plot.hglm {hglm} | R Documentation |
Plots residuals for the mean and dispersion models, individual deviances and hatvalues for hglm
objects
## S3 method for class 'hglm' plot(x, pch = "+", pcol = 'slateblue', lcol = 2, device = NULL, name = NULL, ...)
x |
the |
pch |
symbol used in the plots |
pcol |
color of points |
lcol |
color of lines |
device |
if |
name |
a string gives the main name of the PDF file when |
... |
graphical parameters |
A S3 generic plot method for hglm
objects. It produces a set of diagnostic plots for a hierarchical model.
Xia Shen
# --------------------- # # semiconductor example # # --------------------- # data(semiconductor) h.gamma.normal <- hglm(fixed = y ~ x1 + x3 + x5 + x6, random = ~ 1|Device, family = Gamma(link = log), disp = ~ x2 + x3, data = semiconductor) summary(h.gamma.normal) plot(h.gamma.normal, cex = .6, pch = 1, cex.axis = 1/.6, cex.lab = 1/.6, cex.main = 1/.6, mar = c(3, 4.5, 0, 1.5)) # ------------------- # # redo it using hglm2 # # ------------------- # m1 <- hglm2(y ~ x1 + x3 + x5 + x6 + (1|Device), family = Gamma(link = log), disp = ~ x2 + x3, data = semiconductor) summary(m1) plot(m1, cex = .6, pch = 1, cex.axis = 1/.6, cex.lab = 1/.6, cex.main = 1/.6, mar = c(3, 4.5, 0, 1.5)) # --------------------------------------------- # # simulated example with 2 random effects terms # # --------------------------------------------- # ## Not run: set.seed(911) x1 <- rnorm(100) x2 <- rnorm(100) x3 <- rnorm(100) z1 <- factor(rep(LETTERS[1:10], rep(10, 10))) z2 <- factor(rep(letters[1:5], rep(20, 5))) Z1 <- model.matrix(~ 0 + z1) Z2 <- model.matrix(~ 0 + z2) u1 <- rnorm(10, 0, sqrt(2)) u2 <- rnorm(5, 0, sqrt(3)) y <- 1 + 2*x1 + 3*x2 + Z1%*%u1 + Z2%*%u2 + rnorm(100, 0, sqrt(exp(x3))) dd <- data.frame(x1 = x1, x2 = x2, x3 = x3, z1 = z1, z2 = z2, y = y) (m2.1 <- hglm(X = cbind(rep(1, 100), x1, x2), y = y, Z = cbind(Z1, Z2), RandC = c(10, 5))) summary(m2.1) plot(m2.1) (m2.2 <- hglm2(y ~ x1 + x2 + (1|z1) + (1|z2), data = dd, vcovmat = TRUE)) image(m2.2$vcov) summary(m2.2) plot(m2.2) m3 <- hglm2(y ~ x1 + x2 + (1|z1) + (1|z2), disp = ~ x3, data = dd) print (m3) summary(m3) plot(m3) ## End(Not run)