CRAN Package Check Results for Package cobs

Last updated on 2024-10-28 03:55:59 CET.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	1.3-8	10.91	119.11	130.02	ERROR
r-devel-linux-x86_64-debian-gcc	1.3-8	7.73	86.78	94.51	OK
r-devel-linux-x86_64-fedora-clang	1.3-8			211.53	ERROR
r-devel-linux-x86_64-fedora-gcc	1.3-8			209.26	OK
r-devel-windows-x86_64	1.3-8	15.00	134.00	149.00	OK
r-patched-linux-x86_64	1.3-8	11.11	115.67	126.78	OK
r-release-linux-x86_64	1.3-8	10.21	114.77	124.98	OK
r-release-macos-arm64	1.3-8			69.00	OK
r-release-macos-x86_64	1.3-8			96.00	OK
r-release-windows-x86_64	1.3-8	14.00	131.00	145.00	OK
r-oldrel-macos-arm64	1.3-8			74.00	OK
r-oldrel-macos-x86_64	1.3-8			160.00	OK
r-oldrel-windows-x86_64	1.3-8	17.00	164.00	181.00	OK

Check Details

Version: 1.3-8
Check: tests
Result: ERROR Running ‘0_pt-ex.R’ [2s/3s] Running ‘ex1.R’ [3s/5s] Running ‘ex2-long.R’ [7s/9s] Running ‘ex3.R’ [2s/3s] Comparing ‘ex3.Rout’ to ‘ex3.Rout.save’ ... OK Running ‘multi-constr.R’ [4s/6s] Running ‘roof.R’ [4s/5s] Comparing ‘roof.Rout’ to ‘roof.Rout.save’ ... OK Running ‘small-ex.R’ [3s/4s] Comparing ‘small-ex.Rout’ to ‘small-ex.Rout.save’ ... OK Running ‘spline-ex.R’ [2s/3s] Comparing ‘spline-ex.Rout’ to ‘spline-ex.Rout.save’ ... OK Running ‘temp.R’ [3s/5s] Comparing ‘temp.Rout’ to ‘temp.Rout.save’ ... OK Running ‘wind.R’ [8s/11s] Running the tests in ‘tests/ex1.R’ failed. Complete output: > #### OOps! Running this in 'CMD check' or in *R* __for the first time__ > #### ===== gives a wrong result (at the end) than when run a 2nd time > ####-- problem disappears with introduction of if (psw) call ... in Fortran > > suppressMessages(library(cobs)) > options(digits = 6) > if(!dev.interactive(orNone=TRUE)) pdf("ex1.pdf") > > source(system.file("util.R", package = "cobs")) > > ## Simple example from example(cobs) > set.seed(908) > x <- seq(-1,1, len = 50) > f.true <- pnorm(2*x) > y <- f.true + rnorm(50)/10 > ## specify constraints (boundary conditions) > con <- rbind(c( 1,min(x),0), + c(-1,max(x),1), + c( 0, 0, 0.5)) > ## obtain the median *regression* B-spline using automatically selected knots > coR <- cobs(x,y,constraint = "increase", pointwise = con) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... Warning message: In cobs(x, y, constraint = "increase", pointwise = con) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > summaryCobs(coR) List of 24 $ call : language cobs(x = x, y = y, constraint = "increase", pointwise = con) $ tau : num 0.5 $ degree : num 2 $ constraint : chr "increase" $ ic : chr "AIC" $ pointwise : num [1:3, 1:3] 1 -1 0 -1 1 0 0 1 0.5 $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:50] -1 -0.959 -0.918 -0.878 -0.837 ... $ y : num [1:50] 0.2254 0.0916 0.0803 -0.0272 -0.0454 ... $ resid : num [1:50] 0.148 0.019 0.0105 -0.0962 -0.1156 ... $ fitted : num [1:50] 0.0774 0.0726 0.0698 0.069 0.0702 ... $ coef : num [1:4] 0.0774 0.0226 0.8067 1.074 $ knots : num [1:3] -1 -0.224 1 $ k0 : num 4 $ k : num 4 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 6.19 $ lambda : num 0 $ icyc : int 1 $ ifl : int 21 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 -0.02569206 0.0153529 0.0773974 0.139442 0.180487 2 -0.02467377 0.0149258 0.0747853 0.134645 0.174244 3 -0.02343992 0.0148223 0.0726602 0.130498 0.168760 4 -0.02198644 0.0150449 0.0710223 0.127000 0.164031 5 -0.02030765 0.0155971 0.0698714 0.124146 0.160050 6 -0.01839614 0.0164832 0.0692075 0.121932 0.156811 7 -0.01624274 0.0177089 0.0690308 0.120353 0.154304 8 -0.01383648 0.0192806 0.0693410 0.119401 0.152519 9 -0.01116467 0.0212061 0.0701384 0.119071 0.151441 10 -0.00821304 0.0234939 0.0714227 0.119352 0.151059 11 -0.00496594 0.0261535 0.0731942 0.120235 0.151354 12 -0.00140661 0.0291949 0.0754527 0.121711 0.152312 13 0.00248257 0.0326287 0.0781983 0.123768 0.153914 14 0.00671972 0.0364659 0.0814309 0.126396 0.156142 15 0.01132316 0.0407175 0.0851506 0.129584 0.158978 16 0.01631107 0.0453944 0.0893573 0.133320 0.162404 17 0.02170124 0.0505073 0.0940511 0.137595 0.166401 18 0.02751079 0.0560665 0.0992320 0.142397 0.170953 19 0.03375595 0.0620819 0.1048999 0.147718 0.176044 20 0.04045190 0.0685624 0.1110549 0.153547 0.181658 21 0.04761262 0.0755166 0.1176969 0.159877 0.187781 22 0.05525079 0.0829521 0.1248260 0.166700 0.194401 23 0.06337769 0.0908757 0.1324422 0.174009 0.201507 24 0.07200318 0.0992932 0.1405454 0.181798 0.209088 25 0.08113560 0.1082098 0.1491357 0.190062 0.217136 26 0.09078179 0.1176295 0.1582130 0.198797 0.225644 27 0.10094701 0.1275555 0.1677774 0.207999 0.234608 28 0.11163490 0.1379900 0.1778288 0.217668 0.244023 29 0.12284746 0.1489342 0.1883674 0.227801 0.253887 30 0.13458491 0.1603882 0.1993929 0.238398 0.264201 31 0.14684569 0.1723511 0.2109056 0.249460 0.274965 32 0.15962625 0.1848207 0.2229052 0.260990 0.286184 33 0.17292100 0.1977938 0.2353920 0.272990 0.297863 34 0.18672210 0.2112655 0.2483658 0.285466 0.310009 35 0.20101931 0.2252297 0.2618267 0.298424 0.322634 36 0.21579978 0.2396787 0.2757746 0.311870 0.335749 37 0.23104789 0.2546031 0.2902096 0.325816 0.349371 38 0.24674505 0.2699916 0.3051316 0.340272 0.363518 39 0.26286963 0.2858313 0.3205407 0.355250 0.378212 40 0.27927345 0.3019880 0.3363239 0.370660 0.393374 41 0.29546082 0.3179864 0.3520365 0.386087 0.408612 42 0.31139379 0.3337855 0.3676333 0.401481 0.423873 43 0.32708550 0.3493933 0.3831143 0.416835 0.439143 44 0.34254943 0.3648179 0.3984794 0.432141 0.454409 45 0.35779910 0.3800674 0.4137287 0.447390 0.469658 46 0.37284780 0.3951499 0.4288622 0.462574 0.484877 47 0.38770842 0.4100730 0.4438798 0.477687 0.500051 48 0.40239320 0.4248442 0.4587815 0.492719 0.515170 49 0.41691367 0.4394703 0.4735674 0.507665 0.530221 50 0.43128050 0.4539579 0.4882375 0.522517 0.545195 51 0.44550348 0.4683128 0.5027918 0.537271 0.560080 52 0.45959144 0.4825403 0.5172301 0.551920 0.574869 53 0.47355229 0.4966451 0.5315527 0.566460 0.589553 54 0.48739298 0.5106315 0.5457594 0.580887 0.604126 55 0.50111956 0.5245032 0.5598503 0.595197 0.618581 56 0.51473718 0.5382631 0.5738253 0.609388 0.632913 57 0.52825014 0.5519139 0.5876845 0.623455 0.647119 58 0.54166189 0.5654577 0.6014278 0.637398 0.661194 59 0.55497510 0.5788960 0.6150553 0.651215 0.675136 60 0.56819166 0.5922301 0.6285670 0.664904 0.688942 61 0.58131273 0.6054605 0.6419628 0.678465 0.702613 62 0.59433873 0.6185876 0.6552428 0.691898 0.716147 63 0.60726940 0.6316113 0.6684069 0.705203 0.729544 64 0.62010376 0.6445308 0.6814552 0.718380 0.742807 65 0.63284018 0.6573453 0.6943876 0.731430 0.755935 66 0.64547632 0.6700533 0.7072042 0.744355 0.768932 67 0.65800921 0.6826530 0.7199050 0.757157 0.781801 68 0.67043520 0.6951423 0.7324899 0.769838 0.794545 69 0.68274995 0.7075185 0.7449590 0.782400 0.807168 70 0.69494850 0.7197786 0.7573122 0.794846 0.819676 71 0.70702523 0.7319193 0.7695496 0.807180 0.832074 72 0.71897385 0.7439368 0.7816712 0.819406 0.844369 73 0.73078747 0.7558269 0.7936769 0.831527 0.856566 74 0.74245861 0.7675851 0.8055668 0.843548 0.868675 75 0.75397921 0.7792066 0.8173408 0.855475 0.880702 76 0.76534070 0.7906862 0.8289990 0.867312 0.892657 77 0.77653408 0.8020185 0.8405413 0.879064 0.904549 78 0.78754996 0.8131979 0.8519678 0.890738 0.916386 79 0.79837866 0.8242185 0.8632785 0.902338 0.928178 80 0.80901036 0.8350744 0.8744733 0.913872 0.939936 81 0.81943516 0.8457597 0.8855523 0.925345 0.951669 82 0.82964324 0.8562684 0.8965154 0.936762 0.963388 83 0.83962502 0.8665947 0.9073627 0.948131 0.975100 84 0.84937122 0.8767332 0.9180941 0.959455 0.986817 85 0.85887309 0.8866785 0.9287098 0.970741 0.998546 86 0.86812243 0.8964257 0.9392095 0.981993 1.010297 87 0.87711179 0.9059703 0.9495934 0.993217 1.022075 88 0.88583447 0.9153083 0.9598615 1.004415 1.033889 89 0.89428462 0.9244361 0.9700138 1.015591 1.045743 90 0.90245726 0.9333508 0.9800502 1.026749 1.057643 91 0.91034831 0.9420499 0.9899707 1.037891 1.069593 92 0.91795453 0.9505315 0.9997754 1.049019 1.081596 93 0.92527350 0.9587940 1.0094643 1.060135 1.093655 94 0.93230358 0.9668366 1.0190373 1.071238 1.105771 95 0.93904380 0.9746586 1.0284945 1.082330 1.117945 96 0.94549385 0.9822598 1.0378359 1.093412 1.130178 97 0.95165390 0.9896403 1.0470614 1.104482 1.142469 98 0.95752462 0.9968006 1.0561710 1.115541 1.154817 99 0.96310704 1.0037413 1.0651648 1.126588 1.167223 100 0.96840246 1.0104631 1.0740428 1.137623 1.179683 knots : [1] -1.00000 -0.22449 1.00000 coef : [1] 0.0773974 0.0225871 0.8067413 1.0740429 > coR1 <- cobs(x,y,constraint = "increase", pointwise = con, degree = 1) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... Warning message: In cobs(x, y, constraint = "increase", pointwise = con, degree = 1) : drqssbc2(): Not all flags are normal (== 1), ifl : 20 > summary(coR1) COBS regression spline (degree = 1) from call: cobs(x = x, y = y, constraint = "increase", degree = 1, pointwise = con) **** ERROR in algorithm: ifl = 20 {tau=0.5}-quantile; dimensionality of fit: 3 from {3} x$knots[1:3]: -1.000002, -0.632653, 1.000002 with 3 pointwise constraints coef[1:3]: 0.0781509, 0.0820419, 1.1196697 R^2 = 94.72% ; empirical tau (over all): 25/50 = 0.5 (target tau= 0.5) > > ## compute the median *smoothing* B-spline using automatically chosen lambda > coS <- cobs(x,y,constraint = "increase", pointwise = con, + lambda = -1, trace = 3) Searching for optimal lambda. This may take a while. While you are waiting, here is something you can consider to speed up the process: (a) Use a smaller number of knots; (b) Set lambda==0 to exclude the penalty term; (c) Use a coarser grid by reducing the argument 'lambda.length' from the default value of 25. loo.design2(): -> Xeq 51 x 22 (nz = 151 =^= 0.13%) Xieq 62 x 22 (nz = 224 =^= 0.16%) ........................ Error in drqssbc2(x, y, w, pw = pw, knots = knots, degree = degree, Tlambda = if (select.lambda) lambdaSet else lambda, : The problem is degenerate for the range of lambda specified. Calls: cobs -> drqssbc2 In addition: Warning message: In min(sol1["k", i.keep]) : no non-missing arguments to min; returning Inf Execution halted Running the tests in ‘tests/multi-constr.R’ failed. Complete output: > #### Examples which use the new feature of more than one 'constraint'. > > suppressMessages(library(cobs)) > > ## do *not* show platform info here (as have *.Rout.save), but in 0_pt-ex.R > options(digits = 6) > > if(!dev.interactive(orNone=TRUE)) pdf("multi-constr.pdf") > > source(system.file("util.R", package = "cobs")) > source(system.file(package="Matrix", "test-tools-1.R", mustWork=TRUE)) Loading required package: tools > ##--> tryCatch.W.E(), showProc.time(), assertError(), relErrV(), ... > Lnx <- Sys.info()[["sysname"]] == "Linux" > isMac <- Sys.info()[["sysname"]] == "Darwin" > x86 <- (arch <- Sys.info()[["machine"]]) == "x86_64" > noLdbl <- (.Machine$sizeof.longdouble <= 8) ## TRUE when --disable-long-double > ## IGNORE_RDIFF_BEGIN > Sys.info() sysname "Linux" release "6.10.11-amd64" version "#1 SMP PREEMPT_DYNAMIC Debian 6.10.11-1 (2024-09-22)" nodename "gimli2" machine "x86_64" login "hornik" user "hornik" effective_user "hornik" > noLdbl [1] FALSE > ## IGNORE_RDIFF_END > > > Rsq <- function(obj) { + stopifnot(inherits(obj, "cobs"), is.numeric(res <- obj$resid)) + 1 - sum(res^2)/obj$SSy + } > list_ <- function (...) `names<-`(list(...), vapply(sys.call()[-1L], as.character, "")) > is.cobs <- function(x) inherits(x, "cobs") > > set.seed(908) > x <- seq(-1,2, len = 50) > f.true <- pnorm(2*x) > y <- f.true + rnorm(50)/10 > plot(x,y); lines(x, f.true, col="gray", lwd=2, lty=3) > > ## constraint on derivative at right end: > (con <- rbind(c(2 , max(x), 0))) # f'(x_n) == 0 [,1] [,2] [,3] [1,] 2 2 0 > > ## Using 'trace = 3' --> 'trace = 2' inside drqssbc2() > > ## Regression splines (lambda = 0) > c2 <- cobs(x,y, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Warning message: In cobs(x, y, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > c2i <- cobs(x,y, constraint = c("increase"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 5 x 6 (nz = 15 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 6 x 7 (nz = 18 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) Warning message: In cobs(x, y, constraint = c("increase"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > c2c <- cobs(x,y, constraint = c("concave"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 1 x 3 (nz = 3 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 3 x 5 (nz = 9 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 4 x 6 (nz = 12 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 5 x 7 (nz = 15 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 1 x 3 (nz = 3 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) Warning message: In cobs(x, y, constraint = c("concave"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > c2IC <- cobs(x,y, constraint = c("inc", "concave"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 5 x 4 (nz = 15 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 7 x 5 (nz = 21 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 9 x 6 (nz = 27 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 11 x 7 (nz = 33 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 5 x 4 (nz = 15 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 5 x 4 (nz = 15 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 5 x 4 (nz = 15 =^= 0.75%) Warning message: In cobs(x, y, constraint = c("inc", "concave"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 20 > ## here, it's the same as just "i": > all.equal(fitted(c2i), fitted(c2IC)) [1] TRUE > > c1 <- cobs(x,y, degree = 1, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Warning message: In cobs(x, y, degree = 1, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 22 > c1i <- cobs(x,y, degree = 1, constraint = c("increase"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 1 x 2 (nz = 2 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 2 x 3 (nz = 4 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 4 x 5 (nz = 8 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 5 x 6 (nz = 10 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 4 x 5 (nz = 8 =^= 0.4%) Warning message: In cobs(x, y, degree = 1, constraint = c("increase"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 22 > c1c <- cobs(x,y, degree = 1, constraint = c("concave"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 1 x 3 (nz = 3 =^= 1%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 6 =^= 0.75%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 3 x 5 (nz = 9 =^= 0.6%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 4 x 6 (nz = 12 =^= 0.5%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 6 =^= 0.75%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 6 =^= 0.75%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 6 =^= 0.75%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 3 x 5 (nz = 9 =^= 0.6%) Warning message: In cobs(x, y, degree = 1, constraint = c("concave"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > plot(c1) > lines(predict(c1i), col="forest green") > all.equal(fitted(c1), fitted(c1i), tol = 1e-9)# but not 1e-10 [1] TRUE > > ## now gives warning (not error): > c1IC <- cobs(x,y, degree = 1, constraint = c("inc", "concave"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 1 x 2 (nz = 2 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 12 =^= 0.6%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 7 x 5 (nz = 17 =^= 0.49%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 9 x 6 (nz = 22 =^= 0.41%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 12 =^= 0.6%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 12 =^= 0.6%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 12 =^= 0.6%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 7 x 5 (nz = 17 =^= 0.49%) Warning messages: 1: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' 2: In cobs(x, y, degree = 1, constraint = c("inc", "concave"), trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > cp2 <- cobs(x,y, pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 2 x 5 (nz = 6 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 2 x 6 (nz = 6 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 2 x 7 (nz = 6 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) Warning message: In cobs(x, y, pointwise = con, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > ## Here, warning ".. 'ifl'.. " on *some* platforms (e.g. Windows 32bit) : > r2i <- tryCatch.W.E( cobs(x,y, constraint = "increase", pointwise = con) ) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... > cp2i <- r2i$value > ## IGNORE_RDIFF_BEGIN > r2i$warning <simpleWarning in cobs(x, y, constraint = "increase", pointwise = con): drqssbc2(): Not all flags are normal (== 1), ifl : 21> > ## IGNORE_RDIFF_END > ## when plotting it, we see that it gave a trivial constant!! > cp2c <- cobs(x,y, constraint = "concave", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 4 x 4 (nz = 12 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 5 x 5 (nz = 15 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 6 x 6 (nz = 18 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 7 x 7 (nz = 21 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 4 x 4 (nz = 12 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 4 x 4 (nz = 12 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 4 x 4 (nz = 12 =^= 0.75%) Warning message: In cobs(x, y, constraint = "concave", pointwise = con, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > ## now gives warning (not error): but no warning on M1 mac -> IGNORE > ## IGNORE_RDIFF_BEGIN > cp2IC <- cobs(x,y, constraint = c("inc", "concave"), pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 5 x 3 (nz = 15 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 7 x 4 (nz = 21 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 9 x 5 (nz = 27 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 11 x 6 (nz = 33 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 13 x 7 (nz = 39 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 7 x 4 (nz = 21 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 7 x 4 (nz = 21 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 5 x 3 (nz = 15 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 7 x 4 (nz = 21 =^= 0.75%) Warning message: In cobs(x, y, constraint = c("inc", "concave"), pointwise = con, : drqssbc2(): Not all flags are normal (== 1), ifl : 20 > ## IGNORE_RDIFF_END > cp1 <- cobs(x,y, degree = 1, pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 2 x 2 (nz = 4 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 2 x 3 (nz = 4 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 2 x 5 (nz = 4 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 2 x 6 (nz = 4 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 2 x 5 (nz = 4 =^= 0.4%) Warning message: In cobs(x, y, degree = 1, pointwise = con, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 22 > cp1i <- cobs(x,y, degree = 1, constraint = "increase", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 3 x 2 (nz = 6 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 4 x 3 (nz = 8 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 6 x 5 (nz = 12 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 7 x 6 (nz = 14 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 6 x 5 (nz = 12 =^= 0.4%) Warning message: In cobs(x, y, degree = 1, constraint = "increase", pointwise = con, : drqssbc2(): Not all flags are normal (== 1), ifl : 22 > cp1c <- cobs(x,y, degree = 1, constraint = "concave", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 2 x 2 (nz = 4 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 4 x 4 (nz = 10 =^= 0.62%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 5 x 5 (nz = 13 =^= 0.52%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 6 x 6 (nz = 16 =^= 0.44%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 4 x 4 (nz = 10 =^= 0.62%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 4 x 4 (nz = 10 =^= 0.62%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 4 x 4 (nz = 10 =^= 0.62%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 5 x 5 (nz = 13 =^= 0.52%) Warning message: In cobs(x, y, degree = 1, constraint = "concave", pointwise = con, : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > cp1IC <- cobs(x,y, degree = 1, constraint = c("inc", "concave"), pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 3 x 2 (nz = 6 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 5 x 3 (nz = 11 =^= 0.73%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 7 x 4 (nz = 16 =^= 0.57%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 9 x 5 (nz = 21 =^= 0.47%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 11 x 6 (nz = 26 =^= 0.39%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 7 x 4 (nz = 16 =^= 0.57%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 7 x 4 (nz = 16 =^= 0.57%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 7 x 4 (nz = 16 =^= 0.57%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 9 x 5 (nz = 21 =^= 0.47%) Warning messages: 1: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' 2: In cobs(x, y, degree = 1, constraint = c("inc", "concave"), pointwise = con, : drqssbc2(): Not all flags are normal (== 1), ifl : 21 > > ## Named list of all cobs() results above -- sort() collation order matters for ls() ! > (curLC <- Sys.getlocale("LC_COLLATE")) [1] "C" > Sys.setlocale("LC_COLLATE", "C") [1] "C" > cobsL <- mget(Filter(\(nm) is.cobs(.GlobalEnv[[nm]]), ls(patt="c[12p]")), + envir = .GlobalEnv) > Sys.setlocale("LC_COLLATE", curLC) # reverting [1] "C" > > knL <- lapply(cobsL, `[[`, "knots") > str(knL[order(lengths(knL))]) List of 16 $ c2 : num [1:3] -1 -0.449 2 $ c2IC : num [1:3] -1 -0.449 2 $ c2c : num [1:3] -1 -0.449 2 $ c2i : num [1:3] -1 -0.449 2 $ cp2 : num [1:3] -1 -0.449 2 $ cp2IC: num [1:3] -1 -0.449 2 $ cp2c : num [1:3] -1 -0.449 2 $ cp2i : num [1:3] -1 -0.449 2 $ c1 : num [1:5] -1 -0.449 0.163 0.776 2 $ c1IC : num [1:5] -1 -0.449 0.163 0.776 2 $ c1c : num [1:5] -1 -0.449 0.163 0.776 2 $ c1i : num [1:5] -1 -0.449 0.163 0.776 2 $ cp1 : num [1:5] -1 -0.449 0.163 0.776 2 $ cp1IC: num [1:5] -1 -0.449 0.163 0.776 2 $ cp1c : num [1:5] -1 -0.449 0.163 0.776 2 $ cp1i : num [1:5] -1 -0.449 0.163 0.776 2 > > gotRsqrs <- sapply(cobsL, Rsq) > Rsqrs <- c(c1 = 0.95079126, c1IC = 0.92974549, c1c = 0.92974549, c1i = 0.95079126, + c2 = 0.94637437, c2IC = 0.91375404, c2c = 0.92505977, c2i = 0.95022829, + cp1 = 0.9426453, cp1IC = 0.92223149, cp1c = 0.92223149, cp1i = 0.9426453, + cp2 = 0.94988863, cp2IC= 0.90051964, cp2c = 0.91375409, cp2i = 0.93611487) > ## M1 mac " = " , cp2IC= 0.91704726, " = " , cp2i = 0.94620178 > ## noLD " = " , cp2IC=-0.08244284, " = " , cp2i = 0.94636815 > ## ATLAS " = " , cp2IC= 0.91471729, " = " , cp2i = 0.94506339 > ## openBLAS " = " , cp2IC= 0.91738019, " = " , cp2i = 0.93589404 > ## MKL " = " , cp2IC= 0.91765403, " = " , cp2i = 0.94501205 > ## Intel " = " , cp2IC= 0.91765403, " = " , cp2i = 0.94501205 > ## ^^^^^^^^^^ ^^^^^^^^^^ > ## remove these two from testing, notably for the M1 Mac & noLD .. : > ##iR2 <- if(!x86 || noLdbl) setdiff(names(cobsL), c("cp2IC", "cp2i")) else TRUE > ## actually everywhere, because of ATLAS, openBLAS, MKL, Intel... : > iR2 <- setdiff(names(cobsL), nR2 <- c("cp2IC", "cp2i")) > ## IGNORE_RDIFF_BEGIN > dput(signif(gotRsqrs, digits=8)) c(c1 = 0.95341697, c1IC = 0.95341697, c1c = 0.95341697, c1i = 0.95341697, c2 = 0.94864721, c2IC = 0.94864721, c2c = 0.94864721, c2i = 0.94864721, cp1 = 0.95341697, cp1IC = 0.95341697, cp1c = 0.95341697, cp1i = 0.95341697, cp2 = 0.94864721, cp2IC = 0.94864721, cp2c = 0.94864721, cp2i = 0.94864721 ) > all.equal(Rsqrs[iR2], gotRsqrs[iR2], tolerance=0)# 2.6277e-9 (Lnx F 38); 2.6898e-9 (M1 mac) [1] "Mean relative difference: 0.0179511" > all.equal(Rsqrs[nR2], gotRsqrs[nR2], tolerance=0)# differ; drastically only for 'noLD' [1] "Mean relative difference: 0.0330278" > ## IGNORE_RDIFF_END > stopifnot(exprs = { + all.equal(Rsqrs[iR2], gotRsqrs[iR2]) + identical(c(5L, 3L, 3L, 5L, + 3L, 2L, 3L, 4L, + 5L, 3L, 3L, 5L, + 4L, 2L, 2L, 4L), unname(lengths(knL))) + }) Error: Rsqrs[iR2] and gotRsqrs[iR2] are not equal: Mean relative difference: 0.0179511 Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.3-8
Check: tests
Result: ERROR Running ‘0_pt-ex.R’ Running ‘ex1.R’ [13s/33s] Running ‘ex2-long.R’ [8s/21s] Running ‘ex3.R’ Comparing ‘ex3.Rout’ to ‘ex3.Rout.save’ ... OK Running ‘multi-constr.R’ [7s/19s] Running ‘roof.R’ [5s/14s] Running ‘small-ex.R’ [4s/12s] Comparing ‘small-ex.Rout’ to ‘small-ex.Rout.save’ ... OK Running ‘spline-ex.R’ Comparing ‘spline-ex.Rout’ to ‘spline-ex.Rout.save’ ... OK Running ‘temp.R’ [6s/14s] Comparing ‘temp.Rout’ to ‘temp.Rout.save’ ... OK Running ‘wind.R’ [10s/27s] Running the tests in ‘tests/ex2-long.R’ failed. Complete output: > #### > suppressMessages(library(cobs)) > > source(system.file("util.R", package = "cobs")) > (doExtra <- doExtras()) [1] FALSE > source(system.file("test-tools-1.R", package="Matrix", mustWork=TRUE)) Loading required package: tools > showProc.time() Time (user system elapsed): 0.003 0 0.003 > > options(digits = 5) > if(!dev.interactive(orNone=TRUE)) pdf("ex2.pdf") > > set.seed(821) > x <- round(sort(rnorm(200)), 3) # rounding -> multiple values > sum(duplicated(x)) # 9 [1] 3 > y <- (fx <- exp(-x)) + rt(200,4)/4 > summaryCobs(cxy <- cobs(x,y, "decrease")) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... List of 24 $ call : language cobs(x = x, y = y, constraint = "decrease") $ tau : num 0.5 $ degree : num 2 $ constraint : chr "decrease" $ ic : chr "AIC" $ pointwise : NULL $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:200] -2.56 -2.14 -1.91 -1.81 -1.78 ... $ y : num [1:200] 12.7 8.24 6.67 5.88 6.42 ... $ resid : num [1:200] 0.72 -0.149 0 -0.195 0.545 ... $ fitted : num [1:200] 11.98 8.39 6.67 6.07 5.87 ... $ coef : num [1:5] 11.9769 3.5917 1.0544 0.0295 0.0295 $ knots : num [1:4] -2.557 -0.813 0.418 2.573 $ k0 : num 5 $ k : num 5 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 488 $ lambda : num 0 $ icyc : int 11 $ ifl : int 1 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 11.4448128 11.6875576 11.976923 12.26629 12.50903 2 10.9843366 11.2126114 11.484728 11.75684 11.98512 3 10.5344633 10.7489871 11.004712 11.26044 11.47496 4 10.0951784 10.2966768 10.536874 10.77707 10.97857 5 9.6664684 9.8556730 10.081215 10.30676 10.49596 6 9.2483213 9.4259693 9.637736 9.84950 10.02715 7 8.8407282 9.0075609 9.206435 9.40531 9.57214 8 8.4436848 8.6004453 8.787313 8.97418 9.13094 9 8.0571928 8.2046236 8.380369 8.55612 8.70355 10 7.6812627 7.8201015 7.985605 8.15111 8.28995 11 7.3159159 7.4468904 7.603020 7.75915 7.89012 12 6.9611870 7.0850095 7.232613 7.38022 7.50404 13 6.6171269 6.7344861 6.874385 7.01428 7.13164 14 6.2838041 6.3953578 6.528336 6.66131 6.77287 15 5.9613061 6.0676719 6.194466 6.32126 6.42763 16 5.6497392 5.7514863 5.872775 5.99406 6.09581 17 5.3492272 5.4468683 5.563262 5.67966 5.77730 18 5.0599086 5.1538933 5.265928 5.37796 5.47195 19 4.7819325 4.8726424 4.980774 5.08891 5.17961 20 4.5154542 4.6031999 4.707798 4.81240 4.90014 21 4.2606295 4.3456507 4.447001 4.54835 4.63337 22 4.0176099 4.1000771 4.198383 4.29669 4.37916 23 3.7865383 3.8665567 3.961943 4.05733 4.13735 24 3.5675443 3.6451602 3.737683 3.83021 3.90782 25 3.3607413 3.4359491 3.525601 3.61525 3.69046 26 3.1662231 3.2389744 3.325698 3.41242 3.48517 27 2.9840608 3.0542750 3.137974 3.22167 3.29189 28 2.8142997 2.8818753 2.962429 3.04298 3.11056 29 2.6569546 2.7217833 2.799063 2.87634 2.94117 30 2.5120031 2.5739870 2.647875 2.72176 2.78375 31 2.3793776 2.4384496 2.508867 2.57928 2.63836 32 2.2589520 2.3151025 2.382037 2.44897 2.50512 33 2.1505256 2.2038366 2.267386 2.33094 2.38425 34 2.0538038 2.1044916 2.164914 2.22534 2.27602 35 1.9677723 2.0162522 2.074043 2.13183 2.18031 36 1.8846710 1.9316617 1.987677 2.04369 2.09068 37 1.8024456 1.8486425 1.903712 1.95878 2.00498 38 1.7213655 1.7673410 1.822146 1.87695 1.92293 39 1.6417290 1.6879196 1.742982 1.79804 1.84423 40 1.5638322 1.6105393 1.666217 1.72189 1.76860 41 1.4879462 1.5353474 1.591852 1.64836 1.69576 42 1.4143040 1.4624707 1.519888 1.57731 1.62547 43 1.3430975 1.3920136 1.450324 1.50864 1.55755 44 1.2744792 1.3240589 1.383161 1.44226 1.49184 45 1.2085658 1.2586702 1.318397 1.37812 1.42823 46 1.1454438 1.1958944 1.256034 1.31617 1.36662 47 1.0851730 1.1357641 1.196072 1.25638 1.30697 48 1.0277900 1.0782992 1.138509 1.19872 1.24923 49 0.9733099 1.0235079 1.083347 1.14319 1.19338 50 0.9217268 0.9713870 1.030585 1.08978 1.13944 51 0.8730129 0.9219214 0.980223 1.03852 1.08743 52 0.8271160 0.8750827 0.932262 0.98944 1.03741 53 0.7839554 0.8308269 0.886700 0.94257 0.98945 54 0.7434158 0.7890916 0.843540 0.89799 0.94366 55 0.7053406 0.7497913 0.802779 0.85577 0.90022 56 0.6695233 0.7128138 0.764419 0.81602 0.85931 57 0.6357022 0.6780170 0.728459 0.77890 0.82121 58 0.6035616 0.6452289 0.694899 0.74457 0.78624 59 0.5724566 0.6139693 0.663455 0.71294 0.75445 60 0.5410437 0.5829503 0.632905 0.68286 0.72477 61 0.5094333 0.5521679 0.603110 0.65405 0.69679 62 0.4778879 0.5217649 0.574069 0.62637 0.67025 63 0.4466418 0.4918689 0.545782 0.59970 0.64492 64 0.4158910 0.4625864 0.518250 0.57391 0.62061 65 0.3857918 0.4340022 0.491472 0.54894 0.59715 66 0.3564634 0.4061813 0.465448 0.52471 0.57443 67 0.3279928 0.3791711 0.440179 0.50119 0.55236 68 0.3004403 0.3530042 0.415663 0.47832 0.53089 69 0.2738429 0.3277009 0.391903 0.45610 0.50996 70 0.2482184 0.3032707 0.368896 0.43452 0.48957 71 0.2235676 0.2797141 0.346644 0.41357 0.46972 72 0.1998762 0.2570233 0.325146 0.39327 0.45042 73 0.1771158 0.2351830 0.304402 0.37362 0.43169 74 0.1552452 0.2141706 0.284413 0.35466 0.41358 75 0.1342101 0.1939567 0.265178 0.33640 0.39615 76 0.1139444 0.1745054 0.246697 0.31889 0.37945 77 0.0943704 0.1557743 0.228971 0.30217 0.36357 78 0.0753996 0.1377153 0.211999 0.28628 0.34860 79 0.0569347 0.1202755 0.195781 0.27129 0.33463 80 0.0388708 0.1033980 0.180318 0.25724 0.32177 81 0.0210989 0.0870233 0.165609 0.24419 0.31012 82 0.0035089 0.0710917 0.151654 0.23222 0.29980 83 -0.0140062 0.0555449 0.138454 0.22136 0.29091 84 -0.0315470 0.0403283 0.126008 0.21169 0.28356 85 -0.0492034 0.0253928 0.114316 0.20324 0.27783 86 -0.0670524 0.0106968 0.103378 0.19606 0.27381 87 -0.0851561 -0.0037936 0.093195 0.19018 0.27155 88 -0.1035613 -0.0181039 0.083766 0.18564 0.27109 89 -0.1223000 -0.0322515 0.075091 0.18243 0.27248 90 -0.1413914 -0.0462467 0.067171 0.18059 0.27573 91 -0.1608432 -0.0600938 0.060005 0.18010 0.28085 92 -0.1806546 -0.0737923 0.053594 0.18098 0.28784 93 -0.2008180 -0.0873382 0.047936 0.18321 0.29669 94 -0.2213213 -0.1007247 0.043033 0.18679 0.30739 95 -0.2421494 -0.1139438 0.038884 0.19171 0.31992 96 -0.2632855 -0.1269863 0.035490 0.19797 0.33427 97 -0.2847123 -0.1398427 0.032850 0.20554 0.35041 98 -0.3064126 -0.1525038 0.030964 0.21443 0.36834 99 -0.3283696 -0.1649603 0.029833 0.22463 0.38804 100 -0.3505674 -0.1772037 0.029456 0.23611 0.40948 knots : [1] -2.557 -0.813 0.418 2.573 coef : [1] 11.976924 3.591747 1.054378 0.029456 0.029456 > 1 - sum(cxy $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 97.6% [1] 0.95969 > showProc.time() Time (user system elapsed): 0.783 0.089 2.113 > > if(doExtra) { + ## Interpolation + cxyI <- cobs(x,y, "decrease", knots = unique(x)) + ## takes quite long : 63 sec. (Pent. III, 700 MHz) --- this is because + ## each knot is added sequentially... {{improve!}} + + summaryCobs(cxyI)# only 7 knots remaining! + showProc.time() + } > > summaryCobs(cxy1 <- cobs(x,y, "decrease", lambda = 0.1)) List of 24 $ call : language cobs(x = x, y = y, constraint = "decrease", lambda = 0.1) $ tau : num 0.5 $ degree : num 2 $ constraint : chr "decrease" $ ic : NULL $ pointwise : NULL $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:200] -2.56 -2.14 -1.91 -1.81 -1.78 ... $ y : num [1:200] 12.7 8.24 6.67 5.88 6.42 ... $ resid : num [1:200] 0 -0.315 0 -0.161 0.586 ... $ fitted : num [1:200] 12.7 8.56 6.67 6.04 5.83 ... $ coef : num [1:22] 12.7 5.78 3.16 2.43 2.11 ... $ knots : num [1:20] -2.557 -1.34 -1.03 -0.901 -0.772 ... $ k0 : int 15 $ k : int 15 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 488 $ lambda : num 0.1 $ icyc : int 23 $ ifl : int 1 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 12.0912847 12.4849933 12.6970034 12.90901 13.30272 2 11.5452819 11.9166521 12.1166331 12.31661 12.68798 3 11.0146966 11.3650966 11.5537853 11.74247 12.09287 4 10.4995535 10.8303355 11.0084599 11.18658 11.51737 5 9.9998870 10.3123808 10.4806571 10.64893 10.96143 6 9.5157430 9.8112485 9.9703768 10.12951 10.42501 7 9.0471805 9.3269594 9.4776191 9.62828 9.90806 8 8.5942728 8.8595392 9.0023838 9.14523 9.41049 9 8.1571088 8.4090188 8.5446710 8.68032 8.93223 10 7.7357927 7.9754347 8.1044808 8.23353 8.47317 11 7.3304438 7.5588289 7.6818131 7.80480 8.03318 12 6.9411951 7.1592477 7.2766679 7.39409 7.61214 13 6.5681906 6.7767415 6.8890452 7.00135 7.20990 14 6.2115819 6.4113636 6.5189450 6.62653 6.82631 15 5.8715240 6.0631680 6.1663674 6.26957 6.46121 16 5.5481704 5.7322086 5.8313123 5.93042 6.11445 17 5.2416676 5.4185366 5.5137796 5.60902 5.78589 18 4.9521494 5.1221988 5.2137695 5.30534 5.47539 19 4.6797308 4.8432355 4.9312819 5.01933 5.18283 20 4.4245017 4.5816781 4.6663169 4.75096 4.90813 21 4.1865199 4.3375470 4.4188743 4.50020 4.65123 22 3.9658032 4.1108482 4.1889542 4.26706 4.41211 23 3.7623206 3.9015710 3.9765567 4.05154 4.19079 24 3.5759813 3.7096836 3.7816817 3.85368 3.98738 25 3.4043771 3.5329043 3.6021155 3.67133 3.79985 26 3.2347309 3.3585931 3.4252922 3.49199 3.61585 27 3.0652721 3.1848437 3.2492325 3.31362 3.43319 28 2.8962030 3.0117271 3.0739363 3.13615 3.25167 29 2.7276530 2.8392885 2.8994037 2.95952 3.07115 30 2.5596612 2.6675415 2.7256346 2.78373 2.89161 31 2.3944947 2.4988186 2.5549966 2.61117 2.71550 32 2.2444821 2.3455939 2.4000421 2.45449 2.55560 33 2.1114672 2.2097080 2.2626102 2.31551 2.41375 34 1.9954176 2.0911496 2.1427009 2.19425 2.28998 35 1.8963846 1.9899366 2.0403140 2.09069 2.18424 36 1.8125024 1.9041996 1.9535781 2.00296 2.09465 37 1.7347658 1.8248332 1.8733340 1.92183 2.01190 38 1.6620975 1.7506630 1.7983550 1.84605 1.93461 39 1.5945123 1.6816941 1.7286411 1.77559 1.86277 40 1.5278221 1.6138190 1.6601279 1.70644 1.79243 41 1.4573347 1.5423451 1.5881227 1.63390 1.71891 42 1.3839943 1.4682138 1.5135655 1.55892 1.64314 43 1.3227219 1.4063482 1.4513806 1.49641 1.58004 44 1.2787473 1.3619265 1.4067181 1.45151 1.53469 45 1.2488624 1.3317463 1.3763789 1.42101 1.50390 46 1.2168724 1.2994789 1.3439621 1.38845 1.47105 47 1.1806389 1.2628708 1.3071522 1.35143 1.43367 48 1.1401892 1.2219316 1.2659495 1.30997 1.39171 49 1.0941843 1.1754044 1.2191410 1.26288 1.34410 50 1.0326549 1.1134412 1.1569442 1.20045 1.28123 51 0.9535058 1.0339215 1.0772249 1.12053 1.20094 52 0.8632281 0.9433870 0.9865521 1.02972 1.10988 53 0.7875624 0.8676441 0.9107678 0.95389 1.03397 54 0.7267897 0.8069673 0.8501425 0.89332 0.97350 55 0.6673925 0.7477244 0.7909827 0.83424 0.91457 56 0.6072642 0.6877460 0.7310850 0.77442 0.85491 57 0.5471548 0.6278279 0.6712700 0.71471 0.79539 58 0.4995140 0.5804770 0.6240752 0.66767 0.74864 59 0.4686435 0.5499607 0.5937495 0.63754 0.71886 60 0.4531016 0.5348803 0.5789177 0.62296 0.70473 61 0.4381911 0.5206110 0.5649937 0.60938 0.69180 62 0.4199957 0.5032331 0.5480561 0.59288 0.67612 63 0.4036491 0.4879280 0.5333117 0.57870 0.66297 64 0.3952493 0.4807890 0.5268517 0.57291 0.65845 65 0.3926229 0.4796600 0.5265291 0.57340 0.66044 66 0.3900185 0.4787485 0.5265291 0.57431 0.66304 67 0.3870480 0.4776752 0.5264774 0.57528 0.66591 68 0.3738545 0.4665585 0.5164792 0.56640 0.65910 69 0.3432056 0.4380737 0.4891596 0.54025 0.63511 70 0.2950830 0.3922142 0.4445189 0.49682 0.59395 71 0.2295290 0.3291123 0.3827373 0.43636 0.53595 72 0.1670195 0.2693294 0.3244228 0.37952 0.48183 73 0.1216565 0.2269375 0.2836308 0.34032 0.44561 74 0.0934100 0.2019260 0.2603613 0.31880 0.42731 75 0.0787462 0.1907702 0.2510947 0.31142 0.42344 76 0.0658428 0.1813823 0.2435998 0.30582 0.42136 77 0.0538230 0.1727768 0.2368329 0.30089 0.41984 78 0.0427388 0.1649719 0.2307938 0.29662 0.41885 79 0.0325663 0.1579592 0.2254827 0.29301 0.41840 80 0.0232151 0.1517072 0.2208995 0.29009 0.41858 81 0.0145359 0.1461634 0.2170442 0.28792 0.41955 82 0.0063272 0.1412575 0.2139168 0.28658 0.42151 83 -0.0016568 0.1369034 0.2115173 0.28613 0.42469 84 -0.0096967 0.1330028 0.2098457 0.28669 0.42939 85 -0.0180957 0.1294496 0.2089021 0.28835 0.43590 86 -0.0272134 0.1260791 0.2086264 0.29117 0.44447 87 -0.0387972 0.1210358 0.2071052 0.29317 0.45301 88 -0.0534279 0.1135207 0.2034217 0.29332 0.46027 89 -0.0709531 0.1035871 0.1975762 0.29157 0.46611 90 -0.0912981 0.0912612 0.1895684 0.28788 0.47043 91 -0.1144525 0.0765465 0.1793985 0.28225 0.47325 92 -0.1404576 0.0594287 0.1670665 0.27470 0.47459 93 -0.1693951 0.0398791 0.1525723 0.26527 0.47454 94 -0.2013769 0.0178586 0.1359159 0.25397 0.47321 95 -0.2365365 -0.0066795 0.1170974 0.24087 0.47073 96 -0.2750210 -0.0337868 0.0961167 0.22602 0.46725 97 -0.3169840 -0.0635170 0.0729738 0.20946 0.46293 98 -0.3625797 -0.0959240 0.0476688 0.19126 0.45792 99 -0.4119579 -0.1310604 0.0202016 0.17146 0.45236 100 -0.4652595 -0.1689754 -0.0094278 0.15012 0.44640 knots : [1] -2.557 -1.340 -1.030 -0.901 -0.772 -0.586 -0.448 -0.305 -0.092 0.054 [11] 0.163 0.329 0.481 0.606 0.722 0.859 1.065 1.244 1.837 2.573 coef : [1] 12.6970048 5.7788265 3.1620633 2.4291174 2.1069607 1.8462166 [7] 1.6371062 1.4304905 1.3348346 1.1758220 0.9413974 0.7863913 [13] 0.5998958 0.5697029 0.5265291 0.5265291 0.5265291 0.2707227 [19] 0.2086712 0.2086712 -0.0094278 6.5257497 > 1 - sum(cxy1 $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 98.2% [1] 0.96169 > > summaryCobs(cxy2 <- cobs(x,y, "decrease", lambda = 1e-2)) List of 24 $ call : language cobs(x = x, y = y, constraint = "decrease", lambda = 0.01) $ tau : num 0.5 $ degree : num 2 $ constraint : chr "decrease" $ ic : NULL $ pointwise : NULL $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:200] -2.56 -2.14 -1.91 -1.81 -1.78 ... $ y : num [1:200] 12.7 8.24 6.67 5.88 6.42 ... $ resid : num [1:200] 0 -0.146 0.1468 -0.0463 0.6868 ... $ fitted : num [1:200] 12.7 8.39 6.52 5.92 5.73 ... $ coef : num [1:22] 12.7 5.34 3.59 2.19 2.13 ... $ knots : num [1:20] -2.557 -1.34 -1.03 -0.901 -0.772 ... $ k0 : int 21 $ k : int 21 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 488 $ lambda : num 0.01 $ icyc : int 35 $ ifl : int 1 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 12.0477594 12.4997491 12.6970071 12.89427 13.34625 2 11.4687308 11.8950752 12.0811411 12.26721 12.69355 3 10.9090823 11.3113523 11.4869116 11.66247 12.06474 4 10.3688404 10.7485883 10.9143185 11.08005 11.45980 5 9.8480420 10.2067945 10.3633618 10.51993 10.87868 6 9.3467363 9.6859859 9.8340417 9.98210 10.32135 7 8.8649866 9.1861815 9.3263579 9.46653 9.78773 8 8.4028715 8.7074055 8.8403106 8.97322 9.27775 9 7.9604861 8.2496865 8.3758998 8.50211 8.79131 10 7.5379421 7.8130586 7.9331254 8.05319 8.32831 11 7.1353676 7.3975607 7.5119874 7.62641 7.88861 12 6.7529050 7.0032361 7.1124859 7.22174 7.47207 13 6.3907086 6.6301316 6.7346209 6.83911 7.07853 14 6.0489410 6.2782966 6.3783923 6.47849 6.70784 15 5.7277684 5.9477816 6.0438001 6.13982 6.35983 16 5.4273551 5.6386366 5.7308444 5.82305 6.03433 17 5.1478583 5.3509094 5.4395252 5.52814 5.73119 18 4.8894214 5.0846433 5.1698424 5.25504 5.45026 19 4.6521676 4.8398760 4.9217960 5.00372 5.19142 20 4.4361933 4.6166367 4.6953861 4.77414 4.95458 21 4.2415605 4.4149443 4.4906127 4.56628 4.73966 22 4.0682883 4.2348044 4.3074756 4.38015 4.54666 23 3.9163432 4.0762071 4.1459751 4.21574 4.37561 24 3.7856282 3.9391227 4.0061110 4.07310 4.22659 25 3.6683774 3.8159306 3.8803259 3.94472 4.09227 26 3.5214653 3.6636629 3.7257209 3.78778 3.92998 27 3.3383583 3.4756303 3.5355387 3.59545 3.73272 28 3.1192735 3.2518988 3.3097793 3.36766 3.50028 29 2.8643493 2.9925103 3.0484425 3.10437 3.23254 30 2.5736278 2.6974778 2.7515286 2.80558 2.92943 31 2.2696062 2.3893733 2.4416422 2.49391 2.61368 32 2.0718959 2.1879754 2.2386350 2.28929 2.40537 33 1.9979346 2.1107181 2.1599392 2.20916 2.32194 34 1.9710324 2.0809358 2.1288999 2.17686 2.28677 35 1.9261503 2.0335510 2.0804229 2.12729 2.23470 36 1.8645775 1.9698487 2.0157914 2.06173 2.16701 37 1.7927585 1.8961587 1.9412848 1.98641 2.08981 38 1.7116948 1.8133707 1.8577443 1.90212 2.00379 39 1.6214021 1.7214896 1.7651699 1.80885 1.90894 40 1.5242004 1.6229275 1.6660141 1.70910 1.80783 41 1.4229217 1.5205162 1.5631086 1.60570 1.70330 42 1.3194940 1.4161806 1.4583766 1.50057 1.59726 43 1.2442053 1.3402109 1.3821098 1.42401 1.52001 44 1.2075941 1.3030864 1.3447613 1.38644 1.48193 45 1.2023778 1.2975311 1.3390581 1.38059 1.47574 46 1.1914924 1.2863272 1.3277152 1.36910 1.46394 47 1.1698641 1.2642688 1.3054691 1.34667 1.44107 48 1.1375221 1.2313649 1.2723199 1.31327 1.40712 49 1.0934278 1.1866710 1.2273643 1.26806 1.36130 50 1.0300956 1.1228408 1.1633168 1.20379 1.29654 51 0.9459780 1.0382977 1.0785880 1.11888 1.21120 52 0.8492712 0.9412961 0.9814577 1.02162 1.11364 53 0.7724392 0.8643755 0.9044985 0.94462 1.03656 54 0.7154255 0.8074718 0.8476428 0.88781 0.97986 55 0.6587891 0.7510125 0.7912608 0.83151 0.92373 56 0.5994755 0.6918710 0.7321944 0.77252 0.86491 57 0.5383570 0.6309722 0.6713915 0.71181 0.80443 58 0.4898228 0.5827709 0.6233354 0.66390 0.75685 59 0.4588380 0.5521926 0.5929345 0.63368 0.72703 60 0.4438719 0.5377564 0.5787296 0.61970 0.71359 61 0.4293281 0.5239487 0.5652432 0.60654 0.70116 62 0.4110511 0.5066103 0.5483143 0.59002 0.68558 63 0.3944126 0.4911673 0.5333932 0.57562 0.67237 64 0.3857958 0.4839980 0.5268556 0.56971 0.66792 65 0.3830000 0.4829213 0.5265291 0.57014 0.67006 66 0.3802084 0.4820731 0.5265291 0.57099 0.67285 67 0.3770181 0.4810608 0.5264673 0.57187 0.67592 68 0.3616408 0.4680678 0.5145149 0.56096 0.66739 69 0.3254129 0.4343244 0.4818557 0.52939 0.63830 70 0.2683149 0.3798245 0.4284897 0.47715 0.58866 71 0.1904294 0.3047541 0.3546478 0.40454 0.51887 72 0.1179556 0.2354105 0.2866704 0.33793 0.45539 73 0.0689088 0.1897746 0.2425231 0.29527 0.41614 74 0.0432569 0.1678366 0.2222059 0.27658 0.40115 75 0.0359906 0.1645977 0.2207246 0.27685 0.40546 76 0.0301934 0.1628364 0.2207246 0.27861 0.41126 77 0.0245630 0.1611257 0.2207246 0.28032 0.41689 78 0.0191553 0.1594827 0.2207246 0.28197 0.42229 79 0.0139446 0.1578996 0.2207246 0.28355 0.42750 80 0.0088340 0.1563468 0.2207246 0.28510 0.43262 81 0.0036634 0.1547759 0.2207246 0.28667 0.43779 82 -0.0017830 0.1531211 0.2207246 0.28833 0.44323 83 -0.0077688 0.1513025 0.2207246 0.29015 0.44922 84 -0.0145948 0.1492286 0.2207246 0.29222 0.45604 85 -0.0225859 0.1468007 0.2207246 0.29465 0.46404 86 -0.0321107 0.1438739 0.2206774 0.29748 0.47347 87 -0.0445016 0.1389916 0.2190720 0.29915 0.48265 88 -0.0601227 0.1315395 0.2151851 0.29883 0.49049 89 -0.0788103 0.1215673 0.2090164 0.29647 0.49684 90 -0.1004844 0.1090993 0.2005661 0.29203 0.50162 91 -0.1251339 0.0941388 0.1898342 0.28553 0.50480 92 -0.1528032 0.0766725 0.1768206 0.27697 0.50644 93 -0.1835797 0.0566736 0.1615253 0.26638 0.50663 94 -0.2175834 0.0341058 0.1439484 0.25379 0.50548 95 -0.2549574 0.0089256 0.1240898 0.23925 0.50314 96 -0.2958592 -0.0189149 0.1019496 0.22281 0.49976 97 -0.3404537 -0.0494657 0.0775277 0.20452 0.49551 98 -0.3889062 -0.0827771 0.0508241 0.18443 0.49055 99 -0.4413769 -0.1188979 0.0218389 0.16258 0.48505 100 -0.4980173 -0.1578738 -0.0094279 0.13902 0.47916 knots : [1] -2.557 -1.340 -1.030 -0.901 -0.772 -0.586 -0.448 -0.305 -0.092 0.054 [11] 0.163 0.329 0.481 0.606 0.722 0.859 1.065 1.244 1.837 2.573 coef : [1] 12.697009 5.337850 3.591398 2.187733 2.133993 1.936435 1.631856 [8] 1.340650 1.340650 1.185401 0.931750 0.789326 0.598245 0.570221 [15] 0.526529 0.526529 0.526529 0.220725 0.220725 0.220725 -0.009428 [22] 46.342964 > 1 - sum(cxy2 $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 98.2% (tiny bit better) [1] 0.96257 > > summaryCobs(cxy3 <- cobs(x,y, "decrease", lambda = 1e-6, nknots = 60)) List of 24 $ call : language cobs(x = x, y = y, constraint = "decrease", nknots = 60, lambda = 1e-06) $ tau : num 0.5 $ degree : num 2 $ constraint : chr "decrease" $ ic : NULL $ pointwise : NULL $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:200] -2.56 -2.14 -1.91 -1.81 -1.78 ... $ y : num [1:200] 12.7 8.24 6.67 5.88 6.42 ... $ resid : num [1:200] 0 0 0 -0.382 0.309 ... $ fitted : num [1:200] 12.7 8.24 6.67 6.26 6.11 ... $ coef : num [1:62] 12.7 7.69 6.09 4.35 3.73 3.73 2.74 2.57 2.57 2.25 ... $ knots : num [1:60] -2.56 -1.81 -1.73 -1.38 -1.23 ... $ k0 : int 61 $ k : int 61 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 488 $ lambda : num 1e-06 $ icyc : int 46 $ ifl : int 1 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 12.0247124 12.56890432 12.6970139 12.825123 13.36932 2 11.3797843 11.89599414 12.0175164 12.139039 12.65525 3 10.7668218 11.25721357 11.3726579 11.488102 11.97849 4 10.1860204 10.65259986 10.7624385 10.872277 11.33886 5 9.6375946 10.08219388 10.1868581 10.291522 10.73612 6 9.1217734 9.54603927 9.6459167 9.745794 10.17006 7 8.6387946 9.04418136 9.1396144 9.235048 9.64043 8 8.1888978 8.57666578 8.6679512 8.759237 9.14700 9 7.7723156 8.14353686 8.2309270 8.318317 8.68954 10 7.3892646 7.74483589 7.8285418 7.912248 8.26782 11 7.0399352 7.38059913 7.4607957 7.540992 7.88166 12 6.7244802 7.05085572 7.1276886 7.204521 7.53090 13 6.4430029 6.75562533 6.8292205 6.902816 7.21544 14 6.1955428 6.49491547 6.5653915 6.635868 6.93524 15 5.9820595 6.26871848 6.3362016 6.403685 6.69034 16 5.7696526 6.04428975 6.1089428 6.173596 6.44823 17 5.4339991 5.69759119 5.7596440 5.821697 6.08529 18 5.0454361 5.29908138 5.3587927 5.418504 5.67215 19 4.6993977 4.94405130 5.0016458 5.059240 5.30389 20 4.3963458 4.63268699 4.6883247 4.743962 4.98030 21 4.1365583 4.36504142 4.4188292 4.472617 4.70110 22 3.9202312 4.14115193 4.1931594 4.245167 4.46609 23 3.7474595 3.96103662 4.0113153 4.061594 4.27517 24 3.6182953 3.82478434 3.8733944 3.922005 4.12849 25 3.5335861 3.73343196 3.7804782 3.827524 4.02737 26 3.4937186 3.68729597 3.7328665 3.778437 3.97201 27 3.4752667 3.66292175 3.7070981 3.751274 3.93893 28 3.3043525 3.48641351 3.5292729 3.572132 3.75419 29 2.9458452 3.12249549 3.1640812 3.205667 3.38232 30 2.4899112 2.66132542 2.7016785 2.742031 2.91345 31 2.3652956 2.53186083 2.5710724 2.610284 2.77685 32 2.2382402 2.40029503 2.4384448 2.476594 2.63865 33 2.0486975 2.20653724 2.2436947 2.280852 2.43869 34 2.0511798 2.20522276 2.2414864 2.277750 2.43179 35 2.0553528 2.20601792 2.2414864 2.276955 2.42762 36 2.0385642 2.18623332 2.2209965 2.255760 2.40343 37 1.8391470 1.98414706 2.0182819 2.052417 2.19742 38 1.6312788 1.77395114 1.8075380 1.841125 1.98380 39 1.5314449 1.67192652 1.7049976 1.738069 1.87855 40 1.5208780 1.65927041 1.6918497 1.724429 1.86282 41 1.4986364 1.63513027 1.6672626 1.699395 1.83589 42 1.4498027 1.58470514 1.6164629 1.648221 1.78312 43 1.2247043 1.35830771 1.3897596 1.421211 1.55481 44 1.1772885 1.30980813 1.3410049 1.372202 1.50472 45 1.1781750 1.30997706 1.3410049 1.372033 1.50383 46 1.1786125 1.31005757 1.3410014 1.371945 1.50339 47 1.1644262 1.29555858 1.3264288 1.357299 1.48843 48 1.1223208 1.25286982 1.2836027 1.314336 1.44488 49 1.0583227 1.18805529 1.2185960 1.249137 1.37887 50 1.0360396 1.16504088 1.1954094 1.225778 1.35478 51 1.0366880 1.16516444 1.1954094 1.225654 1.35413 52 0.9728290 1.10089058 1.1310379 1.161185 1.28925 53 0.6458992 0.77387319 0.8039998 0.834127 0.96210 54 0.6278378 0.75589463 0.7860408 0.816187 0.94424 55 0.6233664 0.75144260 0.7815933 0.811744 0.93982 56 0.6203139 0.74853170 0.7787158 0.808900 0.93712 57 0.4831205 0.61171664 0.6419898 0.672263 0.80086 58 0.4152141 0.54435194 0.5747526 0.605153 0.73429 59 0.4143942 0.54419570 0.5747526 0.605309 0.73511 60 0.4133407 0.54399495 0.5747526 0.605510 0.73616 61 0.3912541 0.52305164 0.5540784 0.585105 0.71690 62 0.3615872 0.49479624 0.5261553 0.557514 0.69072 63 0.3595156 0.49440150 0.5261553 0.557909 0.69279 64 0.3572502 0.49396981 0.5261553 0.558341 0.69506 65 0.3545874 0.49346241 0.5261553 0.558848 0.69772 66 0.3515435 0.49288238 0.5261553 0.559428 0.70077 67 0.3482098 0.49224713 0.5261553 0.560063 0.70410 68 0.3447026 0.49157882 0.5261553 0.560732 0.70761 69 0.3265062 0.47651151 0.5118246 0.547138 0.69714 70 0.2579257 0.41132297 0.4474346 0.483546 0.63694 71 0.2081857 0.36515737 0.4021105 0.439064 0.59604 72 0.1349572 0.29569526 0.3335350 0.371375 0.53211 73 0.0020438 0.16674762 0.2055209 0.244294 0.40900 74 -0.0243664 0.14460810 0.1843868 0.224166 0.39314 75 -0.0362635 0.13720915 0.1780468 0.218884 0.39236 76 -0.0421115 0.13609478 0.1780468 0.219999 0.39820 77 -0.0482083 0.13493301 0.1780468 0.221161 0.40430 78 -0.0546034 0.13371440 0.1780468 0.222379 0.41070 79 -0.0610386 0.13248816 0.1780468 0.223605 0.41713 80 -0.0674722 0.13126221 0.1780468 0.224831 0.42357 81 -0.0740291 0.13001276 0.1780468 0.226081 0.43012 82 -0.0809567 0.12869267 0.1780468 0.227401 0.43705 83 -0.0885308 0.12724941 0.1780468 0.228844 0.44462 84 -0.0966886 0.12569491 0.1780468 0.230399 0.45278 85 -0.1053882 0.12403716 0.1780468 0.232056 0.46148 86 -0.1147206 0.12225885 0.1780468 0.233835 0.47081 87 -0.1248842 0.12032213 0.1780468 0.235771 0.48098 88 -0.1360096 0.11820215 0.1780468 0.237891 0.49210 89 -0.1480747 0.11590310 0.1780468 0.240190 0.50417 90 -0.1611528 0.11337745 0.1780053 0.242633 0.51716 91 -0.1772967 0.10838384 0.1756366 0.242889 0.52857 92 -0.1976403 0.09964452 0.1696291 0.239614 0.53690 93 -0.2221958 0.08715720 0.1599828 0.232808 0.54216 94 -0.2510614 0.07090314 0.1466976 0.222492 0.54446 95 -0.2844042 0.05085051 0.1297736 0.208697 0.54395 96 -0.3224450 0.02695723 0.1092109 0.191465 0.54087 97 -0.3654434 -0.00082617 0.0850093 0.170845 0.53546 98 -0.4136843 -0.03255395 0.0571689 0.146892 0.52802 99 -0.4674640 -0.06828261 0.0256897 0.119662 0.51884 100 -0.5270786 -0.10806856 -0.0094284 0.089212 0.50822 knots : [1] -2.557 -1.812 -1.726 -1.384 -1.233 -1.082 -1.046 -1.009 -0.932 -0.902 [11] -0.877 -0.838 -0.813 -0.765 -0.707 -0.665 -0.568 -0.498 -0.460 -0.413 [21] -0.347 -0.333 -0.299 -0.274 -0.226 -0.089 -0.024 -0.011 0.063 0.094 [31] 0.118 0.136 0.231 0.285 0.328 0.392 0.460 0.473 0.517 0.551 [41] 0.602 0.623 0.692 0.715 0.742 0.787 0.812 0.892 0.934 0.988 [51] 1.070 1.162 1.178 1.276 1.402 1.655 1.877 1.988 2.047 2.573 coef : [1] 12.6970155 7.6878537 6.0937652 4.3540061 3.7259911 3.7259911 [7] 2.7408131 2.5727608 2.5727608 2.2478639 2.2414864 2.2414864 [13] 2.2414864 2.2414864 2.2414864 1.9875889 1.6964374 1.6964374 [19] 1.6623718 1.6623718 1.3410049 1.3410049 1.3410049 1.3410049 [25] 1.3410049 1.3410049 1.1954094 1.1954094 1.1954094 1.1954094 [31] 0.9829296 0.8091342 0.7815933 0.7815933 0.7815933 0.5747526 [37] 0.5747526 0.5747526 0.5747526 0.5747526 0.5261553 0.5261553 [43] 0.5261553 0.5261553 0.5261553 0.5261553 0.5261553 0.5261553 [49] 0.5261553 0.5261553 0.4273578 0.3741431 0.2060752 0.1780468 [55] 0.1780468 0.1780468 0.1780468 0.1780468 0.1780468 0.1780468 [61] -0.0094285 432.6957871 > 1 - sum(cxy3 $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 98.36% [1] 0.96502 > showProc.time() Time (user system elapsed): 0.282 0.006 0.649 > > cpuTime(cxy4 <- cobs(x,y, "decrease", lambda = 1e-6, nknots = 100))# ~ 3 sec. Time elapsed: 0.12 > 1 - sum(cxy4 $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 98.443% [1] 0.96603 > > cpuTime(cxy5 <- cobs(x,y, "decrease", lambda = 1e-6, nknots = 150))# ~ 8.7 sec. Time elapsed: 0.152 > 1 - sum(cxy5 $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 98.4396% [1] 0.96835 > showProc.time() Time (user system elapsed): 0.604 0.016 1.651 > > > ## regularly spaced x : > X <- seq(-1,1, len = 201) > xx <- c(seq(-1.1, -1, len = 11), X, + seq( 1, 1.1, len = 11)) > y <- (fx <- exp(-X)) + rt(201,4)/4 > summaryCobs(cXy <- cobs(X,y, "decrease")) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... List of 24 $ call : language cobs(x = X, y = y, constraint = "decrease") $ tau : num 0.5 $ degree : num 2 $ constraint : chr "decrease" $ ic : chr "AIC" $ pointwise : NULL $ select.knots : logi TRUE $ select.lambda: logi FALSE $ x : num [1:201] -1 -0.99 -0.98 -0.97 -0.96 -0.95 -0.94 -0.93 -0.92 -0.91 ... $ y : num [1:201] 2.67 2.77 3.46 3.14 1.79 ... $ resid : num [1:201] 0 0.125 0.84 0.555 -0.77 ... $ fitted : num [1:201] 2.67 2.64 2.62 2.59 2.56 ... $ coef : num [1:4] 2.672 1.556 0.7 0.356 $ knots : num [1:3] -1 -0.2 1 $ k0 : num 4 $ k : num 4 $ x.ps :Formal class 'matrix.csr' [package "SparseM"] with 4 slots $ SSy : num 100 $ lambda : num 0 $ icyc : int 9 $ ifl : int 1 $ pp.lambda : NULL $ pp.sic : NULL $ i.mask : NULL cb.lo ci.lo fit ci.up cb.up 1 2.46750 2.55064 2.67153 2.79242 2.87556 2 2.42251 2.50122 2.61568 2.73013 2.80884 3 2.37783 2.45240 2.56081 2.66923 2.74379 4 2.33345 2.40414 2.50694 2.60973 2.68043 5 2.28933 2.35645 2.45404 2.55164 2.61876 6 2.24548 2.30932 2.40214 2.49496 2.55879 7 2.20189 2.26274 2.35122 2.43970 2.50055 8 2.15855 2.21672 2.30129 2.38586 2.44402 9 2.11547 2.17124 2.25234 2.33344 2.38922 10 2.07265 2.12633 2.20438 2.28244 2.33611 11 2.03013 2.08199 2.15741 2.23283 2.28470 12 1.98791 2.03824 2.11142 2.18461 2.23494 13 1.94605 1.99510 2.06642 2.13775 2.18680 14 1.90459 1.95260 2.02241 2.09222 2.14023 15 1.86359 1.91078 1.97938 2.04799 2.09517 16 1.82311 1.86966 1.93734 2.00502 2.05157 17 1.78322 1.82929 1.89629 1.96328 2.00936 18 1.74397 1.78971 1.85622 1.92273 1.96847 19 1.70544 1.75096 1.81714 1.88332 1.92883 20 1.66769 1.71307 1.77904 1.84502 1.89039 21 1.63079 1.67608 1.74193 1.80779 1.85308 22 1.59478 1.64002 1.70581 1.77160 1.81684 23 1.55972 1.60493 1.67067 1.73642 1.78163 24 1.52564 1.57083 1.63653 1.70222 1.74741 25 1.49260 1.53773 1.60336 1.66899 1.71412 26 1.46062 1.50567 1.57118 1.63670 1.68175 27 1.42972 1.47466 1.53999 1.60533 1.65026 28 1.39994 1.44470 1.50979 1.57488 1.61964 29 1.37128 1.41581 1.48057 1.54533 1.58987 30 1.34375 1.38800 1.45234 1.51668 1.56093 31 1.31736 1.36126 1.42510 1.48893 1.53283 32 1.29211 1.33560 1.39884 1.46207 1.50556 33 1.26800 1.31101 1.37357 1.43612 1.47914 34 1.24500 1.28749 1.34928 1.41107 1.45356 35 1.22310 1.26502 1.32598 1.38694 1.42886 36 1.20228 1.24360 1.30367 1.36374 1.40505 37 1.18250 1.22319 1.28234 1.34150 1.38218 38 1.16372 1.20377 1.26200 1.32023 1.36028 39 1.14589 1.18532 1.24265 1.29998 1.33941 40 1.12894 1.16779 1.22428 1.28077 1.31962 41 1.11271 1.15106 1.20683 1.26259 1.30094 42 1.09639 1.13439 1.18963 1.24488 1.28287 43 1.07982 1.11760 1.17253 1.22747 1.26525 44 1.06303 1.10072 1.15553 1.21034 1.24803 45 1.04607 1.08378 1.13862 1.19346 1.23117 46 1.02898 1.06681 1.12181 1.17681 1.21463 47 1.01180 1.04982 1.10509 1.16037 1.19838 48 0.99458 1.03284 1.08847 1.14411 1.18237 49 0.97734 1.01589 1.07195 1.12801 1.16656 50 0.96011 0.99899 1.05552 1.11205 1.15092 51 0.94294 0.98216 1.03919 1.09621 1.13543 52 0.92585 0.96541 1.02295 1.08049 1.12005 53 0.90885 0.94877 1.00681 1.06485 1.10477 54 0.89197 0.93223 0.99076 1.04930 1.08956 55 0.87523 0.91581 0.97482 1.03382 1.07440 56 0.85865 0.89952 0.95896 1.01840 1.05928 57 0.84223 0.88337 0.94321 1.00304 1.04419 58 0.82598 0.86736 0.92755 0.98773 1.02911 59 0.80991 0.85150 0.91198 0.97246 1.01405 60 0.79403 0.83579 0.89651 0.95723 0.99899 61 0.77834 0.82023 0.88114 0.94205 0.98394 62 0.76284 0.80482 0.86586 0.92690 0.96888 63 0.74753 0.78956 0.85068 0.91180 0.95383 64 0.73241 0.77446 0.83559 0.89673 0.93878 65 0.71747 0.75950 0.82060 0.88171 0.92374 66 0.70271 0.74468 0.80571 0.86674 0.90871 67 0.68812 0.73001 0.79091 0.85182 0.89371 68 0.67368 0.71546 0.77621 0.83696 0.87874 69 0.65939 0.70104 0.76161 0.82217 0.86382 70 0.64523 0.68674 0.74710 0.80745 0.84896 71 0.63118 0.67254 0.73268 0.79282 0.83419 72 0.61722 0.65844 0.71836 0.77829 0.81951 73 0.60333 0.64441 0.70414 0.76388 0.80495 74 0.58948 0.63045 0.69002 0.74958 0.79055 75 0.57565 0.61654 0.67599 0.73544 0.77632 76 0.56181 0.60266 0.66205 0.72145 0.76230 77 0.54792 0.58879 0.64821 0.70764 0.74851 78 0.53395 0.57491 0.63447 0.69403 0.73500 79 0.51986 0.56100 0.62083 0.68065 0.72179 80 0.50563 0.54705 0.60728 0.66750 0.70892 81 0.49121 0.53302 0.59382 0.65462 0.69643 82 0.47657 0.51891 0.58046 0.64202 0.68435 83 0.46169 0.50468 0.56720 0.62972 0.67271 84 0.44652 0.49033 0.55403 0.61774 0.66155 85 0.43105 0.47584 0.54096 0.60609 0.65087 86 0.41526 0.46119 0.52799 0.59478 0.64072 87 0.39912 0.44638 0.51511 0.58383 0.63109 88 0.38264 0.43141 0.50233 0.57324 0.62202 89 0.36579 0.41626 0.48964 0.56302 0.61349 90 0.34858 0.40093 0.47705 0.55317 0.60552 91 0.33101 0.38542 0.46455 0.54368 0.59810 92 0.31307 0.36975 0.45215 0.53456 0.59123 93 0.29478 0.35390 0.43985 0.52580 0.58492 94 0.27615 0.33788 0.42764 0.51741 0.57914 95 0.25717 0.32170 0.41553 0.50936 0.57389 96 0.23787 0.30536 0.40352 0.50167 0.56917 97 0.21824 0.28888 0.39160 0.49431 0.56495 98 0.19830 0.27225 0.37977 0.48730 0.56125 99 0.17806 0.25547 0.36804 0.48062 0.55803 100 0.15752 0.23857 0.35641 0.47426 0.55531 knots : [1] -1.0 -0.2 1.0 coef : [1] 2.67153 1.55592 0.70045 0.35641 > 1 - sum(cXy $ resid ^ 2) / sum((y - mean(y))^2) # R^2 = 77.2% [1] 0.77644 > showProc.time() Time (user system elapsed): 0.177 0.011 0.51 > > (cXy.9 <- cobs(X,y, "decrease", tau = 0.9)) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... COBS regression spline (degree = 2) from call: cobs(x = X, y = y, constraint = "decrease", tau = 0.9) {tau=0.9}-quantile; dimensionality of fit: 6 from {6} x$knots[1:5]: -1.0, -0.6, -0.2, 0.2, 1.0 > (cXy.1 <- cobs(X,y, "decrease", tau = 0.1)) qbsks2(): Performing general knot selection ... WARNING! Since the number of 6 knots selected by AIC reached the upper bound during general knot selection, you might want to rerun cobs with a larger number of knots. Deleting unnecessary knots ... WARNING! Since the number of 6 knots selected by AIC reached the upper bound during general knot selection, you might want to rerun cobs with a larger number of knots. COBS regression spline (degree = 2) from call: cobs(x = X, y = y, constraint = "decrease", tau = 0.1) {tau=0.1}-quantile; dimensionality of fit: 4 from {4} x$knots[1:3]: -1.0, 0.6, 1.0 > (cXy.99<- cobs(X,y, "decrease", tau = 0.99)) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... COBS regression spline (degree = 2) from call: cobs(x = X, y = y, constraint = "decrease", tau = 0.99) {tau=0.99}-quantile; dimensionality of fit: 4 from {4} x$knots[1:3]: -1.0, -0.2, 1.0 > (cXy.01<- cobs(X,y, "decrease", tau = 0.01)) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... COBS regression spline (degree = 2) from call: cobs(x = X, y = y, constraint = "decrease", tau = 0.01) {tau=0.01}-quantile; dimensionality of fit: 6 from {6} x$knots[1:5]: -1.0, -0.6, -0.2, 0.2, 1.0 > plot(X,y, xlim = range(xx), + main = "cobs(*, \"decrease\"), N=201, tau = 50% (Med.), 1,10, 90,99%") > lines(predict(cXy, xx), col = 2) > lines(predict(cXy.1, xx), col = 3) > lines(predict(cXy.9, xx), col = 3) > lines(predict(cXy.01, xx), col = 4) > lines(predict(cXy.99, xx), col = 4) > > showProc.time() Time (user system elapsed): 0.873 0.007 2.057 > > ## Interpolation > cpuTime(cXyI <- cobs(X,y, "decrease", knots = unique(X))) qbsks2(): Performing general knot selection ... Error in x %*% coefficients : NA/NaN/Inf in foreign function call (arg 2) Calls: cpuTime ... cobs -> qbsks2 -> drqssbc2 -> rq.fit.sfnc -> %*% -> %*% In addition: Warning message: In cobs(X, y, "decrease", knots = unique(X)) : The number of knots can't be equal to the number of unique x for degree = 2. 'cobs' has automatically deleted the middle knot. Timing stopped at: 1.287 0.016 3.571 Execution halted Running the tests in ‘tests/multi-constr.R’ failed. Complete output: > #### Examples which use the new feature of more than one 'constraint'. > > suppressMessages(library(cobs)) > > ## do *not* show platform info here (as have *.Rout.save), but in 0_pt-ex.R > options(digits = 6) > > if(!dev.interactive(orNone=TRUE)) pdf("multi-constr.pdf") > > source(system.file("util.R", package = "cobs")) > source(system.file(package="Matrix", "test-tools-1.R", mustWork=TRUE)) Loading required package: tools > ##--> tryCatch.W.E(), showProc.time(), assertError(), relErrV(), ... > Lnx <- Sys.info()[["sysname"]] == "Linux" > isMac <- Sys.info()[["sysname"]] == "Darwin" > x86 <- (arch <- Sys.info()[["machine"]]) == "x86_64" > noLdbl <- (.Machine$sizeof.longdouble <= 8) ## TRUE when --disable-long-double > ## IGNORE_RDIFF_BEGIN > Sys.info() sysname "Linux" release "6.2.15-100.fc36.x86_64" version "#1 SMP PREEMPT_DYNAMIC Thu May 11 16:51:53 UTC 2023" nodename "gannet.stats.ox.ac.uk" machine "x86_64" login "ripley" user "ripley" effective_user "ripley" > noLdbl [1] FALSE > ## IGNORE_RDIFF_END > > > Rsq <- function(obj) { + stopifnot(inherits(obj, "cobs"), is.numeric(res <- obj$resid)) + 1 - sum(res^2)/obj$SSy + } > list_ <- function (...) `names<-`(list(...), vapply(sys.call()[-1L], as.character, "")) > is.cobs <- function(x) inherits(x, "cobs") > > set.seed(908) > x <- seq(-1,2, len = 50) > f.true <- pnorm(2*x) > y <- f.true + rnorm(50)/10 > plot(x,y); lines(x, f.true, col="gray", lwd=2, lty=3) > > ## constraint on derivative at right end: > (con <- rbind(c(2 , max(x), 0))) # f'(x_n) == 0 [,1] [,2] [,3] [1,] 2 2 0 > > ## Using 'trace = 3' --> 'trace = 2' inside drqssbc2() > > ## Regression splines (lambda = 0) > c2 <- cobs(x,y, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) > c2i <- cobs(x,y, constraint = c("increase"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 5 x 6 (nz = 15 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 6 x 7 (nz = 18 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 3 x 4 (nz = 9 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 4 x 5 (nz = 12 =^= 0.6%) > c2c <- cobs(x,y, constraint = c("concave"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 1 x 3 (nz = 3 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 3 x 5 (nz = 9 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 4 x 6 (nz = 12 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 5 x 7 (nz = 15 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 1 x 3 (nz = 3 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) > > c2IC <- cobs(x,y, constraint = c("inc", "concave"), trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 5 x 4 (nz = 15 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 7 x 5 (nz = 21 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 9 x 6 (nz = 27 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 11 x 7 (nz = 33 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) > ## here, it's the same as just "i": > all.equal(fitted(c2i), fitted(c2IC)) [1] "Mean relative difference: 0.0808156" > > c1 <- cobs(x,y, degree = 1, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) > c1i <- cobs(x,y, degree = 1, constraint = c("increase"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 1 x 2 (nz = 2 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 2 x 3 (nz = 4 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 4 x 5 (nz = 8 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 5 x 6 (nz = 10 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 3 x 4 (nz = 6 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 4 x 5 (nz = 8 =^= 0.4%) > c1c <- cobs(x,y, degree = 1, constraint = c("concave"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 1 x 3 (nz = 3 =^= 1%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 6 =^= 0.75%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 3 x 5 (nz = 9 =^= 0.6%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 4 x 6 (nz = 12 =^= 0.5%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 1 x 3 (nz = 3 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 1 x 3 (nz = 3 =^= 1%) l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 1 x 3 (nz = 3 =^= 1%) > > plot(c1) > lines(predict(c1i), col="forest green") > all.equal(fitted(c1), fitted(c1i), tol = 1e-9)# but not 1e-10 [1] TRUE > > ## now gives warning (not error): > c1IC <- cobs(x,y, degree = 1, constraint = c("inc", "concave"), trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 1 x 2 (nz = 2 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 12 =^= 0.6%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 7 x 5 (nz = 17 =^= 0.49%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 9 x 6 (nz = 22 =^= 0.41%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 1 x 2 (nz = 2 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) Warning messages: 1: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' 2: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' > > cp2 <- cobs(x,y, pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 2 x 3 (nz = 6 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 2 x 5 (nz = 6 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 2 x 6 (nz = 6 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 2 x 7 (nz = 6 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 2 x 4 (nz = 6 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 2 x 5 (nz = 6 =^= 0.6%) > > ## Here, warning ".. 'ifl'.. " on *some* platforms (e.g. Windows 32bit) : > r2i <- tryCatch.W.E( cobs(x,y, constraint = "increase", pointwise = con) ) qbsks2(): Performing general knot selection ... Deleting unnecessary knots ... > cp2i <- r2i$value > ## IGNORE_RDIFF_BEGIN > r2i$warning <simpleWarning in cobs(x, y, constraint = "increase", pointwise = con): drqssbc2(): Not all flags are normal (== 1), ifl : 21> > ## IGNORE_RDIFF_END > ## when plotting it, we see that it gave a trivial constant!! > cp2c <- cobs(x,y, constraint = "concave", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 4 x 4 (nz = 12 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 5 x 5 (nz = 15 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 6 x 6 (nz = 18 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 7 x 7 (nz = 21 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 3 x 3 (nz = 9 =^= 1%) > > ## now gives warning (not error): but no warning on M1 mac -> IGNORE > ## IGNORE_RDIFF_BEGIN > cp2IC <- cobs(x,y, constraint = c("inc", "concave"), pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 5 x 3 (nz = 15 =^= 1%) loo.design2(): -> Xeq 50 x 4 (nz = 150 =^= 0.75%) Xieq 7 x 4 (nz = 21 =^= 0.75%) loo.design2(): -> Xeq 50 x 5 (nz = 150 =^= 0.6%) Xieq 9 x 5 (nz = 27 =^= 0.6%) loo.design2(): -> Xeq 50 x 6 (nz = 150 =^= 0.5%) Xieq 11 x 6 (nz = 33 =^= 0.5%) loo.design2(): -> Xeq 50 x 7 (nz = 150 =^= 0.43%) Xieq 13 x 7 (nz = 39 =^= 0.43%) Deleting unnecessary knots ... loo.design2(): -> Xeq 50 x 3 (nz = 150 =^= 1%) Xieq 5 x 3 (nz = 15 =^= 1%) Warning message: In cobs(x, y, constraint = c("inc", "concave"), pointwise = con, : drqssbc2(): Not all flags are normal (== 1), ifl : 18 > ## IGNORE_RDIFF_END > cp1 <- cobs(x,y, degree = 1, pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 2 x 2 (nz = 4 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 2 x 3 (nz = 4 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 2 x 5 (nz = 4 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 2 x 6 (nz = 4 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 2 x 4 (nz = 4 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 2 x 5 (nz = 4 =^= 0.4%) Warning message: In cobs(x, y, degree = 1, pointwise = con, trace = 3) : drqssbc2(): Not all flags are normal (== 1), ifl : 19 > cp1i <- cobs(x,y, degree = 1, constraint = "increase", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 3 x 2 (nz = 6 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 4 x 3 (nz = 8 =^= 0.67%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 6 x 5 (nz = 12 =^= 0.4%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 7 x 6 (nz = 14 =^= 0.33%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 5 x 4 (nz = 10 =^= 0.5%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 6 x 5 (nz = 12 =^= 0.4%) > cp1c <- cobs(x,y, degree = 1, constraint = "concave", pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 2 x 2 (nz = 4 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 4 x 4 (nz = 10 =^= 0.62%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 5 x 5 (nz = 13 =^= 0.52%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 6 x 6 (nz = 16 =^= 0.44%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 2 x 2 (nz = 4 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 3 x 3 (nz = 7 =^= 0.78%) > > cp1IC <- cobs(x,y, degree = 1, constraint = c("inc", "concave"), pointwise = con, trace = 3) qbsks2(): Performing general knot selection ... l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 3 x 2 (nz = 6 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 5 x 3 (nz = 11 =^= 0.73%) l1.design2(): -> Xeq 50 x 4 (nz = 100 =^= 0.5%) Xieq 7 x 4 (nz = 16 =^= 0.57%) l1.design2(): -> Xeq 50 x 5 (nz = 100 =^= 0.4%) Xieq 9 x 5 (nz = 21 =^= 0.47%) l1.design2(): -> Xeq 50 x 6 (nz = 100 =^= 0.33%) Xieq 11 x 6 (nz = 26 =^= 0.39%) Deleting unnecessary knots ... l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 5 x 3 (nz = 11 =^= 0.73%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 5 x 3 (nz = 11 =^= 0.73%) l1.design2(): -> Xeq 50 x 2 (nz = 100 =^= 1%) Xieq 3 x 2 (nz = 6 =^= 1%) l1.design2(): -> Xeq 50 x 3 (nz = 100 =^= 0.67%) Xieq 5 x 3 (nz = 11 =^= 0.73%) Warning messages: 1: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' 2: In l1.design2(x, w, constraint, ptConstr, knots, pw, nrq = n, nl1, : too few knots ==> nk <= 4; could not add constraint 'concave' > > ## Named list of all cobs() results above -- sort() collation order matters for ls() ! > (curLC <- Sys.getlocale("LC_COLLATE")) [1] "C" > Sys.setlocale("LC_COLLATE", "C") [1] "C" > cobsL <- mget(Filter(\(nm) is.cobs(.GlobalEnv[[nm]]), ls(patt="c[12p]")), + envir = .GlobalEnv) > Sys.setlocale("LC_COLLATE", curLC) # reverting [1] "C" > > knL <- lapply(cobsL, `[[`, "knots") > str(knL[order(lengths(knL))]) List of 16 $ c2IC : num [1:2] -1 2 $ cp2IC: num [1:2] -1 2 $ cp2c : num [1:2] -1 2 $ c1IC : num [1:3] -1 0.776 2 $ c1c : num [1:3] -1 0.776 2 $ c2 : num [1:3] -1 -0.449 2 $ c2c : num [1:3] -1 0.163 2 $ cp1IC: num [1:3] -1 0.776 2 $ cp1c : num [1:3] -1 0.776 2 $ c2i : num [1:4] -1 0.163 0.776 2 $ cp2 : num [1:4] -1 -0.449 0.776 2 $ cp2i : num [1:4] -1 0.163 0.776 2 $ c1 : num [1:5] -1 -0.449 0.163 0.776 2 $ c1i : num [1:5] -1 -0.449 0.163 0.776 2 $ cp1 : num [1:5] -1 -0.449 0.163 0.776 2 $ cp1i : num [1:5] -1 -0.449 0.163 0.776 2 > > gotRsqrs <- sapply(cobsL, Rsq) > Rsqrs <- c(c1 = 0.95079126, c1IC = 0.92974549, c1c = 0.92974549, c1i = 0.95079126, + c2 = 0.94637437, c2IC = 0.91375404, c2c = 0.92505977, c2i = 0.95022829, + cp1 = 0.9426453, cp1IC = 0.92223149, cp1c = 0.92223149, cp1i = 0.9426453, + cp2 = 0.94988863, cp2IC= 0.90051964, cp2c = 0.91375409, cp2i = 0.93611487) > ## M1 mac " = " , cp2IC= 0.91704726, " = " , cp2i = 0.94620178 > ## noLD " = " , cp2IC=-0.08244284, " = " , cp2i = 0.94636815 > ## ATLAS " = " , cp2IC= 0.91471729, " = " , cp2i = 0.94506339 > ## openBLAS " = " , cp2IC= 0.91738019, " = " , cp2i = 0.93589404 > ## MKL " = " , cp2IC= 0.91765403, " = " , cp2i = 0.94501205 > ## Intel " = " , cp2IC= 0.91765403, " = " , cp2i = 0.94501205 > ## ^^^^^^^^^^ ^^^^^^^^^^ > ## remove these two from testing, notably for the M1 Mac & noLD .. : > ##iR2 <- if(!x86 || noLdbl) setdiff(names(cobsL), c("cp2IC", "cp2i")) else TRUE > ## actually everywhere, because of ATLAS, openBLAS, MKL, Intel... : > iR2 <- setdiff(names(cobsL), nR2 <- c("cp2IC", "cp2i")) > ## IGNORE_RDIFF_BEGIN > dput(signif(gotRsqrs, digits=8)) c(c1 = 0.95079126, c1IC = 0.92974549, c1c = 0.92974549, c1i = 0.95079126, c2 = 0.94637437, c2IC = 0.91375404, c2c = 0.92505977, c2i = 0.95022829, cp1 = 0.95318954, cp1IC = 0.92223149, cp1c = 0.92223149, cp1i = 0.9426453, cp2 = 0.94988863, cp2IC = 0.90051964, cp2c = 0.91375409, cp2i = 0.94404129 ) > all.equal(Rsqrs[iR2], gotRsqrs[iR2], tolerance=0)# 2.6277e-9 (Lnx F 38); 2.6898e-9 (M1 mac) [1] "Mean relative difference: 0.000805528" > all.equal(Rsqrs[nR2], gotRsqrs[nR2], tolerance=0)# differ; drastically only for 'noLD' [1] "Mean relative difference: 0.00431573" > ## IGNORE_RDIFF_END > stopifnot(exprs = { + all.equal(Rsqrs[iR2], gotRsqrs[iR2]) + identical(c(5L, 3L, 3L, 5L, + 3L, 2L, 3L, 4L, + 5L, 3L, 3L, 5L, + 4L, 2L, 2L, 4L), unname(lengths(knL))) + }) Error: Rsqrs[iR2] and gotRsqrs[iR2] are not equal: Mean relative difference: 0.000805528 Execution halted Running the tests in ‘tests/roof.R’ failed. Complete output: > suppressMessages(library(cobs)) > > data(USArmyRoofs) > attach(USArmyRoofs)#-> "age" and "fci" > > if(!dev.interactive(orNone=TRUE)) pdf("roof.pdf", width=10) > > ## Compute the quadratic median smoothing B-spline with SIC > ## chosen lambda > a50 <- cobs(age,fci,constraint = "decrease",lambda = -1,nknots = 10, + degree = 2,pointwise = rbind(c(0,0,100)), + trace = 2)# trace > 1 : more tracing Searching for optimal lambda. This may take a while. While you are waiting, here is something you can consider to speed up the process: (a) Use a smaller number of knots; (b) Set lambda==0 to exclude the penalty term; (c) Use a coarser grid by reducing the argument 'lambda.length' from the default value of 25. fieq=TRUE -> Tnobs = 184, n0 = 29, |ptConstr| = 2 Error in drqssbc2(x, y, w, pw = pw, knots = knots, degree = degree, Tlambda = if (select.lambda) lambdaSet else lambda, : The problem is degenerate for the range of lambda specified. Calls: cobs -> drqssbc2 In addition: Warning message: In min(sol1["k", i.keep]) : no non-missing arguments to min; returning Inf Execution halted Running the tests in ‘tests/wind.R’ failed. Complete output: > suppressMessages(library(cobs)) > > source(system.file("util.R", package = "cobs")) > (doExtra <- doExtras()) [1] FALSE > source(system.file("test-tools-1.R", package="Matrix", mustWork=TRUE)) Loading required package: tools > showProc.time() # timing here (to be faster by default) Time (user system elapsed): 0.002 0 0.007 > > data(DublinWind) > attach(DublinWind)##-> speed & day (instead of "wind.x" & "DUB.") > iday <- sort.list(day) > > if(!dev.interactive(orNone=TRUE)) pdf("wind.pdf", width=10) > > stopifnot(identical(day,c(rep(c(rep(1:365,3),1:366),4), + rep(1:365,2)))) > co50.1 <- cobs(day, speed, constraint= "periodic", tau= .5, lambda= 2.2, + degree = 1) > co50.2 <- cobs(day, speed, constraint= "periodic", tau= .5, lambda= 2.2, + degree = 2) > > showProc.time() Time (user system elapsed): 0.692 0.033 2.284 > > plot(day,speed, pch = ".", col = "gray20") > lines(day[iday], fitted(co50.1)[iday], col="orange", lwd = 2) > lines(day[iday], fitted(co50.2)[iday], col="sky blue", lwd = 2) > rug(knots(co50.1), col=3, lwd=2) > > nknots <- 13 > > > if(doExtra) { + ## Compute the quadratic median smoothing B-spline using SIC + ## lambda selection + co.o50 <- + cobs(day, speed, knots.add = TRUE, constraint="periodic", nknots = nknots, + tau = .5, lambda = -1, method = "uniform") + summary(co.o50) # [does print] + + showProc.time() + + op <- par(mfrow = c(3,1), mgp = c(1.5, 0.6,0), mar=.1 + c(3,3:1)) + with(co.o50, plot(pp.sic ~ pp.lambda, type ="o", + col=2, log = "x", main = "co.o50: periodic")) + with(co.o50, plot(pp.sic ~ pp.lambda, type ="o", ylim = robrng(pp.sic), + col=2, log = "x", main = "co.o50: periodic")) + of <- 0.64430538125795 + with(co.o50, plot(pp.sic - of ~ pp.lambda, type ="o", ylim = c(6e-15, 8e-15), + ylab = paste("sic -",formatC(of, dig=14, small.m = "'")), + col=2, log = "x", main = "co.o50: periodic")) + par(op) + } > > showProc.time() Time (user system elapsed): 0.05 0.003 0.136 > > ## cobs99: Since SIC chooses a lambda that corresponds to the smoothest > ## possible fit, rerun cobs with a larger lstart value > ## (lstart <- log(.Machine$double.xmax)^3) # 3.57 e9 > ## > co.o50. <- + cobs(day,speed, knots.add = TRUE, constraint = "periodic", nknots = 10, + tau = .5, lambda = -1, method = "quantile") Searching for optimal lambda. This may take a while. While you are waiting, here is something you can consider to speed up the process: (a) Use a smaller number of knots; (b) Set lambda==0 to exclude the penalty term; (c) Use a coarser grid by reducing the argument 'lambda.length' from the default value of 25. The algorithm has converged. You might plot() the returned object (which plots 'sic' against 'lambda') to see if you have found the global minimum of the information criterion so that you can determine if you need to adjust any or all of 'lambda.lo', 'lambda.hi' and 'lambda.length' and refit the model. > summary(co.o50.) COBS smoothing spline (degree = 2) from call: cobs(x = day, y = speed, constraint = "periodic", nknots = 10, method = "quantile", tau = 0.5, lambda = -1, knots.add = TRUE) {tau=0.5}-quantile; dimensionality of fit: 7 from {14,13,11,8,7,30} x$knots[1:10]: 0.999635, 41.000000, 82.000000, ... , 366.000365 lambda = 101002.6, selected via SIC, out of 25 ones. coef[1:12]: 1.121550e+01, 1.139573e+01, 1.089025e+01, 9.954427e+00, 8.148158e+00, ... , 5.373106e-04 R^2 = 8.22% ; empirical tau (over all): 3287/6574 = 0.5 (target tau= 0.5) > summary(pc.5 <- predict(co.o50., interval = "both")) z fit cb.lo cb.up Min. : 0.9996 Min. : 7.212 Min. : 6.351 Min. : 7.951 1st Qu.: 92.2498 1st Qu.: 7.790 1st Qu.: 7.000 1st Qu.: 8.600 Median :183.5000 Median : 9.436 Median : 8.555 Median :10.326 Mean :183.5000 Mean : 9.314 Mean : 8.388 Mean :10.241 3rd Qu.:274.7502 3rd Qu.:10.798 3rd Qu.: 9.716 3rd Qu.:11.787 Max. :366.0004 Max. :11.290 Max. :10.347 Max. :13.416 ci.lo ci.up Min. : 6.782 Min. : 7.598 1st Qu.: 7.370 1st Qu.: 8.213 Median : 8.974 Median : 9.901 Mean : 8.830 Mean : 9.798 3rd Qu.:10.197 3rd Qu.:11.311 Max. :10.797 Max. :12.366 > > showProc.time() Time (user system elapsed): 2.921 0.031 8.021 > > if(doExtra) { ## + repeat.delete.add + co.o50.. <- cobs(day,speed, knots.add = TRUE, repeat.delete.add=TRUE, + constraint = "periodic", nknots = 10, + tau = .5, lambda = -1, method = "quantile") + summary(co.o50..) + showProc.time() + } > > co.o9 <- ## Compute the .9 quantile smoothing B-spline + cobs(day,speed,knots.add = TRUE, constraint = "periodic", nknots = 10, + tau = .9,lambda = -1, method = "uniform") Searching for optimal lambda. This may take a while. While you are waiting, here is something you can consider to speed up the process: (a) Use a smaller number of knots; (b) Set lambda==0 to exclude the penalty term; (c) Use a coarser grid by reducing the argument 'lambda.length' from the default value of 25. Error in x %*% coefficients : NA/NaN/Inf in foreign function call (arg 2) Calls: cobs -> drqssbc2 -> rq.fit.sfnc -> %*% -> %*% Execution halted Flavor: r-devel-linux-x86_64-fedora-clang