rasclass-class: Class "rasclass"

In rasclass: Supervised Raster Image Classification

Description

This class object is a container for all the variables used in the classification algorithms of the rasclass package. The methods provided in the package sequentially fill the slots with data and results.

Objects from the Class

Objects can be created by calls of the form new("rasclass").

Slots

path:

Object of class character. The path from which the data is loaded.

data:

Object of class data.frame. The dataframe that contains the data from the loaded ascii files.

samplename:

Object of class character. The name of a column in the data slot that will be used as sample in the supervised classification.

formula:

Object of class formula, storing the formula passed on to the classification algorithm.

call:

Object of class call, storing the last classification call applied to the object.

gridSkeleton:

Object of class rasclassRaster, containing the skeleton of the output grid. In the structure of rasclass-class objects, only values that are different from null in every input layer (except the sample) are considered in the analysis.

training:

Object of class logical. Is used if the the argument 'splitfraction' of the 'classifyRasclass' function has been used to split data for an in-sample verification. the vector used to split the data is stored in this slot.

maximumLikelihood:

Object of class list. This list is created when the Maximum Likelihood Classifiaction is preformed on the rasclass-class object. It containins the mean vectors and inverse covariance matrices for each class.

randomForest:

Object of class randomForest. This is created when using the Random Forest classification.

logit:

Object of class multinom. Created when using the Random Forest classification.

neuralNetwork:

Object of class, mlp. Created when using the Neural Network classification.

supportVector:

Object of class svm. Created when using the Support Vector Machines classification.

predictedGrid:

Object of class rasclassRaster. The predicted grid resulting from the classification.

overallAccuracy:

Object of class numeric. This slot is used to store the overall accuracy of the classification.

accuracyMatrix:

Object of class matrix. The accuracy matrix of the classification, including user and producer accuracies.

kappa:

Object of class numeric. The kappa accuracy coefficient of the classification, a measure for the accuracy of the classification.

Methods

buildFormula

Builds a formula for classification, based on column names of the data in rasclass object.

checkRasclass

Checks internal consistency of rasclass object.

classifyRasclass

Classification using one of five algorithms.

readRasterFolder

Loads data from several asciigrid files in a folder.

setRasclassData

Sets data in rasclass object based on a dataframe.

summary

Prints a summary of the rasclass-object.

image

Plots the classified raster from the rasclass-object.

View

Shows the data frame in the rasclass-object.

See Also

rasclass-package, rasclassRaster-class,

readRaster, writeRaster,

readRasterFolder, setRasclassData,

buildFormula, checkRasclass,

rasclassMlc, classifyRasclass

rasclass documentation built on May 2, 2019, 6:11 a.m.