pvisgam  R Documentation 
Produces perspective or contour plot views of gam model
predictions of the partial effects interactions. Combines the function
plot.gam
for interaction surfaces with the function
vis.gam
. Similar to plot.gam
,
pvisgam
plots the partial interaction surface, without including
values for other predictors that are not being shown. Similar to
vis.gam
the user can set the two predictors to be
viewed, and colors are added behind the contours to facilitate
interpretation. In contrast to plot.gam
, this function
allows to plotting of interactions with three of more continuous predictors
by breaking it down in twodimensional surfaces.
The code is derivated from the script for vis.gam
.
pvisgam( x, view = NULL, select = NULL, cond = list(), n.grid = 30, too.far = 0, col = NA, color = "terrain", contour.col = NULL, add.color.legend = TRUE, se = 0, plot.type = "contour", zlim = NULL, xlim = NULL, ylim = NULL, nCol = 50, labcex = 0.6, hide.label = FALSE, print.summary = getOption("itsadug_print"), show.diff = FALSE, col.diff = 1, alpha.diff = 0.5, dec = NULL, f = 1.96, ... )
x 
A gam object, produced by 
view 
A twovalue vector containing the names of the two main effect terms to be displayed on the x and y dimensions of the plot. Note that variables coerced to factors in the model formula won't work as view variables. 
select 
A number, selecting a single model term for printing. e.g. if you want the plot for the second smooth term set select=2. 
cond 
A named list of the values to use for the other predictor terms (not in view). Used for choosing between smooths that share the same view predictors. 
n.grid 
The number of grid nodes in each direction used for calculating the plotted surface. 
too.far 
Plot grid nodes that are too far from the points defined by the variables given in 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 variables and then grid nodes more than too.far from the predictor variables are excluded. 
col 
The colors for the facets of the plot. 
color 
The color scheme to use for plots. One of 'topo', 'heat', 'cm', 'terrain', 'gray' or 'bw'. Alternatively a vector with some colors can be provided for a custom color palette (see examples). 
contour.col 
sets the color of contours when using plot. 
add.color.legend 
Logical: whether or not to add a color legend.
Default is TRUE. If FALSE (omitted), one could use the function

se 
If less than or equal to zero then only the predicted surface is plotted, but if greater than zero, then 3 surfaces are plotted, one at the predicted values minus se standard errors, one at the predicted values and one at the predicted values plus se standard errors. 
plot.type 
one of 'contour' or 'persp' (default is 'contour'). 
zlim 
A two item array giving the lower and upper limits for the z axis scale. NULL to choose automatically. 
xlim 
A two item array giving the lower and upper limits for the x axis scale. NULL to choose automatically. 
ylim 
A two item array giving the lower and upper limits for the y axis scale. NULL to choose automatically. 
nCol 
The number of colors to use in color schemes. 
labcex 
Size of the contour labels. 
hide.label 
Logical: whether or not to hide the label (i.e., 'partial effect'). Default is FALSE. 
print.summary 
Logical: whether or not to print summary.
Default set to the print info messages option
(see 
show.diff 
Logical: whether or not to indicate the regions that
are significantly different from zero. Note that these regions are just
an indication and dependent on the value of 
col.diff 
Color to shade the nonsignificant areas. 
alpha.diff 
Level of transparency to mark the nonsignificant areas. 
dec 
Numeric: number of decimals for rounding the color legend.
When NULL (default), no rounding. If 1 the values are automatically determined.
Note: if value = 1 (default), rounding will be applied also when

f 
Scaling factor to determine the CI from the se, for marking the
difference with 0. Only applies when 
... 
other options to pass on to persp, image or contour. In particular ticktype='detailed' will add proper axes labeling to the plots. 
In contrast to vis.gam, do not specify other predictors in cond
that
are not to be plotted.
When the argument show.diff
is set to TRUE a shading area indicates
where the confidence intervals include zero. Or, in other words, the areas
that are not significantly different from zero. Be careful with the
interpretation, however, as the precise shape of the surface is dependent
on model constraints such as the value of choose.k
and the
smooth function used, and the size of the confidence intervals are
dependent on the model fit and model characteristics
(see vignette('acf', package='itsadug')
). In addition, the value of
n.grid
determines the precision of the plot.
Jacolien van Rij. Modification of vis.gam
from
package mgcv
of Simon N. Wood.
vis.gam
, plot.gam
Other Functions for model inspection:
dispersion()
,
fvisgam()
,
gamtabs()
,
inspect_random()
,
plot_data()
,
plot_parametric()
,
plot_smooth()
,
plot_topo()
data(simdat) ## Not run: # Model with random effect and interactions: m1 < bam(Y ~ te(Time, Trial)+s(Time, Subject, bs='fs', m=1), data=simdat, discrete=TRUE) # Plot summed effects: vis.gam(m1, view=c('Time', 'Trial'), plot.type='contour', color='topo') # Partial effect of interaction: pvisgam(m1, view=c('Time', 'Trial'), select=1) # Same: plot(m1, select=1, scheme=2) plot(m1, select=1) # Alternatives: pvisgam(m1, view=c('Trial', 'Time'), select=1) pvisgam(m1, view=c('Trial', 'Time'), select=1, zlim=c(20,20)) pvisgam(m1, view=c('Trial', 'Time'), select=1, zlim=c(20,20), color='terrain') pvisgam(m1, view=c('Trial', 'Time'), select=1, zlim=c(20,20), color=c('blue', 'white', 'red')) # Notes on the color legend: # Labels can easily fall off the plot, therefore the numbers are # automatically rounded. # To undo the rounding, set dec=NULL: pvisgam(m1, view=c('Time', 'Trial'), dec=NULL) # For custom rounding, set dec to a value: pvisgam(m1, view=c('Time', 'Trial'), dec=1) # To increase the left marging of the plot (so that the numbers fit): oldmar < par()$mar par(mar=oldmar + c(0,0,0,1) ) # add one line to the right pvisgam(m1, view=c('Time', 'Trial'), dec=3) par(mar=oldmar) # restore to default settings # too.far: n < which(simdat$Time > 1500 & simdat$Trial > 5) simdat[n,]$Y < NA simdat[simdat$Trial == 3,]$Y < NA m1 < bam(Y ~ te(Time, Trial)+s(Time, Subject, bs='fs', m=1), data=simdat, discrete=TRUE) pvisgam(m1, view=c('Time', 'Trial'), select=1, too.far=0.03) ## End(Not run) # see the vignette for examples: vignette('overview', package='itsadug')
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