Last updated on 2025-03-14 05:49:59 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.1-3 | 21.76 | 183.36 | 205.12 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.1-3 | 16.83 | 128.37 | 145.20 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.1-3 | 354.45 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.1-3 | 351.60 | ERROR | |||
| r-devel-macos-arm64 | 2.1-3 | 89.00 | OK | |||
| r-devel-macos-x86_64 | 2.1-3 | 230.00 | OK | |||
| r-devel-windows-x86_64 | 2.1-3 | 37.00 | 249.00 | 286.00 | OK | |
| r-patched-linux-x86_64 | 2.1-3 | OK | ||||
| r-release-linux-x86_64 | 2.1-3 | 19.37 | 174.99 | 194.36 | OK | |
| r-release-macos-arm64 | 2.1-3 | 121.00 | OK | |||
| r-release-macos-x86_64 | 2.1-3 | 246.00 | OK | |||
| r-release-windows-x86_64 | 2.1-3 | 34.00 | 250.00 | 284.00 | OK | |
| r-oldrel-macos-arm64 | 2.1-3 | 123.00 | OK | |||
| r-oldrel-macos-x86_64 | 2.1-3 | 237.00 | OK | |||
| r-oldrel-windows-x86_64 | 2.1-3 | 40.00 | 290.00 | 330.00 | OK |
Version: 2.1-3
Check: tests
Result: ERROR
Running ‘allier.R’ [5s/16s]
Comparing ‘allier.Rout’ to ‘allier.Rout.save’ ... OK
Running ‘blockkr.R’ [5s/14s]
Comparing ‘blockkr.Rout’ to ‘blockkr.Rout.save’ ... OK
Running ‘covtable.R’ [5s/12s]
Comparing ‘covtable.Rout’ to ‘covtable.Rout.save’ ... OK
Running ‘cv.R’ [5s/13s]
Comparing ‘cv.Rout’ to ‘cv.Rout.save’ ... OK
Running ‘cv3d.R’ [6s/17s]
Comparing ‘cv3d.Rout’ to ‘cv3d.Rout.save’ ... OK
Running ‘fit.R’ [5s/12s]
Comparing ‘fit.Rout’ to ‘fit.Rout.save’ ... OK
Running ‘krige0.R’ [7s/18s]
Comparing ‘krige0.Rout’ to ‘krige0.Rout.save’ ... OK
Running ‘line.R’ [6s/14s]
Comparing ‘line.Rout’ to ‘line.Rout.save’ ... OK
Running ‘merge.R’ [5s/12s]
Comparing ‘merge.Rout’ to ‘merge.Rout.save’ ... OK
Running ‘na.action.R’ [5s/14s]
Comparing ‘na.action.Rout’ to ‘na.action.Rout.save’ ... OK
Running ‘rings.R’ [5s/13s]
Comparing ‘rings.Rout’ to ‘rings.Rout.save’ ... OK
Running ‘sim.R’ [5s/13s]
Comparing ‘sim.Rout’ to ‘sim.Rout.save’ ... OK
Running ‘stars.R’ [74m/81m]
Running ‘unproj.R’ [9s/15s]
Comparing ‘unproj.Rout’ to ‘unproj.Rout.save’ ... OK
Running ‘variogram.R’
Comparing ‘variogram.Rout’ to ‘variogram.Rout.save’ ... OK
Running ‘vdist.R’
Comparing ‘vdist.Rout’ to ‘vdist.Rout.save’ ... OK
Running ‘windst.R’
Running the tests in ‘tests/stars.R’ failed.
Complete output:
> Sys.setenv(TZ = "UTC")
>
> # 0. using sp:
>
> suppressPackageStartupMessages(library(sp))
> demo(meuse, ask = FALSE)
demo(meuse)
---- ~~~~~
> require(sp)
> crs = CRS("EPSG:28992")
> data("meuse")
> coordinates(meuse) <- ~x+y
> proj4string(meuse) <- crs
> data("meuse.grid")
> coordinates(meuse.grid) <- ~x+y
> gridded(meuse.grid) <- TRUE
> proj4string(meuse.grid) <- crs
> data("meuse.riv")
> meuse.riv <- SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
> proj4string(meuse.riv) <- crs
> data("meuse.area")
> meuse.area = SpatialPolygons(list(Polygons(list(Polygon(meuse.area)), "area")))
> proj4string(meuse.area) <- crs
> suppressPackageStartupMessages(library(gstat))
> v = variogram(log(zinc)~1, meuse)
> (v.fit = fit.variogram(v, vgm(1, "Sph", 900, 1)))
model psill range
1 Nug 0.05066243 0.0000
2 Sph 0.59060780 897.0209
> k_sp = krige(log(zinc)~1, meuse[-(1:5),], meuse[1:5,], v.fit)
[using ordinary kriging]
> k_sp_grd = krige(log(zinc)~1, meuse, meuse.grid, v.fit)
[using ordinary kriging]
>
> # 1. using sf:
> suppressPackageStartupMessages(library(sf))
> demo(meuse_sf, ask = FALSE, echo = FALSE)
> # reloads meuse as data.frame, so
> demo(meuse, ask = FALSE)
demo(meuse)
---- ~~~~~
> require(sp)
> crs = CRS("EPSG:28992")
> data("meuse")
> coordinates(meuse) <- ~x+y
> proj4string(meuse) <- crs
> data("meuse.grid")
> coordinates(meuse.grid) <- ~x+y
> gridded(meuse.grid) <- TRUE
> proj4string(meuse.grid) <- crs
> data("meuse.riv")
> meuse.riv <- SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
> proj4string(meuse.riv) <- crs
> data("meuse.area")
> meuse.area = SpatialPolygons(list(Polygons(list(Polygon(meuse.area)), "area")))
> proj4string(meuse.area) <- crs
>
> v = variogram(log(zinc)~1, meuse_sf)
> (v.fit = fit.variogram(v, vgm(1, "Sph", 900, 1)))
model psill range
1 Nug 0.05066243 0.0000
2 Sph 0.59060780 897.0209
> k_sf = krige(log(zinc)~1, meuse_sf[-(1:5),], meuse_sf[1:5,], v.fit)
[using ordinary kriging]
>
> all.equal(k_sp, as(k_sf, "Spatial"), check.attributes = FALSE)
[1] TRUE
> all.equal(k_sp, as(k_sf, "Spatial"), check.attributes = TRUE)
[1] "Attributes: < Component \"bbox\": Attributes: < Component \"dimnames\": Component 1: 2 string mismatches > >"
[2] "Attributes: < Component \"coords\": Attributes: < Component \"dimnames\": Component 2: 2 string mismatches > >"
[3] "Attributes: < Component \"coords.nrs\": Numeric: lengths (2, 0) differ >"
>
> # 2. using stars for grid:
>
> suppressPackageStartupMessages(library(stars))
> st = st_as_stars(meuse.grid)
> st_crs(st)
Coordinate Reference System:
User input: Amersfoort / RD New
wkt:
PROJCRS["Amersfoort / RD New",
BASEGEOGCRS["Amersfoort",
DATUM["Amersfoort",
ELLIPSOID["Bessel 1841",6377397.155,299.1528128,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4289]],
CONVERSION["RD New",
METHOD["Oblique Stereographic",
ID["EPSG",9809]],
PARAMETER["Latitude of natural origin",52.1561605555556,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",5.38763888888889,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9999079,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",155000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",463000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["easting (X)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["northing (Y)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Engineering survey, topographic mapping."],
AREA["Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone."],
BBOX[50.75,3.2,53.7,7.22]],
ID["EPSG",28992]]
>
> # compare inputs:
> sp = as(st, "Spatial")
> fullgrid(meuse.grid) = TRUE
> all.equal(sp, meuse.grid["dist"], check.attributes = FALSE)
[1] "Names: Lengths (5, 1) differ (string compare on first 1)"
[2] "Names: 1 string mismatch"
> all.equal(sp, meuse.grid["dist"], check.attributes = TRUE, use.names = FALSE)
[1] "Names: Lengths (5, 1) differ (string compare on first 1)"
[2] "Names: 1 string mismatch"
[3] "Attributes: < Component 3: Names: 1 string mismatch >"
[4] "Attributes: < Component 3: Length mismatch: comparison on first 1 components >"
[5] "Attributes: < Component 3: Component 1: Mean relative difference: 1.08298 >"
[6] "Attributes: < Component 4: Attributes: < Component 2: names for current but not for target > >"
[7] "Attributes: < Component 4: Attributes: < Component 3: names for current but not for target > >"
>
> # kriging:
> st_crs(st) = st_crs(meuse_sf) = NA # GDAL roundtrip messes them up!
> k_st = if (Sys.getenv("USER") == "travis") {
+ try(krige(log(zinc)~1, meuse_sf, st, v.fit))
+ } else {
+ krige(log(zinc)~1, meuse_sf, st, v.fit)
+ }
[using ordinary kriging]
> k_st
stars object with 2 dimensions and 2 attributes
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
var1.pred 4.7765547 5.2376293 5.5728839 5.7072287 6.1717619 7.4399911 5009
var1.var 0.0854949 0.1372864 0.1621838 0.1853319 0.2116152 0.5002756 5009
dimension(s):
from to offset delta x/y
x 1 78 178440 40 [x]
y 1 104 333760 -40 [y]
>
> # handle factors, when going to stars?
> k_sp_grd$cls = cut(k_sp_grd$var1.pred, c(0, 5, 6, 7, 8, 9))
> st_as_stars(k_sp_grd)
stars object with 2 dimensions and 3 attributes
attribute(s):
var1.pred var1.var cls
Min. :4.777 Min. :0.0855 (0,5]: 316
1st Qu.:5.238 1st Qu.:0.1373 (5,6]:1778
Median :5.573 Median :0.1622 (6,7]: 962
Mean :5.707 Mean :0.1853 (7,8]: 47
3rd Qu.:6.172 3rd Qu.:0.2116 (8,9]: 0
Max. :7.440 Max. :0.5003 NA's :5009
NA's :5009 NA's :5009
dimension(s):
from to offset delta refsys x/y
x 1 78 178440 40 Amersfoort / RD New [x]
y 1 104 333760 -40 Amersfoort / RD New [y]
> if (require(raster, quietly = TRUE)) {
+ print(st_as_stars(raster::stack(k_sp_grd))) # check
+ print(all.equal(st_redimension(st_as_stars(k_sp_grd)), st_as_stars(raster::stack(k_sp_grd)), check.attributes=FALSE))
+ }
stars object with 3 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
var1.pred 0.0854949 0.2116778 2 2.710347 5.237542 7.439991 15027
dimension(s):
from to offset delta refsys values
x 1 78 178440 40 Amersfoort / RD New NULL
y 1 104 333760 -40 Amersfoort / RD New NULL
band 1 3 NA NA NA var1.pred, var1.var , cls
x/y
x [x]
y [y]
band
[1] TRUE
>
> suppressPackageStartupMessages(library(spacetime))
>
> tm = as.POSIXct("2019-02-25 15:37:24 CET")
> n = 4
> s = stars:::st_stars(list(foo = array(1:(n^3), rep(n,3))),
+ stars:::create_dimensions(list(
+ x = stars:::create_dimension(from = 1, to = n, offset = 10, delta = 0.5),
+ y = stars:::create_dimension(from = 1, to = n, offset = 0, delta = -0.7),
+ time = stars:::create_dimension(values = tm + 1:n)),
+ raster = stars:::get_raster(dimensions = c("x", "y")))
+ )
> s
stars object with 3 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
foo 1 16.75 32.5 32.5 48.25 64
dimension(s):
from to offset delta refsys x/y
x 1 4 10 0.5 NA [x]
y 1 4 0 -0.7 NA [y]
time 1 4 2019-02-25 15:37:25 UTC 1 secs POSIXct
>
> as.data.frame(s)
x y time foo
1 10.25 -0.35 2019-02-25 15:37:25 1
2 10.75 -0.35 2019-02-25 15:37:25 2
3 11.25 -0.35 2019-02-25 15:37:25 3
4 11.75 -0.35 2019-02-25 15:37:25 4
5 10.25 -1.05 2019-02-25 15:37:25 5
6 10.75 -1.05 2019-02-25 15:37:25 6
7 11.25 -1.05 2019-02-25 15:37:25 7
8 11.75 -1.05 2019-02-25 15:37:25 8
9 10.25 -1.75 2019-02-25 15:37:25 9
10 10.75 -1.75 2019-02-25 15:37:25 10
11 11.25 -1.75 2019-02-25 15:37:25 11
12 11.75 -1.75 2019-02-25 15:37:25 12
13 10.25 -2.45 2019-02-25 15:37:25 13
14 10.75 -2.45 2019-02-25 15:37:25 14
15 11.25 -2.45 2019-02-25 15:37:25 15
16 11.75 -2.45 2019-02-25 15:37:25 16
17 10.25 -0.35 2019-02-25 15:37:26 17
18 10.75 -0.35 2019-02-25 15:37:26 18
19 11.25 -0.35 2019-02-25 15:37:26 19
20 11.75 -0.35 2019-02-25 15:37:26 20
21 10.25 -1.05 2019-02-25 15:37:26 21
22 10.75 -1.05 2019-02-25 15:37:26 22
23 11.25 -1.05 2019-02-25 15:37:26 23
24 11.75 -1.05 2019-02-25 15:37:26 24
25 10.25 -1.75 2019-02-25 15:37:26 25
26 10.75 -1.75 2019-02-25 15:37:26 26
27 11.25 -1.75 2019-02-25 15:37:26 27
28 11.75 -1.75 2019-02-25 15:37:26 28
29 10.25 -2.45 2019-02-25 15:37:26 29
30 10.75 -2.45 2019-02-25 15:37:26 30
31 11.25 -2.45 2019-02-25 15:37:26 31
32 11.75 -2.45 2019-02-25 15:37:26 32
33 10.25 -0.35 2019-02-25 15:37:27 33
34 10.75 -0.35 2019-02-25 15:37:27 34
35 11.25 -0.35 2019-02-25 15:37:27 35
36 11.75 -0.35 2019-02-25 15:37:27 36
37 10.25 -1.05 2019-02-25 15:37:27 37
38 10.75 -1.05 2019-02-25 15:37:27 38
39 11.25 -1.05 2019-02-25 15:37:27 39
40 11.75 -1.05 2019-02-25 15:37:27 40
41 10.25 -1.75 2019-02-25 15:37:27 41
42 10.75 -1.75 2019-02-25 15:37:27 42
43 11.25 -1.75 2019-02-25 15:37:27 43
44 11.75 -1.75 2019-02-25 15:37:27 44
45 10.25 -2.45 2019-02-25 15:37:27 45
46 10.75 -2.45 2019-02-25 15:37:27 46
47 11.25 -2.45 2019-02-25 15:37:27 47
48 11.75 -2.45 2019-02-25 15:37:27 48
49 10.25 -0.35 2019-02-25 15:37:28 49
50 10.75 -0.35 2019-02-25 15:37:28 50
51 11.25 -0.35 2019-02-25 15:37:28 51
52 11.75 -0.35 2019-02-25 15:37:28 52
53 10.25 -1.05 2019-02-25 15:37:28 53
54 10.75 -1.05 2019-02-25 15:37:28 54
55 11.25 -1.05 2019-02-25 15:37:28 55
56 11.75 -1.05 2019-02-25 15:37:28 56
57 10.25 -1.75 2019-02-25 15:37:28 57
58 10.75 -1.75 2019-02-25 15:37:28 58
59 11.25 -1.75 2019-02-25 15:37:28 59
60 11.75 -1.75 2019-02-25 15:37:28 60
61 10.25 -2.45 2019-02-25 15:37:28 61
62 10.75 -2.45 2019-02-25 15:37:28 62
63 11.25 -2.45 2019-02-25 15:37:28 63
64 11.75 -2.45 2019-02-25 15:37:28 64
> plot(s, col = sf.colors(), axes = TRUE)
> (s.stfdf = as(s, "STFDF"))
An object of class "STFDF"
Slot "data":
foo
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
61 61
62 62
63 63
64 64
Slot "sp":
Object of class SpatialPixels
Grid topology:
cellcentre.offset cellsize cells.dim
x 10.25 0.5 4
y -2.45 0.7 4
SpatialPoints:
x y
[1,] 10.25 -0.35
[2,] 10.75 -0.35
[3,] 11.25 -0.35
[4,] 11.75 -0.35
[5,] 10.25 -1.05
[6,] 10.75 -1.05
[7,] 11.25 -1.05
[8,] 11.75 -1.05
[9,] 10.25 -1.75
[10,] 10.75 -1.75
[11,] 11.25 -1.75
[12,] 11.75 -1.75
[13,] 10.25 -2.45
[14,] 10.75 -2.45
[15,] 11.25 -2.45
[16,] 11.75 -2.45
Coordinate Reference System (CRS) arguments: NA
Slot "time":
timeIndex
2019-02-25 15:37:25 1
2019-02-25 15:37:26 2
2019-02-25 15:37:27 3
2019-02-25 15:37:28 4
Slot "endTime":
[1] "2019-02-25 15:37:26 UTC" "2019-02-25 15:37:27 UTC"
[3] "2019-02-25 15:37:28 UTC" "2019-02-25 15:37:29 UTC"
> stplot(s.stfdf, scales = list(draw = TRUE))
>
> (s2 = st_as_stars(s.stfdf))
stars object with 3 dimensions and 1 attribute
attribute(s):
Min. 1st Qu. Median Mean 3rd Qu. Max.
foo 1 16.75 32.5 32.5 48.25 64
dimension(s):
from to offset delta refsys x/y
x 1 4 10 0.5 NA [x]
y 1 4 -1.11e-16 -0.7 NA [y]
time 1 4 2019-02-25 15:37:25 UTC 1 secs POSIXct
> plot(s2, col = sf.colors(), axes = TRUE)
> all.equal(s, s2, check.attributes = FALSE)
[1] TRUE
>
> # multiple simulations:
> data(meuse, package = "sp")
> data(meuse.grid, package = "sp")
> coordinates(meuse.grid) <- ~x+y
> gridded(meuse.grid) <- TRUE
> meuse.grid = st_as_stars(meuse.grid)
> meuse_sf = st_as_sf(meuse, coords = c("x", "y"))
> g = gstat(NULL, "zinc", zinc~1, meuse_sf, model = vgm(1, "Exp", 300), nmax = 10)
> g = gstat(g, "lead", lead~1, meuse_sf, model = vgm(1, "Exp", 300), nmax = 10, fill.cross = TRUE)
> set.seed(123)
> ## IGNORE_RDIFF_BEGIN
> (p = predict(g, meuse.grid, nsim = 5))
drawing 5 multivariate GLS realisations of beta...
Flavor: r-devel-linux-x86_64-fedora-gcc