# Simulate discrete data. Simulate discrete data from either a correlation matrix or thresholds or probabilities. ```r rdatagen(n = 100, R = diag(1,2), Q = NULL, th = NULL, probs = NULL) ``` ## Arguments - `n`: Number of observations. - `R`: Correlation matrix. - `Q`: Number of discrete values in the random variables. It is a single value or a vector. If `Q` is set to 1 (default), the function returns continuous data distributed according to the normal standardized distribution. - `th`: List of thresholds; each element contains `Q`+1 values. - `probs`: List of probabilities; each elements contains `Q` values. ## Returns Returns a list with four elements: - **data**: The simulated data matrix. - **R**: Correlation matrix. - **thresholds**: The list of thresholds. - **probs**: The list of probabilities. ## References Lombardi, L., Pastore, M. (2014). sgr: A Package for Simulating Conditional Fake Ordinal Data. **The R Journal**, 6, 164-177. Pastore, M., Lombardi, L. (2014). The impact of faking on Cronbach's Alpha for dichotomous and ordered rating scores. **Quality & Quantity**, 48, 1191-1211. ## Author(s) Massimiliano Pastore, Luigi Lombardi & Marco Bressan ## Note Defaults work like in the `mvrnorm` function of the `MASS` package. ## Examples ```r require(MASS) ## only default rdatagen() ## set correlations only R <- matrix(c(1,.4,.4,1),2,2) Dx <- rdatagen(n=10000,R=R)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) hist(Dx[,j]) ## set correlations and Q Dx <- rdatagen(n=10000,R=R,Q=2)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) barplot(table(Dx[,j])/nrow(Dx)) ## set correlations and thresholds th <- list(c(-Inf,qchisq(pbinom(0:3,4,.5),1),Inf), c(-Inf,qnorm(pbinom(0:2,3,.5)),Inf)) Dx <- rdatagen(n=10000,R=R,th=th)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) barplot(table(Dx[,j])/nrow(Dx)) ## set correlations and probabilities [1] probs <- list(c(.125,.375,.375,.125),c(.125,.375,.375,.125)) Dx <- rdatagen(n=10000,R=R,probs=probs)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) barplot(table(Dx[,j])/nrow(Dx)) ## set correlations and probabilities [2] probs <- c(.125,.375,.375,.125) Dx <- rdatagen(n=10000,R=R,probs=probs)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) barplot(table(Dx[,j])/nrow(Dx)) ## set different values for Q Dx <- rdatagen(n=1000,Q=c(2,4),R=R)$data par(mfrow=c(1,2)) for (j in 1:ncol(Dx)) barplot(table(Dx[,j])/nrow(Dx)) ```

rdatagen function