msr: Moran spectral randomization
In adespatial: Multivariate Multiscale Spatial Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
This function allows to generate spatially-constrained random variables
preserving the global autocorrelation (Moran's I) and the spatial structures
at multiple scales. Multiscale property is defined by the power spectrum
(i.e. decomposition of the variance of the original variables) on a basis of
orthonormal eigenvectors (Moran's Eigenvector Maps, MEM). The function
provides methods for univariate randomization, joint randomization of a
group of variables while keeping within-group correlations fixed and
univariate randomization with a fixed correlation between original data and
randomized replicates.
Usage
1
2
Arguments
x
a vector, a matrix or a data.frame with the
original variables. If NCOL(x) > 1, then the joint randomization
procedure that preserves the correlations among variables is used.
listwORorthobasis
an object of the class listw (spatial
weights) created by the functions of the spdep package or an object of
class orthobasis
nrepet
an integer indicating the number of replicates
method
an character specifying which algorithm should be used to
produce spatial replicates (see Details).
cor.fixed
if not missing, the level of correlation between the
original variable and its randomized replicates
nmax
the number of trials used in the "triplet" procedure.
simplify
A logical value. If TRUE, the outputs for univariate
procedures are returned in a matrix where each column corresponds to a
replicate. If FALSEn a list is returned.
Details
Three procedures are implemented in the function. The "pair" procedure is
the more general as it can be applied in the three cases (univariate,
univariate with fixed correlation and multivariate). This procedure
preserves the power spectrum by pair of MEMs but not strictly the global
autocorrelation level (Moran's I). The "singleton" procedure can be used for
univariate and multivariate cases. It preserves strictly the global level of
autocorrelation and the power spectrum. The "triplet" procedure can only be
applied in the univariate case. It preserves the power spectrum by triplet
of MEMs and strictly the global autocorrelation level.
Value
Either a matrix (if simplify is TRUE) or a list with
randomized replicates.
Author(s)
Stephane Dray stephane.dray@univ-lyon1.fr and Helene H
Wagner helene.wagner@utoronto.ca
References
Wagner, H.H. and Dray S. (2015) Generating spatially-constrained
null models for irregularly spaced data using Moran spectral randomization
methods
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
library(spdep)
x1 <- matrix(rnorm(81*5), nrow = 81)
lw1 <- nb2listw(cell2nb(9, 9))
moran.mc(x1[,1], lw1, 2)$statistic
## singleton
x1.1 <- msr(x1[,1], lw1, nrepet = 9, method = "singleton")
apply(x1.1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## triplet
x1.2 <- msr(x1[,1], lw1, nrepet = 9, method = "triplet")
apply(x1.2, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## pair
x1.3 <- msr(x1[,1], lw1, nrepet = 9, method = "pair")
apply(x1.3, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## pair with cor.fixed
x1.4 <- msr(x1[,1], lw1, nrepet = 9, cor.fixed = 0.5)
apply(x1.4, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
cor(x1[,1], x1.4)
## pair preserving correlations for multivariate data
x1.5 <- msr(x1, lw1, nrepet = 9, cor.fixed = 0.5)
cor(x1)
lapply(x1.5, cor)
apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
apply(x1.5[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## singleton preserving correlations for multivariate data
x1.6 <- msr(x1, lw1, nrepet = 9, method = "singleton")
cor(x1)
lapply(x1.6, cor)
apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
apply(x1.6[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
Example output
Registered S3 methods overwritten by 'adegraphics':
method from
biplot.dudi ade4
kplot.foucart ade4
kplot.mcoa ade4
kplot.mfa ade4
kplot.pta ade4
kplot.sepan ade4
kplot.statis ade4
scatter.coa ade4
scatter.dudi ade4
scatter.nipals ade4
scatter.pco ade4
score.acm ade4
score.mix ade4
score.pca ade4
screeplot.dudi ade4
Registered S3 method overwritten by 'spdep':
method from
plot.mst ape
Registered S3 methods overwritten by 'adespatial':
method from
plot.multispati adegraphics
print.multispati ade4
summary.multispati ade4
Loading required package: sp
Loading required package: spData
To access larger datasets in this package, install the spDataLarge
package with: `install.packages('spDataLarge',
repos='https://nowosad.github.io/drat/', type='source')`
Loading required package: sf
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
statistic
0.005473461
[1] 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461
[7] 0.005473461 0.005473461 0.005473461
[1] 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461
[7] 0.005473461 0.005473461 0.005473461
[1] 0.0018640994 -0.0008526903 0.0048132034 -0.0016266943 0.0029023627
[6] -0.0043914763 0.0002661407 -0.0046162051 -0.0035681100
[1] -0.0002684298 -0.0037324849 0.0008879352 -0.0026023747 -0.0014087511
[6] -0.0057628513 -0.0023621150 -0.0020706085 -0.0002162435
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[1]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[2]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[3]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[4]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[5]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[6]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[7]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[8]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[9]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
[1] 0.001789976 0.126765900 -0.082323912 -0.026963819 0.001891579
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[1]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[2]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[3]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[4]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[5]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[6]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[7]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[8]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[9]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
adespatial documentation built on May 2, 2019, 4:43 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Author(s) References See Also Examples
Description
This function allows to generate spatially-constrained random variables preserving the global autocorrelation (Moran's I) and the spatial structures at multiple scales. Multiscale property is defined by the power spectrum (i.e. decomposition of the variance of the original variables) on a basis of orthonormal eigenvectors (Moran's Eigenvector Maps, MEM). The function provides methods for univariate randomization, joint randomization of a group of variables while keeping within-group correlations fixed and univariate randomization with a fixed correlation between original data and randomized replicates.
Usage
1 2 |
Arguments
x |
a |
listwORorthobasis |
an object of the class |
nrepet |
an |
method |
an character specifying which algorithm should be used to produce spatial replicates (see Details). |
cor.fixed |
if not missing, the level of correlation between the original variable and its randomized replicates |
nmax |
the number of trials used in the "triplet" procedure. |
simplify |
A logical value. If |
Details
Three procedures are implemented in the function. The "pair" procedure is the more general as it can be applied in the three cases (univariate, univariate with fixed correlation and multivariate). This procedure preserves the power spectrum by pair of MEMs but not strictly the global autocorrelation level (Moran's I). The "singleton" procedure can be used for univariate and multivariate cases. It preserves strictly the global level of autocorrelation and the power spectrum. The "triplet" procedure can only be applied in the univariate case. It preserves the power spectrum by triplet of MEMs and strictly the global autocorrelation level.
Value
Either a matrix (if simplify is TRUE) or a list with
randomized replicates.
Author(s)
Stephane Dray stephane.dray@univ-lyon1.fr and Helene H Wagner helene.wagner@utoronto.ca
References
Wagner, H.H. and Dray S. (2015) Generating spatially-constrained null models for irregularly spaced data using Moran spectral randomization methods
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | library(spdep)
x1 <- matrix(rnorm(81*5), nrow = 81)
lw1 <- nb2listw(cell2nb(9, 9))
moran.mc(x1[,1], lw1, 2)$statistic
## singleton
x1.1 <- msr(x1[,1], lw1, nrepet = 9, method = "singleton")
apply(x1.1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## triplet
x1.2 <- msr(x1[,1], lw1, nrepet = 9, method = "triplet")
apply(x1.2, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## pair
x1.3 <- msr(x1[,1], lw1, nrepet = 9, method = "pair")
apply(x1.3, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## pair with cor.fixed
x1.4 <- msr(x1[,1], lw1, nrepet = 9, cor.fixed = 0.5)
apply(x1.4, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
cor(x1[,1], x1.4)
## pair preserving correlations for multivariate data
x1.5 <- msr(x1, lw1, nrepet = 9, cor.fixed = 0.5)
cor(x1)
lapply(x1.5, cor)
apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
apply(x1.5[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
## singleton preserving correlations for multivariate data
x1.6 <- msr(x1, lw1, nrepet = 9, method = "singleton")
cor(x1)
lapply(x1.6, cor)
apply(x1, 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
apply(x1.6[[1]], 2, function(x) moran.mc(x, listw = lw1, nsim = 2)$statistic)
|
Example output
Registered S3 methods overwritten by 'adegraphics':
method from
biplot.dudi ade4
kplot.foucart ade4
kplot.mcoa ade4
kplot.mfa ade4
kplot.pta ade4
kplot.sepan ade4
kplot.statis ade4
scatter.coa ade4
scatter.dudi ade4
scatter.nipals ade4
scatter.pco ade4
score.acm ade4
score.mix ade4
score.pca ade4
screeplot.dudi ade4
Registered S3 method overwritten by 'spdep':
method from
plot.mst ape
Registered S3 methods overwritten by 'adespatial':
method from
plot.multispati adegraphics
print.multispati ade4
summary.multispati ade4
Loading required package: sp
Loading required package: spData
To access larger datasets in this package, install the spDataLarge
package with: `install.packages('spDataLarge',
repos='https://nowosad.github.io/drat/', type='source')`
Loading required package: sf
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
statistic
0.005473461
[1] 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461
[7] 0.005473461 0.005473461 0.005473461
[1] 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461 0.005473461
[7] 0.005473461 0.005473461 0.005473461
[1] 0.0018640994 -0.0008526903 0.0048132034 -0.0016266943 0.0029023627
[6] -0.0043914763 0.0002661407 -0.0046162051 -0.0035681100
[1] -0.0002684298 -0.0037324849 0.0008879352 -0.0026023747 -0.0014087511
[6] -0.0057628513 -0.0023621150 -0.0020706085 -0.0002162435
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[1]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[2]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[3]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[4]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[5]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[6]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[7]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[8]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[9]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
[1] 0.001789976 0.126765900 -0.082323912 -0.026963819 0.001891579
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[1]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[2]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[3]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[4]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[5]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[6]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[7]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[8]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[[9]]
[,1] [,2] [,3] [,4] [,5]
[1,] 1.00000000 0.19704292 0.02704586 -0.03659142 0.01186029
[2,] 0.19704292 1.00000000 0.06915438 -0.16207077 -0.16990708
[3,] 0.02704586 0.06915438 1.00000000 -0.15454345 0.14949111
[4,] -0.03659142 -0.16207077 -0.15454345 1.00000000 0.14745899
[5,] 0.01186029 -0.16990708 0.14949111 0.14745899 1.00000000
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
[1] 0.0054734606 0.1201329327 -0.0864690562 -0.0256945645 -0.0001781331
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.