Last chance! 50% off unlimited learning
Sale ends in
gam
model
predictions, fixing all but the values in view
to the values supplied in cond
.vis.gam(x,view=NULL,cond=list(),n.grid=30,too.far=0,col=NA,
color="heat",contour.col=NULL,se=-1,type="link",
plot.type="persp",zlim=NULL,nCol=50,...)
gam
object, produced by gam()
view
). Variables omitted from this list will have the closest observed value to the median
for continuous variables, or the most commonly occuring level for factors.view
can be excluded from the plot. too.far
determines what is too far. The grid is scaled into the unit
square along with the view
NA
then if se
>0 the facets are transparent,
otherwise the colour scheme specified in color
is used. If col
is not NA
then it is used as thse
<=0. one="" of="" "topo", "heat"
, "cm"
,
"terrain"
, "gray"
or "bw"
. Schemes "gray"
and
"bw"
also modif
plot.type="contour"
. Default scheme used if NULL
.se
standard errors, one at the predicted values and one at
the predicted value"link"
to plot on linear predictor scale and "response"
to plot on the response scale."contour"
or "persp"
.NULL
to choose automatically.view
variable. When setting
default view
variables it can not detect this situation either, which can cause failures
if the coerced variables are the first, otherwise suitable, variables encountered.view
. If se
<=0 then="" a="" single="" (height="" colour="" coded,="" by="" default)="" surface="" is="" produced,="" otherwise="" three="" (by="" default see-through)="" meshes="" are="" produced="" at="" mean="" and="" +="" -="" se standard errors. Parts of the x-y plane too far from
data can be excluded by setting too.far
All options to the underlying graphics functions can be reset by passing them
as extra arguments ...
: such supplied values will always over-ride the
default values used by vis.gam
.
persp
and gam
.library(mgcv)
set.seed(0)
n<-200;sig2<-4
x0 <- runif(n, 0, 1);x1 <- runif(n, 0, 1)
x2 <- runif(n, 0, 1)
y<-x0^2+x1*x2 +runif(n,-0.3,0.3)
g<-gam(y~s(x0,x1,x2))
old.par<-par(mfrow=c(2,2))
# display the prediction surface in x0, x1 ....
vis.gam(g,ticktype="detailed",color="heat",theta=-35)
vis.gam(g,se=2,theta=-35) # with twice standard error surfaces
vis.gam(g, view=c("x1","x2"),cond=list(x0=0.75)) # different view
vis.gam(g, view=c("x1","x2"),cond=list(x0=.75),theta=210,phi=40,
too.far=.07)
# ..... areas where there is no data are not plotted
# contour examples....
vis.gam(g, view=c("x1","x2"),plot.type="contour",color="heat")
vis.gam(g, view=c("x1","x2"),plot.type="contour",color="terrain")
vis.gam(g, view=c("x1","x2"),plot.type="contour",color="topo")
vis.gam(g, view=c("x1","x2"),plot.type="contour",color="cm")
par(old.par)
# Examples with factor and "by" variables
fac<-rep(1:4,20)
x<-runif(80)
y<-fac+2*x^2+rnorm(80)*0.1
fac<-factor(fac)
b<-gam(y~fac+s(x))
vis.gam(b,theta=-35,color="heat") # factor example
z<-rnorm(80)*0.4
y<-as.numeric(fac)+3*x^2*z+rnorm(80)*0.1
b<-gam(y~fac+s(x,by=z))
vis.gam(b,theta=-35,color="heat",cond=list(z=1)) # by variable example
vis.gam(b,view=c("z","x"),theta= 35) # plot against by variable
Run the code above in your browser using DataLab