plot.Rchoice function

Plot the distribution of conditional expectation for random parameters.

Plot the distribution of the conditional expectation of the random parameters or compensating variations for objects of class Rchoice.

## S3 method for class 'Rchoice'
plot(
  x,
  par = NULL,
  effect = c("ce", "cv"),
  wrt = NULL,
  type = c("density", "histogram"),
  adjust = 1,
  main = NULL,
  col = "indianred1",
  breaks = 10,
  ylab = NULL,
  xlab = NULL,
  ind = FALSE,
  id = NULL,
  ...
)

Arguments

• x: a object of class Rchoice,

• par: a string giving the name of the variable with random parameter,

• effect: a string indicating what should be plotted: the conditional expectation of the individual coefficients "ce", or the conditional expectation of the individual compensating variations "cv",

• wrt: a string indicating repect to which variable should be computed the compensating variation,

• type: a string indicating the type of distribution: it can be a histogram or a density of the conditional expectation,

• adjust: bandwidth for the kernel density,

• main: an overall title for the plot,

• col: color for the graph,

• breaks: number of breaks for the histrogram if type = "histogram",

• ylab: a title for the y axis,

• xlab: a title for the x axis,

• ind: a boolean. If TRUE, a 95

  As default, the conditional expectation of par for the first 10 individual is plotted,

• id: only relevant if ind is not NULL. This is a vector indicating the individuals for which the confidence intervals are plotted,

• ...: further arguments. Ignored.

Examples

# Poisson with random parameters
data("Articles")
poisson.ran <- Rchoice(art ~ fem + mar + kid5 + phd + ment, 
                       data = Articles,  family = poisson,
                       ranp = c(kid5 = "n", phd = "n", ment = "n"), 
                       R = 10)

## Plot the distribution of the conditional mean for ment
plot(poisson.ran, par = "ment", type = "density")

## Plot the conditional mean for the first 20 individuals
plot(poisson.ran, par = "ment", ind =  TRUE, id = 1:20, col = "blue")

## Plot the compensating variation with respect to fem
plot(poisson.ran, par = "ment", effect = "cv", wrt = "fem", type = "histogram")

References

• Greene, W. H. (2012). Econometric analysis, Seventh Edition. Pearson Hall.
• Train, K. (2009). Discrete choice methods with simulation. Cambridge university press.

See Also

Rchoice for the estimation of different discrete choice models with individual parameters.

Author(s)

Mauricio Sarrias