Tidied up a bit.

ChangeLog

@@ -209,17 +209,3 @@ Further tweaks to the vignette.
 
 Incremented the version number.
 Version 0.1-6.
-
-01/08/2024
-
-Added a file First.R to produce a startup message.
-
-Introduced "Gaussian sampling" as a means of generating data, under the
-null hypothesis, upon which to base Monte Carlo tests.
-
-- changed argument name "permtype" to "infertype", with possible values
-  "resperm", "datperm", and "gaussSample"
-- replaced function permSumFns() by simSimFns()
-
-Incremented the version number.
-Version 0.1-7.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: kanova
-Version: 0.1-7
-Date: 2024-08-01
+Version: 0.1-6
+Date: 2024-07-25
 Title: Pseudo Anova for K-functions
 Author: Rolf Turner <rolfturner@posteo.net>
 Maintainer: Rolf Turner <rolfturner@posteo.net>
@@ -13,3 +13,5 @@ VignetteBuilder: R.rsp
 LazyData: true
 Depends: R (>= 3.2.2)
 License: GPL (>=2)
+NeedsCompilation: no
+Packaged: 2024-07-25 00:22:58 UTC; rolf

R/initPrep.R

@@ -126,7 +126,9 @@ if(is.null(B)) {
     AB <- interaction(B,A)
 }
 
-# Set "splif" and check on adequacy of cell counts.
+# Build Khat (the overall estimate of the unique K function, common
+# to all groups under the null hypothesis of no group effects),
+# and s2, the overall sample variance.
 if(type %in% c("oneway","addit")) {
     splif <- A
 } else if(type == "interac") {

R/kanova.R

@@ -1,15 +1,15 @@
 kanova <- function(fmla,data,sumFnNm=c("Kest","Fest","Gest","Jest"),
-                   test=TRUE,infertype=c("resperm","datperm","gaussSample"),
-                   nsam=99,brief=TRUE,verb=TRUE) {
+                   test=TRUE,permtype=c("resids","data"),nperm=99,
+                   brief=TRUE,verb=TRUE) {
 #
-# Function to conduct one or two-way pseudo analysis of variance of
+# Function to conduct one or two-way analysis of variance of
 # summary functions (Kest, Fest, Gest, or Jest)  of replicated point
 # patterns classified by a grouping factor A or two grouping factors
 # A and B.
 #
 
-sumFnNm   <- match.arg(sumFnNm)
-infertype <- match.arg(infertype)
+sumFnNm  <- match.arg(sumFnNm)
+permtype <- match.arg(permtype)
 
 if(length(fmla)==2) {
     if(inherits(data,"hyperframe")) {
@@ -57,8 +57,8 @@ switch(EXPR=npreds,
      if(preds[3] != paste0(Anm,":",Bnm)) {
          stop("Argument \"fmla\" is of an incorrect form.\n")
      }
-     if(infertype=="data")
-        stop("Cannot use infertype=\"data\" when there is interaction in the model.\n")
+     if(permtype=="data")
+        stop("Cannot use permtype=\"data\" when there is interaction in the model.\n")
      type <- "interac"
      effNm <- paste0("interaction of ",Anm," with ",Bnm)
     }
@@ -73,46 +73,36 @@ if(!test) {
        rslt <- list(stat=Tobs)
    } else {
        rslt <- list(fmla=fmla,data=data,sumFnNm=sumFnNm,
-                    infertype=infertype,stat=Tobs)
+                    permtype=permtype,stat=Tobs)
    }
    class(rslt) <- "kanova"
    return(rslt)
 }
 
 # Testing;  carry out the Monte Carlo test.
-# If infertype is "resperm", create the fitted values and residuals.
-if(infertype=="resperm") {
+# If permtype is "resids", create the fitted values and residuals.
+if(permtype=="resids") {
    rAndF <- resAndFit(iDat,type) # List with components "resids" and "fitVals".
 } else {
    rAndF <- NULL
 }
-if(infertype=="gaussSample") {
-    splif <- switch(EXPR=type,oneway="A",addit="A",interac="AB")
-    xxx   <- with(iDat,builds2Khat(sumFns,wts,splif,do.s2=TRUE))
-    s2    <- xxx$s2
-    Khat  <- xxx$Khat
-} else {
-    splif <- NULL
-    s2    <- NULL
-    Khat  <- NULL
-}
 
-Tstar <- numeric(nsam)
-for(i in 1:nsam) {
-    sSumFns  <- simSumFns(iDat[["sumFns"]],iDat[["B"]],rAndF,splif,s2,Khat,infertype)
-    Tstar[i] <- with(iDat,testStat(sSumFns,A,B,AB,wts,r,type=type))
+Tstar <- numeric(nperm)
+for(i in 1:nperm) {
+    pSumFns  <- permSumFns(iDat[["sumFns"]],iDat[["B"]],rAndF,permtype)
+    Tstar[i] <- with(iDat,testStat(pSumFns,A,B,AB,wts,r,type=type))
     if(verb) cat(i,"")
     if(verb & i%%10 == 0) cat("\n")
 }
 if(verb & i%%10 != 0) cat("\n")
 m    <- sum(Tstar >= Tobs)
-pv   <- (m+1)/(nsam+1)
+pv   <- (m+1)/(nperm+1)
 bres <- list(EffectName=effNm,stat=Tobs,pvalue=pv)
 if(brief) {
     rslt <- bres
 } else {
     rslt <- c(bres,list(fmla=fmla,data=data,sumFnNm=sumFnNm,
-                        infertype=infertype,Tstar=Tstar))
+                        permtype=permtype,Tstar=Tstar))
 }
 class(rslt) <- "kanova"
 rslt