plot function

Plot Diagnostics for gel and gmm objects

It is a plot method for gel or gmm objects.

## S3 method for class 'gel'
plot(x, which = c(1L:4),
            main = list("Residuals vs Fitted values", "Normal Q-Q",
            "Response variable and fitted values","Implied probabilities"),
            panel = if(add.smooth) panel.smooth else points,
            ask = prod(par("mfcol")) < length(which) && dev.interactive(), ...,
            add.smooth = getOption("add.smooth"))

## S3 method for class 'gmm'
plot(x, which = c(1L:3),
            main = list("Residuals vs Fitted values", "Normal Q-Q",
            "Response variable and fitted values"),
            panel = if(add.smooth) panel.smooth else points,
            ask = prod(par("mfcol")) < length(which) && dev.interactive(), ...,
            add.smooth = getOption("add.smooth"))

Arguments

• x: gel or gmm object, typically result of gel or gmm.
• which: if a subset of the plots is required, specify a subset of the numbers 1:4 for gel or 1:3 for gmm.
• main: Vector of titles for each plot.
• panel: panel function. The useful alternative to points, panel.smooth can be chosen by add.smooth = TRUE.
• ask: logical; if TRUE, the user is asked before each plot, see par(ask=.).
• ...: other parameters to be passed through to plotting functions.
• add.smooth: logical indicating if a smoother should be added to most plots; see also panel above.

Details

It is a beta version of a plot method for gel objects. It is a modified version of plot.lm. For now, it is available only for linear models expressed as a formula. Any suggestions are welcome regarding plots or options to include. The first two plots are the same as the ones provided by plot.lm, the third is the dependant variable $y$ with its mean $\hat{y}$ (the fitted values) and the last plots the implied probabilities with the empirical density $1/T$.

Examples

# GEL #
n = 500
phi<-c(.2,.7)
thet <- 0
sd <- .2
x <- matrix(arima.sim(n = n,list(order = c(2,0,1), ar = phi, ma = thet, sd = sd)), ncol = 1)
y <- x[7:n]
ym1 <- x[6:(n-1)]
ym2 <- x[5:(n-2)]

H <- cbind(x[4:(n-3)], x[3:(n-4)], x[2:(n-5)], x[1:(n-6)])
g <- y ~ ym1 + ym2
x <- H
t0 <- c(0,.5,.5)

res <- gel(g, x, t0)

plot(res, which = 3)
plot(res, which = 4)

# GMM #

res <- gmm(g, x)
plot(res, which = 3)