mgcViz (version 0.2.0)

pterm: Extracting parametric effects from a GAM model

Description

This function can be used to extract a parametric effect from an object of class gamViz.

Usage

pterm(o, select)

Value

An object of class "pTermSomething" where "Something" is substituted with the class of the variable of interest. For instance if this "numeric", the pterm

will return an object of class "ptermNumeric".

Arguments

o

an object of class gamViz, the output of a getViz() call.

select

index of the selected parametric effect.

Examples

####### 1. Gaussian GAM 
library(mgcViz)
set.seed(3)
dat <- gamSim(1,n=1500,dist="normal",scale=20)
dat$fac <- as.factor( sample(c("A1", "A2", "A3"), nrow(dat), replace = TRUE) ) 
dat$logi <- as.logical( sample(c(TRUE, FALSE), nrow(dat), replace = TRUE) ) 
bs <- "cr"; k <- 12
b <- gam(y ~ x0 + x1 + I(x1^2) + s(x2,bs=bs,k=k) + fac + x3:fac + I(x1*x2) + logi,data=dat)
o <- getViz(b)

# Plot effect of 'x0'
pt <- pterm(o, 1)
plot(pt, n = 60) + l_ciPoly() + l_fitLine() + l_ciLine() + l_points()

# Plot effect of 'x3'
pt <- pterm(o, 1)
plot(pt, n = 60) + l_fitLine() + l_ciLine(colour = 2)

# Plot effect of 'fac'
pt <- pterm(o, 4)
plot(pt) + l_ciBar(colour = "blue") + l_fitPoints(colour = "red") + 
           l_rug(alpha = 0.3)

# Plot effect of 'logi'
pt <- pterm(o, 6)
plot(pt) + l_fitBar(a.aes = list(fill = I("light blue"))) + l_ciBar(colour = "blue")

# Plot effect of 'x3:fac': no method available yet available for second order terms
pt <- pterm(o, 7)
plot(pt)

if (FALSE) {
####### 1. Continued: Quantile GAMs
b <- mqgamV(y ~ x0 + x1 + I(x1^2) + s(x2,bs=bs,k=k) + x3:fac + 
              I(x1*x2) + logi, data=dat, qu = c(0.3, 0.5, 0.8))

plot(pterm(b, 3)) + l_ciBar(colour = 2) + l_fitPoints()

plot(pterm(b, 4)) + l_fitBar(colour = "blue", fill = 3) + l_ciBar(colour = 2) 

# Don't know how to plot this interaction
plot(pterm(b, 6))
 
####### 2. Gaussian GAMLSS model
library(MASS)
mcycle$fac <- as.factor( sample(c("z", "k", "a", "f"), nrow(mcycle), replace = TRUE) ) 
b <- gam(list(accel~times + I(times^2) + s(times,k=10), ~ times + fac + s(times)),
          data=mcycle,family=gaulss(), optimizer = "efs")
o <- getViz(b)

# Plot effect of 'I(times^2)' on mean: notice that partial residuals
# are unavailable for GAMLSS models, hence l_point does not do anything here.
pt <- pterm(o, 2)
plot(pt) + l_ciPoly() + l_fitLine() + l_ciLine() + l_points()

# Plot effect of 'times' in second linear predictor.
# Notice that partial residuals are unavailable.
pt <- pterm(o, 3)
plot(pt) + l_ciPoly() + l_fitLine() + l_ciLine(linetype = 3) + l_rug()

# Plot effect of 'fac' in second linear predictor.
pt <- pterm(o, 4)
plot(pt) + l_ciBar(colour = "blue") + l_fitPoints(colour = "red") + 
           l_rug() 
}