ParallelInit_Test: Data initialization function is the first step to complete...

In ParallelDSM: Parallel Digital Soil Mapping using Machine Learning

Data initialization function is the first step to complete parallel training

Description

Data initialization function is the first step to complete parallel training

Usage

ParallelInit_Test(
  fn = "",
  icsv = NULL,
  dsmformula = NULL,
  nblock = 6,
  ncore = 2
)

Arguments

fn	: Name of the folder in which the soil data is stored
icsv	: Use df.input from the built-in dataset
dsmformula	:Symbolic description of a soil fitting model
nblock	: the number of blocks for data cutting
ncore	: Computes the CPU's kernel in parallel(fill in according to the computer configuration)

References

Breiman, L. (2001). Random forests. Mach. Learn. 45, 5???32. Meinshausen, N. (2006) "Quantile Regression Forests", Journal of Machine Learning Research 7, 983-999 http://jmlr.csail.mit.edu/papers/v7/

Examples

############################################################################
## Example code                                                           ##
## If you want to use test cases, load the relevant data sets             ##
## Select the data set that comes with this package                       ##
############################################################################
library(ParallelDSM)
data("df.input",package = "ParallelDSM")
data("df.dem",package = "ParallelDSM")
data("df.twi",package = "ParallelDSM")
sampledata <- system.file("extdata", "covariate", package = "ParallelDSM")
ParallelInit_Test(sampledata,df.input,dsmformula = "socd030 ~ twi + dem")
#ParallelComputing(outpath = "qrfOutput",mymodels = "QRF")

############################################################################
##  Use the data file references that come with this package              ##
############################################################################
# sampledatas <- system.file("extdata", "covariate", package = "ParallelDSM")

############################################################################
## Use ParallelInit_Test functions to process the data that is loaded in  ##
############################################################################
# ParallelInit_Test(sampledata,df.input,dsmformula = "socd030 ~ dem + twi")

############################################################################
## This function is the main function that performs parallel computations ##
## The outpath field refers to the filename of the data output            ##
## The mymodels field has three modes to choose from: QRF,RF and MLR      ##
## 'QRF' stands for Random Forest Model Prediction Method                 ##
## 'RF' stands for Machine Learning Model Prediction Method               ##
## 'MLR' stands for Multiple Linear Regression Prediction Model           ##
## 'from' and 'to' are reserved fields that can be left unused by the user##
############################################################################
# ParallelComputing(outpath = "myoutputs",mymodels = "MLR",from=1,to=200)

ParallelDSM documentation built on Nov. 16, 2022, 9:05 a.m.