* using log directory 'd:/Rcompile/CRANpkg/local/2.13/psgp.Rcheck'
* using R version 2.13.2 (2011-09-30)
* using platform: i386-pc-mingw32 (32-bit)
* using session charset: ISO8859-1
* checking for file 'psgp/DESCRIPTION' ... OK
* this is package 'psgp' version '0.3-0'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking whether package 'psgp' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking for portable file names ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking for unstated dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking for portable compilation flags in Makevars ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking sizes of PDF files under inst/doc ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in tests ... OK
* checking tests ...
** running tests for arch 'i386' OK
  Running 'meuse_psgp.R'
  Comparing 'meuse_psgp.Rout' to 'meuse_psgp.Rout.save' ...1,42d0
< 
< 
< > library(psgp)
< 
< Attaching package: 'zoo'
< 
< The following object(s) are masked from 'package:base':
< 
<     as.Date, as.Date.numeric
< 
< Geospatial Data Abstraction Library extensions to R successfully loaded
< Loaded GDAL runtime: GDAL 1.8.1, released 2011/07/09
< Path to GDAL shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/gdal
< Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009, [PJ_VERSION: 470]
< Path to PROJ.4 shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/proj
< 
< Attaching package: 'lattice'
< 
< The following object(s) are masked from 'package:evd':
< 
<     qq
< 
< > set.seed(100)
< > # set up data:
< > data(meuse)
< > coordinates(meuse) = ~x+y
< > meuse$value = log(meuse$zinc)
< > data(meuse.grid)
< > gridded(meuse.grid) = ~x+y
< > proj4string(meuse) = CRS("+init=epsg:28992")
< > proj4string(meuse.grid) = CRS("+init=epsg:28992")
< > 
< > # set up intamap object:
< > psgpObject = createIntamapObject(
< +   observations = meuse,
< +   formulaString=as.formula(value~1),
< +   predictionLocations = meuse.grid,
< +   class = "psgp"
< + )
< > 
< > # run test:
< > checkSetup(psgpObject)
44,46d1
< > 
< > # do interpolation steps:
< > psgpObject = estimateParameters(psgpObject)
396,398d350
< > 
< > # make prediction
< > psgpObject = spatialPredict(psgpObject)
723c675
<    Range  (P0) :844.492790
---
>    Range  (P0) :844.492816
727c679
<    Length scale  (P0) :1752.485472
---
>    Length scale  (P0) :1752.485482
732,736d683
< > 
< > # Plot prediction
< > # plotIntamap(psgpObject)
< > # plotIntamap(meuse, pch=1, cex=sqrt(meuse$value)/20, add=TRUE)
< > 
  Running 'psgp.R'
  Comparing 'psgp.Rout' to 'psgp.Rout.save' ...1,40d0
< 
< 
< > library(psgp) # requires intamap
< 
< Attaching package: 'zoo'
< 
< The following object(s) are masked from 'package:base':
< 
<     as.Date, as.Date.numeric
< 
< Geospatial Data Abstraction Library extensions to R successfully loaded
< Loaded GDAL runtime: GDAL 1.8.1, released 2011/07/09
< Path to GDAL shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/gdal
< Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009, [PJ_VERSION: 470]
< Path to PROJ.4 shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/proj
< 
< Attaching package: 'lattice'
< 
< The following object(s) are masked from 'package:evd':
< 
<     qq
< 
< > 
< > data(meuse)
< > observations = data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
< > coordinates(observations) = ~x+y
< > set.seed(13531)
< > predictionLocations = spsample(observations, 50, "regular")
< > 
< > krigingObject = createIntamapObject(
< + 	observations = observations,
< + 	predictionLocations = predictionLocations,
< +   formulaString = as.formula(value~1),
< + 	params =  list(doAnisotropy = TRUE, thresh = quantile(observations$value,0.9)),
< +   outputWhat = list(mean=TRUE, variance=TRUE, excprob = 5.9, cumdistr = 5.9, 
< + 		quantile = .1)
< + )
< > class(krigingObject) = c("psgp")
< > 
< > checkSetup(krigingObject)
42,43d1
< > krigingObject = preProcess(krigingObject)
< > krigingObject = estimateParameters(krigingObject)
393d350
< > krigingObject = spatialPredict(krigingObject)
712c669
<    Range  (P0) :844.492798
---
>    Range  (P0) :844.492802
716c673
<    Length scale  (P0) :1752.485476
---
>    Length scale  (P0) :1752.485475
721,751d677
< > krigingObject = postProcess(krigingObject)
< > 
< > # Send predictions back to Java. Not sure how to deal with this spatial object though...?
< > summary(krigingObject$outputTable)
<        x                y               mean          variance      
<  Min.   :179019   Min.   :330013   Min.   :4.762   Min.   :0.03764  
<  1st Qu.:179485   1st Qu.:330829   1st Qu.:5.314   1st Qu.:0.07613  
<  Median :180183   Median :331644   Median :5.874   Median :0.25617  
<  Mean   :180183   Mean   :331644   Mean   :6.234   Mean   :0.45727  
<  3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:7.409   3rd Qu.:0.64260  
<  Max.   :181348   Max.   :333275   Max.   :8.013   Max.   :1.81668  
<    excprob5.9         cumdistr5.9         quantile0.1   
<  Min.   :0.0000153   Min.   :1.622e-09   Min.   :4.048  
<  1st Qu.:0.0561157   1st Qu.:2.101e-02   1st Qu.:4.646  
<  Median :0.4837435   Median :5.163e-01   Median :5.177  
<  Mean   :0.5164719   Mean   :4.835e-01   Mean   :5.478  
<  3rd Qu.:0.9789940   3rd Qu.:9.439e-01   3rd Qu.:6.461  
<  Max.   :1.0000000   Max.   :1.000e+00   Max.   :7.228  
< > summary(krigingObject$observations)
< Object of class SpatialPointsDataFrame
< Coordinates:
<      min    max
< x 178605 181390
< y 329714 333611
< Is projected: NA 
< proj4string : [NA]
< Number of points: 155
< Data attributes:
<    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
<   4.727   5.288   5.787   5.886   6.514   7.517 
< > summary(autoKrige(value~1,krigingObject$observations,predictionLocations)$krige_output)
753,773d678
< Object of class SpatialPointsDataFrame
< Coordinates:
<         min      max
< x1 179018.6 181348.1
< x2 330013.4 333274.7
< Is projected: NA 
< proj4string : [NA]
< Number of points: 48
< Data attributes:
<    var1.pred        var1.var        var1.stdev    
<  Min.   :4.929   Min.   :0.1153   Min.   :0.3395  
<  1st Qu.:5.518   1st Qu.:0.1615   1st Qu.:0.4018  
<  Median :6.047   Median :0.3582   Median :0.5963  
<  Mean   :5.978   Mean   :0.3827   Mean   :0.5909  
<  3rd Qu.:6.337   3rd Qu.:0.6026   3rd Qu.:0.7762  
<  Max.   :7.472   Max.   :0.6752   Max.   :0.8217  
< > autofitVariogram(value~1,krigingObject$observations)$var_model
<   model      psill    range
< 1   Nug 0.04847876   0.0000
< 2   Sph 0.58754476 889.8912
< > 
** running tests for arch 'x64' OK
  Running 'meuse_psgp.R'
  Comparing 'meuse_psgp.Rout' to 'meuse_psgp.Rout.save' ...1,42d0
< 
< 
< > library(psgp)
< 
< Attaching package: 'zoo'
< 
< The following object(s) are masked from 'package:base':
< 
<     as.Date, as.Date.numeric
< 
< Geospatial Data Abstraction Library extensions to R successfully loaded
< Loaded GDAL runtime: GDAL 1.8.1, released 2011/07/09
< Path to GDAL shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/gdal
< Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009, [PJ_VERSION: 470]
< Path to PROJ.4 shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/proj
< 
< Attaching package: 'lattice'
< 
< The following object(s) are masked from 'package:evd':
< 
<     qq
< 
< > set.seed(100)
< > # set up data:
< > data(meuse)
< > coordinates(meuse) = ~x+y
< > meuse$value = log(meuse$zinc)
< > data(meuse.grid)
< > gridded(meuse.grid) = ~x+y
< > proj4string(meuse) = CRS("+init=epsg:28992")
< > proj4string(meuse.grid) = CRS("+init=epsg:28992")
< > 
< > # set up intamap object:
< > psgpObject = createIntamapObject(
< +   observations = meuse,
< +   formulaString=as.formula(value~1),
< +   predictionLocations = meuse.grid,
< +   class = "psgp"
< + )
< > 
< > # run test:
< > checkSetup(psgpObject)
44,46d1
< > 
< > # do interpolation steps:
< > psgpObject = estimateParameters(psgpObject)
396,398d350
< > 
< > # make prediction
< > psgpObject = spatialPredict(psgpObject)
723c675
<    Range  (P0) :844.492401
---
>    Range  (P0) :844.492816
727c679
<    Length scale  (P0) :1752.485270
---
>    Length scale  (P0) :1752.485482
732,736d683
< > 
< > # Plot prediction
< > # plotIntamap(psgpObject)
< > # plotIntamap(meuse, pch=1, cex=sqrt(meuse$value)/20, add=TRUE)
< > 
  Running 'psgp.R'
  Comparing 'psgp.Rout' to 'psgp.Rout.save' ...1,40d0
< 
< 
< > library(psgp) # requires intamap
< 
< Attaching package: 'zoo'
< 
< The following object(s) are masked from 'package:base':
< 
<     as.Date, as.Date.numeric
< 
< Geospatial Data Abstraction Library extensions to R successfully loaded
< Loaded GDAL runtime: GDAL 1.8.1, released 2011/07/09
< Path to GDAL shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/gdal
< Loaded PROJ.4 runtime: Rel. 4.7.1, 23 September 2009, [PJ_VERSION: 470]
< Path to PROJ.4 shared files: d:/RCompile/CRANpkg/lib/2.13/rgdal/proj
< 
< Attaching package: 'lattice'
< 
< The following object(s) are masked from 'package:evd':
< 
<     qq
< 
< > 
< > data(meuse)
< > observations = data.frame(x = meuse$x,y = meuse$y,value = log(meuse$zinc))
< > coordinates(observations) = ~x+y
< > set.seed(13531)
< > predictionLocations = spsample(observations, 50, "regular")
< > 
< > krigingObject = createIntamapObject(
< + 	observations = observations,
< + 	predictionLocations = predictionLocations,
< +   formulaString = as.formula(value~1),
< + 	params =  list(doAnisotropy = TRUE, thresh = quantile(observations$value,0.9)),
< +   outputWhat = list(mean=TRUE, variance=TRUE, excprob = 5.9, cumdistr = 5.9, 
< + 		quantile = .1)
< + )
< > class(krigingObject) = c("psgp")
< > 
< > checkSetup(krigingObject)
42,43d1
< > krigingObject = preProcess(krigingObject)
< > krigingObject = estimateParameters(krigingObject)
393d350
< > krigingObject = spatialPredict(krigingObject)
712c669
<    Range  (P0) :844.492711
---
>    Range  (P0) :844.492802
716c673
<    Length scale  (P0) :1752.485435
---
>    Length scale  (P0) :1752.485475
721,751d677
< > krigingObject = postProcess(krigingObject)
< > 
< > # Send predictions back to Java. Not sure how to deal with this spatial object though...?
< > summary(krigingObject$outputTable)
<        x                y               mean          variance      
<  Min.   :179019   Min.   :330013   Min.   :4.762   Min.   :0.03763  
<  1st Qu.:179485   1st Qu.:330829   1st Qu.:5.314   1st Qu.:0.07613  
<  Median :180183   Median :331644   Median :5.875   Median :0.25617  
<  Mean   :180183   Mean   :331644   Mean   :6.235   Mean   :0.45727  
<  3rd Qu.:180882   3rd Qu.:332459   3rd Qu.:7.411   3rd Qu.:0.64259  
<  Max.   :181348   Max.   :333275   Max.   :8.015   Max.   :1.81668  
<    excprob5.9         cumdistr5.9         quantile0.1   
<  Min.   :0.0000153   Min.   :2.238e-09   Min.   :4.048  
<  1st Qu.:0.0561275   1st Qu.:2.100e-02   1st Qu.:4.646  
<  Median :0.4838715   Median :5.161e-01   Median :5.176  
<  Mean   :0.5164961   Mean   :4.835e-01   Mean   :5.479  
<  3rd Qu.:0.9790043   3rd Qu.:9.439e-01   3rd Qu.:6.461  
<  Max.   :1.0000000   Max.   :1.000e+00   Max.   :7.228  
< > summary(krigingObject$observations)
< Object of class SpatialPointsDataFrame
< Coordinates:
<      min    max
< x 178605 181390
< y 329714 333611
< Is projected: NA 
< proj4string : [NA]
< Number of points: 155
< Data attributes:
<    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
<   4.727   5.288   5.787   5.886   6.514   7.517 
< > summary(autoKrige(value~1,krigingObject$observations,predictionLocations)$krige_output)
753,773d678
< Object of class SpatialPointsDataFrame
< Coordinates:
<         min      max
< x1 179018.6 181348.1
< x2 330013.4 333274.7
< Is projected: NA 
< proj4string : [NA]
< Number of points: 48
< Data attributes:
<    var1.pred        var1.var        var1.stdev    
<  Min.   :4.929   Min.   :0.1153   Min.   :0.3395  
<  1st Qu.:5.518   1st Qu.:0.1615   1st Qu.:0.4018  
<  Median :6.047   Median :0.3582   Median :0.5963  
<  Mean   :5.978   Mean   :0.3827   Mean   :0.5909  
<  3rd Qu.:6.337   3rd Qu.:0.6026   3rd Qu.:0.7762  
<  Max.   :7.472   Max.   :0.6752   Max.   :0.8217  
< > autofitVariogram(value~1,krigingObject$observations)$var_model
<   model      psill    range
< 1   Nug 0.04847876   0.0000
< 2   Sph 0.58754476 889.8912
< > 
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... OK
* checking re-building of vignettes ... OK
* checking PDF version of manual ... OK