Extracting parametric effects from a GAM model

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

pterm(o, select)

Arguments

o	an object of class gamViz, the output of a getViz() call.
select	index of the selected parametric effect.

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".

Examples

####### 1. Gaussian GAM 
library(mgcViz)
set.seed(3)
dat <- gamSim(1,n=1500,dist="normal",scale=20)
#> Gu & Wahba 4 term additive model
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)
#> mgcViz does not know how to plot interactions. Returning NULL.

####### 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))
#> Estimating learning rate. Each dot corresponds to a loss evaluation. 
#> qu = 0.5........done 
#> qu = 0.3........done 
#> qu = 0.8........done 

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))
#> mgcViz does not know how to plot interactions. Returning NULL.

####### 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()
#> l_points(): Partial residuals are not available

# 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()