randomForestSRC (version 2.5.0)

partial: Acquire Partial Effect of a Variable

Description

Acquire the partial effect of a variable on the ensembles.

Usage

partial.rfsrc(object, outcome.target = NULL,
  partial.type = NULL, partial.xvar = NULL, partial.values = NULL,
  partial.xvar2 = NULL, partial.values2 = NULL,
  partial.time = NULL, oob = TRUE, seed = NULL, do.trace = FALSE, ...)

Arguments

object

An object of class (rfsrc, grow).

outcome.target

Character value for multivariate families specifying the target outcome to be used. The default is to use the first coordinate.

partial.type

Character value of the type of predicted value. See details below.

partial.xvar

Character value specifying the single primary partial x-variable to be used.

partial.values

Vector of values that the primary partialy x-variable will assume.

partial.xvar2

Vector of character values specifying the second order x-variables to be used.

partial.values2

Vector of values that the second order x-variables will assume. Each second order x-variable can only assume a single value. This the length of partial.xvar2 and partial.values2 will be the same. In addition, the user must do the appropriate conversion for factors, and represent a value as a numeric element.

partial.time

For survival families, the time at which the predicted survival value is evaluated at (depends on partial.type).

oob

OOB (TRUE) or in-bag (FALSE) predicted values.

seed

Negative integer specifying seed for the random number generator.

do.trace

Number of seconds between updates to the user on approximate time to completion.

...

Further arguments passed to or from other methods.

Details

1. For regression, the predicted response is used.

2. For survival, the choices are:

   • Relative frequency of mortality (rel.freq) or mortality (mort) is of dim [n] x [length(partial.values)].

   • The cumulative hazard function (chf) is of dim [n] x [length(partial.time)] x [length(partial.values)].

   • The survival function (surv) is of dim [n] x [length(partial.time)] x [length(partial.values)].

3. For competing risks, the choices are:

   • The expected number of life years lost (years.lost) is of dim [n] x [length(event.info$event.type)] x [length(partial.values)].

   • The cumulative incidence function (cif) is of dim [n] x [length(partial.time)] x [length(event.info$event.type)] x [length(partial.values)].

   • The cumulative hazard function (chf) is of dim [n] x [length(partial.time)] x [length(event.info$event.type)] x [length(partial.values)].

4. For regression, it is of dim [n] x [length(partial.values)].

5. For classification, it is of dim [n] x [1 + yvar.nlevels[.]] x [length(partial.values)].

References

Ishwaran H., Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.

Ishwaran H., Gerds T.A., Kogalur U.B., Moore R.D., Gange S.J. and Lau B.M. (2014). Random survival forests for competing risks. Biostatistics, 15(4):757-773.

See Also

plot.variable.rfsrc

Examples

# NOT RUN {
## ------------------------------------------------------------
## survival/competing risk
## ------------------------------------------------------------

## survival
data(veteran, package = "randomForestSRC")
v.obj <- rfsrc(Surv(time,status)~., veteran, nsplit = 10, ntree = 100)
partial.obj <- partial(v.obj,
  partial.type = "rel.freq",
  partial.xvar = "age",
  partial.values = v.obj$xvar[, "age"],
  partial.time = v.obj$time.interest)

## competing risks
data(follic, package = "randomForestSRC")
follic.obj <- rfsrc(Surv(time, status) ~ ., follic, nsplit = 3, ntree = 100)
partial.obj <- partial(follic.obj,
  partial.type = "cif",
  partial.xvar = "age",
  partial.values = follic.obj$xvar[, "age"],
  partial.time = follic.obj$time.interest,
  oob = TRUE)

## regression
airq.obj <- rfsrc(Ozone ~ ., data = airquality)
partial.obj <- partial(airq.obj,
  partial.xvar = "Wind",
  partial.values = airq.obj$xvar[, "Wind"],
  oob = FALSE)

## classification
iris.obj <- rfsrc(Species ~., data = iris)
partial.obj <- partial(iris.obj,
  partial.xvar = "Sepal.Length",
  partial.values = iris.obj$xvar[, "Sepal.Length"])

## multivariate mixed outcomes
mtcars2 <- mtcars
mtcars2$carb <- factor(mtcars2$carb)
mtcars2$cyl <- factor(mtcars2$cyl)
mtcars.mix <- rfsrc(Multivar(carb, mpg, cyl) ~ ., data = mtcars2)
partial.obj <- partial(mtcars.mix,
  partial.xvar = "disp",
  partial.values = mtcars.mix$xvar[, "disp"])

## second order variable specification
mtcars.obj <- rfsrc(mpg ~., data = mtcars)
partial.obj <- partial(mtcars.obj,
  partial.xvar = "cyl",
  partial.values = c(4, 8),
  partial.xvar2 = c("gear", "disp", "carb"),
  partial.values2 = c(4, 200, 3))

# }