@@ -2,6 +2,7 @@

 export(geom_hatching)

 export(hatchingPlot)

+export(inhomLocalK)

 export(lisa)

 export(lisaClust)

 export(regionMap)

@@ -58,7 +58,7 @@ lisa <-

            lisaFunc = "K",

            minLambda = 0.05,

            fast = TRUE,

-           spatialCoords = c("x","y"),

+           spatialCoords = c("x", "y"),

            cellType = "cellType",

            imageID = "imageID") {

@@ -329,18 +329,54 @@ weightCounts <- function(dt, X, maxD, lam) {

   mat

 }

+#' Calculate the inhomogenous local K function.

+#'

+#' @param data The data.

+#' @param Rs

+#'   A vector of the radii that the measures of association should be

+#'   calculated.

+#' @param sigma

+#'   A numeric variable used for scaling when filting inhomogeneous L-curves.

+#' @param window

+#'   Should the window around the regions be 'square', 'convex' or 'concave'.

+#' @param window.length

+#'   A tuning parameter for controlling the level of concavity.

+#' @param minLambda

+#'   Minimum value for density for scaling when fitting inhomogeneous L-curves.

+#' @param lisaFunc Either "K" or "L" curve.

+#' 

+#' @return A matrix of LISA curves

+#'

+#' @examples

+#' library(spicyR)

+#' # Read in data as a SegmentedCells objects

+#' isletFile <- system.file("extdata", "isletCells.txt.gz", package = "spicyR")

+#' cells <- read.table(isletFile, header = TRUE)

+#' cells$x <- cells$AreaShape_Center_X

+#' cells$y <- cells$AreaShape_Center_Y

+#' cells$cellType <- as.factor(sample(

+#'     c("big", "medium", "small"),

+#'     length(cells$AreaShape_Center_Y),

+#'     replace = TRUE

+#' ))

+#' cells$cellID <- as.factor(cells$ObjectNumber)

+#'

+#' inhom <- inhomLocalK(cells[1:100,])

+#'

+#' @export

+#' @rdname inhomLocalK

 #' @importFrom spatstat.geom ppp closepairs marks area

 #' @importFrom spatstat.explore density.ppp

 #' @importFrom tidyr pivot_longer

 #' @importFrom dplyr left_join

 inhomLocalK <-

-  function (data,

-            Rs = c(20, 50, 100, 200),

-            sigma = 10000,

-            window = "convex",

-            window.length = NULL,

-            minLambda = 0.05,

-            lisaFunc = "K") {

+  function(data,

+           Rs = c(20, 50, 100, 200),

+           sigma = 10000,

+           window = "convex",

+           window.length = NULL,

+           minLambda = 0.05,

+           lisaFunc = "K") {

     ow <- makeWindow(data, window, window.length)

     X <-

@@ -350,15 +386,15 @@ inhomLocalK <-

         window = ow,

         marks = data$cellType

       )

-    

+

     if (is.null(Rs))

-      Rs = c(20, 50, 100, 200)

+      Rs <- c(20, 50, 100, 200)

     if (is.null(sigma))

-      sigma = 100000

-    

+      sigma <- 100000

+

     maxR <- min(ow$xrange[2]- ow$xrange[1], ow$yrange[2]- ow$yrange[1])/2.01

     Rs <- unique(pmin(c(0, sort(Rs)),maxR))

-    

+

     den <- spatstat.explore::density.ppp(X, sigma = sigma)

     den <- den / mean(den)

     den$v <- pmax(den$v, minLambda)

@@ -367,37 +403,36 @@ inhomLocalK <-

     n <- X$n

     p$j <- data$cellID[p$j]

     p$i <- data$cellID[p$i]

-    

+

     cT <- data$cellType

     names(cT) <- data$cellID

-    

+

     p$d <- cut(p$d, Rs, labels = Rs[-1], include.lowest = TRUE)

-    

+

     # inhom density

     np <- spatstat.geom::nearest.valid.pixel(X$x, X$y, den)

     w <- den$v[cbind(np$row, np$col)]

     names(w) <- data$cellID

-    p$wt <- 1/w[p$j]*mean(w)

+    p$wt <- 1 / w[p$j] * mean(w)

     rm(np)

-    

-    lam <- table(data$cellType)/spatstat.geom::area(X)

-    

- 

+

+    lam <- table(data$cellType) / spatstat.geom::area(X)

+

+

     p$cellTypeJ <- cT[p$j]

     p$cellTypeI <- cT[p$i]

     p$i <- factor(p$i, levels = data$cellID)

-    

-    edge <- sapply(Rs[-1],function(x)borderEdge(X,x))

+    edge <- sapply(Rs[-1], function(x) borderEdge( X, x))

     edge <- as.data.frame(edge)

     colnames(edge) <- Rs[-1]

     edge$i <- data$cellID

-    edge <- tidyr::pivot_longer(edge,-i, names_to = "d")

-    

+    edge <- tidyr::pivot_longer(edge, -i, names_to = "d")

+

     p <- dplyr::left_join(as.data.frame(p), edge, c("i", "d"))

     p$d <- factor(p$d, levels = Rs[-1])

-  

-    

+

+

     p <- as.data.frame(p)

@@ -471,7 +506,6 @@ getL <-

 #' @importFrom spicyR cellSummary

 #' @importFrom SummarizedExperiment colData

-#' @importFrom spicyR as.data.frame

 #' @import SpatialExperiment SingleCellExperiment

 prepCellSummary <- function(cells, spatialCoords, cellType, imageID, region = NULL, bind = FALSE){

   if (is.data.frame(cells)) {

new file mode 100644

@@ -0,0 +1,55 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/LISA.R

+\name{inhomLocalK}

+\alias{inhomLocalK}

+\title{Calculate the inhomogenous local K function.}

+\usage{

+inhomLocalK(

+  data,

+  Rs = c(20, 50, 100, 200),

+  sigma = 10000,

+  window = "convex",

+  window.length = NULL,

+  minLambda = 0.05,

+  lisaFunc = "K"

+)

+}

+\arguments{

+\item{data}{The data.}

+

+\item{Rs}{A vector of the radii that the measures of association should be

+calculated.}

+

+\item{sigma}{A numeric variable used for scaling when filting inhomogeneous L-curves.}

+

+\item{window}{Should the window around the regions be 'square', 'convex' or 'concave'.}

+

+\item{window.length}{A tuning parameter for controlling the level of concavity.}

+

+\item{minLambda}{Minimum value for density for scaling when fitting inhomogeneous L-curves.}

+

+\item{lisaFunc}{Either "K" or "L" curve.}

+}

+\value{

+A matrix of LISA curves

+}

+\description{

+Calculate the inhomogenous local K function.

+}

+\examples{

+library(spicyR)

+# Read in data as a SegmentedCells objects

+isletFile <- system.file("extdata", "isletCells.txt.gz", package = "spicyR")

+cells <- read.table(isletFile, header = TRUE)

+cells$x <- cells$AreaShape_Center_X

+cells$y <- cells$AreaShape_Center_Y

+cells$cellType <- as.factor(sample(

+    c("big", "medium", "small"),

+    length(cells$AreaShape_Center_Y),

+    replace = TRUE

+))

+cells$cellID <- as.factor(cells$ObjectNumber)

+

+inhom <- inhomLocalK(cells[1:100,])

+

+}