plot.ldt.estim {ldt}

Plot Diagnostics for ldt.estim Object

Description

This function creates diagnostic plots for estimated regression models of ldt.estim class.

Usage

## S3 method for class 'ldt.estim'
plot(
  x,
  equation = 1,
  type = c(1, 2, 3, 4, 5, 6),
  ablineArgs = list(col = "lightblue"),
  textArgs = list(pos = 3, cex = 0.7, col = "red"),
  ...
)

Arguments

x

An object of type ldt.estim.

equation

A number or a name of endogenous variable specifying an equation in the estimated system.

type

One of these numbers: 1, 2, 3, or 5. See which argument in plot.lm documentation.

ablineArgs

A list of additional arguments to customize the "text" function used for labeling influential observations.

textArgs

A list of additional arguments to customize the "abline" function.

...

additional arguments to be passed to "plot" (or "qqnorm" function for type=2, or "barplot" for type=4).

Details

This function is designed to be similar to plot.lm function. However, note that an ldt.estim object might be a system estimation.

Some plots use standardized residuals. Note that they are not calculated in a system estimation context. See residuals.ldt.estim documentation for a description. Cook's distance is also calculated equation-wise. Its formula is:

d = \frac{r_i^2}{k*var(r)}\frac{h_{ii}}{(1-h_{ii})^2}

where r_i and h_{ii} are residual and leverage in i-th observation, respectively. var(r) is variance of residuals and k is the number of estimated coefficients in the equation. Note that Cook's distance is not implemented for weighted observations.

Value

This function creates diagnostic plots for regression models. It also returns a list with x and y data used in plot functions.