plot.RRglm: Plot diagnostics for a RRglm object In GLMMRR: Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data

Description

Six plots (selectable by `which`) are currently available: (1) a plot of estimated population prevalence per RR model, (2) a plot of estimated population prevalence per protection level, (3) a plot of ungrouped residuals against predicted honest response, (4) a plot of grouped (on covariates) residuals against predicted honest response, (5) a plot of grouped Hosmer-Lemeshow residuals against predicted response, and (6) a Normal Q-Q plot of grouped (on covariates) residuals. By default, plots 1, 3, 4 and 6 are provided. Reference: Fox, J-P, Klotzke, K. and Veen, D. (2016). Generalized Linear Mixed Models for Randomized Responses. Manuscript submitted for publication.

Usage

 ```1 2 3``` ```## S3 method for class 'RRglm' plot(x, which = c(1, 3, 4, 6), type = c("deviance", "pearson"), ngroups = 10, ...) ```

Arguments

 `x` an object of class RRglm. `which` if a subset of the plots is required, specify a subset of the numbers 1:6 (default: 1, 3, 4, 6). `type` the type of residuals which should be used to be used for plots 3, 4 and 6. The alternatives are: "deviance" (default) and "pearson". `ngroups` the number of groups to compute the Hosmer-Lemeshow residuals for (default: 10). `...` further arguments passed to or from other methods.

Examples

 ```1 2 3``` ```out <- RRglm(response ~ Gender + RR + pp + age, link="RRlink.logit", RRmodel=RRmodel, p1=RRp1, p2=RRp2, data=Plagiarism, etastart=rep(0.01, nrow(Plagiarism))) plot(out, which = 1:6, type = "deviance", ngroups = 50) ```

GLMMRR documentation built on May 30, 2017, 3:31 a.m.