| stepgbm {stepgbm} | R Documentation |
Select predictive variables for generalized boosted regression modeling (gbm) by various variable influence methods and predictive accuracy in a stepwise algorithm
Description
This function is to select predictive variables for generalized boosted regression modeling (gbm) based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) and predictive accuracy. It is implemented via the functions 'stepgbmRVI' and 'steprf::steprfAVIPredictors'. It should be noted that this function can be slow, may taking several minutes, hours or even days depending on the number of the predictive variables to be selected and the specifications of relevant arguments.
Usage
stepgbm(
trainx,
trainy,
method = "KIRVI",
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,
rpt = 2,
predacc = "VEcv",
min.n.var = 2,
delta.predacc = 0.001,
rseed = 1234,
...
)
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. |
method |
a variable selection method for 'GBM'; can be: "RVI", "KIRVI" and "KIRVI2". If "RVI" is used, it would produce the same results as 'stepgbmRVI'. By default, "KIRVI" is used. |
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. By default, 0.001 is used. |
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. |
rpt |
iteration of cross validation. |
predacc |
"VEcv" for vecv in function pred.acc. |
min.n.var |
minimum number of predictive variables remained in the final predictive model the default is 1. |
delta.predacc |
minimum changes between the accuracy of two consecutive predictive models. By default, 0.01 is used. |
rseed |
random seed. By default, 1234 is used. |
... |
other arguments passed on to gbm. |
Value
A list with the following components: 1) stepgbmPredictorsFinal: the variables selected for the last GBM model, whether it is of the highest predictive accuracy need to be confirmed using 'max.predictive.accuracy' that is listed next; 2) max.predictive.accuracy: the predictive accuracy of the most accurate GBM model for each run of 'stepgbmRVI', which can be used to confirm the model with the highest accuracy, 3) numberruns: number of runs of 'stepgbmRVI'; 4) laststepRVI: the outpouts of last run of 'stepgbmRVI'; 5) stepgbmRVIOutputsAll: the outpouts of all 'stepgbmRVI' produced during the variable selection process; 6) stepgbmPredictorsAll: the outpouts of 'stepgbmRVIPredictors' for all 'stepgbmRVI' produced during the variable selection process; 7) KIRVIPredictorsAll: predictors used for all 'stepgbmRVI' produced during the variable selection process; for a method "RVI", if the variables are different from those in the traning dataset, it suggests that these variables should be tested if the predictive accuracy can be further improved.
Author(s)
Jin Li
References
Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton, Chapman and Hall/CRC.
Li, J., Siwabessy, J., Huang, Z., Nichol, S. (2019). "Developing an optimal spatial predictive model for seabed sand content using machine learning, geostatistics and their hybrid methods." Geosciences 9 (4):180.
Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). "Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness." Environmental Modelling & Software 97: 112-129.
Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). Selecting predictors to form the most accurate predictive model for count data. International Congress on Modelling and Simulation (MODSIM) 2017, Hobart.
Examples
library(spm)
data(petrel)
stepgbm1 <- stepgbm(trainx = petrel[, c(1,2, 6:9)], trainy =
log(petrel[, 5] + 1), method = "KIRVI", family = "gaussian", rpt = 2,
predacc = "VEcv", cv.fold = 5, min.n.var = 2, n.cores = 2,
delta.predacc = 0.01, rseed = 1234)
names(stepgbm1)
stepgbm1$stepgbmPredictorsFinal$variables.most.accurate
stepgbm1$max.predictive.accuracy
stepgbm1$stepgbmPredictorsAll[[1]]
# The variables selected can be derived with
stepgbm1$stepgbmPredictorsAll[[1]]$variables.most.accurate
data(sponge)
stepgbm2 <- stepgbm(trainx = sponge[, -3], trainy = sponge[, 3], method = "KIRVI",
family = "poisson", rpt = 2, cv.fold = 5, predacc = "VEcv", min.n.var = 2,
n.cores = 2, delta.predacc = 0.01, rseed = 1234)
stepgbm2
stepgbm2$max.predictive.accuracy
# The variables selected can be derived with
stepgbm2$stepgbmPredictorsAll[[1]]$variables.most.accurate