Package 'spm'

Averaged variable importance based on random forest

Description

This function is to derive an averaged variable importance based on random forest

Usage

avi(
  trainx,
  trainy,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  ntree = 500,
  importance = TRUE,
  maxk = c(4),
  nsim = 100,
  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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
importance	imprtance of predictive variables.
maxk	maxk split value. By default, 4 is used.
nsim	iteration number. By default, 100 is used.
corr.threshold	correlation threshold and the defaults value is 0.5.
...	other arguments passed on to randomForest.

Value

A list with the following components: averaged variable importance (avi), column number of importance variable in trainx arranged from the most important to the least important (impvar), names of importance variable arranged from the most important to the least important (impvar2)

Author(s)

Jin Li

References

Smith, S.J., Ellis, N., Pitcher, C.R., 2011. Conditional variable importance in R package extendedForest.

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.

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

Examples

## Not run: 
data(petrel)
set.seed(1234)
avi1 <- avi(petrel[, c(1,2, 6:9)], petrel[, 5], nsim = 10)
avi1

avi1 <- avi(petrel[, c(1), drop = FALSE], petrel[, 5], nsim = 10)
avi1

## End(Not run)

---

Note on notes

Description

This is my first submission.

## R CMD check results 0 errors | 0 warnings | 0 notes

Author(s)

Jin Li

---

Cross validation, n-fold for generalized boosted regression modeling (gbm)

Description

This function is a cross validation function for generalized boosted regression modeling.

Usage

gbmcv(
  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,
  predacc = "VEcv",
  ...
)

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 folds in the cross-validation. it is also the 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.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to gbm.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

This function is largely based on rf.cv (see Li et al. 2013), rfcv in randomForest and gbm.

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 content using 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 by randomForest. R News 2(3), 18-22.

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)

gbmcv1 <- gbmcv(sponge[, -c(3)], sponge[, 3], cv.fold = 10,
family = "poisson", n.cores=2, predacc = "ALL")
gbmcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
gbmcv1 <- gbmcv(sponge[, -c(3)], sponge[, 3], cv.fold = 10,
family = "poisson",  n.cores=2, predacc = "VEcv")
VEcv [i] <- gbmcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for gbm", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)

Description

This function is a cross validation function for the hybrid method of generalized boosted regression modeling and inverse distance weighting.

Usage

gbmidwcv(
  longlat,
  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,
  idp = 2,
  nmax = 12,
  predacc = "VEcv",
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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 folds in the cross-validation. it is also the 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.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
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 list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

this function is largely based on rf.cv (see Li et al. 2013), rfcvrandomForest and gbm

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 content using 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 by randomForest. R News 2(3), 18-22.

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)

gbmidwcv1 <- gbmidwcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "ALL")
gbmidwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
gbmidwcv1 <- gbmidwcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "VEcv")
VEcv [i] <- gbmidwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for gbmidw", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmidw)

Description

This function is to make spatial predictions using the hybrid method of generalized boosted regression modeling and inverse distance weighting.

Usage

gbmidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  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,
  idp = 2,
  nmax = 12,
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
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 folds in the cross-validation. it is also the 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.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are 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 list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse and vecv; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

This function is largely based on gbm.

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 content using 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 by randomForest. R News 2(3), 18-22.

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(petrel)
data(petrel.grid)
gbmidwpred1 <- gbmidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
  petrel.grid[, c(1,2)], petrel.grid, family = "gaussian", n.cores=6,
  nmax = 12)
names(gbmidwpred1)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)

Description

This function is a cross validation function for the hybrid method of generalized boosted regression modeling and ordinary kriging.

Usage

gbmokcv(
  longlat,
  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,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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 folds in the cross-validation. it is also the 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.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
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 list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

This function is largely based on rf.cv (see Li et al. 2013), rfcv in randomForest and gbm. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

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 content using 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 by randomForest. R News 2(3), 18-22.

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)

gbmokcv1 <- gbmokcv(sponge[, c(1,2)], sponge[,-c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "ALL")
gbmokcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
gbmokcv1 <- gbmokcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "VEcv")
VEcv [i] <- gbmokcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for gbmok", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

## End(Not run)

---

Cross validation, n-fold for the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmokgbmidw)

Description

This function is a cross validation function for the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting.

Usage

gbmokgbmidwcv(
  longlat,
  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,
  idp = 2,
  nmaxidw = 12,
  nmaxok = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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 folds in the cross-validation. it is also the 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.
idp	numeric; specify the inverse distance weighting power.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgmgstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
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 list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv for categorical data: correct classification rate (ccr.cv) and kappa (kappa.cv)

Note

This function is largely based on rf.cv (see Li et al. 2013), rfcv in randomForest and gbm. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

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 content using 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 by randomForest. R News 2(3), 18-22.

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)

gbmokgbmidw1 <- gbmokgbmidwcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "ALL")
gbmokgbmidw1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
gbmokgbmidw1 <- gbmokgbmidwcv(sponge[, c(1,2)], sponge[, -c(3)], sponge[, 3],
cv.fold = 10, family = "poisson", n.cores=2, predacc = "VEcv")
VEcv [i] <- gbmokgbmidw1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for gbmokgbmidw", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting (gbmokgbmidw)

Description

This function is to make spatial predictions using the average of the hybrid method of generalized boosted regression modeling and ordinary kriging and the hybrid method of generalized boosted regression modeling and inverse distance weighting.

Usage

gbmokgbmidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  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 = 0,
  weights = rep(1, nrow(trainx)),
  keep.data = FALSE,
  verbose = TRUE,
  idp = 2,
  nmaxidw = 12,
  nmaxok = 12,
  vgm.args = ("Sph"),
  block = 0,
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
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.
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.
idp	numeric; specify the inverse distance weighting power.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
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 longitude, latitude, predictions and variances. The variances are the same as the variances of gbmokpred.

Note

This function is largely based on gbm. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

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(petrel)
data(petrel.grid)
gbmokgbmidwpred1 <- gbmokgbmidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)],
petrel[, 3], petrel.grid[, c(1,2)], petrel.grid, family = "gaussian",
n.cores=6, nmaxidw = 12, nmaxok = 12, vgm.args = ("Sph"))
names(gbmokgbmidwpred1)

## End(Not run)

---

Generate spatial predictions using the hybrid method of generalized boosted regression modeling and ordinary kriging (gbmok)

Description

This function is to make spatial predictions using the hybrid method of generalized boosted regression modeling and ordinary kriging.

Usage

gbmokpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  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,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  n.cores = 6,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
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.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
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 longitude, latitude, predictions and variances. The variances are produced by OK based on the residuals of gbm.

Note

This function is largely based on gbm. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

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(petrel)
data(petrel.grid)
gbmokpred1 <- gbmokpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
  petrel.grid[, c(1,2)], petrel.grid, family = "gaussian", n.cores=6,
  nmax = 12, vgm.args = ("Sph"))
names(gbmokpred1)

## End(Not run)

---

Generate spatial predictions using generalized boosted regression modeling ('gbm')

Description

This function is to make spatial predictions using generalized boosted regression modeling.

Usage

gbmpred(
  trainx,
  trainy,
  longlatpredx,
  predx,
  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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
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 longitude, latitude and predictions.

Note

This function is largely based on 'gbm'.

Author(s)

Jin Li

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)
data(sponge.grid)
gbmpred1 <- gbmpred(sponge[, -c(3)], sponge[, 3], sponge.grid[, c(1:2)],
sponge.grid, family = "poisson", n.cores=2)
names(gbmpred1)

## End(Not run)

---

A dataset of seabed hardness in the eastern Joseph Bonaparte Golf, northern Australia marine margin

Description

This dataset contains 137 samples of 17 variables including area surveyed (Area), easting, northing, prock, bathymetry (bathy), backscatter (bs), local Moran I (bathy.moran), plannar curvature (planar.curv), profile curvature (profile.curv), topographic relief (relief), slope (slope), surface area (surface), topographic position index (tpi), homogeneity of backscatter (homogeneity), local Moran I of backscatter (bs.moran), variance of backscatter (variance) and seabed hardness (hardness).

Usage

data("hard")

Format

A data frame with 137 observations on the following 17 variables.

Area

a catergorical vector, no unit

easting

a numeric vector, m

northing

a numeric vector, m

prock

a numeric vector, no unit

bathy

a numeric vector, meter

bs

a numeric vector, dB

bathy.moran

a numeric vector, no unit

planar.curv

a numeric vector, no unit

profile.curv

a numeric vector, no unit

relief

a numeric vector, meter

slope

a numeric vector, no unit

surface

a numeric vector, no unit

tpi

a numeric vector, no unit

homogeneity

a numeric vector, no unit

bs.moran

a numeric vector, no unit

variance

a numeric vector, dB^2

hardness

a catergorical vector, no unit

Details

For details, please see the source. This dataset was modified by removing 3 samples with missing values from Appendix AA of the book chapter listed in the source.

Source

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.

---

Cross validation, n-fold for inverse distance weighting (IDW)

Description

This function is a cross validation function for inverse distance weighting.

Usage

idwcv(longlat, trainy, cv.fold = 10, nmax = 12, idp = 2, predacc = "VEcv", ...)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
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.
nmax	for a local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
idp	numeric; specify the inverse distance weighting power.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to gstat.

Value

A list with the following components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv only.

Note

This function is largely based on rfcv in randomForest and some functions in library(gstat).

Author(s)

Jin Li

References

Li, J., 2013. Predictive Modelling Using Random Forest and Its Hybrid Methods with Geostatistical Techniques in Marine Environmental Geosciences, In: Christen, P., Kennedy, P., Liu, L., Ong, K.-L., Stranieri, A., Zhao, Y. (Eds.), The proceedings of the Eleventh Australasian Data Mining Conference (AusDM 2013), Canberra, Australia, 13-15 November 2013. Conferences in Research and Practice in Information Technology, Vol. 146.

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

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

## Not run: 
library(sp)
data(swmud)
data(petrel)

idwcv1 <- idwcv(swmud[, c(1,2)], swmud[, 3], nmax = 12, idp = 2)
idwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
idwcv1 <- idwcv(petrel[, c(1,2)], petrel[, 3], nmax = 12, predacc = "VEcv")
VEcv [i] <- idwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for IDW", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd=2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
idwcv1 <- idwcv(swmud[, c(1,2)], swmud[, 3], predacc = "ALL")
measures <- rbind(measures, idwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for IDW", ylab="VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using inverse distance weighting (IDW)

Description

This function is to make spatial predictions using inverse distance weighting.

Usage

idwpred(longlat, trainy, longlat2, nmax = 12, idp = 2, ...)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
longlat2	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
nmax	for a local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
idp	numeric; specify the inverse distance weighting power.
...	other arguments passed on to gstat.

Value

A dataframe of longitude, latitude and predictions.

Note

This function is largely based on library(gstat).

Author(s)

Jin Li

References

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

## Not run: 
library(sp)
data(swmud)
data(sw)
idwpred1 <- idwpred(swmud[, c(1,2)], swmud[, 3], sw, nmax = 12, idp = 2)
names(idwpred1)

## End(Not run)

---

Cross validation, n-fold for ordinary kriging (OK)

Description

This function is a cross validation function for ordinary kriging.

Usage

okcv(
  longlat,
  trainy,
  cv.fold = 10,
  nmax = 12,
  transformation = "none",
  delta = 1,
  vgm.args = ("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
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.
nmax	for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
transformation	transform the response variable to normalise the data; can be "sqrt" for square root, "arcsine" for arcsine, "log" or "none" for non transformation. By default, "none" is used.
delta	numeric; to avoid log(0) in the log transformation.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
anis	anisotropy parameters: see notes vgm in gstat for details.
alpha	direction in plane (x,y). see variogram in gstat for details.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to gstat.

Value

A list with the following components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv only

Note

This function is largely based on rfcv in randomForest and some functions in library(gstat). When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method. If it still occur for okpred function, different method should be used.

Author(s)

Jin Li

References

Li, J., 2013. Predictive Modelling Using Random Forest and Its Hybrid Methods with Geostatistical Techniques in Marine Environmental Geosciences, In: Christen, P., Kennedy, P., Liu, L., Ong, K.-L., Stranieri, A., Zhao, Y. (Eds.), The proceedings of the Eleventh Australasian Data Mining Conference (AusDM 2013), Canberra, Australia, 13-15 November 2013. Conferences in Research and Practice in Information Technology, Vol. 146.

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

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

## Not run: 
library(sp)
data(swmud)
data(petrel)

okcv1 <- okcv(swmud[, c(1,2)], swmud[, 3], nmax = 7, transformation =
"arcsine", vgm.args = ("Sph"), predacc = "VEcv")
okcv1

n <- 20 # number of iterations,60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
okcv1 <- okcv(petrel[, c(1,2)], petrel[, 5], nmax = 12,
transformation = "arcsine", predacc = "VEcv")
VEcv [i] <- okcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for OK", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
okcv1 <- okcv(petrel[, c(1,2)], petrel[, 3], nmax = 12, transformation =
"arcsine", predacc = "ALL")
measures <- rbind(measures, okcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for OK", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using ordinary kriging (OK)

Description

This function is to make spatial predictions using ordinary kriging.

Usage

okpred(
  longlat,
  trainy,
  longlat2,
  nmax = 12,
  transformation = "none",
  delta = 1,
  vgm.args = ("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
longlat2	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
nmax	for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
transformation	transform the response variable to normalise the data; can be "sqrt" for square root, "arcsine" for arcsine, "log" or "none" for non transformation. By default, "none" is used.
delta	numeric; to avoid log(0) in the log transformation.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
anis	anisotropy parameters: see notes vgm in gstat for details.
alpha	direction in plane (x,y). see variogram in gstat for details.
block	block size. see krige in gstat for details.
...	other arguments passed on to gstat.

Value

A dataframe of longitude, latitude, predictions and variances.

Author(s)

Jin Li

References

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

Examples

## Not run: 
library(sp)
data(swmud)
data(sw)
okpred1 <- okpred(swmud[, c(1,2)], swmud[, 3], sw, nmax = 7, transformation =
"arcsine", vgm.args = ("Sph"))
names(okpred1)

## End(Not run)

---

A dataset of seabed sediments in the Petrel sub-basin in Australia Exclusive Economic Zone

Description

This dataset contains 237 samples of 9 variables including longitude (long), latitude (lat), mud content (mud), sand content (sand), gravel content (gravel), bathymetry (bathy), disttance to coast (dist), seabe relief (relief), seabed slope (slope).

Usage

data("petrel")

Format

A data frame with 237 observations on the following 9 variables.

long

a numeric vector, decimal degree

lat

a numeric vector, decimal degree

mud

a numeric vector, percentage

sand

a numeric vector, percentage

gravel

a numeric vector, percentage

bathy

a numeric vector, meter bellow sea level

dist

a numeric vector, degree

relief

a numeric vector, meter

slope

a numeric vector, no unit

Details

For details, please check the reference.

Source

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

---

A dataset of grids for producing spatial predictions of seabed sediment content in the Petrel sub-basin in Australia Exclusive Economic Zone

Description

This dataset contains 248675 rows of 6 variables including longitude (long), latitude (lat), bathymetry (bathy), disttance to coast (dist), seabe relief (relief), seabed slope (slope).

Usage

data("petrel")

Format

A data frame with 248675 observations on the following 6 variables.

long

a numeric vector, decimal degree

lat

a numeric vector, decimal degree

bathy

a numeric vector, meter bellow sea level

dist

a numeric vector, degree

relief

a numeric vector, meter

slope

a numeric vector, no unit

Details

For details, please check the reference.

Source

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

---

Predictive error and accuracy measures for predictive models based on cross-validation

Description

This function is used to calculate the mean error (me), mean absolute error (mae), mean squared error (mse), relative me (rme), relative mae (rmae), root mse (rmse), relative rmse (rrmse), variance explained by predictive models based on cross-validation (vecv), and Legates and McCabe's E1 (e1) for numerical data; and it also calculates correct classification rate (ccr), kappa (kappa), sensitivity (sens), specificity (spec), and true skill statistic (tss) for categorical data with the observed (obs) data specified as factor. They are based on the differences between the predicted values for and the observed values of validation samples for cross-validation. For 0 and 1 data, the observed values need to be specified as factor in order to use accuracy measures for categorical data. Moreover, sens, spec, tss and rmse are for categorical data with two levels (e.g. presence and absence data).

Usage

pred.acc(obs, pred)

Arguments

obs	a vector of observation values of validation samples.
pred	a vector of prediction values of predictive models for validation samples.

Value

A list with the following components: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1 for numerical data; ccr, kappa, sens, spec and tss for categorical data with two levels; and ccr, kappa for categorical data with more than two levels.

Author(s)

Jin Li

References

Li, J., 2016. Assessing spatial predictive models in the environmental sciences: accuracy measures, data variation and variance explained. Environmental Modelling & Software 80 1-8.

Li, J., 2017. Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what? PLOS ONE 12 (8): e0183250.

Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and true skill statistic (TSS). Journal of Applied Ecology 43 1223-1232.

Examples

set.seed(1234)
x <- sample(1:30, 30)
e <- rnorm(30, 1)
y <- x + e
pred.acc(x, y)

y <- 0.8 * x + e
pred.acc(x, y)

---

Cross validation, n-fold for random forest (RF)

Description

This function is a cross validation function for random forest.

Usage

RFcv(
  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 = "ALL",
  ...
)

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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv. for categorical data: correct classification rate (ccr), kappa (kappa), sensitivity (sens), specificity (spec) and true skill statistic (tss)

Note

This function is largely based on rf.cv (see Li et al. 2013) and rfcv 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 content using 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 by randomForest. R News 2(3), 18-22.

Examples

## Not run: 
data(hard)
data(petrel)

rfcv1 <- RFcv(petrel[, c(1,2, 6:9)], petrel[, 5], predacc = "ALL")
rfcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rfcv1 <- RFcv(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 <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rfcv1 <- RFcv(hard[, c(4:6)], hard[, 17])
measures <- rbind(measures, rfcv1$ccr) # for kappa, replace ccr with kappa
}
plot(measures ~ c(1:n), xlab = "Iteration for RF", ylab = "Correct
classification rate  (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of random forest and inverse distance weighting (RFIDW)

Description

This function is a cross validation function for the hybrid method of random forest and inverse distance weighting (RFIDW).

Usage

rfidwcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  idp = 2,
  nmax = 12,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rf.cv (see Li et al. 2013) and rfcv in randomForest.

Author(s)

Jin Li

References

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.

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

Examples

## Not run: 
data(petrel)

rfidwcv1 <- rfidwcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rfidwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rfidwcv1 <- rfidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rfidwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rfidwcv1 <- rfidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rfidwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the hybrid method of random forest and inverse distance weighting (RFIDW)

Description

This function is to make spatial predictions using the hybrid method of random forest and inverse distance weighting (RFIDW).

Usage

rfidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  idp = 2,
  nmax = 12,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude and predictions.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rfidwpred1 <- rfidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
petrel.grid[, c(1,2)], petrel.grid, ntree = 500, idp = 2, nmax = 12)
names(rfidwpred1)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of random forest and ordinary kriging (RFOK)

Description

This function is a cross validation function for the hybrid method of random forest and ordinary kriging (RFOK).

Usage

rfokcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rf.cv (see Li et al. 2013) and rfcv in randomForest. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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.

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

Examples

## Not run: 
data(petrel)

rfokcv1 <- rfokcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rfokcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rfokcv1 <- rfokcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rfokcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFOK", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rfokcv1 <- rfokcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rfokcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFOK", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the hybrid method of random forest and ordinary kriging (RFOK)

Description

This function is to make spatial predictions using the hybrid method of random forest and ordinary kriging (RFOK).

Usage

rfokpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude, predictions and variances. The variances are produced by OK based on the residuals of rf.

Note

This function is largely based rfcv in randomForest. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rfokpred1 <- rfokpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
petrel.grid[, c(1,2)], petrel.grid, ntree = 500, nmax = 12, vgm.args =
("Sph"))
names(rfokpred1)

## End(Not run)

---

Cross validation, n-fold for the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW)

Description

This function is a cross validation function for the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW).

Usage

rfokrfidwcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  idp = 2,
  nmaxok = 12,
  nmaxidw = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
idp	numeric; specify the inverse distance weighting power.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rf.cv (see Li et al. 2013) and rfcv in randomForest. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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.

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

Examples

## Not run: 
data(petrel)

rfokrfidwcv1 <- rfokrfidwcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rfokrfidwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rfokrfidwcv1 <- rfokrfidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rfokrfidwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFOKRFIDW", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rfokrfidwcv1 <- rfokrfidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rfokrfidwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFOKRFIDW", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW)

Description

This function is to make spatial predictions using the average of the hybrid method of random forest and ordinary kriging and the hybrid method of random forest and inverse distance weighting (RFOKRFIDW).

Usage

rfokrfidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  ntree = 500,
  idp = 2,
  nmaxok = 12,
  nmaxidw = 12,
  vgm.args = ("Sph"),
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
idp	numeric; specify the inverse distance weighting power.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude, predictions and variances. The variances are the same as the variances of rfokpred.

Note

This function is largely based rfcv in randomForest. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rfokrfidwpred1 <- rfokrfidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)],
petrel[, 3], petrel.grid[, c(1,2)], petrel.grid, ntree = 500, idp = 2,
nmaxok = 12, nmaxidw = 12)
names(rfokrfidwpred1)

## End(Not run)

---

Generate spatial predictions using random forest (RF)

Description

This function is to make spatial predictions using random forest.

Usage

rfpred(
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  ntree = 500,
  ...
)

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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
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.
...	other arguments passed on to randomForest.

Value

A dataframe of longitude, latitude and predictions.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rfpred1 <- rfpred(petrel[, c(1,2, 6:9)], petrel[, 5], petrel.grid[, c(1,2)],
petrel.grid, ntree = 500)
names(rfpred1)

## End(Not run)

---

Cross validation, n-fold for random forest in ranger (RG)

Description

This function is a cross validation function for random forest in ranger.

Usage

rgcv(
  trainx,
  trainy,
  cv.fold = 10,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  num.trees = 500,
  min.node.size = NULL,
  num.threads = NULL,
  verbose = FALSE,
  predacc = "ALL",
  ...
)

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.
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.
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv. for categorical data: correct classification rate (ccr), kappa (kappa), sensitivity (sens), specificity (spec) and true skill statistic (tss)

Note

This function is largely based on RFcv.

Author(s)

Jin Li

References

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.

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(hard)
data(petrel)

rgcv1 <- rgcv(petrel[, c(1,2, 6:9)], petrel[, 5], predacc = "ALL")
rgcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rgcv1 <- rgcv(petrel[, c(1,2,6:9)], petrel[, 5], predacc = "VEcv")
VEcv [i] <- rgcv1
}
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 <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rgcv1 <- rgcv(hard[, c(4:6)], hard[, 17])
measures <- rbind(measures, rgcv1$ccr) # for kappa, replace ccr with kappa
}
plot(measures ~ c(1:n), xlab = "Iteration for RF", ylab = "Correct
classification rate  (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)

Description

This function is a cross validation function for the hybrid method of random forest in ranger and inverse distance weighting (RGIDW).

Usage

rgidwcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  num.threads = NULL,
  verbose = FALSE,
  idp = 2,
  nmax = 12,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rfidwcv.

Author(s)

Jin Li

References

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.

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)

rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rgidwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rgidwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rgidwcv1 <- rgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rgidwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFIDW", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the hybrid method of random forest in ranger and inverse distance weighting (RGIDW)

Description

This function is to make spatial predictions using the hybrid method of random forest in ranger and inverse distance weighting (RGIDW).

Usage

rgidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  type = "response",
  num.threads = NULL,
  verbose = FALSE,
  idp = 2,
  nmax = 12,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
idp	numeric; specify the inverse distance weighting power.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude and predictions.

Note

This function is largely based on rfidwpred.

Author(s)

Jin Li

References

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rgidwpred1 <- rgidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
petrel.grid[, c(1,2)], petrel.grid, num.trees = 500, idp = 2, nmax = 12)
names(rgidwpred1)

## End(Not run)

---

Cross validation, n-fold for the hybrid method of random forest in ranger and ordinary kriging (RGFOK)

Description

This function is a cross validation function for the hybrid method of random forest in ranger and ordinary kriging (RFOK).

Usage

rgokcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  num.threads = NULL,
  verbose = FALSE,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rfokcv. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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.

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)

rgokcv1 <- rgokcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rgokcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rgokcv1 <- rgokcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rgokcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFOK", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rgokcv1 <- rgokcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rgokcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFOK", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the hybrid method of random forest in ranger and ordinary kriging (RGOK)

Description

This function is to make spatial predictions using the hybrid method of random forest in ranger and ordinary kriging (RGOK).

Usage

rgokpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  type = "response",
  num.threads = NULL,
  verbose = FALSE,
  nmax = 12,
  vgm.args = ("Sph"),
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
nmax	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude, predictions and variances. The variances are produced by OK based on the residuals of rf.

Note

This function is largely based rfokpred. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rgokpred1 <- rgokpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 3],
petrel.grid[, c(1,2)], petrel.grid, num.trees = 500, nmax = 12, vgm.args =
("Sph"))
names(rgokpred1)

## End(Not run)

---

Cross validation, n-fold for the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW)

Description

This function is a cross validation function for the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW).

Usage

rgokrgidwcv(
  longlat,
  trainx,
  trainy,
  cv.fold = 10,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  num.threads = NULL,
  verbose = FALSE,
  idp = 2,
  nmaxok = 12,
  nmaxidw = 12,
  vgm.args = ("Sph"),
  block = 0,
  predacc = "VEcv",
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
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.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
idp	numeric; specify the inverse distance weighting power.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
predacc	can be either "VEcv" for vecv or "ALL" for all measures in function pred.acc.
...	other arguments passed on to randomForest or gstat.

Value

A list with the following components: for numerical data: me, rme, mae, rmae, mse, rmse, rrmse, vecv and e1; or vecv.

Note

This function is largely based on rfokrfidw. When 'A zero or negative range was fitted to variogram' occurs, to allow gstat running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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.

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)

rgokrgidwcv1 <- rgokrgidwcv(petrel[, c(1,2)], petrel[, c(1,2, 6:9)], petrel[, 5],
predacc = "ALL")
rgokrgidwcv1

n <- 20 # number of iterations, 60 to 100 is recommended.
VEcv <- NULL
for (i in 1:n) {
rgokrgidwcv1 <- rgokrgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "VEcv")
VEcv [i] <- rgokrgidwcv1
}
plot(VEcv ~ c(1:n), xlab = "Iteration for RFOKRFIDW", ylab = "VEcv (%)")
points(cumsum(VEcv) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(VEcv), col = 'blue', lwd = 2)

n <- 20 # number of iterations, 60 to 100 is recommended.
measures <- NULL
for (i in 1:n) {
rgokrgidwcv1 <- rgokrgidwcv(petrel[, c(1,2)], petrel[, c(1,2,6:9)], petrel[, 5],
predacc = "ALL")
measures <- rbind(measures, rgokrgidwcv1$vecv)
}
plot(measures ~ c(1:n), xlab = "Iteration for RFOKRFIDW", ylab = "VEcv (%)")
points(cumsum(measures) / c(1:n) ~ c(1:n), col = 2)
abline(h = mean(measures), col = 'blue', lwd = 2)

## End(Not run)

---

Generate spatial predictions using the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW)

Description

This function is to make spatial predictions using the average of the hybrid method of random forest in ranger (RG) and ordinary kriging and the hybrid method of RG and inverse distance weighting (RGOKRGIDW).

Usage

rgokrgidwpred(
  longlat,
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = function(p) max(1, floor(sqrt(p))),
  num.trees = 500,
  min.node.size = NULL,
  type = "response",
  num.threads = NULL,
  verbose = FALSE,
  idp = 2,
  nmaxok = 12,
  nmaxidw = 12,
  vgm.args = ("Sph"),
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples (i.e., trainx and trainy).
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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	a function of number of remaining predictor variables to use as the mtry parameter in the randomForest call.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
idp	numeric; specify the inverse distance weighting power.
nmaxok	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for OK.
nmaxidw	for local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used for IDW.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
block	block size. see krige in gstat for details.
...	other arguments passed on to randomForest or gstat.

Value

A dataframe of longitude, latitude, predictions and variances. The variances are the same as the variances of rfokpred.

Note

This function is largely based rfokrfidwpred. When 'A zero or negative range was fitted to variogram' occurs, to allow OK running, the range was set to be positive by using min(vgm1$dist). In this case, caution should be taken in applying this method, although sometimes it can still outperform IDW and OK.

Author(s)

Jin Li

References

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

Examples

## Not run: 
data(petrel)
data(petrel.grid)
rgokrgidwpred1 <- rgokrgidwpred(petrel[, c(1,2)], petrel[, c(1,2, 6:9)],
petrel[, 3], petrel.grid[, c(1,2)], petrel.grid, num.trees = 500, idp = 2,
nmaxok = 12, nmaxidw = 12)
names(rgokrgidwpred1)

## End(Not run)

---

Generate spatial predictions using random forest in ranger (RG)

Description

This function is to make spatial predictions using random forest in ranger.

Usage

rgpred(
  trainx,
  trainy,
  longlatpredx,
  predx,
  mtry = if (!is.null(trainy) && !is.factor(trainy)) max(floor(ncol(trainx)/3), 1) else
    floor(sqrt(ncol(trainx))),
  num.trees = 500,
  min.node.size = NULL,
  type = "response",
  num.threads = NULL,
  verbose = FALSE,
  ...
)

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.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
mtry	Number of variables to possibly split at in each node. Default is the (rounded down) square root of the number variables.
num.trees	number of trees. By default, 500 is used.
min.node.size	Default 1 for classification, 5 for regression.
type	Type of prediction. One of 'response', 'se', 'terminalNodes' with default 'response'. See ranger::predict.ranger for details.
num.threads	number of threads. Default is number of CPUs available.
verbose	Show computation status and estimated runtime.Default is FALSE.
...	other arguments passed on to randomForest.

Value

A dataframe of longitude, latitude and predictions.

Note

This function is largely based on rfpred.

Author(s)

Jin Li

References

Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. http://dx.doi.org/10.18637/jss.v077.i01.

Examples

## Not run: 
data(petrel)
data(petrel.grid)
set.seed(1234)
rgpred1 <- rgpred(petrel[, c(1,2, 6:9)], petrel[, 5], petrel.grid[, c(1,2)],
petrel.grid, num.trees = 500)
names(rgpred1)

## End(Not run)

---

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

Note

This function is largely based on gbm.

Author(s)

Jin Li

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

## End(Not run)

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