rasclass (version 0.1.0)

setRasclassData: Add data from dataframe to rasclass object

Description

This function adds data from a dataframe to an existing rasclass-class object.

Usage

setRasclassData(newdata, object, ncols = NA, nrows = NA,
	xllcorner = NA, yllcorner = NA, cellsize = NA, NAvalue = -9999,
	samplename = 'sample')

Arguments

newdata

A data frame containing raster cell values in its columns.

object

A rasclass object where the data should be updated to.

ncols

The number of columns of the raster grid. Optional if the values are already specified in the rasclass object.

nrows

The number of rows of the raster grid. Optional if the values are already specified in the rasclass object.

xllcorner

Coordinates of the X coordinate of the lower left corner. Optional if the values are already specified in the rasclass object.

yllcorner

Coordinates of the Y coordinate of the lower left corner. Optional if the values are already specified in the rasclass object.

cellsize

The cell size of the grid. Optional if the values are already specified in the rasclass object.

NAvalue

The value in the raster that represents NA values. Optional if the values are already specified in the rasclass object.

samplename

Optional name of the column in the input data frame that will be used as sample data. The default is 'sample'.

Value

• A rasclass-class object containing the loaded data as a dataframe in the data slot.

Memory Issues

The setRasclassData function only keeps track of data raster cells that have an observed value (non-NA) in every column of the input dataframe provided. The resulting grid structure will be retained in the gridSkeleton slot.

Details

This function adds data from a dataframe to a rasclass object for subsequent classification using the classifiers from the rasclass package. Similar to the readRasterFolder, the name of the column containing the sample data has to be specified. The default sample column name is "sample".

The data in the input dataframe is assumed to be from raster files with the same projection, grid size and location. For ascii files this is equivalent to having an identical header in all the layers. If the common header has not been specified in the gridSkeleton slot of the input object before, the values have to be given as arguments to the function.

See Also

accuracyAssessment, buildFormula, checkRasclass, classifyMLC, classifyMlogit, rasclass-class, rasclassRaster-class, readRaster, readRasterFolder, setRasclassData, writeRaster

Examples

# For this example, create data from a random dataframe
mysample <- sample(c(NA, 1, 2, 3), 20000, rep = TRUE)
red <- sample(c(NA,1:255), 20000, rep = TRUE)
green <- sample(c(NA,1:255), 20000, rep = TRUE)
blue <- sample(c(NA,1:255), 20000, rep = TRUE)
newdata <- data.frame(mysample,red,green,blue)

# Prepare object using the dataframe and specifying raster properties
object <- new('rasclass')
object <- setRasclassData(newdata, object, ncols = 100, nrows = 200,
	xllcorner = 0, yllcorner = 0, cellsize = 10, NAvalue = -9999,
	samplename = 'mysample')

# Classify and compute accuracy
object <- classifyMlogit(object, anova = TRUE)
object <- accuracyAssessment(object)

# Summarize and plot results
summary(object)
image(object@predictedGrid)