boostmtree (version 1.5.1)

marginalPlot: Marginal plot analysis

Description

Marginal plot of x against the unadjusted predicted y. This is mainly used to obtain marginal relationships between x and the unadjusted predicted y. Marginal plots have a faster execution compared to partial plots (Friedman, 2001).

Usage

marginalPlot(object,
             xvar.names,
             tm.unq,
             subset,
             plot.it = FALSE,
             path_saveplot = NULL,
             Verbose = TRUE,
             ...)

Arguments

object

A boosting object of class (boostmtree, grow).

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.

subset

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

plot.it

Should plots be displayed? If xvar.names is a vector with more than one variable name, then instead of displaying, plot is stored as "MarginalPlot.pdf" in the location specified by path_saveplot.

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?

...

Further arguments passed to or from other methods.

Details

Marginal plot of x values specified by xvar.names against the unadjusted 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.

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
marginalPlot(boost.grow, "x1",plot.it = TRUE)
marginalPlot(boost.grow, "x2",plot.it = TRUE)

#Plot of all covariates. The plot will be stored as the "MarginalPlot.pdf"
# in the current working directory.
marginalPlot(boost.grow,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
marginalPlot(boost.grow, "x1",plot.it = TRUE)
marginalPlot(boost.grow, "x2",plot.it = TRUE)

#Plot of all covariates. The plot will be stored as the "MarginalPlot.pdf"
# in the current working directory.
marginalPlot(boost.grow,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)

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

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

#combine the two plots
dltx <- dltx[[2]][[1]]
sltx <- sltx[[2]][[1]]
plot(range(c(dltx[[1]][, 1], sltx[[1]][, 1])), range(c(dltx[[1]][, -1], sltx[[1]][, -1])),
     xlab = "age", ylab = "predicted y", type = "n")
lines(dltx[[1]][, 1][order(dltx[[1]][, 1]) ], dltx[[1]][, -1][order(dltx[[1]][, 1]) ], 
      lty = 1, lwd = 2, col = "red")
lines(sltx[[1]][, 1][order(sltx[[1]][, 1]) ], sltx[[1]][, -1][order(sltx[[1]][, 1]) ], 
      lty = 1, lwd = 2, col = "blue")
legend("topright", legend = c("DLTx", "SLTx"), lty = 1, fill = c(2,4))
# }