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Last data update: 2014.03.03

R: Computation of the spatial potential

calcMultiPotential

R Documentation

Computation of the spatial potential

Description

Compute the regional potential of a spatial field. Call the calcMultiPotential_cpp C++ function.

Usage

calcMultiPotential(W_SR, W_LR, distance.ref, sample, coords,
         threshold = 0.01, nbGroup_min = 100, check.args = TRUE, verbose = TRUE)

Arguments

`W_SR`	The local neighborhood matrix. dgCMatrix. Should be normalized by row (i.e. `rowSums(Wweight_SR)=1`). REQUIRED.
`W_LR`	The regional neighborhood matrix. dgCMatrix. Should contain the distances between the observations (`0` indicating infinite distance). REQUIRED.
`distance.ref`	The intervals of distance defining the several neighborhood orders in `W_LR`. numeric vector. REQUIRED.
`sample`	The probability membership to the group. numeric vector or character vector. REQUIRED.
`coords`	The voxel coordinates. matrix. REQUIRED.
`threshold`	The minimum value to consider non-negligible group membership. numeric. Default is `0.01`.
`nbGroup_min`	The minimum group size of the spatial groups required for computing the potential. integer. Default is `100`.
`check.args`	Should the validity of the arguments be checked ? logical.
`verbose`	Should the radius of the spatial groups be displayed ? logical.

Details

If check.args is set to TRUE, argument ccords must be a matrix (and not a data.frame) and the x slot of argument W_LR must contain categories of distances instead of distances. Distance categories must begin at 0 and end at length(distance.ref)-1.

Value

A numeric vector containing the regional potential.

Examples

# spatial field
## Not run: 
n <- 30

## End(Not run)

G <- 3
coords <- data.frame(which(matrix(0, nrow = n * G, ncol = n * G) == 0, arr.ind = TRUE), 1)
optionsMRIaggr(quantiles.legend = FALSE, axes = FALSE, num.main = FALSE)

# neighborhood matrix
W_SR <- calcW(coords, range = sqrt(2), row.norm = TRUE)$W
W_LR <- calcW(coords, range = 10, row.norm = FALSE)$W
distance.ref <- seq(1, 10, 1)

# data
set.seed(10)
sample <- simulPotts(W = W_SR, G = G, rho = 3.5, iter_max = 500)

# 
multiplot(coords,sample$simulation[,2])

V1 <- calcMultiPotential(W_SR = W_SR, W_LR = W_LR, distance.ref = seq(1, 10, 1),
     sample = sample$simulation[,2], coords = coords, verbose = TRUE)

multiplot(coords, V1)		


#
sampleV <- (apply(sample$simulation, 1, which.max) - 1) / 2
multiplot(coords, sampleV)

system.time(
  V2 <- calcMultiPotential(W_SR = W_SR, W_LR = W_LR, distance.ref = seq(1, 10, 1),
     sample = sampleV, coords = coords, verbose = TRUE)
)

Wcat_LR <- W_LR
Wcat_LR@x <- findInterval(x = Wcat_LR@x, vec = distance.ref) - 1

system.time(
  V2 <- calcMultiPotential(W_SR = W_SR, W_LR = Wcat_LR, distance.ref = seq(1, 10, 1),
     sample = sampleV, coords = as.matrix(coords), verbose = TRUE, check.args = FALSE)
)
# quicker but arguments have to be correctly specified

multiplot(coords, V2)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(MRIaggr)
Loading required package: Rcpp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MRIaggr/sfMM-calcMultiPotential.Rd_%03d_medium.png", width=480, height=480)
> ### Name: calcMultiPotential
> ### Title: Computation of the spatial potential
> ### Aliases: calcMultiPotential
> ### Keywords: functions
> 
> ### ** Examples
> 
> # spatial field
> ## Not run: 
> ##D n <- 30
> ## End(Not run)
> ## Don't show: 
> n <- 10
> ## End(Don't show)
> G <- 3
> coords <- data.frame(which(matrix(0, nrow = n * G, ncol = n * G) == 0, arr.ind = TRUE), 1)
> optionsMRIaggr(quantiles.legend = FALSE, axes = FALSE, num.main = FALSE)
> 
> # neighborhood matrix
> W_SR <- calcW(coords, range = sqrt(2), row.norm = TRUE)$W
> W_LR <- calcW(coords, range = 10, row.norm = FALSE)$W
> distance.ref <- seq(1, 10, 1)
> 
> # data
> set.seed(10)
> sample <- simulPotts(W = W_SR, G = G, rho = 3.5, iter_max = 500)
NOTE : specifying argument 'site_order' would speed up the execution of the function 
0% 10   20   30   40   50   60   70   80   90   100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
|
> 
> # 
> multiplot(coords,sample$simulation[,2])
> 
> V1 <- calcMultiPotential(W_SR = W_SR, W_LR = W_LR, distance.ref = seq(1, 10, 1),
+      sample = sample$simulation[,2], coords = coords, verbose = TRUE)
segment x slot of argument 'W_LR' according to argument 'distance.ref' 
radius :  10.22   
> 
> multiplot(coords, V1)		
> 
> 
> #
> sampleV <- (apply(sample$simulation, 1, which.max) - 1) / 2
> multiplot(coords, sampleV)
> 
> system.time(
+   V2 <- calcMultiPotential(W_SR = W_SR, W_LR = W_LR, distance.ref = seq(1, 10, 1),
+      sample = sampleV, coords = coords, verbose = TRUE)
+ )
segment x slot of argument 'W_LR' according to argument 'distance.ref' 
radius :  11.42   
   user  system elapsed 
  0.008   0.000   0.007 
> 
> Wcat_LR <- W_LR
> Wcat_LR@x <- findInterval(x = Wcat_LR@x, vec = distance.ref) - 1
> 
> system.time(
+   V2 <- calcMultiPotential(W_SR = W_SR, W_LR = Wcat_LR, distance.ref = seq(1, 10, 1),
+      sample = sampleV, coords = as.matrix(coords), verbose = TRUE, check.args = FALSE)
+ )
radius :  11.42   
   user  system elapsed 
  0.000   0.000   0.002 
> # quicker but arguments have to be correctly specified
> 
> multiplot(coords, V2)		
> 	
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>