The table present the different plot functions in the packages mgcv** [@Wood_2006; @Wood_2011] and **itsadug for visualizing GAMM models.
| Partial effect | Sum of all effects | Sum of “fixed” effects1 | |
|---|---|---|---|
| surface | plot.gam() | vis.gam() | |
| pvisgam() | fvisgam() | ||
| smooth | plot.gam() | plot_smooth() | |
| group estimates | plot.gam()2 | plot_parametric() | |
| random smooths | get_random(), inspect_random() | ||
1: include rm.ranef=TRUE to zero all random effects.
2: include all.terms=TRUE to visualize parametric terms.
library(itsadug)
library(mgcv)
data(simdat)
## Not run:
# Model with random effect and interactions:
m1 <- bam(Y ~ Group + te(Time, Trial, by=Group)
+ s(Time, Subject, bs='fs', m=1, k=5),
data=simdat)
# Simple model with smooth:
m2 <- bam(Y ~ Group + s(Time, by=Group)
+ s(Subject, bs='re')
+ s(Subject, Time, bs='re'),
data=simdat)
Summary model m1:
gamtabs(m1, type='html')
Summary model m2:
gamtabs(m2, type='html')
Plotting the partial effects of te(Time,Trial):GroupAdults and te(Time,Trial):GroupChildren.
par(mfrow=c(1,2), cex=1.1)
pvisgam(m1, view=c("Time", "Trial"), select=1,
main="Children", zlim=c(-12,12))
pvisgam(m1, view=c("Time", "Trial"), select=2,
main="Adults", zlim=c(-12,12))
Notes:
Plots same data as plot(m1, select=1): partial effects plot, i.e., the plot does not include intercept or any other effects.
Make sure to set the zlim values the same when comparing surfaces
Use the argument cond to specify the value of other predictors in a more complex interaction. For example, for plotting a modelterm te(A,B,C) use something like pvisgam(model, view=c("A", "B"), select=1, cond=list(C=5)).
Plotting the fitted effects of te(Time,Trial):GroupAdults and te(Time,Trial):GroupChildren.
par(mfrow=c(1,2), cex=1.1)
fvisgam(m1, view=c("Time", "Trial"), cond=list(Group="Children"),
main="Children", zlim=c(-12,12), rm.ranef=TRUE)
fvisgam(m1, view=c("Time", "Trial"), cond=list(Group="Adults"),
main="Adults", zlim=c(-12,12), rm.ranef=TRUE)
Notes:
Plots the fitted effects, i.e., the plot includes intercept and estimates for the other modelterms.
Make sure to set the zlim values the same when comparing surfaces
Use the argument cond to specify the value of other predictors in the model.
The argument rm.ranef cancels the contribution of random effects.
The argument transform accepts a function for transforming the fitted values into the original scale.
Plotting the partial effects of s(Time):GroupAdults and s(Time):GroupChildren.
par(mfrow=c(1,2), cex=1.1)
plot(m2, select=1, shade=TRUE, rug=FALSE, ylim=c(-15,10))
abline(h=0)
plot(m2, select=2, shade=TRUE, rug=FALSE, ylim=c(-15,10))
abline(h=0)
Notes:
Alternatively:
par(mfrow=c(1,1), cex=1.1)
# Get model term data:
st1 <- get_modelterm(m2, select=1)
st2 <- get_modelterm(m2, select=2)
# plot model terms:
emptyPlot(2000, c(-15,10), h=0,
main='s(Time)')
plot_error(st1$terms, st1$fit, st1$se.fit, shade=TRUE)
plot_error(st2$terms, st2$fit, st2$se.fit, shade=TRUE, col='red', lty=5, lwd=2)
# add legend:
legend('bottomleft',
legend=c('Children', 'Adults'),
fill=c(alpha('black'), alpha('red')),
bty='n')
Plotting the fitted effects of te(Time,Trial):GroupAdults and te(Time,Trial):GroupChildren i.e., the plot includes intercept and estimates for the other modelterms.
par(mfrow=c(1,2), cex=1.1)
plot_smooth(m1, view="Time", cond=list(Group="Children"),
rm.ranef=TRUE, ylim=c(-6,10))
plot_smooth(m1, view="Time", cond=list(Group="Adults"),
col="red", rug=FALSE, add=TRUE,
rm.ranef=TRUE)
# or alternatively:
plot_smooth(m1, view="Time", plot_all="Group",
rm.ranef=TRUE)
Notes:
Use the argument cond to specify the value of other predictors in the model.
The argument rm.ranef cancels the contribution of random effects.
The argument transform accepts a function for transforming the fitted values into the original scale.
The argument plot_all plots all levels for the given predictor(s).
Plotting the partial effect of grouping predictors such as Group:
par(mfrow=c(1,1), cex=1.1)
plot.gam(m1, select=4, all.terms=TRUE, rug=FALSE)
Alternatively, use get_coefs() to extract the coefficients and plot these:
coefs <- get_coefs(m1)
coefs
par(mfrow=c(1,2), cex=1.1)
b <- barplot(coefs[,1], beside=TRUE,
main="Parametric terms",
ylim=c(0,5))
errorBars(b, coefs[,1], coefs[,2], xpd=TRUE)
# Note that the effect of Group is a *difference* estimate
# between intercept (=GroupChildren) and Group Adults
b2 <- barplot(coefs[1,1], beside=TRUE,
main="Estimate for Group",
ylim=c(0,5), xlim=c(0.1,2.5))
mtext(row.names(coefs), at=b, side=1, line=1)
abline(h=coefs[1,1], lty=2)
rect(b[2]-.4, coefs[1,1], b[2]+.4, coefs[1,1]+coefs[2,1],
col='gray')
errorBars(b, coefs[,1]+c(0,coefs[1,1]), coefs[,2], xpd=TRUE)
## Estimate Std. Error
## (Intercept) 1.337552 0.4860860
## GroupAdults 2.450473 0.7012568
Notes:
get_coefs() is faster than summary(model).Plotting the fitted effects of grouping predictors such as Group:
pp <- plot_parametric(m1, pred=list(Group=c("Children", "Adults")) )
pp
## $fv
## Group Time Trial Subject fit CI
## 1 Children 989.899 0 a01 0.7700983 1.7731153
## 2 Adults 989.899 0 a01 6.4457358 0.1681991
For extracting the random effects coefficients (random adjustments of intercept and slopes):
par(mfrow=c(1,2), cex=1.1)
plot(m2, select=3)
plot(m2, select=4)
Or alternatively:
pp <- get_random(m2)
emptyPlot(range(pp[[1]]), range(pp[[2]]), h=0,v=0,
xlab='Random intercepts', ylab='Random slopes',
main='Correlation')
text(pp[[1]], pp[[2]], labels=names(pp[[1]]),
col='steelblue', xpd=TRUE)
For plotting and extracting the random smooths:
par(mfrow=c(1,2), cex=1.1)
inspect_random(m1, select=3, main='s(Time, Subject)')
children <- unique(simdat[simdat$Group=="Children", "Subject"])
adults <- unique(simdat[simdat$Group=="Adults", "Subject"])
inspect_random(m1, select=3, main='Averages',
fun=mean,
cond=list(Subject=children))
inspect_random(m1, select=3,
fun=mean, cond=list(Subject=adults),
add=TRUE, col='red', lty=5)
# add legend:
legend('bottomleft',
legend=c('Children', 'Adults'),
col=c('black', 'red'), lty=c(1,5),
bty='n')
