partial.BoostMLR {BoostMLR} | R Documentation |
Partial plot analysis
Description
Partial dependence plot of x and time against adjusted predicted y.
Usage
## S3 method for class 'BoostMLR'
partial(Object,
xvar.name,
n.x = 10,
n.tm = 10,
x.unq = NULL,
tm.unq = NULL,
Mopt,
plot.it = TRUE,
path_saveplot = NULL,
Verbose = TRUE,
...)
Arguments
Object |
A boosting object of class |
xvar.name |
Name of the x-variable to be used for partial plot. |
n.x |
Maximum number of unique points used for |
n.tm |
Maximum number of unique points used for |
x.unq |
Unique values used for the partial plot for variable |
tm.unq |
Unique time points used for the partial plots of x against y.
Default is NULL in which case
unique values are obtained uniformaly based on the range of |
Mopt |
The optimal number of boosting iteration. If missing, the value from
the |
plot.it |
Should partial plot be displayed? |
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
Partial dependence plot (Friedman, 2001) of x values specified by
xvar.name
against the adjusted predicted y-values over a set
of time points specified by tm.unq
.
Value
x.unq |
Unique values used for the partial plot for variable |
tm.unq |
Unique time points used for the partial plots of x against y. |
pList |
List with number of elements equal to number of multivariate response.
Each element of the list is a matrix with number of rows equal to length of |
sList |
List with number of elements equal to number of multivariate response.
Each element is a matrix with the same dimension as described in |
Author(s)
Amol Pande and Hemant Ishwaran
References
Friedman J.H. Greedy function approximation: a gradient boosting machine, Ann. of Statist., 5:1189-1232, 2001.
Examples
##------------------------------------------------------------
## Generate partial plot for covariate x1
##-------------------------------------------------------------
dta <- simLong(n = 100, N = 5, rho =.80, model = 1, q_x = 0,
q_y = 0,type = "corCompSym")$dtaL
# Boosting call: Raw values of covariates, B-spline for time,
# no shrinkage, no estimate of rho and phi
boost.grow <- BoostMLR(x = dta$features, tm = dta$time, id = dta$id,
y = dta$y, M = 100, VarFlag = FALSE)
Partial_Plot_x1 <- partial.BoostMLR(Object = boost.grow, xvar.name = "x1",plot.it = FALSE)