| Type: | Package |
| Title: | Calculate and Rectify Moran's I |
| Version: | 2.3.0 |
| Author: | Ivan Fuentes, Thomas DeWitt, Thomas Ioerger, Michael Bishop |
| Maintainer: | Ivan Fuentes <jivfur@tamu.edu> |
| 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) <doi:10.48550/arXiv.1606.03658>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.tamu.edu/jivfur/rectifiedI |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | ggplot2, sp, e1071, graphics, grDevices, stats, utils,Rdpack, fBasics, imager, reshape2 |
| RoxygenNote: | 6.1.1 |
| RdMacros: | Rdpack |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-11-21 17:15:53 UTC; jivfur |
| Repository: | CRAN |
| Date/Publication: | 2019-11-21 17:40:02 UTC |
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))
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 |
times |
the number of times |
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) 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 |
times |
the number of times |
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) 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$data)
Given a 2D data structure, it calculates the euclidean distance among all the points.
Description
calculateEuclideanDistance Computes the euclidean distance betwen all pairs of nodes provided in the input vector.
Usage
calculateEuclideanDistance(data)
Arguments
data |
2D data structure for latitute and longitute respectively. |
Details
Computes the euclidean distance, \sqrt{(x_1-x_2)^2 + (y_1-y_2)^2}, matrix between each pair of points.
Value
Matrix, of size nrow(data) \times nrow(data), with the distance between all the pair of points.
Examples
fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data<-loadFile(fileInput)
distM<-calculateEuclideanDistance(data$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) \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$data)
Calculates the Moran's I using the algorithm proposed by Chen (Chen 2013).
Description
calculateMoranI Moran's I computing method.
I = varOfInterest^t \times weightedM \times varOfInterest
Usage
calculateMoranI(distM, varOfInterest, scaling = TRUE)
Arguments
distM |
the distance matrix. Altough the equation asks for weighted distant matrix, the paramenter that is required is only the distance matrix because this procedure calculate calculates the weighted distance mantrix by itself. |
varOfInterest |
the variable of interest to calculate Moran's I. |
scaling |
if the values are previously scaled, set this parameter to False. The default value is TRUE. |
Value
Moran's I
References
Chen Y (2013). “New approaches for calculating Moran’s index of spatial autocorrelation.” PloS one, 8(7), e68336.
Examples
inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$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 \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$data)
distW<-calculateWeightedDistMatrix(distM)
Plots the convexhull polygon from the data (latitude, longitude), and calculates the center of the convexhull and its area.
Description
convexHull Computes the area and centroid of the convex hull from the (latitute, longitude) vector.
It provides a plot of how the points are dispersed in the field of interest.
Usage
convexHull(X, varOfInterest)
Arguments
X |
dataframe with two colums, latitute and longitude respectively. |
varOfInterest |
variable of interest to plot. This variable is needed to color the points on the convexhull. |
Details
Consideration for this function:
It makes usage of chull from rgeos and Polygon from graphics.
The centroid of the polygon is calculated by averaging the vertices of it.
The shown plot uses the basic
plotcommand.
Value
A vector with two elements, the first element is the area and the second one is the centroid.
The centroid is a list of two elements, latitude and longitude that represents the centroid.
To have a visual idea of the returned object, it has the following shape [area,[latitude,longitude], plotObject].
Examples
fileInput <- system.file("testdata", "chen.csv", package="Irescale")
data<-loadFile(fileInput)
area_centroid<-convexHull(data$data,data$varOfInterest)
Transforms a x,y position in a cartesian plane into a position in a 1D array.
Description
coor Transforms a x,y position in a cartesian plane into a position in a 1D array.
Usage
coor(i, j, size)
Arguments
i |
the value of the row. |
j |
the value of the column. |
size |
the maximum between row and columns of the matrix. |
Value
an integer value that represents the position in the array.
Examples
pos<-coor(1,1,10)
Calculates the expected value for local I
Description
expectedValueI Calculates the expected value for local I
Usage
expectedValueI(W)
Arguments
W |
Weighted Distance Matrix. |
Value
Expected Value
Examples
W<-matrix(1:100,nrow=10,ncol=10)
evI<-expectedValueI(W)
Scaling process for Moran's I.
Description
iCorrection . consists in centering the I value (I-median) and scaling by the difference between the median and 1st or 99th quantile. The correction is according to the following equation:
I = \left\{
\begin{array}{lr}
\frac{(I-median)}{(median - Q1)}& I < median\\
\frac{(I-median)}{(Q99-median)}& I>median\\
\end{array}\right\}
Usage
iCorrection(I, vI, statsVI, scalingUpTo = "Quantile", sd = 1)
Arguments
I |
Moran's I, It could be computed using calculateMoranI function. |
vI |
the vector obtained by resamplingI. |
statsVI |
the statistic vector obtained from summaryVector. |
scalingUpTo |
the rescaling could be done up to the 0.01% and 99.9% quantile or max and min values. The two possible options are: "MaxMin", or "Quantile". The default value for this parameter is Quantile. |
sd |
this represents upto which standard deviation you want to scale I |
Value
rescaled I
Examples
inputFileName<-system.file("testdata", "chen.csv", package="Irescale")
input<-loadFile(inputFileName)
distM<-calculateEuclideanDistance(input$data)
I<-calculateMoranI(distM = distM,varOfInterest = input$varOfInterest)
vI<-resamplingI(distM, input$varOfInterest)
statsVI<-summaryVector(vI)
corrections<-iCorrection(I,vI,scalingUpTo="Quantile")
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 data and varOfInterest. The variable data is a 2D list with the latitude and longitude respectly, while the variable varOfInterest 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 = \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 |
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 Null 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 Null 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 |
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 = \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)