boostmtree (version 1.5.1)

partialPlot: Partial plot analysis

Description

Partial dependence plot of x against adjusted predicted y.

Usage

partialPlot(object,
            M = NULL,
            xvar.names,
            tm.unq,
            xvar.unq = NULL,
            npts = 25,
            subset,
            prob.class = FALSE,
            conditional.xvars = NULL,
            conditional.values = NULL,
            plot.it = FALSE,
            Variable_Factor = FALSE,
            path_saveplot = NULL,
            Verbose = TRUE,
            useCVflag = FALSE,
            ...)

Arguments

object

A boosting object of class (boostmtree, grow).

M

Fixed value for the boosting step number. If NULL, then use Mopt if it is available from the object, else use M

xvar.names

Names of the x-variables to be used. By default, all variables are plotted.

tm.unq

Unique time points used for the plots of x against y. By default, the deciles of the observed time values are used.

xvar.unq

Unique values used for the partial plot. Default is NULL in which case unique values are obtained uniformaly based on the range of variable. Values must be provided using list with same length as lenght of xvar.names.

npts

Maximum number of points used for x. Reduce this value if plots are slow.

subset

Vector indicating which rows of the x-data to be used for the analysis. The default is to use the entire data.

prob.class

In case of ordinal response, use class probability rather than cumulative probability.

conditional.xvars

Vector of character values indicating names of the x-variables to be used for further conditioning (adjusting) the predicted y values. Variable names should be different from xvar.names.

conditional.values

Vector of values taken by the variables from conditional.xvars. The length of the vector should be same as the length of the vector for conditional.xvars, which means only one value per conditional variable.

plot.it

Should plots be displayed?

Variable_Factor

Default is FALSE. Use TRUE if the variable specified in xvar.names is a factor.

path_saveplot

Provide the location where plot should be saved. By default the plot will be saved at temporary folder.

Verbose

Display the path where the plot is saved?

useCVflag

Should the predicted value be based on the estimate derived from oob sample?

...

Further arguments passed to or from other methods.

Details

Partial dependence plot (Friedman, 2001) of x values specified by xvar.names against the adjusted predicted y-values over a set of time points specified by tm.unq. Analysis can be restricted to a subset of the data using subset. Further conditioning can be imposed using conditional.xvars.

References

Friedman J.H. Greedy function approximation: a gradient boosting machine, Ann. of Statist., 5:1189-1232, 2001.

Examples

# NOT RUN {
##------------------------------------------------------------
## Synthetic example (Response is continuous)
## high correlation, quadratic time with quadratic interaction
##-------------------------------------------------------------
#simulate the data
dta <- simLong(n = 50, N = 5, rho =.80, model = 2,family = "Continuous")$dtaL

#basic boosting call
boost.grow <- boostmtree(dta$features, dta$time, dta$id, dta$y,family = "Continuous",M = 300)

#plot results
#x1 has a linear main effect
#x2 is quadratic with quadratic time trend
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",plot.it = TRUE)
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x2",plot.it = TRUE)

#partial plot using "x2" as the conditional variable
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",
                      conditional.xvar = "x2", conditional.values = 1,plot.it = TRUE)
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",
                      conditional.xvar = "x2", conditional.values = 2,plot.it = TRUE)

##------------------------------------------------------------
## Synthetic example (Response is binary)
## high correlation, quadratic time with quadratic interaction
##-------------------------------------------------------------
#simulate the data
dta <- simLong(n = 50, N = 5, rho =.80, model = 2,family = "Binary")$dtaL

#basic boosting call
boost.grow <- boostmtree(dta$features, dta$time, dta$id, dta$y,family = "Binary",M = 300)

#plot results
#x1 has a linear main effect
#x2 is quadratic with quadratic time trend
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x1",plot.it = TRUE)
pp.obj <- partialPlot(object = boost.grow, xvar.names = "x2",plot.it = TRUE)

##----------------------------------------------------------------------------
## spirometry data
##----------------------------------------------------------------------------
data(spirometry, package = "boostmtree")

#boosting call: cubic B-splines with 15 knots
spr.obj <- boostmtree(spirometry$features, spirometry$time, spirometry$id, spirometry$y,
            family = "Continuous",M = 300, nu = .025, nknots = 15)

#partial plot of double-lung group at 5 years
dltx <- partialPlot(object = spr.obj, xvar.names = "AGE",
                    tm.unq = 5, subset=spr.obj$x$DOUBLE==1,plot.it = TRUE)

#partial plot of single-lung group at 5 years
sltx <- partialPlot(object = spr.obj, xvar.names = "AGE",
                    tm.unq = 5, subset=spr.obj$x$DOUBLE==0,plot.it = TRUE)

#combine the two plots: we use lowess smoothed values
dltx <- dltx$l.obj[[1]]
sltx <- sltx$l.obj[[1]]
plot(range(c(dltx[, 1], sltx[, 1])), range(c(dltx[, -1], sltx[, -1])),
     xlab = "age", ylab = "predicted y (adjusted)", type = "n")
lines(dltx[, 1], dltx[, -1], lty = 1, lwd = 2, col = "red")
lines(sltx[, 1], sltx[, -1], lty = 1, lwd = 2, col = "blue")
legend("topright", legend = c("DLTx", "SLTx"), lty = 1, fill = c(2,4))
# }