the upper and lower bounds of the confidence band
d.hat	y-values of the comparison density
sparL	"spar" value in "smooth.spline" of lower bound of the null band
sparU	"spar" value in "smooth.spline" of upper bound of the null band
out.above	Quantile intervals where group 1 dominates the pooled distribution
out.below	Quantile intervals where group 0 dominates the pooled distribution
LP.comoment.0	LP comoments, conditioned on X=0
LP.coef.0	LP coefficients, conditioned on X=0
LP.comoment.1	LP comoments, conditioned on X=1
LP.coef.1	LP coefficients, conditioned on X=1
LPINFOR	Test statistics value
pval	The p-value for testing equality of two distributions F0=F1

Author(s)

David Jungreis

Subhadeep Mukhopadhyay

References

Jungreis, D., (2019) "Unification of Continuous, Discrete, and Mixed Distribution Two-Sample Testing with Inferences in the Quantile Domain"

Mukhopadhyay, S. and Parzen, E. (2014), "LP Approach to Statistical Modeling", arXiv:1405.2601.

Examples

x <- c(rep(0,200),rep(1,200))
y <- c(rnorm(200,0,1),rnorm(200,1,1))
L <- LP.QDC(x,y)
L$pval

QDComparison documentation built on June 24, 2019, 9:04 a.m.