LFL: Check linearity assumption

In CastleLi/BetaPSS: Calculate Power and Sample Size with Beta Regression.

Description

Check linearity assumption in a model using a given link function.

Usage

LFL(dsn,X,Y,nkat,link.type,outlier)

Arguments

dsn	data set name containing X and Y;
X	continuous covariate
Y	outcome variable
nkat	number of categories into which X should be divided (an even number)
link.type	link function to be used: link.type is coded as follows: "identity" = identity link; "logit" = logit link; "probit" = probit link; "log" = log link; "clog-log" = complementary log-log link; "log-log" = log-log link; "reciprocal" = reciprocal link;
outlier	exclude outliers from the plot.

Value

Return a matrix with 6 columns:

X	intervals of continuous covariate.
Frequency	number of observations in each group.
mean	group means of outcome variable.
link	name of link function.
gmu	group means of outcome variable with link function.
midpt	median of continuous covariate in each group.

Examples

Data.X <- runif(1000)
Data.Y <- exp(Data.X+1)+rnorm(1000,0,1)
Data <- data.frame(cbind(Data.X,Data.Y))
LFL(Data,"Data.Y","Data.X",20,4)
##Use NH data:
NH$quality.rating <- (scale(NH$quality.rating)/2+0.5)
LFL(dsn = NH,Y = "quality.rating", X = "RNHRD", nkat = 40, link.type = "logit", outlier = F)

CastleLi/BetaPSS documentation built on May 15, 2019, 10:34 p.m.