Package 'Irescale'

Title: Calculate and Rectify Moran's I
Description: Provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.
Authors: Ivan Fuentes, Thomas DeWitt, Thomas Ioerger, Michael Bishop
Maintainer: Ivan Fuentes <[email protected]>
License: GPL (>= 2)
Version: 2.3.0
Built: 2025-02-25 03:30:44 UTC
Source: https://github.com/cran/Irescale

Help Index

Finds how many iterations are necessary to achieve stability in resampling method.

Description

buildStabilityTable finds how many iterations are necessary to achieve stability in resampling method, plotting in a log scale.

Usage

buildStabilityTable(data, times = 10, samples = 100, plots = TRUE,
  scalingUpTo = "Quantile")

Arguments

data

data structure after loading the file using loadFile function
times

the number of times rescaleI will be executed. The default value is 100.
samples

size of the resampling method. The default value is 1000
plots

to draw the significance plot
scalingUpTo

the rescaling could be done up to the 0.01% and 99.99% quantile or max and min values. The two possible options are: "MaxMin", or "Quantile". The default value for this parameter is "Quantile"

Value

A vector with the average log⁡(samples)\log(samples) averages I

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
resultsChen<-buildStabilityTable(data=data,times=10,samples=100,plots=TRUE,scalingUpTo="Quantile")

Finds how many iterations are necessary to achieve stability in resampling method for rectifying I through pearson corrrelation.

Description

buildStabilityTableForCorrelation finds how many iterations are necessary to achieve stability in resampling method, plotting in a log scale.

Usage

buildStabilityTableForCorrelation(data, times = 10, samples = 100,
  plots = TRUE)

Arguments

data

data structure after loading the file using loadFile function
times

the number of times rescaleI will be executed. The default value is 100.
samples

size of the resampling method. The default value is 1000
plots

to draw the significance plot

Value

A vector with the average log⁡(samples)\log(samples) averages I

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
resultsChen<-buildStabilityTableForCorrelation(data=data,times=10,samples=100,plots=TRUE)

Calculates the distance in a chessboard-alike structure.

Description

calculateDistMatrixFromBoard calculates the distance matrix when the field is divided in a matrix shape (rows and columns). This board could have different number of columns for each row. For example:

1 1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4

The dimension of obtained squared matrix is given by the square of the maximumn dimension of the original matrix. In the previous example, the matrix will have a size of (36,36).

Usage

calculateDistMatrixFromBoard(data)

Arguments

data

is a 2D data structure.

Value

distM the distance between each cell.

Examples

fileInput <- system.file("testdata", "chessboard.csv", package="Irescale")
data<-loadChessBoard(fileInput)
distM<-calculateEuclideanDistance(data$data)

Given a 2D data structure, it calculates the euclidean distance among all the points.

Description

calculateEuclideanDistance Computes the euclidean distance betwen all pairs of nodes provided in the input vector.

Usage

calculateEuclideanDistance(data)

Arguments

data

2D data structure for latitute and longitute respectively.

Details

Computes the euclidean distance, (x1−x2)2+(y1−y2)2\sqrt{(x_1-x_2)^2 + (y_1-y_2)^2}, matrix between each pair of points.

Value

Matrix, of size nrow(data)×nrow(data)nrow(data) \times nrow(data), with the distance between all the pair of points.

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data<-loadFile(fileInput)
distM<-calculateEuclideanDistance(data$data)

Computing the Local Moran's I

Description

calculateLocalI calculates the local Moran's I without rescaling

Usage

calculateLocalI(z, distM, scaling = TRUE)

Arguments

z

vector with the var of interest
distM

distance matrix
scaling

to scale the variable of interest. The default value is set to TRUE

Value

a vector with the local Moran's I

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
input <- loadFile(fileInput)
distM<-calculateEuclideanDistance(input$data)
localI<-calculateLocalI(input$varOfInterest,distM)

Calculates the manhattan distance.

Description

calculateManhattanDistance Calculates the manhattan distance between each pair of nodes.

Usage

calculateManhattanDistance(data)

Arguments

data

2D structure with n rows and 2 colums that represents the coordinate in a plane.

Value

Matrix, of size nrow(data)×nrow(data)nrow(data) \times nrow(data), with the distance between each the pair of points.

Examples

fileInput <- system.file("testdata", "chessboard.csv", package="Irescale")
data<-loadChessBoard(fileInput)
distM<-calculateManhattanDistance(data$data)

Calculates the Moran's I using the algorithm proposed by Chen (Chen 2013).

Description

calculateMoranI Moran's I computing method.

I=varOfInterestt×weightedM×varOfInterestI = varOfInterest^t \times weightedM \times varOfInterest

Usage

calculateMoranI(distM, varOfInterest, scaling = TRUE)

Arguments

distM

the distance matrix. Altough the equation asks for weighted distant matrix, the paramenter that is required is only the distance matrix because this procedure calculate calculates the weighted distance mantrix by itself.
varOfInterest

the variable of interest to calculate Moran's I.
scaling

if the values are previously scaled, set this parameter to False. The default value is TRUE.

Value

Moran's I

References

Chen Y (2013). “New approaches for calculating Moran’s index of spatial autocorrelation.” PloS one, 8(7), e68336.

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)

p-value calculation.

Description

calculatePvalue calculates a p-value for the null hypothesis.

Usage

calculatePvalue(sample, value, mean)

Arguments

sample

the vector that will be used as reference.
value

the value of interest.
mean

the mean of interest.

Examples

fileInput<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(fileInput)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest, n=1000) # This is the permutation
statsVI<-summaryVector(vI)
corrections<-iCorrection(I,vI)
pv<-calculatePvalue(corrections$scaledData,corrections$newI,corrections$summaryScaledD$mean)

Calculates a weighted representation of the distance matrix.

Description

calculateWeightedDistMatrix The weighted matrix is used as a standardized version of the distance matrix.

Usage

calculateWeightedDistMatrix(distM)

Arguments

distM

2D matrix with the distance among all pair of coordinates.

Details

Computes the similarity matrix of the distance by taking the reciprocal of the distance 1d\frac{1}{d}. A value of Zero is assigned when this value can not be calculated. The whole reciprocal matrix is scaled by dividing each value by the sum of all the elements of the matrix.

Value

weighted distance matrix. The sum of this matrix is 1.

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data<-loadFile(fileInput)
distM<-calculateEuclideanDistance(data$data)
distW<-calculateWeightedDistMatrix(distM)

Plots the convexhull polygon from the data (latitude, longitude), and calculates the center of the convexhull and its area.

Description

convexHull Computes the area and centroid of the convex hull from the (latitute, longitude) vector. It provides a plot of how the points are dispersed in the field of interest.

Usage

convexHull(X, varOfInterest)

Arguments

X

dataframe with two colums, latitute and longitude respectively.
varOfInterest

variable of interest to plot. This variable is needed to color the points on the convexhull.

Details

Consideration for this function:

  • It makes usage of chull from rgeos and Polygon from graphics.

  • The centroid of the polygon is calculated by averaging the vertices of it.

  • The shown plot uses the basic plot command.

Value

A vector with two elements, the first element is the area and the second one is the centroid. The centroid is a list of two elements, latitude and longitude that represents the centroid. To have a visual idea of the returned object, it has the following shape [area,[latitude,longitude],plotObject][area,[latitude,longitude], plotObject].

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data<-loadFile(fileInput)
area_centroid<-convexHull(data$data,data$varOfInterest)

Transforms a x,y position in a cartesian plane into a position in a 1D array.

Description

coor Transforms a x,y position in a cartesian plane into a position in a 1D array.

Usage

coor(i, j, size)

Arguments

i

the value of the row.
j

the value of the column.
size

the maximum between row and columns of the matrix.

Value

an integer value that represents the position in the array.

Examples

pos<-coor(1,1,10)

Calculates the expected value for local I

Description

expectedValueI Calculates the expected value for local I

Usage

expectedValueI(W)

Arguments

W

Weighted Distance Matrix.

Value

Expected Value

Examples

W<-matrix(1:100,nrow=10,ncol=10)
evI<-expectedValueI(W)

Scaling process for Moran's I.

Description

iCorrection . consists in centering the I value (I-median) and scaling by the difference between the median and 1st or 99th quantile. The correction is according to the following equation:

I={(I−median)(median−Q1)I<median(I−median)(Q99−median)I>median}I = \left\{ \begin{array}{lr} \frac{(I-median)}{(median - Q1)}& I < median\\ \frac{(I-median)}{(Q99-median)}& I>median\\ \end{array}\right\}

Usage

iCorrection(I, vI, statsVI, scalingUpTo = "Quantile", sd = 1)

Arguments

I

Moran's I, It could be computed using calculateMoranI function.
vI

the vector obtained by resamplingI.
statsVI

the statistic vector obtained from summaryVector.
scalingUpTo

the rescaling could be done up to the 0.01% and 99.9% quantile or max and min values. The two possible options are: "MaxMin", or "Quantile". The default value for this parameter is Quantile.
sd

this represents upto which standard deviation you want to scale I

Value

rescaled I

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest)
statsVI<-summaryVector(vI)
corrections<-iCorrection(I,vI,scalingUpTo="Quantile")

Calculate the equivalence r from the I percentile in the I-Null Distribution.

Description

ItoPearsonCorrelation It calculates the Null distribution of I and determine what is the percentile of the real value of I, then It calculates the inverse of the Normal Distribution(qnorm) to obtain the value of R to which this percentile belongs to.

Usage

ItoPearsonCorrelation(vI, n, medianCenter = TRUE)

Arguments

vI

the vector obtained by resamplingI.
n

sample size
medianCenter

to center all the values to the median. The defaul value is TRUE

Value

a list with r correlation equivalence and the rectified vector

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
distM<-calculateEuclideanDistance(data$data)
vI<-resamplingI(distM,data$varOfInterest,n = 1000)
rectifiedI<- ItoPearsonCorrelation(vI, length(data))

Loads a chessboard or matrix alike input file.

Description

loadChessBoard is used when the input file has a 2D shape, this is a board shape, and it is only one variable of interest. For example:

1 1 1 1 1 1
2 2 2 2 2
3 3 3 3 3
4 4 4 4 4

Usage

loadChessBoard(fileName)

Arguments

fileName

the path and file's name to load.

Value

data frame with two variables, the first variable is a vector with coordinate x (latitude) and y (longitude), the second variable contains the values of the variable of interest.

Examples

fileInput <- system.file("testdata", "chessboard.csv", package="Irescale")
data<-loadChessBoard(fileInput)

Loads a distance matrix. Instead of computing the distance from latitute and longitude LoadDistanceMatrix Loads the distance matrix, avoiding computing it from latitude and longitude.

Description

Loads a distance matrix. Instead of computing the distance from latitute and longitude LoadDistanceMatrix Loads the distance matrix, avoiding computing it from latitude and longitude.

Usage

loadDistanceMatrix(fileName, colnames = TRUE, rownames = TRUE)

Arguments

fileName

file's name and path to the file
colnames

If the first row of the file is the names for the columns. The default value is TRUE
rownames

If the first column is the the row names. The default value is TRUE

Value

The distance matrix

Examples

fileInput <- system.file("testdata", "chenDistance.csv", package="Irescale")
distM<-loadDistanceMatrix(fileInput)

Loads a file with latitude, longitude and variable of interest

Description

loadFile loads the input file with the following format:

  • Column 1 represents the sample Id. It has to be Unique.

  • Column 2,3 Lat/Long respectively.

  • Column 4 and beyond the variables of interest.

Usage

loadFile(fileName)

Arguments

fileName

the file's name and path.

Value

it returns a data frame with two variables datadata and varOfInterestvarOfInterest. The variable datadata is a 2D list with the latitude and longitude respectly, while the variable varOfInterestvarOfInterest is a matrix with all the variables to calculate and rescale Moran's I.

Examples

fileInput <- system.file("testdata", "chessboard.csv", package="Irescale")
data<-loadFile(fileInput)

Loads a Satellite image in PNG format

Description

loadSatelliteImage Loads a Satellite image in PNG format. It does not matter the number of chanells it will return it in grayscale (One channel)

Usage

loadSatelliteImage(fileName)

Arguments

fileName

file's name and path to the file

Value

An cimg object in gray scale.

Examples

fileInput <- system.file("testdata", "imageGray.png", package="Irescale")
img<-loadSatelliteImage(fileInput)

Scaling process for Local Moran's I.

Description

localICorrection . consists in centering the local I value (I-median) and scaling by the difference between the median and 1st or 99th quantile. The correction is according to the following equation:

I={(I−median)(median−Q1)I<median(I−median)(Q99−median)I>median}I = \left\{ \begin{array}{lr} \frac{(I-median)}{(median - Q1)}& I < median\\ \frac{(I-median)}{(Q99-median)}& I>median\\ \end{array}\right\}

Usage

localICorrection(localI, vI, statsVI, scalingUpTo = "Quantile")

Arguments

localI

Local Moran's I, It could be computed using calculateLocalMoranI function.
vI

the vector obtained by resamplingI.
statsVI

the statistic vector obtained from summaryLocalIVector.
scalingUpTo

the rescaling could be done up to the 0.01% and 99.9% quantile or max and min values. The two possible options are: "MaxMin", or "Quantile". The default value for this parameter is Quantile.

Value

rescaled local I vector

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
localI <- calculateLocalI(input$varOfInterest,distM)
vI<-resamplingLocalI(input$varOfInterest,distM)
statsVI<-summaryLocalIVector(vI)
corrections<-localICorrection(localI,vI,scalingUpTo="Quantile")

Calculate a distribution of how the var of interest is correlated to a

Description

nullDristribution Calculate a linear regression between variable of interest and latitude, longitude and latitude*longitude. The residuals of this data set is calculated The variable of interest is shuffle by numReplicates times and each time the linear regression and residuals are calculated. At each interation the correlation between the original residuals and the shuffle residuals is calculated This vector os correlations is returned and plot it as histogram.

Usage

nullDristribution(data, numReplicates)

Arguments

data

the distance matrix. Altough the equation asks for weighted distant matrix, the paramenter that is required is only the distance matrix because this procedure calculate calculates the weighted distance mantrix by itself.
numReplicates

the variable of interest to calculate Moran's I.

Value

Histogram and the vector of correlations between residuals

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
c<-nullDristribution(input,1000)

Creates an overlay of the histogram of the data and the theorical normal distribution.

Description

plotHistogramOverlayCorrelation Overlays the histogram and the theorical normal distribution.

Usage

plotHistogramOverlayCorrelation(originalVec, vec, I, n, bins = 50,
  main = "Histogram")

Arguments

originalVec

The original vector of I, it should be sorted.
vec

the vector to plot.
I

the value of I to plot
n

number of observations in the sample.
bins

the number of bins for the histogram, The default value is 30.
main

the title of the histogram, The default value is "Histogram".

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
originalI<-resamplingI(distM, input$varOfInterest)
correlationI<-ItoPearsonCorrelation(originalI,length(input$varOfInterest))
plotHistogramOverlayCorrelation(originalI,correlationI,I,length(input$varOfInterest))

Creates an overlay of the histogram of the data and the theorical normal distribution.

Description

plotHistogramOverlayNormal Overlays the histogram and the theorical normal distribution.

Usage

plotHistogramOverlayNormal(vec, stats, bins = 50, main = "Histogram")

Arguments

vec

the vector to plot.
stats

the stats obtained from summaryVector.
bins

the number of bins for the histogram, The default value is 30.
main

the title of the histogram, The default value is "Histogram".

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest)
statsVI<-summaryVector(vI)
plotHistogramOverlayNormal(vI,statsVI)

Procrustes distance between two surfaces

Description

procrustes Procrustes distance between two surfaces. The Procrustes distance is used to quantify the similarity or dissimilarity of (3-dimensional) shapes, and extensively used in biological morphometrics.

Usage

procrustes(U, V)

Arguments

U

Vector of the first surface.
V

Vector of the second surface.

Value

Procrustes distance

Rectify I using a correlation method for all the variables in an input file.

Description

rescaleI It executes the whole rectifying using theorical R distribution for all the measurements in the csv file.

  • It plots the histogram with the theorical distribution.

  • It plots the convexHull for each variable.

  • It calcualtes the area and centroid of the convex hull for each variable.

  • It calculates the I and rescale it for every variable.

  • It returns an object with the computations.

Usage

rectifyIrho(data, samples = 10000)

Arguments

data

the data frame obtained from loadFile.
samples

number of permutations for the resampling method.

Value

An object with I, rescaleI and statistic summary for the inputs without scaling, the same statistics after scaling them, the p-value and the convexhull information

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
rectifiedI<-rectifyIrho(data,100)

Calculates n permutations of the variable of interest to calculate n different I in order to create the NullNull distribution.

Description

resamplingI Permute n-1 times the values of the variable of interest to calculate a Null distribution for I. It is done n-1, because one order is the original one, to make sure it is included.

Usage

resamplingI(distM, varOfInterest, n = 1000, scaling = TRUE)

Arguments

distM

the distance matrix. Although the equation requires a weighted distant matrix, the only parameter that will be needed is the distance matrix. This procedure is able to calculate the weighted distance matrix by itself.
varOfInterest

the variable name or position of the variable we are interested to calculate the spatial autocorrelation.
n

number of permutations. The default value is 1000
scaling

The default value is TRUE. However, if the values are previously scaled, this parameter must be set to FALSE..

Value

A vector with the n calculated Moran's I.

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest)

Calculates n permutations of the variable of interest to calculate n different I in order to create the NullNull distribution.

Description

resamplingLocalI Permute n-1 times the values of the variable of interest to calculate a Null distribution for I. It is done n-1, because one order is the original one, to make sure it is included.

Usage

resamplingLocalI(varOfInterest, distM, n = 1000, scaling = TRUE)

Arguments

varOfInterest

the variable name or position of the variable we are interested to calculate the spatial autocorrelation.
distM

the distance matrix. Although the equation requires a weighted distant matrix, the only parameter that will be needed is the distance matrix. This procedure is able to calculate the weighted distance matrix by itself.
n

number of permutations.
scaling

The default value is TRUE. However, if the values are previously scaled, this parameter must be set to FALSE..

Value

A vector with the n calculated Local Moran's I.

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
vI<-resamplingLocalI(input$varOfInterest,distM,n=100)

Performs the rescale for all the variables in an input file.

Description

rescaleI It executes the whole analysis for all the measurements in the field.

  • It plots the histogram with the theorical distribution.

  • It plots the convexHull for each variable.

  • It calcualtes the area and centroid of the convex hull for each variable.

  • It calculates the I and rescale it for every variable.

  • It returns an object with the computations.

Usage

rescaleI(data, samples = 10000, scalingUpTo = "Quantile", sd = 1)

Arguments

data

the data frame obtained from loadFile.
samples

number of permutations for the resampling method.
scalingUpTo

the rescaling could be done up to the 0.01% and 99.99% quantile or max and min values. The two possible options are: "MaxMin", or "Quantile". The default value for this parameter is "Quantile"
sd

this represents upto which standard deviation you want to scale I

Value

An object with I, rescaleI and statistic summary for the inputs without scaling, the same statistics after scaling them, the p-value and the convexhull information

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
scaledI<-rescaleI(data,100)

Saves a report with important statistics to describe the sample.

Description

saveFile Saves a csv report with the following columns: Convex Hull Area, Convex Hull Centroid X, Convex Hull Centroid Y, Sample Size, Ichen, Iscaled, pvalue , Mean, MeanScaled, STD DEV, SDScaled, Q_1%, Q_1%Scaled, $Q_99%, Q_99%Scaled, Max, Max_Scaled, Min, Min_Scaled, Skew,Skew_Scaled, Kutorsis,Kutorsis_Scaled.

Usage

saveFile(fileName, results)

Arguments

fileName

the name of the file with the path where the CSV file will be saved.
results

is the vector obtained from running the rescaling process over all the variables of interest.

Examples

fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data <- loadFile(fileInput)
scaledI<-rescaleI(data,1000)
fn = file.path(tempdir(),"output.csv",fsep = .Platform$file.sep)
saveFile(fn,scaledI)
if (file.exists(fn)){
    file.remove(fn)
}

Standardize the input vector

Description

standardize Calculates the z-values of the input vector. #'

z=vectorI−meanIvarIz = \frac{vectorI - meanI}{\sqrt{varI}}

Usage

standardize(vectorI, W)

Arguments

vectorI

vector to be standardized.
W

weighed distance matrix

Value

z values

Examples

W<-matrix(runif(100, min=0, max=1),nrow=10,ncol=10)
vectorI<-runif(10, min=0, max=1)
standardize(vectorI,W)

Scales a matrix by column.

Description

standardizedByColumn It considers each column independently to scale them.

Usage

standardizedByColumn(M)

Arguments

M

Matrix to be scaled by column.

Value

a matrix scaled by column.

Calculates statistic for the received Matrix.

Description

summaryLocalIVector. Calculates basic statistic of the received Matrix, like mean, standard deviation, maximum, minimum, 0.1% and 99.9% quantile and median.

Usage

summaryLocalIVector(vec)

Arguments

vec

the vector to calculate the summary.

Value

a list with mean, standard deviation, maximum, minimum, 0.1% and 99.9% quantile and median of the received vector.

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
vI<-resamplingLocalI(input$varOfInterest,distM)
statsVI<-summaryLocalIVector(vI)

Calculates statistic for the received vector.

Description

summaryVector. Calculates basic statistic of the received vector, like mean, standard deviation, maximum, minimum, 0.1% and 99.9% quantile and median.

Usage

summaryVector(vec)

Arguments

vec

the vector to calculate the summary.

Value

a list with mean, standard deviation, maximum, minimum, 0.1% and 99.9% quantile and median of the received vector.

Examples

inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest)
statsVI<-summaryVector(vI)

Transforms the image in the object need it to run the analysis.

Description

transformImageToList transforms the image into a list with two variables, data and varOfInterest, which are the identificators needed to execute the rectification.

Usage

transformImageToList(im)

Arguments

im

cimg object.

Examples

fileInput <- system.file("testdata", "imageGray.png", package="Irescale")
img<-loadSatelliteImage(fileInput)
data<-transformImageToList(img)

Transforms the image to a matrix.

Description

transformImageToMatrix transforms the image into a 2D matrix.

Usage

transformImageToMatrix(im)

Arguments

im

cimg object.

Examples

fileInput <- system.file("testdata", "imageGray.png", package="Irescale")
img<-loadSatelliteImage(fileInput)
data<-transformImageToList(img)