- using R version 2.15.3 (2013-03-01)
- using platform: i386-w64-mingw32 (32-bit)
- using session charset: ISO8859-1
- checking for file 'psgp/DESCRIPTION' ... OK
- this is package 'psgp' version '0.3-2'
- checking package namespace information ... OK
- checking package dependencies ... OK
- checking if this is a source package ... OK
- checking if there is a namespace ... 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 for left-over 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
** checking loading without being on the library search path ... 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 loading without being on the library search path ... 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 $(LAPACK_LIBS) ... OK
- checking compiled code ... OK
- checking sizes of PDF files under 'inst/doc' ... OK
- checking installed files from 'inst/doc' ... OK
- checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
- checking for unstated dependencies in tests ... OK
- checking tests ...
** running tests for arch 'i386' OK
Running 'meuse_psgp.R'
Comparing 'meuse_psgp.Rout' to 'meuse_psgp.Rout.save' ...1,35d0
<
<
< > library(psgp)
<
< Attaching package: 'lattice'
<
< The following object(s) are masked from 'package:evd':
<
< qq
<
< > set.seed(100)
< > # set up data:
< > data(meuse)
< > coordinates(meuse) = ~x+y
< > meuse$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"
< + )
< Warning messages:
< 1: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< 2: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< >
< > # run test:
< > checkSetup(psgpObject)
37,42c2,4
< >
< > # do interpolation steps:
< > psgpObject = estimateParameters(psgpObject)
< Range: 716.729191
< Sill: 0.766279
< Nugget: 0.007457
---
> Range: 727.943976
> Sill: 0.752485
> Nugget: 0.005943
45c7
< Defaulting to GAUSSIAN with variance 0.000075
---
> Defaulting to GAUSSIAN with variance 0.000059
359,360c321,322
< Range (P0) :716.729191
< Variance (P1) :0.766279
---
> Range (P0) :727.943976
> Variance (P1) :0.752485
363,364c325,326
< Length scale (P0) :716.729191
< Variance (P1) :0.766279
---
> Length scale (P0) :727.943976
> Variance (P1) :0.752485
370,375c332,337
< Variance (P0) :0.007457
< Finding optimal parametersCycle 1 Error 101.037136 Scale 1.000000
< Cycle 2 Error 99.254923 Scale 0.500000
< Cycle 3 Error 99.086845 Scale 0.250000
< Cycle 4 Error 98.047846 Scale 0.125000
< Cycle 5 Error 98.002947 Scale 0.062500
---
> Variance (P0) :0.005943
> Finding optimal parametersCycle 1 Error 101.557831 Scale 1.000000
> Cycle 2 Error 99.668320 Scale 0.500000
> Cycle 3 Error 99.416729 Scale 0.250000
> Cycle 4 Error 98.418564 Scale 0.125000
> Cycle 5 Error 98.357160 Scale 0.062500
377,381c339,343
< Cycle 1 Error 97.968412 Scale 1.000000
< Cycle 2 Error 97.507097 Scale 0.500000
< Cycle 3 Error 97.494582 Scale 0.250000
< Cycle 4 Error 97.494582 Scale 0.125000
< Cycle 5 Error 97.167484 Scale 0.500000
---
> Cycle 1 Error 98.328484 Scale 1.000000
> Cycle 2 Error 97.891931 Scale 0.500000
> Cycle 3 Error 97.885781 Scale 0.250000
> Cycle 4 Error 97.493976 Scale 0.125000
> Cycle 5 Error 97.476106 Scale 0.062500
383,387c345,349
< Cycle 1 Error 97.138869 Scale 1.000000
< Cycle 2 Error 97.044464 Scale 0.500000
< Cycle 3 Error 97.035699 Scale 0.250000
< Cycle 4 Error 97.023483 Scale 0.125000
< Cycle 5 Error 97.021590 Scale 0.062500
---
> Cycle 1 Error 97.472655 Scale 1.000000
> Cycle 2 Error 97.334270 Scale 0.500000
> Cycle 3 Error 97.327172 Scale 0.250000
> Cycle 4 Error 97.310690 Scale 0.125000
> Cycle 5 Error 97.310470 Scale 0.062500
389,391d350
< >
< > # make prediction
< > psgpObject = spatialPredict(psgpObject)
394c353
< Defaulting to GAUSSIAN with variance 0.000346
---
> Defaulting to GAUSSIAN with variance 0.000302
716,717c675,676
< Range (P0) :853.719568
< Variance (P1) :1.070282
---
> Range (P0) :844.492816
> Variance (P1) :1.111596
720,721c679,680
< Length scale (P0) :1306.632751
< Variance (P1) :0.881519
---
> Length scale (P0) :1752.485482
> Variance (P1) :0.793301
724,729c683
< Amplitude (P0) :0.023702
< >
< > # Plot prediction
< > # plotIntamap(psgpObject)
< > # plotIntamap(meuse, pch=1, cex=sqrt(meuse$value)/20, add=TRUE)
< >
---
> Amplitude (P0) :0.022145
Running 'psgp.R'
Comparing 'psgp.Rout' to 'psgp.Rout.save' ...1,28d0
<
<
< > library(psgp) # requires intamap
<
< 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)
30,34c2,4
< > krigingObject = preProcess(krigingObject)
< > krigingObject = estimateParameters(krigingObject)
< Range: 716.729191
< Sill: 0.766279
< Nugget: 0.007457
---
> Range: 727.943976
> Sill: 0.752485
> Nugget: 0.005943
37c7
< Defaulting to GAUSSIAN with variance 0.000075
---
> Defaulting to GAUSSIAN with variance 0.000059
351,352c321,322
< Range (P0) :716.729191
< Variance (P1) :0.766279
---
> Range (P0) :727.943976
> Variance (P1) :0.752485
355,356c325,326
< Length scale (P0) :716.729191
< Variance (P1) :0.766279
---
> Length scale (P0) :727.943976
> Variance (P1) :0.752485
362,367c332,337
< Variance (P0) :0.007457
< Finding optimal parametersCycle 1 Error 101.037136 Scale 1.000000
< Cycle 2 Error 99.254923 Scale 0.500000
< Cycle 3 Error 99.086845 Scale 0.250000
< Cycle 4 Error 98.047846 Scale 0.125000
< Cycle 5 Error 98.002947 Scale 0.062500
---
> Variance (P0) :0.005943
> Finding optimal parametersCycle 1 Error 101.557831 Scale 1.000000
> Cycle 2 Error 99.668320 Scale 0.500000
> Cycle 3 Error 99.416729 Scale 0.250000
> Cycle 4 Error 98.418564 Scale 0.125000
> Cycle 5 Error 98.357160 Scale 0.062500
369,373c339,343
< Cycle 1 Error 97.968412 Scale 1.000000
< Cycle 2 Error 97.507097 Scale 0.500000
< Cycle 3 Error 97.494582 Scale 0.250000
< Cycle 4 Error 97.494582 Scale 0.125000
< Cycle 5 Error 97.167484 Scale 0.500000
---
> Cycle 1 Error 98.328484 Scale 1.000000
> Cycle 2 Error 97.891931 Scale 0.500000
> Cycle 3 Error 97.885781 Scale 0.250000
> Cycle 4 Error 97.493976 Scale 0.125000
> Cycle 5 Error 97.476106 Scale 0.062500
375,379c345,349
< Cycle 1 Error 97.138869 Scale 1.000000
< Cycle 2 Error 97.044464 Scale 0.500000
< Cycle 3 Error 97.035699 Scale 0.250000
< Cycle 4 Error 97.023483 Scale 0.125000
< Cycle 5 Error 97.021590 Scale 0.062500
---
> Cycle 1 Error 97.472655 Scale 1.000000
> Cycle 2 Error 97.334270 Scale 0.500000
> Cycle 3 Error 97.327172 Scale 0.250000
> Cycle 4 Error 97.310690 Scale 0.125000
> Cycle 5 Error 97.310470 Scale 0.062500
381d350
< > krigingObject = spatialPredict(krigingObject)
384c353
< Defaulting to GAUSSIAN with variance 0.000346
---
> Defaulting to GAUSSIAN with variance 0.000302
700,701c669,670
< Range (P0) :853.719570
< Variance (P1) :1.070282
---
> Range (P0) :844.492802
> Variance (P1) :1.111596
704,705c673,674
< Length scale (P0) :1306.632748
< Variance (P1) :0.881519
---
> Length scale (P0) :1752.485475
> Variance (P1) :0.793301
708,742c677
< Amplitude (P0) :0.023702
< > krigingObject = postProcess(krigingObject)
< Warning message:
< In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< >
< > # Send predictions back to Java. Not sure how to deal with this spatial object though...?
< > summary(krigingObject$outputTable)
< x y mean variance
< Min. :179019 Min. :330013 Min. :4.764 Min. :0.03598
< 1st Qu.:179485 1st Qu.:330829 1st Qu.:5.308 1st Qu.:0.07071
< Median :180183 Median :331644 Median :5.847 Median :0.25432
< Mean :180183 Mean :331644 Mean :6.257 Mean :0.48081
< 3rd Qu.:180882 3rd Qu.:332459 3rd Qu.:7.478 3rd Qu.:0.69749
< Max. :181348 Max. :333275 Max. :8.178 Max. :1.91187
< excprob5.9 cumdistr5.9 quantile0.1
< Min. :0.0000083 Min. :0.00000 Min. :3.999
< 1st Qu.:0.0549936 1st Qu.:0.01972 1st Qu.:4.638
< Median :0.4629030 Median :0.53710 Median :5.227
< Mean :0.5165047 Mean :0.48350 Mean :5.491
< 3rd Qu.:0.9802820 3rd Qu.:0.94501 3rd Qu.:6.480
< Max. :1.0000000 Max. :0.99999 Max. :7.298
< > summary(krigingObject$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)
---
> Amplitude (P0) :0.022145
744,764d678
< 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' ...1,35d0
<
<
< > library(psgp)
<
< 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"
< + )
< Warning messages:
< 1: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< 2: In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< >
< > # run test:
< > checkSetup(psgpObject)
37,42c2,4
< >
< > # do interpolation steps:
< > psgpObject = estimateParameters(psgpObject)
< Range: 716.729191
< Sill: 0.766279
< Nugget: 0.007457
---
> Range: 727.943976
> Sill: 0.752485
> Nugget: 0.005943
45c7
< Defaulting to GAUSSIAN with variance 0.000075
---
> Defaulting to GAUSSIAN with variance 0.000059
359,360c321,322
< Range (P0) :716.729191
< Variance (P1) :0.766279
---
> Range (P0) :727.943976
> Variance (P1) :0.752485
363,364c325,326
< Length scale (P0) :716.729191
< Variance (P1) :0.766279
---
> Length scale (P0) :727.943976
> Variance (P1) :0.752485
370,375c332,337
< Variance (P0) :0.007457
< Finding optimal parametersCycle 1 Error 101.037136 Scale 1.000000
< Cycle 2 Error 99.254923 Scale 0.500000
< Cycle 3 Error 99.086845 Scale 0.250000
< Cycle 4 Error 98.047846 Scale 0.125000
< Cycle 5 Error 98.002947 Scale 0.062500
---
> Variance (P0) :0.005943
> Finding optimal parametersCycle 1 Error 101.557831 Scale 1.000000
> Cycle 2 Error 99.668320 Scale 0.500000
> Cycle 3 Error 99.416729 Scale 0.250000
> Cycle 4 Error 98.418564 Scale 0.125000
> Cycle 5 Error 98.357160 Scale 0.062500
377,381c339,343
< Cycle 1 Error 97.968412 Scale 1.000000
< Cycle 2 Error 97.507097 Scale 0.500000
< Cycle 3 Error 97.494582 Scale 0.250000
< Cycle 4 Error 97.494582 Scale 0.125000
< Cycle 5 Error 97.167484 Scale 0.500000
---
> Cycle 1 Error 98.328484 Scale 1.000000
> Cycle 2 Error 97.891931 Scale 0.500000
> Cycle 3 Error 97.885781 Scale 0.250000
> Cycle 4 Error 97.493976 Scale 0.125000
> Cycle 5 Error 97.476106 Scale 0.062500
383,387c345,349
< Cycle 1 Error 97.138869 Scale 1.000000
< Cycle 2 Error 97.044464 Scale 0.500000
< Cycle 3 Error 97.035699 Scale 0.250000
< Cycle 4 Error 97.023483 Scale 0.125000
< Cycle 5 Error 97.021590 Scale 0.062500
---
> Cycle 1 Error 97.472655 Scale 1.000000
> Cycle 2 Error 97.334270 Scale 0.500000
> Cycle 3 Error 97.327172 Scale 0.250000
> Cycle 4 Error 97.310690 Scale 0.125000
> Cycle 5 Error 97.310470 Scale 0.062500
389,391d350
< >
< > # make prediction
< > psgpObject = spatialPredict(psgpObject)
394c353
< Defaulting to GAUSSIAN with variance 0.000346
---
> Defaulting to GAUSSIAN with variance 0.000302
716,717c675,676
< Range (P0) :853.719595
< Variance (P1) :1.070282
---
> Range (P0) :844.492816
> Variance (P1) :1.111596
720,721c679,680
< Length scale (P0) :1306.632729
< Variance (P1) :0.881519
---
> Length scale (P0) :1752.485482
> Variance (P1) :0.793301
724,729c683
< Amplitude (P0) :0.023702
< >
< > # Plot prediction
< > # plotIntamap(psgpObject)
< > # plotIntamap(meuse, pch=1, cex=sqrt(meuse$value)/20, add=TRUE)
< >
---
> Amplitude (P0) :0.022145
Running 'psgp.R'
Comparing 'psgp.Rout' to 'psgp.Rout.save' ...1,28d0
<
<
< > library(psgp) # requires intamap
<
< Attaching package: 'lattice'
<
< The following object(s) are masked from 'package:evd':
<
< qq
<
< >
< > data(meuse)
< > observations = data.frame(x = meuse$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)
30,34c2,4
< > krigingObject = preProcess(krigingObject)
< > krigingObject = estimateParameters(krigingObject)
< Range: 716.729191
< Sill: 0.766279
< Nugget: 0.007457
---
> Range: 727.943976
> Sill: 0.752485
> Nugget: 0.005943
37c7
< Defaulting to GAUSSIAN with variance 0.000075
---
> Defaulting to GAUSSIAN with variance 0.000059
351,352c321,322
< Range (P0) :716.729191
< Variance (P1) :0.766279
---
> Range (P0) :727.943976
> Variance (P1) :0.752485
355,356c325,326
< Length scale (P0) :716.729191
< Variance (P1) :0.766279
---
> Length scale (P0) :727.943976
> Variance (P1) :0.752485
362,367c332,337
< Variance (P0) :0.007457
< Finding optimal parametersCycle 1 Error 101.037136 Scale 1.000000
< Cycle 2 Error 99.254923 Scale 0.500000
< Cycle 3 Error 99.086845 Scale 0.250000
< Cycle 4 Error 98.047846 Scale 0.125000
< Cycle 5 Error 98.002947 Scale 0.062500
---
> Variance (P0) :0.005943
> Finding optimal parametersCycle 1 Error 101.557831 Scale 1.000000
> Cycle 2 Error 99.668320 Scale 0.500000
> Cycle 3 Error 99.416729 Scale 0.250000
> Cycle 4 Error 98.418564 Scale 0.125000
> Cycle 5 Error 98.357160 Scale 0.062500
369,373c339,343
< Cycle 1 Error 97.968412 Scale 1.000000
< Cycle 2 Error 97.507097 Scale 0.500000
< Cycle 3 Error 97.494582 Scale 0.250000
< Cycle 4 Error 97.494582 Scale 0.125000
< Cycle 5 Error 97.167484 Scale 0.500000
---
> Cycle 1 Error 98.328484 Scale 1.000000
> Cycle 2 Error 97.891931 Scale 0.500000
> Cycle 3 Error 97.885781 Scale 0.250000
> Cycle 4 Error 97.493976 Scale 0.125000
> Cycle 5 Error 97.476106 Scale 0.062500
375,379c345,349
< Cycle 1 Error 97.138869 Scale 1.000000
< Cycle 2 Error 97.044464 Scale 0.500000
< Cycle 3 Error 97.035699 Scale 0.250000
< Cycle 4 Error 97.023483 Scale 0.125000
< Cycle 5 Error 97.021590 Scale 0.062500
---
> Cycle 1 Error 97.472655 Scale 1.000000
> Cycle 2 Error 97.334270 Scale 0.500000
> Cycle 3 Error 97.327172 Scale 0.250000
> Cycle 4 Error 97.310690 Scale 0.125000
> Cycle 5 Error 97.310470 Scale 0.062500
381d350
< > krigingObject = spatialPredict(krigingObject)
384c353
< Defaulting to GAUSSIAN with variance 0.000346
---
> Defaulting to GAUSSIAN with variance 0.000302
700,701c669,670
< Range (P0) :853.719627
< Variance (P1) :1.070282
---
> Range (P0) :844.492802
> Variance (P1) :1.111596
704,705c673,674
< Length scale (P0) :1306.632668
< Variance (P1) :0.881519
---
> Length scale (P0) :1752.485475
> Variance (P1) :0.793301
708,742c677
< Amplitude (P0) :0.023702
< > krigingObject = postProcess(krigingObject)
< Warning message:
< In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE, :
< there is no package called 'rgdal'
< >
< > # 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. :4.764 Min. :0.03600
< 1st Qu.:179485 1st Qu.:330829 1st Qu.:5.308 1st Qu.:0.07071
< Median :180183 Median :331644 Median :5.847 Median :0.25432
< Mean :180183 Mean :331644 Mean :6.256 Mean :0.48082
< 3rd Qu.:180882 3rd Qu.:332459 3rd Qu.:7.477 3rd Qu.:0.69749
< Max. :181348 Max. :333275 Max. :8.177 Max. :1.91187
< excprob5.9 cumdistr5.9 quantile0.1
< Min. :0.0000083 Min. :0.00000 Min. :3.999
< 1st Qu.:0.0549833 1st Qu.:0.01972 1st Qu.:4.638
< Median :0.4628215 Median :0.53718 Median :5.228
< Mean :0.5164843 Mean :0.48352 Mean :5.491
< 3rd Qu.:0.9802752 3rd Qu.:0.94502 3rd Qu.:6.480
< Max. :1.0000000 Max. :0.99999 Max. :7.298
< > 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)$krige_output)
---
> Amplitude (P0) :0.022145
744,764d678
< Object of class SpatialPointsDataFrame
< Coordinates:
< min max
< x1 179018.6 181348.1
< x2 330013.4 333274.7
< Is projected: NA
< proj4string : [NA]
< Number of points: 48
< Data attributes:
< var1.pred var1.var var1.stdev
< Min. :4.929 Min. :0.1153 Min. :0.3395
< 1st Qu.:5.518 1st Qu.:0.1615 1st Qu.:0.4018
< Median :6.047 Median :0.3582 Median :0.5963
< Mean :5.978 Mean :0.3827 Mean :0.5909
< 3rd Qu.:6.337 3rd Qu.:0.6026 3rd Qu.:0.7762
< Max. :7.472 Max. :0.6752 Max. :0.8217
< > autofitVariogram(value~1,krigingObject$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 vignette PDFs ... OK
- checking PDF version of manual ... OK