R Under development (unstable) (2025-08-21 r88668 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > > # REDUNDANT cat("CANNOT TEST LGCPMETHODS ...\n") > > library(lgcp) Welcome to 'lgcp': Log-Gaussian Cox Process B. M. Taylor & T. M. Davies & B. S. Rowlingson & P. J. Diggle. Type '?lgcp' for details. Type 'lgcpvignette()' to view the basic package vignette. Type 'lgcpbayes()' to view the Bayesian package vignette. Type 'citation("lgcp")' to view the citation for this package. Please see the lgcp package NEWS file for latest additions, changes and bug fixes. Note, RandomFields covariance functions are currently unavailable. > library(spatstat.explore) Loading required package: spatstat.data Loading required package: spatstat.univar spatstat.univar 3.1-4 Loading required package: spatstat.geom spatstat.geom 3.5-0 Loading required package: spatstat.random spatstat.random 3.4-1 Loading required package: nlme spatstat.explore 3.5-2 > library(spatstat.utils) > library(sp) > library(raster) Attaching package: 'raster' The following object is masked from 'package:nlme': getData > > set.seed(1) > > sd <- lgcpSimSpatial() FFT Grid size: [64 , 64] Warning message: 23 points were rejected as lying outside the specified window > > xyt <- lgcpSim() FFT Grid size: [64 , 64] | |======== | 11% | |================ | 22% | |======================= | 33% | |=============================== | 44% | |======================================= | 56% | |=============================================== | 67% | |====================================================== | 78% | |============================================================== | 89% | |======================================================================| 100% Warning message: In temporalAtRisk.function(f, tlim, warn = warn) : No xyt specified, given temporal intensity treated is treated as known > > # check plotting > > plot(sd) > plot(xyt) > > # now check fitting algorithms > > sdsave <- file.path(tempdir(),"lgm") > xytsave <- file.path(tempdir(),"lg") > > exceed <- exceedProbs(c(1.5,2)) > > lgm <- lgcpPredictSpatial( sd=sd, + model.parameters=lgcppars(sigma=2,phi=0.1), + spatial.covmodel="exponential", + cellwidth=0.1, + spatial.intensity=density(sd), + mcmc.control=mcmcpars(mala.length=200,burnin=20, + retain=20,adaptivescheme=andrieuthomsh(inith=1,alpha=0.5,C=1, + targetacceptance=0.574)), + output.control=setoutput(gridfunction= + dump2dir(dirname=sdsave,forceSave=TRUE), + gridmeans=MonteCarloAverage("exceed")), + gradtrunc=Inf, # no gradient truncation + ext=2) FFT Grid size: [32 , 32] WARNING: disk space required for saving is approximately 0.02 Mb, Netcdf file: D:\temp\2025_08_22_19_05_18_31607\Rtmp6t41By/lgm/simout.nc created | |== | 5% : Burn-in | |===== | 11% : Burn-in | |======= | 16% : Burn-in | |========= | 21% : Burn-in | |============ | 26% : Burn-in | |============== | 32% : Burn-in | |================= | 37% : Burn-in | |=================== | 42% : Burn-in | |===================== | 47% : Burn-in | |======================== | 53% : Burn-in | |========================== | 58% : Burn-in | |============================ | 63% : Burn-in | |=============================== | 68% : Burn-in | |================================= | 74% : Burn-in | |==================================== | 79% : Burn-in | |====================================== | 84% : Burn-in | |======================================== | 89% : Burn-in | |=========================================== | 95% : Burn-in | |=============================================| 100% : Burn-in | | | 1% : Run 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spatial.intensity=density(xyt), + temporal.intensity=function(x){return(100)}, + mcmc.control=mcmcpars(mala.length=200,burnin=20, + retain=20,adaptivescheme=andrieuthomsh(inith=0.01,alpha=0.5,C=1,targetacceptance=0.574)), + output.control=setoutput(gridfunction= + dump2dir(dirname=xytsave,forceSave=TRUE), + gridmeans=MonteCarloAverage("exceed")), + autorotate=FALSE, + gradtrunc=Inf) Warning in lgcpPredict(xyt = xyt, T = 8, laglength = 4, model.parameters = lgcppars(sigma = 2, : Converting T into integer value, see ?as.integer Warning in lgcpPredict(xyt = xyt, T = 8, laglength = 4, model.parameters = lgcppars(sigma = 2, : Converting laglength into integer values, see ?as.integer Warning in lgcpPredict(xyt = xyt, T = 8, laglength = 4, model.parameters = lgcppars(sigma = 2, : Converting xyt$t into integer values, see ?as.integer FFT Grid size: [32 , 32] WARNING: disk space required for saving is approximately 0.02 Mb, Netcdf file: D:\temp\2025_08_22_19_05_18_31607\Rtmp6t41By/lg/simout.nc 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Length: 1 Matsize: [ 16 , 16 ] > meanfield(lg) lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > varfield(lgm) lgcpgrid object. Length: 1 Matsize: [ 16 , 16 ] > varfield(lg) lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > rr(lgm) lgcpgrid object. Length: 1 Matsize: [ 16 , 16 ] > rr(lg) lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > serr(lgm) lgcpgrid object. Length: 1 Matsize: [ 16 , 16 ] > serr(lg) lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > intens(lgm) [1] 16 16 lgcpgrid object. Length: 1 Matsize: [ 16 , 16 ] > intens(lg) [1] 16 16 lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > seintens(lgm) lgcpgrid object. Length: 1 Matsize: [ 16 , 16 ] > seintens(lg) lgcpgrid object. Length: 5 Matsize: [ 16 , 16 ] > > > # now check conversion > > as.array(lgm$y.mean) , , 1 [,1] [,2] [,3] [,4] [,5] [,6] [1,] -2.8562712 -2.6720829 -4.1716362 -4.341476325 -3.3391237 -2.2973844 [2,] -2.2924831 -1.9038685 -2.9399855 -3.779645371 -4.2422542 -3.3618382 [3,] -2.3310343 -1.6669850 -1.5605267 -1.853716217 -2.8641735 -1.9091339 [4,] -2.5006823 -2.3426668 -3.3913567 -1.237167046 -2.9731461 -1.5932486 [5,] -3.9633508 -4.6269848 -2.1579590 0.829186288 -0.7546476 -0.6200280 [6,] -3.3695951 -5.8958513 -3.0506446 0.048561334 0.6268950 -1.7541101 [7,] -4.2463514 -2.8651411 -4.3806260 -2.337235000 -1.0434394 -1.8547938 [8,] -1.7786856 -3.4748470 -1.6085408 0.939160790 -0.7757700 -1.9002626 [9,] -0.8375963 -2.4575745 -0.4684239 -1.316979190 -2.5195693 -2.3499766 [10,] -3.4519296 -2.3187874 -1.9723325 -1.104843428 -2.4955406 -1.1903768 [11,] -1.6108297 -0.4723489 -3.8983365 -1.300557433 -3.0536038 -1.4050360 [12,] -0.8898868 -2.0888121 -5.1655494 -3.055742806 -2.3301146 -1.0167969 [13,] -2.0327192 -1.0438099 -2.1759817 -1.426639165 -0.4210945 -3.0101976 [14,] -2.1467608 -1.4710988 -1.1382424 -1.986015229 -0.2042578 -1.8075652 [15,] -2.1274005 -2.2576185 -0.6016715 -0.000295192 -1.3659537 -0.6038447 [16,] -1.9518844 -2.1865152 -0.4993777 -0.596495945 -2.8242375 -2.1516736 [,7] [,8] [,9] [,10] [,11] [,12] [1,] -2.5010138 -3.42285616 -3.04344888 -3.39670068 -1.3727848 0.05879353 [2,] -3.5654406 -3.54307730 -3.78791357 -3.48368385 0.1927845 -1.97021095 [3,] -2.2541478 -1.64212439 -3.26767122 -4.27753607 0.4650946 -1.03693511 [4,] -3.0265417 -2.45309219 -1.77058101 -2.46154991 -2.8256303 -1.40419877 [5,] -1.5444281 -0.83149670 -2.36739031 -2.85319247 -3.7216709 -2.14197330 [6,] -1.5808226 -2.14411458 -4.22115798 -3.16136491 -3.5062685 -3.11233584 [7,] -1.7562348 -2.57928876 -2.40303923 -2.65792074 -1.1949936 -0.63638084 [8,] -2.0920384 -2.07863209 -4.13133495 -1.96925596 1.3460792 1.74230291 [9,] -0.9364289 -0.93841821 -4.13660261 -2.28592383 -1.1279458 -0.96835922 [10,] 0.1656676 -0.67268866 -3.18296689 -0.96578842 -0.7182940 -0.46594551 [11,] -1.3878694 -1.52229704 0.08774107 -1.79965325 -2.3822338 -1.07992693 [12,] -0.5543015 -0.01697613 -2.98034974 -0.05867336 -1.0403756 -2.85113089 [13,] -0.8306581 -1.23639505 -0.20772522 0.18333197 -0.1140661 -0.26750427 [14,] -0.7541764 -0.95473727 0.89705580 0.24328314 0.3458440 0.14155697 [15,] -1.3339217 -0.51481549 -1.79432115 -1.54524659 -1.1631490 -1.70135029 [16,] -3.9625844 -3.25655183 -2.84819021 -3.16286479 -2.6698152 -1.61361949 [,13] [,14] [,15] [,16] [1,] -1.81730209 -2.0174624 -0.1702415 -0.6793184 [2,] -0.20218163 -3.2315473 -2.3991369 1.3146527 [3,] -0.57932382 -1.9116708 -0.9859012 0.4129744 [4,] -1.02631786 -0.1276615 0.1521228 -1.9521516 [5,] -0.76096521 -2.2186121 -2.3437914 -1.4446096 [6,] -1.94648824 -2.3120439 -3.6229907 -4.6302679 [7,] -1.45525748 -0.6648328 -2.7503151 -0.2091537 [8,] -0.03298162 -1.1062377 -3.9922828 -1.3485443 [9,] -2.01763416 -0.2952399 -3.3646736 -1.8419460 [10,] -0.22337603 -1.7283071 -0.9498217 -0.8159199 [11,] -0.18986944 -0.7558743 -1.7919381 -1.6191935 [12,] -1.93471888 -0.9921065 -2.0119647 -1.0297746 [13,] -0.55431459 -2.4740895 -1.0014783 -2.4936415 [14,] -0.57186191 -3.6888728 -3.3645056 -4.0808664 [15,] -2.16281734 -3.2526473 -0.8492190 -2.0024890 [16,] -3.12101190 -3.4469273 -2.8911099 -2.4209902 > as.array(lg$y.mean) , , 1 [,1] [,2] [,3] [,4] [,5] [,6] [1,] -3.2871060 -1.742714 -2.3689888 -2.0047342 -1.2640217 -1.9982705 [2,] -2.9136136 -1.302055 -1.1970713 -1.8105276 -1.6819094 -1.8455312 [3,] -2.1382553 -2.165281 -2.6062506 -2.3179128 -3.0016361 -2.8701289 [4,] -1.4172207 -2.065171 -2.1377846 -3.4420040 -5.1458690 -3.0093363 [5,] -1.8311196 -2.949503 -2.9651653 -3.0846501 -4.9131869 -2.6197973 [6,] -1.4285263 -1.674985 -3.1069765 -1.3949359 -3.2554631 -3.2620936 [7,] -1.9857028 -1.704956 -2.3406612 -0.9411565 -2.1110187 -1.5609551 [8,] -3.1531732 -1.667979 -0.2616109 0.7939099 -0.1643552 0.1514163 [9,] -3.6332893 -2.339159 -1.1773063 1.4098435 -1.0421643 -0.5694856 [10,] -1.6079724 -1.239907 -0.1126911 2.9070274 1.8586296 -1.1466029 [11,] -0.5690956 -1.793233 -0.2782758 1.7958600 0.7610438 0.0373569 [12,] -2.6756426 -2.729422 -2.1207511 -1.3656270 -2.0109015 -1.7900442 [13,] -3.1710942 -1.717064 -2.0397637 -1.3320727 -2.3120060 -1.9421243 [14,] -3.3146740 -4.730987 -4.3024152 -2.8554250 -2.9414306 -2.8283587 [15,] -3.4737049 -3.770431 -3.1450117 -1.8677072 -2.2866403 -3.3358844 [16,] -1.6454155 -3.275253 -0.2991627 0.2808755 -2.0339785 -2.1736448 [,7] [,8] [,9] [,10] [,11] [,12] [1,] -2.57489218 -2.3243255 -3.0070618 -2.0219298 -2.39575261 -0.6170183 [2,] -2.62735610 -1.8095275 -1.8553303 -2.3618159 -2.47310952 -2.3972059 [3,] -2.65294311 -1.0108349 -2.1093754 -1.6365695 -3.03247666 -3.4371209 [4,] -2.87722760 -2.8507906 -1.2034448 -2.8504405 -1.55779618 -1.2960785 [5,] -1.73482467 -2.7651349 -2.6600438 -1.7475693 -1.96615891 -0.5051111 [6,] -1.55388160 -2.0298976 -1.4021966 -1.8855514 -1.04642741 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-2.2551554 -0.7731682 [11,] -1.5422086 -1.5162559 -1.3981962 -1.716399 -1.9201049 -1.4489514 [12,] -0.9286972 -1.0173408 -0.8114453 -1.401444 -2.6411824 -3.5081086 [13,] -1.0310144 -1.5051613 -3.1074206 -2.542145 -2.0139965 -2.7169221 [14,] -1.2601997 -0.9035410 -0.8115148 -2.103324 -3.5718598 -3.1854355 [15,] -2.3822825 -1.1965263 -1.8857873 -1.692040 -2.8104102 -3.3818317 [16,] -2.4466553 -0.7334052 -1.3073938 -2.602704 -2.5537977 -3.3034143 [,13] [,14] [,15] [,16] [1,] -2.954165 -1.3068557 -1.7155571 -2.8711670 [2,] -2.831447 -1.8025721 -0.7021949 -0.6038477 [3,] -2.671843 -2.7837206 -1.4440338 -1.3010558 [4,] -1.371642 -2.3707022 -1.7184953 -1.9043386 [5,] -3.046988 -2.2844989 -2.7858697 -2.7347350 [6,] -2.136846 -2.0764240 -2.3656498 -1.5349431 [7,] -1.485527 -3.6080767 -1.7834775 -1.9923068 [8,] -2.159671 -2.3433912 -1.6663483 -2.1411508 [9,] -1.974536 -1.8700526 -1.1130324 -1.6444930 [10,] -1.526786 -2.3185413 -1.9315960 -0.3627363 [11,] -1.079379 -0.2144062 -2.1567752 -1.9715424 [12,] -1.379904 -1.4191772 -3.1542404 -3.5514226 [13,] -2.480933 -1.5266328 -1.6392073 -1.1372558 [14,] -2.956895 -2.9890284 -2.3610452 -1.6416803 [15,] -5.027488 -2.0716461 -1.5079855 -0.7073722 [16,] -3.443790 -2.7759842 -1.1179179 -2.3933001 > > raster(meanfield(lgm)) class : RasterLayer dimensions : 16, 16, 256 (nrow, ncol, ncell) resolution : 0.1, 0.1 (x, y) extent : -0.3, 1.3, -0.3, 1.3 (xmin, xmax, ymin, ymax) crs : NA source : memory names : layer values : -5.895851, 1.742303 (min, max) > raster(varfield(lg)) class : RasterBrick dimensions : 16, 16, 256, 5 (nrow, ncol, ncell, nlayers) resolution : 0.1, 0.1 (x, y) extent : -0.3, 1.3, -0.3, 1.3 (xmin, xmax, ymin, ymax) crs : NA source : memory names : Time4, Time5, Time6, Time7, Time8 min values : 0.06924851, 0.03464016, 0.04539036, 0.07853337, 0.04933288 max values : 2.807208, 3.417839, 2.846012, 2.940088, 3.417774 > > as.SpatialPixelsDataFrame(lg$y.mean) Object of class SpatialPixelsDataFrame Object of class 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0.15 1.15 [230,] 0.25 1.15 [231,] 0.35 1.15 [232,] 0.45 1.15 [233,] 0.55 1.15 [234,] 0.65 1.15 [235,] 0.75 1.15 [236,] 0.85 1.15 [237,] 0.95 1.15 [238,] 1.05 1.15 [239,] 1.15 1.15 [240,] 1.25 1.15 [241,] -0.25 1.25 [242,] -0.15 1.25 [243,] -0.05 1.25 [244,] 0.05 1.25 [245,] 0.15 1.25 [246,] 0.25 1.25 [247,] 0.35 1.25 [248,] 0.45 1.25 [249,] 0.55 1.25 [250,] 0.65 1.25 [251,] 0.75 1.25 [252,] 0.85 1.25 [253,] 0.95 1.25 [254,] 1.05 1.25 [255,] 1.15 1.25 [256,] 1.25 1.25 Coordinate Reference System (CRS) arguments: NA Data summary: outTime4 outTime5 outTime6 outTime7 Min. :-5.1459 Min. :-4.273 Min. :-4.761 Min. :-4.8505 1st Qu.:-2.4732 1st Qu.:-2.654 1st Qu.:-2.584 1st Qu.:-2.3862 Median :-1.7005 Median :-2.042 Median :-1.881 Median :-1.7024 Mean :-1.6531 Mean :-2.025 Mean :-1.862 Mean :-1.7682 3rd Qu.:-0.9966 3rd Qu.:-1.413 3rd Qu.:-1.178 3rd Qu.:-1.0618 Max. : 2.9070 Max. : 0.815 Max. : 2.035 Max. : 0.6877 outTime8 Min. :-5.027 1st Qu.:-2.289 Median :-1.677 Mean :-1.598 3rd Qu.:-1.082 Max. : 2.101 > as.SpatialPixelsDataFrame(lgm$y.var) Object of class SpatialPixelsDataFrame Object of class SpatialPixels Grid topology: cellcentre.offset cellsize cells.dim Var1 -0.25 0.1 16 Var2 -0.25 0.1 16 SpatialPoints: Var1 Var2 [1,] -0.25 -0.25 [2,] -0.15 -0.25 [3,] -0.05 -0.25 [4,] 0.05 -0.25 [5,] 0.15 -0.25 [6,] 0.25 -0.25 [7,] 0.35 -0.25 [8,] 0.45 -0.25 [9,] 0.55 -0.25 [10,] 0.65 -0.25 [11,] 0.75 -0.25 [12,] 0.85 -0.25 [13,] 0.95 -0.25 [14,] 1.05 -0.25 [15,] 1.15 -0.25 [16,] 1.25 -0.25 [17,] -0.25 -0.15 [18,] -0.15 -0.15 [19,] -0.05 -0.15 [20,] 0.05 -0.15 [21,] 0.15 -0.15 [22,] 0.25 -0.15 [23,] 0.35 -0.15 [24,] 0.45 -0.15 [25,] 0.55 -0.15 [26,] 0.65 -0.15 [27,] 0.75 -0.15 [28,] 0.85 -0.15 [29,] 0.95 -0.15 [30,] 1.05 -0.15 [31,] 1.15 -0.15 [32,] 1.25 -0.15 [33,] -0.25 -0.05 [34,] -0.15 -0.05 [35,] -0.05 -0.05 [36,] 0.05 -0.05 [37,] 0.15 -0.05 [38,] 0.25 -0.05 [39,] 0.35 -0.05 [40,] 0.45 -0.05 [41,] 0.55 -0.05 [42,] 0.65 -0.05 [43,] 0.75 -0.05 [44,] 0.85 -0.05 [45,] 0.95 -0.05 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[107,] 0.75 0.35 [108,] 0.85 0.35 [109,] 0.95 0.35 [110,] 1.05 0.35 [111,] 1.15 0.35 [112,] 1.25 0.35 [113,] -0.25 0.45 [114,] -0.15 0.45 [115,] -0.05 0.45 [116,] 0.05 0.45 [117,] 0.15 0.45 [118,] 0.25 0.45 [119,] 0.35 0.45 [120,] 0.45 0.45 [121,] 0.55 0.45 [122,] 0.65 0.45 [123,] 0.75 0.45 [124,] 0.85 0.45 [125,] 0.95 0.45 [126,] 1.05 0.45 [127,] 1.15 0.45 [128,] 1.25 0.45 [129,] -0.25 0.55 [130,] -0.15 0.55 [131,] -0.05 0.55 [132,] 0.05 0.55 [133,] 0.15 0.55 [134,] 0.25 0.55 [135,] 0.35 0.55 [136,] 0.45 0.55 [137,] 0.55 0.55 [138,] 0.65 0.55 [139,] 0.75 0.55 [140,] 0.85 0.55 [141,] 0.95 0.55 [142,] 1.05 0.55 [143,] 1.15 0.55 [144,] 1.25 0.55 [145,] -0.25 0.65 [146,] -0.15 0.65 [147,] -0.05 0.65 [148,] 0.05 0.65 [149,] 0.15 0.65 [150,] 0.25 0.65 [151,] 0.35 0.65 [152,] 0.45 0.65 [153,] 0.55 0.65 [154,] 0.65 0.65 [155,] 0.75 0.65 [156,] 0.85 0.65 [157,] 0.95 0.65 [158,] 1.05 0.65 [159,] 1.15 0.65 [160,] 1.25 0.65 [161,] -0.25 0.75 [162,] -0.15 0.75 [163,] -0.05 0.75 [164,] 0.05 0.75 [165,] 0.15 0.75 [166,] 0.25 0.75 [167,] 0.35 0.75 [168,] 0.45 0.75 [169,] 0.55 0.75 [170,] 0.65 0.75 [171,] 0.75 0.75 [172,] 0.85 0.75 [173,] 0.95 0.75 [174,] 1.05 0.75 [175,] 1.15 0.75 [176,] 1.25 0.75 [177,] -0.25 0.85 [178,] -0.15 0.85 [179,] -0.05 0.85 [180,] 0.05 0.85 [181,] 0.15 0.85 [182,] 0.25 0.85 [183,] 0.35 0.85 [184,] 0.45 0.85 [185,] 0.55 0.85 [186,] 0.65 0.85 [187,] 0.75 0.85 [188,] 0.85 0.85 [189,] 0.95 0.85 [190,] 1.05 0.85 [191,] 1.15 0.85 [192,] 1.25 0.85 [193,] -0.25 0.95 [194,] -0.15 0.95 [195,] -0.05 0.95 [196,] 0.05 0.95 [197,] 0.15 0.95 [198,] 0.25 0.95 [199,] 0.35 0.95 [200,] 0.45 0.95 [201,] 0.55 0.95 [202,] 0.65 0.95 [203,] 0.75 0.95 [204,] 0.85 0.95 [205,] 0.95 0.95 [206,] 1.05 0.95 [207,] 1.15 0.95 [208,] 1.25 0.95 [209,] -0.25 1.05 [210,] -0.15 1.05 [211,] -0.05 1.05 [212,] 0.05 1.05 [213,] 0.15 1.05 [214,] 0.25 1.05 [215,] 0.35 1.05 [216,] 0.45 1.05 [217,] 0.55 1.05 [218,] 0.65 1.05 [219,] 0.75 1.05 [220,] 0.85 1.05 [221,] 0.95 1.05 [222,] 1.05 1.05 [223,] 1.15 1.05 [224,] 1.25 1.05 [225,] -0.25 1.15 [226,] -0.15 1.15 [227,] -0.05 1.15 [228,] 0.05 1.15 [229,] 0.15 1.15 [230,] 0.25 1.15 [231,] 0.35 1.15 [232,] 0.45 1.15 [233,] 0.55 1.15 [234,] 0.65 1.15 [235,] 0.75 1.15 [236,] 0.85 1.15 [237,] 0.95 1.15 [238,] 1.05 1.15 [239,] 1.15 1.15 [240,] 1.25 1.15 [241,] -0.25 1.25 [242,] -0.15 1.25 [243,] -0.05 1.25 [244,] 0.05 1.25 [245,] 0.15 1.25 [246,] 0.25 1.25 [247,] 0.35 1.25 [248,] 0.45 1.25 [249,] 0.55 1.25 [250,] 0.65 1.25 [251,] 0.75 1.25 [252,] 0.85 1.25 [253,] 0.95 1.25 [254,] 1.05 1.25 [255,] 1.15 1.25 [256,] 1.25 1.25 Coordinate Reference System (CRS) arguments: NA Data summary: out Min. :0.04106 1st Qu.:1.00000 Median :1.74061 Mean :2.10025 3rd Qu.:2.60472 Max. :9.44692 > > # check quantiles > > quantile(lgm,c(0,0.1,0.5)) | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 40% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |============================== | 44% | |=============================== | 44% | |=============================== | 45% | |================================ | 45% | |================================ | 46% | |================================= | 47% | |================================= | 48% | |================================== | 48% | |================================== | 49% | |=================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================ | 64% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 80% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |========================================================== | 84% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================= | 88% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 89% | |=============================================================== | 90% | |=============================================================== | 91% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 100% , , 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA -6.0578786 -4.752999 -5.199752 -5.107915 -3.8125182 [5,] NA NA NA -0.9241159 -1.458312 -3.040016 -2.740651 -3.0332420 [6,] NA NA NA -1.9189757 -1.173783 -3.717613 -3.382016 -5.1352318 [7,] NA NA NA -4.1566490 -2.182564 -4.645342 -3.556354 -5.0485562 [8,] NA NA NA -0.6031064 -2.062174 -3.478986 -2.976654 -4.5585347 [9,] NA NA NA -3.6520728 -4.097702 -3.666254 -2.909498 -3.3020768 [10,] NA NA NA -3.1998319 -3.458086 -3.146081 -2.899160 -2.6395522 [11,] NA NA NA -3.1352184 -6.592253 -2.702453 -3.201767 -2.5960499 [12,] NA NA NA -5.4221101 -3.565465 -2.549564 -2.933129 -0.8611791 [13,] NA NA NA -3.9384144 -3.250740 -6.327203 -4.094320 -2.9016963 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] -5.162547 -6.634063 -5.817255 -3.405690 -3.207343 NA NA NA [5,] -3.778066 -5.924774 -6.340662 -3.838092 -3.053222 NA NA NA [6,] -5.853153 -4.819822 -6.333250 -5.623172 -3.888487 NA NA NA [7,] -4.218973 -4.688009 -2.194347 -2.528054 -2.552721 NA NA NA [8,] -5.327917 -4.283272 1.011641 1.549346 -1.483488 NA NA NA [9,] -6.769536 -4.200363 -2.796268 -1.477862 -4.743459 NA NA NA [10,] -4.479335 -2.185923 -1.249349 -1.788159 -1.692351 NA NA NA [11,] -1.029457 -2.910265 -4.501240 -2.692024 -1.594824 NA NA NA [12,] -5.251820 -1.834116 -3.095849 -4.879462 -4.150083 NA NA NA [13,] -2.582604 -1.690076 -3.411019 -1.786181 -2.237258 NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA , , 2 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA -4.0583985 -3.871051 -2.758467 -3.890769 -3.654589 [5,] NA NA NA 0.2220934 -1.318848 -1.942101 -2.676394 -2.674320 [6,] NA NA NA -0.7456025 -1.066910 -3.038229 -3.318096 -4.507444 [7,] NA NA NA -3.9458299 -1.873441 -3.217891 -3.091763 -4.729182 [8,] NA NA NA 0.1091450 -2.008504 -3.130066 -2.882252 -3.307323 [9,] NA NA NA -2.3228875 -3.486695 -3.369428 -2.369812 -2.307976 [10,] NA NA NA -2.5312544 -3.409267 -2.650318 -1.775528 -2.177577 [11,] NA NA NA -3.0098943 -5.860693 -2.485546 -3.050135 -1.937987 [12,] NA NA NA -4.4012491 -3.539724 -2.280669 -2.220573 -0.761831 [13,] NA NA NA -3.1165908 -3.118380 -5.467935 -2.253215 -2.390173 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] -4.0598992 -6.2993297 -3.999779 -2.469160 -1.801194 NA NA NA [5,] -3.4053411 -5.4365870 -6.067459 -3.484431 -1.789890 NA NA NA [6,] -5.3594317 -4.0511740 -5.887559 -5.281723 -3.601825 NA NA NA [7,] -4.1672271 -4.5668599 -1.686651 -1.654686 -2.491629 NA NA NA [8,] -4.8385424 -4.1742503 1.170690 1.568474 -1.303133 NA NA NA [9,] -5.8321862 -4.1947175 -2.434826 -1.453002 -4.330950 NA NA NA [10,] -4.4705340 -2.0108078 -1.170762 -1.343274 -1.228500 NA NA NA [11,] -0.8064627 -2.7183167 -3.958960 -2.552255 -1.018924 NA NA NA [12,] -3.9521065 -0.7711591 -1.846705 -4.405790 -4.095418 NA NA NA [13,] -1.3243812 -1.3782543 -1.842862 -1.687885 -1.563646 NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA , , 3 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA -0.2899128 -2.91701743 -1.4980038 -3.1479475 -2.3374443 [5,] NA NA NA 0.7526183 -0.82005904 -0.5183286 -1.2529396 -0.9371537 [6,] NA NA NA 0.2758388 0.91430689 -2.3162276 -1.9902250 -1.8169674 [7,] NA NA NA -2.5035485 -1.15114487 -1.6094382 -1.6904350 -2.6960212 [8,] NA NA NA 0.9514327 -0.99745872 -1.9605295 -2.0500762 -2.1710444 [9,] NA NA NA -1.4860472 -2.35249149 -2.1542062 -1.0956198 -0.6644498 [10,] NA NA NA -0.5407403 -2.92537419 -0.9685352 0.7124888 -0.8331127 [11,] NA NA NA -1.1929914 -2.84520555 -1.4675242 -1.6489004 -1.7328935 [12,] NA NA NA -2.9980340 -2.58462286 -0.9035632 -0.3713678 -0.1042836 [13,] NA NA NA -1.7219752 0.04719354 -3.3601733 -0.2728513 -1.2602662 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [1,] NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA [4,] -1.8779902 -1.017454876 -2.7402285 -0.9957598 -1.0888752 NA NA [5,] -2.6565631 -2.062990736 -2.8904892 -1.9700504 -0.7721791 NA NA [6,] -4.4022353 -2.890915876 -3.5838735 -2.7736719 -2.5152469 NA NA [7,] -2.2524695 -2.108369721 -1.3027816 -0.7338287 -1.6287905 NA NA [8,] -4.0809507 -2.221434190 1.3248304 1.6172327 0.1201446 NA NA [9,] -4.1255930 -2.124084135 -1.0317276 -0.9387340 -1.6154612 NA NA [10,] -3.1781980 -0.982452447 -0.8776559 -0.6613035 0.2381970 NA NA [11,] 0.2408290 -2.160209579 -2.1529465 -1.0808149 -0.1827466 NA NA [12,] -2.8908713 0.007148476 -0.8536168 -2.3059202 -1.6682379 NA NA [13,] 0.3699054 -0.005334825 0.4298401 -0.3924010 -1.1607223 NA NA [14,] NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA [,16] [1,] NA [2,] NA [3,] NA [4,] NA [5,] NA [6,] NA [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [15,] NA [16,] NA attr(,"quantiles") [1] 0.0 0.1 0.5 attr(,"xcoords") [1] -0.25 -0.15 -0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 [13] 0.95 1.05 1.15 1.25 attr(,"ycoords") [1] -0.25 -0.15 -0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 [13] 0.95 1.05 1.15 1.25 attr(,"window") window: polygonal boundary enclosing rectangle: [-0.3, 1.3] x [-0.3, 1.3] units attr(,"ParentWindow") window: polygonal boundary enclosing rectangle: [-0.3, 1.3] x [-0.3, 1.3] units attr(,"class") [1] "lgcpQuantiles" "array" > quantile(lg,c(0,0.1,0.5)) | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 40% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |============================== | 44% | |=============================== | 44% | |=============================== | 45% | |================================ | 45% | |================================ | 46% | |================================= | 47% | |================================= | 48% | |================================== | 48% | |================================== | 49% | |=================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================ | 64% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 80% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |========================================================== | 84% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================= | 88% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 89% | |=============================================================== | 90% | |=============================================================== | 91% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 100% , , 1 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA -0.02962839 -0.9338264 -1.539671 -3.754142 -2.401741 [5,] NA NA NA 0.33293900 -0.5171376 -1.689263 -3.345072 -3.555829 [6,] NA NA NA -1.24187273 -1.1441125 -1.780011 -1.321830 -2.000988 [7,] NA NA NA -1.60339161 -1.7047465 -4.121943 -2.981072 -2.095036 [8,] NA NA NA -2.66634002 -2.9330718 -3.992537 -2.689161 -1.067768 [9,] NA NA NA -2.92356938 -3.5879405 -2.850266 -4.289019 -3.063121 [10,] NA NA NA -2.58200798 -3.2096242 -3.668595 -1.997523 -4.416937 [11,] NA NA NA -2.86467487 -3.6058642 -3.993929 -2.306349 -2.130217 [12,] NA NA NA -1.87804425 -2.9487129 -2.131784 -1.723532 -2.091761 [13,] NA NA NA -2.85599078 -1.5501783 -2.116070 -2.090567 -2.676557 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] -1.599231 -2.376213 -1.282258 -1.575100 -1.999655 NA NA NA [5,] -4.979177 -1.820323 -3.325153 -3.449793 -4.128936 NA NA NA [6,] -2.162047 -3.915713 -3.041198 -2.351919 -3.048554 NA NA NA [7,] -2.831931 -2.748930 -1.852901 -2.855762 -2.352126 NA NA NA [8,] -3.308592 -2.386928 -2.393898 -2.170618 -3.278353 NA NA NA [9,] -3.135992 -3.498476 -2.314516 -2.369958 -2.795569 NA NA NA [10,] -3.607592 -1.853295 -3.439574 -2.247691 -2.060384 NA NA NA [11,] -1.776336 -2.657577 -2.910391 -2.161026 -1.723481 NA NA NA [12,] -2.148221 -2.830232 -3.980010 -5.133389 -2.141436 NA NA NA [13,] -4.210209 -3.732414 -2.924684 -3.490376 -3.232421 NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA , , 2 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA 0.3513046 -0.5944815 -1.429257 -3.013343 -1.7602226 [5,] NA NA NA 0.7223915 -0.1921502 -1.218376 -3.126350 -2.9584809 [6,] NA NA NA -0.6223810 -0.6522721 -1.573052 -0.982602 -1.9059335 [7,] NA NA NA -1.0986261 -1.6748791 -3.682483 -2.456267 -1.9225140 [8,] NA NA NA -2.0636986 -2.7063342 -3.383930 -2.631501 -0.7910639 [9,] NA NA NA -2.3838826 -3.3163267 -2.746832 -4.205482 -2.9417445 [10,] NA NA NA -2.0012539 -3.0718713 -3.290246 -1.891908 -4.0137267 [11,] NA NA NA -2.4104115 -3.0395418 -3.688824 -2.261457 -1.9720666 [12,] NA NA NA -1.6193876 -2.8730155 -1.994888 -1.460049 -2.0236387 [13,] NA NA NA -2.5129208 -1.1480527 -1.700653 -1.871193 -2.4913722 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] -1.512767 -2.196626 -1.096320 -1.240144 -1.882481 NA NA NA [5,] -4.325561 -1.776401 -2.831654 -3.376948 -3.552335 NA NA NA [6,] -1.583918 -3.714294 -2.905295 -2.255194 -2.952677 NA NA NA [7,] -2.690772 -2.539883 -1.587424 -2.679764 -2.299788 NA NA NA [8,] -3.118448 -2.054463 -2.365751 -1.958441 -2.845826 NA NA NA [9,] -2.663076 -3.209915 -1.490321 -2.012666 -2.489102 NA NA NA [10,] -2.865724 -1.798144 -2.924242 -2.114994 -2.012328 NA NA NA [11,] -1.740593 -2.649379 -2.359127 -2.155288 -1.530033 NA NA NA [12,] -2.004612 -2.496362 -3.678580 -4.626382 -2.018656 NA NA NA [13,] -3.582072 -3.277444 -2.632041 -3.118295 -2.982360 NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA , , 3 [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] NA NA NA 2.0583955 0.5555907 -0.01118931 -2.1014920 -0.9858830 [5,] NA NA NA 2.4806229 1.3666331 0.78540137 -2.4159863 -1.9358795 [6,] NA NA NA 0.1597315 0.1000519 -0.19783712 0.6565438 -1.4519219 [7,] NA NA NA -0.2921212 -1.1067625 -2.89086525 -1.6801629 -1.5401311 [8,] NA NA NA -1.3182806 -2.2906943 -2.37819480 -2.5049030 0.6663891 [9,] NA NA NA -1.5272537 -2.0842884 -1.94815065 -2.9364776 -2.4136270 [10,] NA NA NA -1.1778554 -2.0170930 -2.42516476 -1.3057180 -2.1565510 [11,] NA NA NA -2.0478841 -2.5741746 -2.19300164 -1.5986701 -1.6952115 [12,] NA NA NA -1.0264884 -1.9764923 -1.66204564 -0.9872374 -1.4734528 [13,] NA NA NA -2.0161972 -0.2953709 -0.72722446 -1.0785619 -1.1263978 [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] NA NA NA NA NA NA NA NA [2,] NA NA NA NA NA NA NA NA [3,] NA NA NA NA NA NA NA NA [4,] -1.0263674 -1.8671942 -0.2972772 -0.9908955 -1.360338 NA NA NA [5,] -3.1961574 -0.7731363 -1.9422980 -2.8608960 -2.882800 NA NA NA [6,] -1.2956836 -2.4374932 -1.6232642 -1.7490272 -1.789348 NA NA NA [7,] -1.6360167 -1.7826329 -1.1640874 -2.2514416 -1.429218 NA NA NA [8,] -2.2861613 -1.8219427 -1.5952464 -1.3981515 -2.150127 NA NA NA [9,] -1.1738582 -2.4320275 -0.5013499 -1.1739738 -2.217763 NA NA NA [10,] -2.0931756 -1.4640528 -2.2062325 -1.2502359 -1.450782 NA NA NA [11,] -1.5527537 -2.1236677 -1.8718298 -1.7900730 -1.275009 NA NA NA [12,] -0.9712371 -1.8993344 -2.4023390 -3.3088390 -1.107028 NA NA NA [13,] -3.1625457 -2.4514316 -2.1681860 -2.6660178 -2.332695 NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA attr(,"quantiles") [1] 0.0 0.1 0.5 attr(,"xcoords") [1] -0.25 -0.15 -0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 [13] 0.95 1.05 1.15 1.25 attr(,"ycoords") [1] -0.25 -0.15 -0.05 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 [13] 0.95 1.05 1.15 1.25 attr(,"window") window: polygonal boundary enclosing rectangle: [-0.3, 1.3] x [-0.3, 1.3] units attr(,"ParentWindow") window: polygonal boundary enclosing rectangle: [-0.3, 1.3] x [-0.3, 1.3] units attr(,"class") [1] "lgcpQuantiles" "array" > > t1 <- all.equal(expectation(lgm,function(x){return(x)})[[1]],lgm$y.mean$grid[[1]]) | | | 0% | |========= | 12% | |================== | 25% | |========================== | 38% | |=================================== | 50% | |============================================ | 62% | |==================================================== | 75% | |============================================================= | 88% | |======================================================================| 100% > if(!t1){stop("error in computing expectation in lgcpMethodsTest.R")} > t2 <- all.equal(expectation(lg,function(x){return(x)})[[1]],lg$y.mean$grid[[length(lg$y.mean$grid)]]) | | | 0% | |========= | 12% | |================== | 25% | |========================== | 38% | |=================================== | 50% | |============================================ | 62% | |==================================================== | 75% | |============================================================= | 88% | |======================================================================| 100% > if(!t2){stop("error in computing expectation in lgcpMethodsTest.R")} > > proc.time() user system elapsed 14.79 1.18 18.46