steprf {steprf}

Select predictive variables for random forest by various variable importance methods and predictive accuracy in a stepwise algorithm

Description

This function is to select predictive variables for random forest by various variable importance methods (i.e., AVI, Knowledge informed AVI (KIAVI), KIAVI2) and predictive accuracy. It is implemented via the functions 'steprfAVI' and 'steprfAVIPredictors'.

Usage

steprf(
  trainx,
  trainy,
  method = "KIAVI",
  cv.fold = 10,
  ntree = 500,
  rpt = 20,
  predacc = "VEcv",
  importance = TRUE,
  maxk = c(4),
  nsim = 100,
  delta.predacc = 0.001,
  min.n.var = 2,
  corr.threshold = 0.5,
  ...
)

Arguments

trainx

a dataframe or matrix contains columns of predictor variables.

trainy

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

method

a variable selection method for 'RF'; can be: "AVI", "KIAVI" and "KIAVI2". If "AVI" is used, it would produce tha same results as 'steprfAVI'. By default, "KIAVI" is used.

cv.fold

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

ntree

number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times. By default, 500 is used.

rpt

iteration of cross validation.

predacc

"VEcv" for vecv for numerical data, or "ccr" (i.e., correct classification rate) or "kappa" for categorical data.

importance

imprtance of predictive variables.

maxk

maxk split value. By default, 4 is used.

nsim

iteration number. By default, 100 is used.

delta.predacc

minimum changes between the accuracies of two consecutive predictive models.

min.n.var

minimum number of predictive variables remained in the final predictive model the default is 2. If 1 is used, then warnings: 'invalid mtry: reset to within valid range' will be issued, which should be ignored.

corr.threshold

correlation threshold and the defaults value is 0.5.

...

other arguments passed on to randomForest.

Value

A list with the following components: 1) steprfPredictorsFinal: the variables selected for the last RF 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 RF model for each run of 'steprfAVI', which can be used to confirm the model with the highest accuracy, 3) numberruns: number of runs of 'steprfAVI'; 4) laststepAVI: the outpouts of last run of 'steprfAVI'; 5) steprfAVIOutputsAll: the outpouts of all 'steprfAVI' produced during the variable selection process; 6) steprfPredictorsAll: the outpouts of 'steprfAVIPredictors' for all 'steprfAVI' produced during the variable selection process; 7) KIAVIPredictorsAll: predictors used for all 'steprfAVI' produced during the variable selection process; for a method "AVI", 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.

Note

In 'steprf', 'steprfAVI' is used instead of 'steprfAVI1' and 'steprfAVI2'. This is because: 1) 'avi' is expected to change with the removal of each predictor, but in 'steprfAVI1' the averaged variable importance is calculated only once and is from the full model only, so its use is expected to produce a less optimal model, hence not used; and 2) the 'steprf' would lead to the same set of predictors as that for 'steprfAVI2' if 'steprfAVI2' is used, so it is not used either.

Author(s)

Jin Li

References

Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton, Chapman and Hall/CRC.

Li, J. (2019). "A critical review of spatial predictive modeling process in environmental sciences with reproducible examples in R." Applied Sciences 9: 2048.

Li, J. 2013. Predicting the spatial distribution of seabed gravel content using random forest, spatial interpolation methods and their hybrid methods. Pages 394-400 The International Congress on Modelling and Simulation (MODSIM) 2013, Adelaide.

Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F. and 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., Siwabessy, J., Huang, Z., and 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., Siwabessy, J., Tran, M., Huang, Z. and Heap, A., 2014. Predicting Seabed Hardness Using Random Forest in R. Data Mining Applications with R. Y. Zhao and Y. Cen. Amsterdam, Elsevier: 299-329.

Li, J., Tran, M. and Siwabessy, J., 2016. Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness. PLOS ONE 11(2): e0149089.

Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variable importance in R package extendedForest. R vignette [http://gradientforest.r-forge.r-project.org/Conditional-importance.pdf].

Examples

library(spm)
data(petrel)
set.seed(1234)
steprf1 <- steprf(trainx = petrel[, c(1,2, 6:9)], trainy =
petrel[, 5], method = "KIAVI", rpt = 2, predacc = "VEcv", importance = TRUE,
 nsim = 3, delta.predacc = 0.01)
names(steprf1)
steprf1$steprfPredictorsFinal$variables.most.accurate
steprf1$max.predictive.accuracy