R/llrplot.R

In musto101/wilcox_R: What the package does (short line)

llrplot <-
function(x, y)
{
# Makes ESSP, the weighted forward response and residual plots for loglinear regression.
#
#   If q is changed, change the formula in the glm statement.
	q <- 5	# change formula to x[,1]+ ... + x[,q] with q
	out <- glm(y ~ x[, 1] + x[, 2] + x[, 3] + x[, 4] + x[, 5], family =
		poisson)
	ESP <- x %*% out$coef[-1] + out$coef[1]
	Y <- y
	par(mfrow = c(2, 2))
	plot(ESP, Y)
	abline(mean(y), 0)
	Ehat <- exp(ESP)
	indx <- sort.list(ESP)
	lines(ESP[indx], Ehat[indx])
	lines(lowess(ESP, y), type = "s")
	title("a) ESSP")
	Vhat <- (y - Ehat)^2
	plot(Ehat, Vhat)
	abline(0, 1)
	#abline(lsfit(Ehat, Vhat)$coef)
	title("b)")
	Z <- y
	Z[y < 1] <- Z[y < 1] + 0.5
	MWRES <- sqrt(Z) * (log(Z) - x %*% out$coef[-1] - out$coef[1])
	MWFIT <- sqrt(Z) * log(Z) - MWRES
	plot(MWFIT, sqrt(Z) * log(Z))
	abline(0, 1)
	#abline(lsfit(MWFIT, sqrt(Z) * log(Z))$coef)
	title("c) WFRP Based on MLE")
	plot(MWFIT, MWRES)
	title("d) WRP Based on MLE")
}