minor changes

DESCRIPTION

 Package: gmGeostats
-Version: 0.10-6
+Version: 0.10-6.9000
 Date: 2020-09-07
 Title: Geostatistics for Compositional Analysis
 Authors@R: c(person(given = "Raimon", 

NAMESPACE

@@ -59,7 +59,10 @@ S3method(getStackElement,default)
 S3method(getStackElement,list)
 S3method(gmApply,DataFrameStack)
 S3method(gmApply,default)
+S3method(gsi.gstatCokriging2compo,data.frame)
 S3method(gsi.gstatCokriging2compo,default)
+S3method(gsi.gstatCokriging2rmult,data.frame)
+S3method(gsi.gstatCokriging2rmult,default)
 S3method(has.missings,data.frame)
 S3method(image,logratioVariogramAnisotropy)
 S3method(image,mask)

NEWS.md

+# gmGeostats 0.10.x
+
+* (2020-09-18) conversion of variogram models of class CompLinModCoReg -> LMCAnisCompo -> variogramModel(List)
+* (2020-09-18) `gsi.gstatCokriging2compo()` and `gsi.gstatCokriging2rmult()` now work with systems of 3 components resp. 2 variables (systems of 2 components or 1 variable still fail)
+
+# gmGeostats 0.10.6
+
+* (2020-09-17) we are in CRAN! 
\ No newline at end of file

R/geostats.R

@@ -207,22 +207,24 @@ gsi.CondTurningBands <- function(Xout, Xin, Zin, vgram,
 ### tests --
 # if(!exists("do.test")) do.test=FALSE
 # if(do.test){
-#   library("gstat")
-#   library("magrittr")
-#   data("jura", package = "gstat")
-#   dt = jura.pred %>% dplyr::select(Cd:Zn)
-#   X = jura.pred[,1:2]
-#   spc = SpatialPointsDataFrame(SpatialPoints(X),acomp(dt))
-#   a2 = make.gmCompositionalGaussianSpatialModel(spc)
-#   #attach(gsi)
-#   a3gs = as.gstat(a2)
-#   a3vg = variogram(a3gs) 
-#   a3gs = fit_lmc(a3vg, a3gs, vgm("Sph", psill=1, nugget=0, range=1))
-#   a4 = make.gmCompositionalGaussianSpatialModel(spc, V="alr", model=a3gs$model)
-#   a4gs = as.gstat(a4)
-#   p1 = predict(a4gs, newdata = jura.grid)
-#   p2 = predict(a4gs, newdata = SpatialPoints(jura.grid[,1:2]))
-#   p3 = predict(a4gs, newdata = SpatialPixels(SpatialPoints(jura.grid[,1:2])))
+  # library("compositions")
+  # library("sp")
+  # library("magrittr")
+  # library("gstat")
+  # data("jura", package = "gstat")
+  # dt = jura.pred %>% dplyr::select(Cd:Cr)
+  # X = jura.pred[,1:2]
+  # spc = SpatialPointsDataFrame(SpatialPoints(X),acomp(dt))
+  # a2 = make.gmCompositionalGaussianSpatialModel(spc)
+  # #attach(gsi)
+  # a3gs = as.gstat(a2)
+  # a3vg = variogram(a3gs)
+  # a3gs = fit_lmc(a3vg, a3gs, vgm("Sph", psill=1, nugget=0, range=1))
+  # a4 = make.gmCompositionalGaussianSpatialModel(spc, V="alr", model=a3gs$model)
+  # a4gs = as.gstat(a4)
+  # p1 = predict(a4gs, newdata = jura.grid)
+  # p2 = predict(a4gs, newdata = jura.grid[,1:2])
+  # p3 = predict(a4gs, newdata = SpatialPixels(SpatialPoints(jura.grid[,1:2])))
 #   
 # }
 

R/grids.R

@@ -152,6 +152,7 @@ gsi.gstatCokriging2compo.default <- function(COKresult, ...){
 #' @param tol for generalized inversion of the matrix (**rarely touched!**)
 #' @param nscore boolean, were the data normal score-transformed? (**deprecated**)
 #' @param gg in the case that normal score transformation was applied, provide the gstat object! (**deprecated**)
+#' @export
 gsi.gstatCokriging2compo.data.frame = function(COKresult, # output of predict.gstat
                                                V=NULL,    # string or matrix describing which logratio was applied
                                                orignames=NULL, # names of the original components (OPTIONAL)
@@ -171,7 +172,7 @@ gsi.gstatCokriging2compo.data.frame = function(COKresult, # output of predict.gs
   if(is.null(orignames)) orignames = paste("v", 1:D, sep="")
   if(length(orignames)!=D) stop("names provided not consistent with number of logratio variables. Did you forget the rest?")
   prefix = "ilr"
-  prediccions = COKresult[,prednames]
+  prediccions = COKresult[,prednames, drop=FALSE]
   if(is.character(V)){
     if(V=="ilr"){
       V = ilrBase(prediccions)
@@ -205,12 +206,12 @@ gsi.gstatCokriging2compo.data.frame = function(COKresult, # output of predict.gs
     # add cokriging variance matrices as an attribute as well
     noms = sub(".pred", "", prednames)
     cvmat = array(0, dim=c(nrow(rg), D-1, D-1), dimnames=list(NULL, noms, noms))
-    vrs = COKresult[,varnames]
+    vrs = COKresult[,varnames, drop=FALSE]
     colnames(vrs) = sub(".var", "", varnames)
     for(ivr in noms){
       cvmat[ ,ivr, ivr] = vrs[,ivr]
     }
-    cvs = COKresult[,covnames]
+    cvs = COKresult[,covnames, drop=FALSE]
     colnames(cvs) = sub("cov.", "", covnames)
     for(ivr in noms){
       for(jvr in noms){
@@ -234,6 +235,7 @@ gsi.gstatCokriging2rmult <- function(COKresult, ...) UseMethod("gsi.gstatCokrigi
 
 #' @describeIn gsi.gstatCokriging2compo Reorganisation of cokriged multivariate data
 #' @method gsi.gstatCokriging2rmult default
+#' @export
 gsi.gstatCokriging2rmult.default <- function(COKresult, ...){
   stopifnot(is(COKresult, "Spatial"), "data" %in% slotNames(COKresult))
   coord = sp::coordinates(COKresult)
@@ -251,6 +253,7 @@ gsi.gstatCokriging2rmult.default <- function(COKresult, ...){
 
 #' @describeIn gsi.gstatCokriging2compo Reorganisation of cokriged multivariate data
 #' @method gsi.gstatCokriging2rmult data.frame
+#' @export
 gsi.gstatCokriging2rmult.data.frame = function(COKresult, # output of predict.gstat
                                     nscore=FALSE, # were the data NS-transformed?
                                     gg=NULL, # in the case that NS was applied, provide the gstat object!
@@ -264,7 +267,7 @@ gsi.gstatCokriging2rmult.data.frame = function(COKresult, # output of predict.gs
   D = length(prednames)
   noms = sub(".pred", "", prednames)
   
-  prediccions = COKresult[,prednames]
+  prediccions = COKresult[,prednames, drop=FALSE]
   if(nscore){
     ## space to back-transform the predictions
     #if(is.null(gg))stop("To apply a nscore backtransformation, the gstat object must be provided!")
@@ -283,12 +286,12 @@ gsi.gstatCokriging2rmult.data.frame = function(COKresult, # output of predict.gs
   if(!nscore){
     # add cokriging variance matrices as an attribute as well
     cvmat = array(0, dim=c(nrow(rg), D, D), dimnames=list(NULL, noms, noms))
-    vrs = COKresult[,varnames]
+    vrs = COKresult[,varnames, drop=FALSE]
     colnames(vrs) = sub(".var", "", varnames)
     for(ivr in noms){
       cvmat[ ,ivr, ivr] = vrs[,ivr]
     }
-    cvs = COKresult[,covnames]
+    cvs = COKresult[,covnames, drop=FALSE]
     colnames(cvs) = sub("cov.", "", covnames)
     for(ivr in noms){
       for(jvr in noms){