# Probability-scale residual `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. ```r presid(object, ...) ``` ## Arguments - `object`: The model object for which the probability-scale residual is calculated - `...`: Additional arguements passed to methods ## Returns The probability-scale residual for the model ## 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. ## Examples ```r 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) ``` ## 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.

presid function