spm (version 1.2.2)

rvi: Relative variable influence based on generalized boosted regression modeling (gbm)

Description

This function is to to derive a relative variable influence based on generalized boosted regression modeling.

Usage

rvi(
  trainx,
  trainy,
  var.monotone = rep(0, ncol(trainx)),
  family = "gaussian",
  n.trees = 3000,
  learning.rate = 0.001,
  interaction.depth = 2,
  bag.fraction = 0.5,
  train.fraction = 1,
  n.minobsinnode = 10,
  cv.fold = 10,
  weights = rep(1, nrow(trainx)),
  keep.data = FALSE,
  verbose = TRUE,
  n.cores = 6,
  ...
)

Arguments

trainx

a dataframe or matrix contains columns of predictive variables.

trainy

a vector of response, must have length equal to the number of rows in trainx.

var.monotone

an optional vector, the same length as the number of predictors, indicating which variables have a monotone increasing (+1), decreasing (-1), or arbitrary (0) relationship with the outcome. By default, a vector of 0 is used.

family

either a character string specifying the name of the distribution to use or a list with a component name specifying the distribution and any additional parameters needed. See gbm for details. By default, "gaussian" is used.

n.trees

the total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. By default, 3000 is used.

learning.rate

a shrinkage parameter applied to each tree in the expansion. Also known as step-size reduction.

interaction.depth

the maximum depth of variable interactions. 1 implies an additive model, 2 implies a model with up to 2-way interactions, etc. By default, 2 is used.

bag.fraction

the fraction of the training set observations randomly selected to propose the next tree in the expansion. By default, 0.5 is used.

train.fraction

The first train.fraction * nrows(data) observations are used to fit the gbm and the remainder are used for computing out-of-sample estimates of the loss function.

n.minobsinnode

minimum number of observations in the trees terminal nodes. Note that this is the actual number of observations not the total weight. By default, 10 is used.

cv.fold

integer; number of cross-validation folds to perform within gbm. if > 1, then apply n-fold cross validation; the default is 10, i.e., 10-fold cross validation that is recommended.

weights

an optional vector of weights to be used in the fitting process. Must be positive but do not need to be normalized. If keep.data = FALSE in the initial call to gbm then it is the user's responsibility to resupply the weights to gbm.more. By default, a vector of 1 is used.

keep.data

a logical variable indicating whether to keep the data and an index of the data stored with the object. Keeping the data and index makes subsequent calls to gbm.more faster at the cost of storing an extra copy of the dataset. By default, 'FALSE' is used.

verbose

If TRUE, gbm will print out progress and performance indicators. By default, 'TRUE' is used.

n.cores

The number of CPU cores to use. See gbm for details. By default, 6 is used.

...

other arguments passed on to gbm.

Value

A dataframe of variables (var), and relative influence (rel.inf) arranged from the most influential to the least influential

References

Greg Ridgeway with contributions from others (2015). gbm: Generalized Boosted Regression Models. R package version 2.1.1. https://CRAN.R-project.org/package=gbm

Examples

# NOT RUN {
data(sponge)
set.seed(1234)
rvi1 <- rvi(sponge[, -c(3)], sponge[, 3], family = "poisson", n.cores=2)
names(rvi1)
# The least influence variable
livar <- as.character(rvi1$gbm.rvi$var[dim(sponge[, -3])[2]])
livar

# The least influence variable to be removed from the training dataset
rmvar <- which(names(sponge[, -c(3)]) == livar)
rmvar
# }
# NOT RUN {
# }