| Title: | Stepwise Predictive Variable Selection for Random Forest |
| Version: | 1.0.2 |
| Date: | 2022-6-28 |
| Description: | An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <doi:10.13140/RG.2.2.27686.22085>. |
| Depends: | R (≥ 4.0) |
| Imports: | spm, randomForest, spm2, psy |
| License: | GPL-2 |GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 7.1.1 |
| Encoding: | UTF-8 |
| Suggests: | knitr, rmarkdown, lattice, reshape2 |
| NeedsCompilation: | no |
| Packaged: | 2022-06-28 06:37:55 UTC; Jin |
| Author: | Jin Li [aut, cre] |
| Maintainer: | Jin Li <jinli68@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-06-29 11:20:02 UTC |
Cross validation, n-fold for random forest (RF)
Description
This function is a cross validation functionfor random forest. It is for functions 'steprf', 'steprfAVI', ect.
Usage
RFcv2( trainx, trainy, cv.fold = 10, mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else floor(sqrt(ncol(trainx))), ntree = 500, predacc = "VEcv", ...)Arguments
trainx | a dataframe or matrix contains columns of predictor variables. |
trainy | a vector of response, must have length equal to the number ofrows in trainx. |
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 crossvalidation that is recommended. |
mtry | a function of number of remaining predictor variables to use asthe mtry parameter in the randomForest call. |
ntree | number of trees to grow. This should not be set to too small anumber, to ensure that every input row gets predicted at least a few times.By default, 500 is used. |
predacc | "VEcv" for vecv for numerical data, or "ccr" (i.e., correctclassification rate) or "kappa" for categorical data. |
... | other arguments passed on to randomForest. |
Value
A list with the following component:vecv for numerical data: ; orccr (correct classification rate) for categorical data: .
Note
This function is largely based on rf.cv (see Li et al. 2013) andrfcv in randomForest.
Author(s)
Jin Li
References
Li, J., J. Siwabessy, M. Tran, Z. Huang, and A. Heap. 2013.Predicting Seabed Hardness Using Random Forest in R. Pages 299-329 in Y.Zhao and Y. Cen, editors. Data Mining Applications with R. Elsevier.
Li, J. 2013. Predicting the spatial distribution of seabed gravel contentusing random forest, spatial interpolation methods and their hybrid methods.Pages 394-400 The International Congress on Modelling and Simulation(MODSIM) 2013, Adelaide.
Liaw, A. and M. Wiener (2002). Classification and Regression byrandomForest. R News 2(3), 18-22.
Examples
library(spm)data(hard)data(petrel)rfcv1 <- RFcv2(petrel[, c(1,2, 6:9)], petrel[, 5], predacc = "VEcv")rfcv1rfcv2 <- RFcv2(hard[, -c(1, 17)], hard[, 17], predacc = "ccr")rfcv2rfcv3 <- RFcv2(hard[, -c(1, 17)], hard[, 17], predacc = "kappa")rfcv3n <- 10 # number of iterations, 60 to 100 is recommended.VEcv <- NULLfor (i in 1:n) {rfcv1 <- RFcv2(petrel[, c(1,2,6:9)], petrel[, 5], predacc = "VEcv")VEcv [i] <- rfcv1}plot(VEcv ~ c(1:n), xlab = "Iteration for RF", ylab = "VEcv (%)")points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)abline(h = mean(VEcv), col = 'blue', lwd = 2)n <- 10 # number of iterations, 60 to 100 is recommended.measures <- NULLfor (i in 1:n) {rfcv1 <- RFcv2(hard[, c(4:6)], hard[, 17], predacc = "ccr")measures <- rbind(measures, rfcv1)}plot(measures ~ c(1:n), xlab = "Iteration for RF", ylab = "Correctclassification Rate (%)")points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)abline(h = mean(measures), col = 'blue', lwd = 2)Note on notes
Description
This is an updated and extended version of ‘spm' package. The change in package name from 'spm' to 'spm2' is due to the change in Author’s support from Geoscience Australia to Data2Action Australia.
## R CMD check results0 errors | 0 warnings | 0 notes
Author(s)
Jin Li
Select predictive variables for random forest by various variable importance methods and predictive accuracyin a stepwise algorithm
Description
This function is to select predictive variables for random forestby 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 ofrows 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 crossvalidation that is recommended. |
ntree | number of trees to grow. This should not be set to too small anumber, 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., correctclassification 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 consecutivepredictive models. |
min.n.var | minimum number of predictive variables remained in the finalpredictive 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: thevariables selected for the last RF model, whether it is of the highestpredictive accuracy need to be confirmed using 'max.predictive.accuracy'that is listed next; 2) max.predictive.accuracy: the predictive accuracyof the most accurate RF model for each run of 'steprfAVI', which can be usedto confirm the model with the highest accuracy, 3) numberruns: number of runsof 'steprfAVI'; 4) laststepAVI: the outpouts of last run of 'steprfAVI'; 5)steprfAVIOutputsAll: the outpouts of all 'steprfAVI' produced during thevariable selection process; 6) steprfPredictorsAll: the outpouts of'steprfAVIPredictors' for all 'steprfAVI' produced during the variableselection 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 predictiveaccuracy 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 theremoval of each predictor, but in 'steprfAVI1' the averaged variableimportance is calculated only once and is from the full model only, soits use is expected to produce a less optimal model, hence not used; and2) 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 processin environmental sciences with reproducible examples in R." AppliedSciences 9: 2048.
Li, J. 2013. Predicting the spatial distribution of seabedgravel content using random forest, spatial interpolation methods andtheir hybrid methods. Pages 394-400 The International Congress on Modellingand 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 geostatisticaltechniques to count data: Predicting sponge species richness."Environmental Modelling & Software 97: 112-129.
Li, J., Siwabessy, J., Huang, Z., and Nichol, S. (2019). "Developing anoptimal spatial predictive model for seabed sand content using machinelearning, geostatistics and their hybrid methods." Geosciences 9 (4):180.
Li, J., Siwabessy, J., Tran, M., Huang, Z. and Heap, A., 2014. PredictingSeabed 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 forestpredictive models: a case study on predicting the spatial distribution ofseabed hardness. PLOS ONE 11(2): e0149089.
Liaw, A. and M. Wiener (2002). Classification and Regression byrandomForest. R News 2(3), 18-22.
Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variableimportance 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.accuratesteprf1$max.predictive.accuracySelect predictive variables for random forest by AVI and accuracyin a stepwise algorithm
Description
This function is to select predictive variables for random forestby their averaged variable importance (AVI) that is calculated for each modelafter excluding the least important variable, and returns the corresponding predictiveaccuracy. It is also developed for 'steprf' function.
Usage
steprfAVI( trainx, trainy, cv.fold = 10, ntree = 500, rpt = 20, predacc = "VEcv", importance = TRUE, maxk = c(4), nsim = 100, 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 ofrows in trainx. |
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 crossvalidation that is recommended. |
ntree | number of trees to grow. This should not be set to too small anumber, to ensure that every input row gets predicted at least a few times.By default, 500 is used. |
rpt | iteration number of cross validation. |
predacc | "VEcv" for vecv for numerical data, or "ccr" (i.e., correctclassification rate) or "kappa" for categorical data. |
importance | importance of predictive variables. |
maxk | maxk split value. By default, 4 is used. |
nsim | iteration number for 'avi'. By default, 100 is used. |
min.n.var | minimum number of predictive variables remained in the finalpredictive 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) variable.removed:variable removed based on AVI, 2) predictive.accuracy: averaged predictiveaccuracy of the model after excluding the variable.removed, 3)delta.accuracy: contribution to accuracy by each variable.removed, and 4)predictive.accuracy2: predictive accuracy matrix of the model afterexcluding the variable.removed for each iteration.
Author(s)
Jin Li
References
Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton,Chapman and Hall/CRC.
Li, J. 2013. Predicting the spatial distribution of seabedgravel content using random forest, spatial interpolation methods andtheir hybrid methods. Pages 394-400 The International Congress onModelling 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 geostatisticaltechniques to count data: Predicting sponge species richness."Environmental Modelling & Software 97: 112-129.
Li, J., Siwabessy, J., Huang, Z., Nichol, S. (2019). "Developing an optimalspatial 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. PredictingSeabed Hardness Using Random Forest in R. Data Mining Applications with R. Y.Zhao and Y. Cen. Amsterdam, Elsevier: 299-329.
Liaw, A. and M. Wiener (2002). Classification and Regression byrandomForest. R News 2(3), 18-22.
Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variableimportance 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 <- steprfAVI(trainx = petrel[, c(1,2, 6:9)], trainy = petrel[, 5], rpt = 2, predacc = "VEcv", importance = TRUE, nsim = 3, min.n.var = 2)steprf1steprf2 <- steprfAVI(trainx = hard[, -c(1, 17)], trainy = hard[, 17], rpt = 2, predacc = "ccr", importance = TRUE, nsim = 3, min.n.var = 2)steprf2#plot steprf1 resultslibrary(reshape2)pa1 <- as.data.frame(steprf1$predictive.accuracy2)names(pa1) <- steprf1$variable.removedpa2 <- melt(pa1, id = NULL)names(pa2) <- c("Variable","VEcv")library(lattice)par (font.axis=2, font.lab=2)with(pa2, boxplot(VEcv~Variable, ylab="VEcv (%)", xlab="Predictive variable removed"))barplot(steprf1$delta.accuracy, col = (1:length(steprf1$variable.removed)),names.arg = steprf1$variable.removed, main = "Predictive accuracy vs variable removed",font.main = 4, cex.names=1, font=2, ylab="Increase in VEcv (%)")Select predictive variables for random forest by avi and accuracyin a stepwise algorithm
Description
This function is to select predictive variables for random forestby their averaged variable importance which is derived from the full model andreturns the corresponding predictive accuracy. That is, in comparison with 'steprfAVI',the averaged variable importance is calculated only once and is from thefull model only.
Usage
steprfAVI1( trainx, trainy, cv.fold = 10, mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else floor(sqrt(ncol(trainx))), ntree = 500, rpt = 2, predacc = "VEcv", importance = TRUE, maxk = c(4), nsim = 100, 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 ofrows in trainx. |
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 crossvalidation that is recommended. |
mtry | a function of number of remaining predictor variables to use asthe mtry parameter in the randomForest call. |
ntree | number of trees to grow. This should not be set to too small anumber, 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., correctclassification 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. |
min.n.var | minimum number of predictive variables remained in the finalpredictive model the default is 1. |
corr.threshold | correlation threshold and the defaults value is 0.5. |
... | other arguments passed on to randomForest. |
Value
A list with the following components:variable removed based on avi (variable.removed), averaged predictive accuracyof the model after excluding variable.removed (predictive.accuracy),contribution to accuracy by each variable.removed (delta.accuracy), andpredictive accuracy matrix of the model after excluding variable.removed foreach iteration (predictive.accuracy2)
Author(s)
Jin Li
References
Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton,Chapman and Hall/CRC.
Li, J. 2013. Predicting the spatial distribution of seabedgravel content using random forest, spatial interpolation methods andtheir hybrid methods. Pages 394-400 The International Congress on Modellingand Simulation (MODSIM) 2013, Adelaide.
Li, J. (2019). "A critical review of spatial predictive modeling processin environmental sciences with reproducible examples in R." AppliedSciences 9: 2048.
Li, J., Siwabessy, J., Huang, Z., Nichol, S. (2019). "Developing an optimalspatial 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. PredictingSeabed 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 forestpredictive models: a case study on predicting the spatial distribution ofseabed hardness. PLOS ONE 11(2): e0149089.
Liaw, A. and M. Wiener (2002). Classification and Regression byrandomForest. R News 2(3), 18-22.
Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variableimportance in R package extendedForest. R vignette[http://gradientforest.r-forge.r-project.org/Conditional-importance.pdf].
Examples
library(spm)data(petrel)set.seed(1234)steprfAVI1.1 <- steprfAVI1(trainx = petrel[, c(1,2, 6:9)], trainy =petrel[, 5], predacc = "VEcv", nsim = 10)steprfAVI1.1#plot steprf1 resultslibrary(reshape2)pa1 <- as.data.frame(steprfAVI1.1$predictive.accuracy2)names(pa1) <- steprfAVI1.1$variable.removedpa2 <- melt(pa1, id = NULL)names(pa2) <- c("Variable","VEcv")library(lattice)par (font.axis=2, font.lab=2)with(pa2, boxplot(VEcv~Variable, ylab="VEcv (%)", xlab="Predictive variable removed"))barplot(steprfAVI1.1$delta.accuracy, col = (1:length(steprfAVI1.1$variable.removed)),names.arg = steprfAVI1.1$variable.removed, main = "Predictive accuracy vs variable removed",font.main = 4, cex.names=1, font=2, ylab="Increase rate in VEcv (%)")barplot(steprfAVI1.1$delta.accuracy, col = (1:length(steprfAVI1.1$variable.removed)),names.arg = steprfAVI1.1$variable.removed, main = "Predictive accuracy vs variable removed",font.main = 4, cex.names=1, font=2, ylab="Increase in VEcv (%)")Select predictive variables for random forest by AVI and accuracyin a stepwise algorithm
Description
This function is similar to 'steprfAVI'; the only differenceis that 'set.seed()' is added before each code line that involvesrandomness and such additions alter the results considerably.
Usage
steprfAVI2( trainx, trainy, cv.fold = 10, ntree = 500, rpt = 20, predacc = "VEcv", importance = TRUE, maxk = c(4), nsim = 100, min.n.var = 2, corr.threshold = 0.5, rseed = 1234, ...)Arguments
trainx | a dataframe or matrix contains columns of predictor variables. |
trainy | a vector of response, must have length equal to the number ofrows in trainx. |
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 crossvalidation that is recommended. |
ntree | number of trees to grow. This should not be set to too small anumber, 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., correctclassification 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. |
min.n.var | minimum number of predictive variables remained in the finalpredictive 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. |
rseed | random seed. By default, 1234 is used. |
... | other arguments passed on to randomForest. |
Value
A list with the following components: 1) variable.removed:variable removed based on AVI, 2) predictive.accuracy: averaged predictiveaccuracy of the model after excluding the variable.removed, 3)delta.accuracy: contribution to accuracy by each variable.removed, and 4)predictive.accuracy2: predictive accuracy matrix of the model afterexcluding the variable.removed for each iteration.
Author(s)
Jin Li
References
Li, J. (2022). Spatial Predictive Modeling with R. Boca Raton,Chapman and Hall/CRC.
Li, J. 2013. Predicting the spatial distribution of seabedgravel content using random forest, spatial interpolation methods andtheir hybrid methods. Pages 394-400 The International Congress onModelling 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 geostatisticaltechniques to count data: Predicting sponge species richness."Environmental Modelling & Software 97: 112-129.
Li, J., Siwabessy, J., Huang, Z., Nichol, S. (2019). "Developing an optimalspatial 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. PredictingSeabed Hardness Using Random Forest in R. Data Mining Applications with R. Y.Zhao and Y. Cen. Amsterdam, Elsevier: 299-329.
Liaw, A. and M. Wiener (2002). Classification and Regression byrandomForest. R News 2(3), 18-22.
Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variableimportance in R package extendedForest. R vignette[http://gradientforest.r-forge.r-project.org/Conditional-importance.pdf].
Chang, W. 2021. Cookbook for R. http://www.cookbook-r.com/.
Examples
library(spm)data(petrel)steprf1 <- steprfAVI2(trainx = petrel[, c(1,2, 6:9)], trainy = petrel[, 5], rpt = 2, predacc = "VEcv", importance = TRUE, nsim = 3, min.n.var = 2)steprf1#plot steprf1 resultslibrary(reshape2)pa1 <- as.data.frame(steprf1$predictive.accuracy2)names(pa1) <- steprf1$variable.removedpa2 <- melt(pa1, id = NULL)names(pa2) <- c("Variable","VEcv")library(lattice)par (font.axis=2, font.lab=2)with(pa2, boxplot(VEcv~Variable, ylab="VEcv (%)", xlab="Predictive variable removed"))barplot(steprf1$delta.accuracy, col = (1:length(steprf1$variable.removed)),names.arg = steprf1$variable.removed, main = "Predictive accuracy vs variable removed",font.main = 4, cex.names=1, font=2, ylab="Increase in VEcv (%)")Extract names of the selected predictive variables by steprf
Description
This function is to extract names of the selected predictive variables by steprfAVI.
Usage
steprfAVIPredictors(steprf1, trainx)Arguments
steprf1 | a list of output of 'steprf' function. |
trainx | a dataframe or matrix contains columns of predictor variables. |
Value
A list with the following components:1) variables.most.accurate: a list of predictive variables contained in themost accurate RF model, 2) PABV: a list of predictive variables with positivecontributions to the predictive accuracy of RF models, that is,predictive accuracy boosting variable (PABV), 3) PARV: a list ofpredictive variables with negative contributions to the predictiveaccuracy of RF models, that is, predictive accuracy reducing variable,and 4) max.predictive.accuracy: the predictive accuracy of the mostaccurate RF model.
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 modelingprocess in environmental sciences with reproducible examples in R." AppliedSciences 9: 2048.
Li, J., Siwabessy, J., Huang, Z., and Nichol, S. (2019). "Developing anoptimal spatial predictive model for seabed sand content using machinelearning, geostatistics and their hybrid methods." Geosciences 9 (4):180.
Examples
library(spm)data(petrel)set.seed(1234)steprf1 <- steprfAVI(trainx = petrel[, c(1,2, 6:9)], trainy = petrel[, 5], nsim = 10, min.n.var = 2)steprfAVIPredictors(steprf1, trainx = petrel[, c(1,2, 6:9)])