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Last data update: 2014.03.03

R: Build a raster Look-UP-Table for training dataset

build.rastLUT

R Documentation

Build a raster Look-UP-Table for training dataset

Description

GUI prompts will help the user build a Look-Up-Table to associated predictor variable with their corresponding spatial rasters.

Usage

build.rastLUT(imageList=NULL,predList=NULL,qdata.trainfn=NULL,
rastLUTfn=NULL,folder=NULL)

Arguments

`imageList`	Vector. A vector of character strings giving names and full paths to all raster data files used in model.
`predList`	Vector. A vector of character strings giving the predictor names used as headers in the model training data.
`qdata.trainfn`	String. The name (full path or base name with path specified by `folder`) of the training data file used for building the model. The file must be a comma-delimited file `*.csv` with column headings. `qdata.trainfn` can also be an `R` dataframe. The column headers from `qdata.trainfn` are used to generate a list of possible predictors for the raster Look-UP-Table.
`rastLUTfn`	String. The name of the file output for the Look-Up-Table. By default, if a file name is provided by the `"qdatatrainfn"` argument `"_rastLUT.csv"` appended after `"qdatatrainfn"`. Otherwise, default filename for look-up-table is`"rastLUT.csv"`
`folder`	String. The folder used for output. Do not add ending slash to path string. If `folder = NULL` (default), a GUI interface prompts user to browse to a folder. To use the working directory, specify `folder = getwd()`.

Details

This function helps the user create a raster Look-Up-Table to be used later by model.mapmake(). Currently this function only works in a Windows environment.

First, if "folder" is not given, the user selects the output folder for the Look-UP-Table.

Second, if "predList" or "qdatatrainfn" are not given, the user selects the file containing the training data. The header of the file is used to generate a selection list of possible predictor variables.

Third, if "imageList" is not provided, the user selects the rasters.

Finally, the function steps through each band of each raster, and the user selects the appropriate predictor.

Value

Returns a data frame containing the raster Look-Up-Table. Also Writes a .csv file containing the raster Look-Up-Table.

Author(s)

Elizabeth Freeman

Examples


folder<-system.file("external", "helpexamples", package = "ModelMap")
qdata.trainfn = paste(folder,"/DATATRAIN.csv",sep="")

#build.rastLUT(	qdata.trainfn=qdata.trainfn,
#		folder=folder)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ModelMap)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: raster
Loading required package: sp
Loading required package: rgdal
rgdal: version: 1.1-10, (SVN revision 622)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 1.11.3, released 2015/09/16
 Path to GDAL shared files: /usr/share/gdal/1.11
 Loaded PROJ.4 runtime: Rel. 4.9.2, 08 September 2015, [PJ_VERSION: 492]
 Path to PROJ.4 shared files: (autodetected)
 Linking to sp version: 1.2-3 
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ModelMap/build.rastLUT.Rd_%03d_medium.png", width=480, height=480)
> ### Name: build.rastLUT
> ### Title: Build a raster Look-UP-Table for training dataset
> ### Aliases: build.rastLUT
> ### Keywords: models
> 
> ### ** Examples
> 
> 
> folder<-system.file("external", "helpexamples", package = "ModelMap")
> qdata.trainfn = paste(folder,"/DATATRAIN.csv",sep="")
> 
> #build.rastLUT(	qdata.trainfn=qdata.trainfn,
> #		folder=folder)
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>