It produces a histogram of the response along with the estimated density from the assumed distribution as well as a normal Q-Q plot for the normalised quantile response. It also provides the log-likelihood for AIC calculation, for instance.
resp.check(y, margin = "N", main = "Histogram and Density of Response", xlab = "Response", print.par = FALSE, plots = TRUE, loglik = FALSE, ...)TRUE then the estimated parameters used to construct the plots are returned.FALSE then no plots are produced and only parameter estimates returned.TRUE then it returns the logLik.Prior to fitting a model with binary-continuous or continuous-continuous bivariate response, a distribution for the continuous response(s) may be chosen by looking at the histogram of the response along with the estimated density from the assumed distribution, and at the normalised quantile responses. These will provide a rough guide to the adequacy of the chosen distribution. The latter are defined as the quantile standard normal function of the cumulative distribution function of the response with scale and location estimated by MLE. These should behave approximately as normally distributed variables (even though the original observations are not). Therefore, a normal Q-Q plot is appropriate here.
If loglik = TRUE then this function also provides the log-likelihood for AIC calculation, for instance.
SemiParBIVProbit
## see example 5 for SemiParBIVProbit
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