partial.BoostMLR {BoostMLR} | R Documentation |
Partial dependence plot of x and time against adjusted predicted y.
## 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, ...)
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. |
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
.
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 |
Amol Pande and Hemant Ishwaran
Friedman J.H. Greedy function approximation: a gradient boosting machine, Ann. of Statist., 5:1189-1232, 2001.
##------------------------------------------------------------ ## 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)