a model fitted using the model fitting function plmm.
heteroX
at most two variables conditioning the heteroskedasticity of the regression error variance. If there are two variables, they can be passed either as a 2-element list or a 2-column matrix.
data
an optional data frame containing the variables in the model. If relevant variables are not found in data, the variables are taken from the environment from which var.plot was called.
var.fun.bws
the bandwidth selection method for the kernel regression estimation of the variance function. A rule-of-thumb type method “ROT” (default), “h.select” (cross validation using binning technique) or “hcv” (ordinary cross validation are available.
var.fun.poly.index
the degree of polynomial of the kernel regression to estimate the nonparametric variance function: either 1 for local linear or 0 (default) for local constant.
...
optional arguments relevant to estimation and plotting with sm.regression.
Details
The variance function plotted is an unconditional estimate, i.e. the sum of the estimated variances of the random effects and the regression error. As opposed to wplmm, var.plot does not trim negative estimates of the variance function values. var.fun.values returned from var.plot are also untrimmed estimates.
“ROT” selects the bandwidths for heteroskedasticity conditioning variable w by sd(w)N^{-1/(4+q)} where q is th the number of the conditioning variables (1 or 2) and N is the sample size. Some of the relevant optional arguments include display, nbins and ngrid. See sm.options.
Value
The following values are returned invisibly (they are not printed, but can be assigned).
var.fun.values
the estimated untrimmed conditional variance function values at the data points.
var.comp
the estimated variance of the random effects.
h.var.fun
the bandwidths used to estimate the nonparametric variance function.
See Also
wplmm, sm.regression, sm.options
Examples
data(plmm.data)
model <- plmm(y1~x1+x2+x3|t1, random=cluster, data=plmm.data)
var.plot(model, heteroX=x3, data=plmm.data)
result <- var.plot(model, heteroX=x3, data=plmm.data, display="none")
result$var.fun.values
providing 
.