SemiParBIVProbit (version 3.6-1)

resp.check: Plots for response variable

Description

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.

Usage

resp.check(y, margin = "N", main = "Histogram and Density of Response",
           xlab = "Response", print.par = FALSE, plots = TRUE, ...)

Arguments

y

Response.

margin

The distributions allowed are: normal ("N"), log-normal ("LN"), Gumbel ("GU"), reverse Gumbel ("rGU"), logistic ("LO"), Weibull ("WEI"), inverse Gaussian ("iG"), gamma ("GA").

main

Title for the plot.

xlab

Title for the x axis.

print.par

If TRUE then the estimated parameters used to construct the plots are returned.

plots

If FALSE then no plots are produced and only parameter estimates returned.

...

Other graphics parameters to pass on to plotting commands.

Details

Prior to fitting a model with binary-continuous bivariate response, a distribution for the continuous response 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.

See Also

SemiParBIVProbit

Examples

## see example 5 for SemiParBIVProbit