PResiduals (version 1.0-2)

presid: Probability-scale residual

Description

presid calculates the probability-scale residual for various model function objects. Currently supported models include glm (Poisson, binomial, and gaussian families), lm in the stats library; survreg (Weibull, exponential, gaussian, logistic, and lognormal distributions) and coxph in the survival library; polr and glm.nb in the MASS library; and ols, cph, lrm, orm, psm, and Glm in the rms library.

Usage

presid(object, ...)

Value

The probability-scale residual for the model

Arguments

object

The model object for which the probability-scale residual is calculated

...

Additional arguements passed to methods

Details

Probability-scale residual is \(P(Y < y) - P(Y > y)\) where \(y\) is the observed outcome and \(Y\) is a random variable from the fitted distribution.

References

Shepherd BE, Li C, Liu Q (2016) Probability-scale residuals for continuous, discrete, and censored data. The Canadian Jouranl of Statistics. 44:463--476.

Li C and Shepherd BE (2012) A new residual for ordinal outcomes. Biometrika. 99: 473--480.

Examples

library(survival)
library(stats)

set.seed(100)
n <- 1000
x <- rnorm(n)
t <- rweibull(n, shape=1/3, scale=exp(x))
c <- rexp(n, 1/3)
y <- pmin(t, c)
d <- ifelse(t<=c, 1, 0)

mod.survreg <- survreg(Surv(y, d) ~ x, dist="weibull")
summary(presid(mod.survreg))
plot(x, presid(mod.survreg))

##### example for proprotional hazards model
n <- 1000
x <- rnorm(n)
beta0 <- 1
beta1 <- 0.5
t <- rexp(n, rate = exp(beta0 + beta1*x))
c <- rexp(n, rate=1)
y <- ifelse(t py[,i])
y <-  as.factor(y)


mod.polr <- polr(y~x, method="logistic")
summary(mod.polr)
presid <- presid(mod.polr)
summary(presid)
plot(x, presid, cex=0.4)