# plot.RRlog: Plot Logistic RR Regression In danheck/RRreg: Correlation and Regression Analyses for Randomized Response Data

## Description

Plot predicted logit values/probabilities of a randomized response logistic regression model.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## S3 method for class 'RRlog' plot( x, predictor = NULL, type = c("link", "response", "attribute"), center.preds = TRUE, plot.mean = TRUE, ci = 0.95, xlim = NULL, steps = 50, ... ) ```

## Arguments

 `x` a fitted RRlog object `predictor` character name of a predictor of the model to be fitted `type` `"response"` returns predicted probabilities for the (observable) RR responses, `"link"` returns predicted logit-values for the (latent) sensitive attribute, and `"attribute"` returns predicted probabilities of having the (latent) sensitive attribute. `center.preds` whether to compute predictions by assuming that all other predictors are at their respective mean values (if `FALSE`: all other predictors are set to zero) `plot.mean` whether to plot the mean of the predictor as a vertical line `ci` level for confidence intervals. Use `ci=0` to omit. `xlim` if provided, these boundaries are used for the predictor on the x-axis `steps` number of steps for plotting `...` other arguments passed to the function plot (e.g., `ylim=c(0,1)`).

`predict.RRlog`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ``` # generate data n <- 500 x <- data.frame(x1=rnorm(n)) pi.true <- 1/(1+exp(.3+1.5*x\$x1)) true <- rbinom(n, 1, plogis(pi.true)) dat <- RRgen(n, trueState=true, model="Warner", p=.1) x\$response <- dat\$response # fit and plot RR logistic regression mod <- RRlog(response ~ x1, data=x, model="Warner", p=.1) plot(mod, "x1" ,ci=.95, type = "attribute", ylim = 0:1) ```