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Type 'q()' to quit R. > ### test subsample > ### LU decomposition and singular subsamples handling > require(robustbase) Loading required package: robustbase > source(system.file("xtraR/subsample-fns.R", package = "robustbase", mustWork=TRUE)) > ## instead of relying on system.file("test-tools-1.R", package="Matrix"): > source(system.file("xtraR/test-tools.R", package = "robustbase")) # assert.EQ(), showProc.time() .. > options(nwarnings = 4e4) > > cat("doExtras:", doExtras <- robustbase:::doExtras(),"\n") doExtras: FALSE > showProc.time() Time (user system elapsed): 0 0 0 > > A <- rbind(c(0.001, 1), + c(1, 2)) > set.seed(11) > str(sa <- tstSubsample(A)) Loading required package: Matrix List of 21 $ x : num [1:2, 1:2] 0.001 1 1 2 $ y : num [1:2] -0.591 0.0266 $ n : int 2 $ m : int 2 $ beta : num [1:2] 1.211 -0.592 $ ind_space: int [1:2] 0 1 $ idc : int [1:2] 0 1 $ idr : int [1:2] 1 0 $ lu : num [1:2, 1:2] 1 0.001 2 0.998 $ v : num [1:2] 1 0.998 $ pivot : int 1 $ Dr : num [1:2] 1 0.5 $ Dc : num [1:2] 2 1 $ rowequ : int 0 $ colequ : int 0 $ status : int 0 $ sample : logi FALSE $ mts : int 0 $ ss : int 1 $ tolinv : num 1e-07 $ solve : logi TRUE > > A <- rbind(c(3, 17, 10), + c(2, 4, -2), + c(6, 18, 12)) > tstSubsample(A) > > ## test some random matrix > set.seed(1002) > A <- matrix(rnorm(100), 10) > tstSubsample(A) > > ## test singular matrix handling > A <- rbind(c(1, 0, 0), + c(0, 1, 0), + c(0, 1, 0), + c(0, 0, 1)) > tstSubsample(A) > > > ## test subsample with mts > 0 > data <- data.frame(y = rnorm(9), expand.grid(A = letters[1:3], B = letters[1:3])) > x <- model.matrix(y ~ ., data) > y <- data$y > ## this should produce a warning and return status == 2 > showSys.time(z <- Rsubsample(x, y, mts=2)) Time user system elapsed Time 0 0 0 Warning message: In Rsubsample(x, y, mts = 2) : Too many singular resamples. Aborting subsample(). See parameter 'subsampling; in help of lmrob.config(). > stopifnot(z$status == 2) > > > ## test equilibration > ## columns only > X <- rbind(c(1e-7, 1e-10), + c(2 , 0.2)) > y <- 1:2 > tstSubsample(t(X), y) kappa before equilibration = 2.0404e+08, after = 21.2097 > > ## rows only > X <- rbind(c(1e-7, 1e+10), + c(2 , 0.2)) > y <- 1:2 > tstSubsample(X, y) kappa before equilibration = 5e+09, after = 1.15762 > > ## both > X <- rbind(c(1e-7, 2 ), + c(1e10, 2e12)) > y <- 1:2 > tstSubsample(X, y) kappa before equilibration = 2.01e+14, after = 3.33338 > showProc.time() Time (user system elapsed): 0.9 0.08 0.98 > > > ## test real data example > data(possumDiv)## 151 * 9; the last two variables are factors > with(possumDiv, table(eucalyptus, aspect)) aspect eucalyptus NW-NE NW-SE SE-SW SW-NW regnans 27 34 32 17 delegatensis 6 8 9 6 nitens 2 5 3 2 > > mf <- model.frame(Diversity ~ .^2, possumDiv) > X <- model.matrix(mf, possumDiv) > y <- model.response(mf) > stopifnot(qr(X)$rank == ncol(X)) > > ## this used to fail: different pivots in step 37 > str(s1 <- tstSubsample(X, y)) kappa before equilibration = 16194.6, after = 736.342 List of 21 $ x : num [1:151, 1:63] 1 1 1 1 1 1 1 1 1 1 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:151] "1" "2" "3" "4" ... .. ..$ : chr [1:63] "(Intercept)" "Shrubs" "Stumps" "Stags" ... ..- attr(*, "assign")= int [1:63] 0 1 2 3 4 5 6 7 7 8 ... ..- attr(*, "contrasts")=List of 2 .. ..$ eucalyptus: chr "contr.treatment" .. ..$ aspect : chr "contr.treatment" $ y : num [1:151] 3 2 1 2 3 2 3 2 0 0 ... $ n : int 151 $ m : int 63 $ beta : num [1:63] 6732.1 -463.6 1799.4 -205.2 -65.2 ... $ ind_space: int [1:151] 0 1 2 3 4 5 6 7 8 9 ... $ idc : int [1:151] 0 1 2 3 4 5 6 7 8 9 ... $ idr : int [1:151] 40 31 14 33 37 54 56 52 45 13 ... $ lu : num [1:63, 1:63] 1 0.387 0.194 0.414 0 ... $ v : num [1:63] 1 0.897 0.567 0.353 0.584 ... $ pivot : int [1:62] 40 31 14 33 37 54 56 52 45 13 ... $ Dr : num [1:151] 0.00111 0.00556 0.00481 0.00391 0.00313 ... $ Dc : num [1:63] 6 2 10 2 2 3 3 6 16 20 ... $ rowequ : int 1 $ colequ : int 1 $ status : int 0 $ sample : logi FALSE $ mts : int 0 $ ss : int 1 $ tolinv : num 1e-07 $ solve : logi TRUE > s2 <- tstSubsample(X / max(abs(X)), y / max(abs(X))) kappa before equilibration = 16194.6, after = 736.342 > s3 <- tstSubsample(X * 2^-50, y * 2^-50) kappa before equilibration = 16194.6, after = 736.342 > ## all components *BUT* x, y, lu, Dr, Dc, rowequ, colequ : > nm <- names(s1); nm <- nm[is.na(match(nm, c("x","y","lu", "Dr", "Dc", "rowequ", "colequ")))] > stopifnot(all.equal(s1[nm], s2[nm], tolerance=1e-10), + all.equal(s1[nm], s3[nm], tolerance=1e-10)) > showProc.time() Time (user system elapsed): 0.07 0.02 0.08 > > set.seed(10) > nsing <- sum(replicate(if(doExtras) 200 else 20, tstSubsampleSing(X, y))) > stopifnot(nsing == 0) > showProc.time() Time (user system elapsed): 0.12 0.01 0.14 > > ## test example with many categorical predictors - 2 different random seeds: > set.seed(10) ; r1 <- lmrob(Diversity ~ .^2 , data = possumDiv, cov="none") > set.seed(108); r2 <- lmrob(Diversity ~ .^2 , data = possumDiv, cov="none")# lmrob.S() failed > (i1 <- r1$init) # print() S-estimator lmrob.S(): Coefficients: (Intercept) Shrubs 0.722871 -0.041500 Stumps Stags -2.504527 -0.223348 Bark Habitat 0.056598 0.112795 BAcacia eucalyptusdelegatensis -0.065380 -2.018232 eucalyptusnitens aspectNW-SE 1.517607 1.350060 aspectSE-SW aspectSW-NW 0.920044 1.429090 Shrubs:Stumps Shrubs:Stags 0.119261 0.012629 Shrubs:Bark Shrubs:Habitat -0.017849 -0.035020 Shrubs:BAcacia Shrubs:eucalyptusdelegatensis 0.011194 -0.172258 Shrubs:eucalyptusnitens Shrubs:aspectNW-SE -0.209467 0.127213 Shrubs:aspectSE-SW Shrubs:aspectSW-NW -0.068075 0.302488 Stumps:Stags Stumps:Bark -1.195520 0.037637 Stumps:Habitat Stumps:BAcacia 1.854153 -0.125780 Stumps:eucalyptusdelegatensis Stumps:eucalyptusnitens -11.212340 -10.851453 Stumps:aspectNW-SE Stumps:aspectSE-SW -0.292179 6.426255 Stumps:aspectSW-NW Stags:Bark -2.615428 -0.005088 Stags:Habitat Stags:BAcacia 0.028734 -0.001261 Stags:eucalyptusdelegatensis Stags:eucalyptusnitens 0.546997 0.259457 Stags:aspectNW-SE Stags:aspectSE-SW 0.163733 0.174822 Stags:aspectSW-NW Bark:Habitat 0.118977 0.041449 Bark:BAcacia Bark:eucalyptusdelegatensis 0.007768 0.014562 Bark:eucalyptusnitens Bark:aspectNW-SE -0.191179 -0.261191 Bark:aspectSE-SW Bark:aspectSW-NW 0.094818 -0.149431 Habitat:BAcacia Habitat:eucalyptusdelegatensis -0.004190 -0.101211 Habitat:eucalyptusnitens Habitat:aspectNW-SE 0.176561 -0.299827 Habitat:aspectSE-SW Habitat:aspectSW-NW -0.343471 -0.770122 BAcacia:eucalyptusdelegatensis BAcacia:eucalyptusnitens 0.081777 -0.039539 BAcacia:aspectNW-SE BAcacia:aspectSE-SW 0.042317 -0.063962 BAcacia:aspectSW-NW eucalyptusdelegatensis:aspectNW-SE 0.015849 -0.646063 eucalyptusnitens:aspectNW-SE eucalyptusdelegatensis:aspectSE-SW -0.200390 0.891429 eucalyptusnitens:aspectSE-SW eucalyptusdelegatensis:aspectSW-NW -0.732987 0.177784 eucalyptusnitens:aspectSW-NW -0.676201 scale = 0.7557 ; converged in 73 refinement steps Algorithmic parameters: tuning.chi bb tuning.psi refine.tol rel.tol scale.tol solve.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 1.000e-07 1.000e-10 1.000e-07 zero.tol 1.000e-10 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" "none" seed : int(0) > (i2 <- r1$init) # ... and they are "somewhat" close: S-estimator lmrob.S(): Coefficients: (Intercept) Shrubs 0.722871 -0.041500 Stumps Stags -2.504527 -0.223348 Bark Habitat 0.056598 0.112795 BAcacia eucalyptusdelegatensis -0.065380 -2.018232 eucalyptusnitens aspectNW-SE 1.517607 1.350060 aspectSE-SW aspectSW-NW 0.920044 1.429090 Shrubs:Stumps Shrubs:Stags 0.119261 0.012629 Shrubs:Bark Shrubs:Habitat -0.017849 -0.035020 Shrubs:BAcacia Shrubs:eucalyptusdelegatensis 0.011194 -0.172258 Shrubs:eucalyptusnitens Shrubs:aspectNW-SE -0.209467 0.127213 Shrubs:aspectSE-SW Shrubs:aspectSW-NW -0.068075 0.302488 Stumps:Stags Stumps:Bark -1.195520 0.037637 Stumps:Habitat Stumps:BAcacia 1.854153 -0.125780 Stumps:eucalyptusdelegatensis Stumps:eucalyptusnitens -11.212340 -10.851453 Stumps:aspectNW-SE Stumps:aspectSE-SW -0.292179 6.426255 Stumps:aspectSW-NW Stags:Bark -2.615428 -0.005088 Stags:Habitat Stags:BAcacia 0.028734 -0.001261 Stags:eucalyptusdelegatensis Stags:eucalyptusnitens 0.546997 0.259457 Stags:aspectNW-SE Stags:aspectSE-SW 0.163733 0.174822 Stags:aspectSW-NW Bark:Habitat 0.118977 0.041449 Bark:BAcacia Bark:eucalyptusdelegatensis 0.007768 0.014562 Bark:eucalyptusnitens Bark:aspectNW-SE -0.191179 -0.261191 Bark:aspectSE-SW Bark:aspectSW-NW 0.094818 -0.149431 Habitat:BAcacia Habitat:eucalyptusdelegatensis -0.004190 -0.101211 Habitat:eucalyptusnitens Habitat:aspectNW-SE 0.176561 -0.299827 Habitat:aspectSE-SW Habitat:aspectSW-NW -0.343471 -0.770122 BAcacia:eucalyptusdelegatensis BAcacia:eucalyptusnitens 0.081777 -0.039539 BAcacia:aspectNW-SE BAcacia:aspectSE-SW 0.042317 -0.063962 BAcacia:aspectSW-NW eucalyptusdelegatensis:aspectNW-SE 0.015849 -0.646063 eucalyptusnitens:aspectNW-SE eucalyptusdelegatensis:aspectSE-SW -0.200390 0.891429 eucalyptusnitens:aspectSE-SW eucalyptusdelegatensis:aspectSW-NW -0.732987 0.177784 eucalyptusnitens:aspectSW-NW -0.676201 scale = 0.7557 ; converged in 73 refinement steps Algorithmic parameters: tuning.chi bb tuning.psi refine.tol rel.tol scale.tol solve.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 1.000e-07 1.000e-10 1.000e-07 zero.tol 1.000e-10 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" "none" seed : int(0) > stopifnot(all.equal(r1[names(r1) != "init.S"], + r2[names(r2) != "init.S"], tol = 0.40)) > c1 <- coef(r1) > c2 <- coef(r2) > relD <- (c1-c2)*2/(c1+c2) > xCf <- which(abs(relD) >= 10) > stopifnot(exprs = { + identical(xCf, c(`Bark:aspectSW-NW` = 46L)) + all.equal(c1[-xCf], c2[-xCf], tol = 0.35) # 0.3418 + sign(c1[-xCf]) == sign(c2[-xCf]) + }) > showProc.time() Time (user system elapsed): 0.92 0 0.92 > > ## investigate problematic subsample: > idc <- 1 + c(140, 60, 12, 13, 89, 90, 118, 80, 17, 134, 59, 94, 36, + 43, 46, 93, 107, 62, 57, 116, 11, 45, 35, 38, 120, 34, 29, + 33, 147, 105, 115, 92, 61, 91, 104, 141, 138, 129, 130, 84, + 119, 132, 6, 135, 112, 16, 67, 41, 102, 76, 111, 82, 148, 24, + 131, 10, 96, 0, 87, 21, 127, 56, 124) > > rc <- lm(Diversity ~ .^2 , data = possumDiv, subset = idc) > > X <- model.matrix(rc) > y <- possumDiv$Diversity[idc] > tstSubsample(X, y)## have different pivots ... could not find non-singular kappa before equilibration = 3.51068e+20, after = 2.03776e+19 LU.gaxpy() and Rsubsample() have different pivots: [1] 34 9 41 15 14 18 38 46 17 62 11 55 47 60 56 37 18 20 24 54 22 50 33 39 32 [26] 54 37 40 38 30 45 60 56 35 62 57 50 44 51 45 62 55 52 48 53 46 55 48 59 61 [51] 58 54 54 63 62 57 57 58 61 61 61 63 [1] 34 9 41 15 14 18 38 46 17 62 11 55 47 60 56 37 18 20 24 54 22 50 33 39 32 [26] 54 37 40 38 30 45 60 56 42 62 57 50 44 51 45 42 55 52 48 53 46 62 48 59 61 [51] 58 54 54 63 55 57 57 58 61 61 61 63 ... are different at indices: [1] 34 41 47 55 Warning message: In Rsubsample(x, y, tolInverse = tolInverse) : subsample(): could not find non-singular subsample. > > lu <- LU.gaxpy(t(X)) > stopifnot(lu$sing) > zc <- Rsubsample(X, y) Warning message: In Rsubsample(X, y) : subsample(): could not find non-singular subsample. > stopifnot(zc$status > 0) > ## column 52 is linearly dependent and should have been discarded > ## qr(t(X))$pivot > > image(as(round(zc$lu - (lu$L + lu$U - diag(nrow(lu$U))), 10), "Matrix")) > image(as( sign(zc$lu) - sign(lu$L + lu$U - diag(nrow(lu$U))), "Matrix")) > showProc.time() Time (user system elapsed): 0.22 0.02 0.24 > > ## test equilibration > ## colequ only > X <- matrix(c(1e-7, 2, 1e-10, 0.2), 2) > y <- 1:2 > tstSubsample(t(X), y) kappa before equilibration = 2.0404e+08, after = 21.2097 > > ## rowequ only > X <- matrix(c(1e-7, 2, 1e10, 0.2), 2) > y <- 1:2 > tstSubsample(X, y) kappa before equilibration = 5e+09, after = 1.15762 > > ## both > X <- matrix(c(1e-7, 1e10, 2, 2e12), 2) > y <- 1:2 > tstSubsample(X, y) kappa before equilibration = 2.01e+14, after = 3.33338 > showProc.time() Time (user system elapsed): 0.02 0 0.01 > > ### real data, see MM's ~/R/MM/Pkg-ex/robustbase/hedlmrob.R > ## close to singular cov(): > attach(system.file("external", "d1k27.rda", package="robustbase", mustWork=TRUE)) > > fm1 <- lmrob(y ~ a + I(a^2) + tf + I(tf^2) + A + I(A^2) + . , data = d1k27) Warning message: In lf.cov(init, x = x) : X'WX is almost singular. Consider using cov = ".vcov.w" > ## ^^^^^ gave error, earlier, now with a warning -- use ".vcov.w" > ## --> cov = ".vcov.w" > fm2 <- lmrob(y ~ a + I(a^2) + tf + I(tf^2) + A + I(A^2) + . , data = d1k27, + cov = ".vcov.w", trace = TRUE) lmrob_S(n = 1000, nRes = 500): fast_s() [non-large n]: Subsampling 500 times to find candidate betas: Now refine() to convergence for 2 very best ones: Best[0]: convergence (83 iter.): -> improved scale to 0.166554423946168 Best[1]: convergence (88 iter.): -> improved scale to 0.166554423946079 lmrob.S(): scale = 0.166554; coeff.= [1] 1.075654e+01 -1.154462e-02 1.106946e-04 3.571354e-04 -1.297327e-08 [6] 7.327823e-01 -3.256861e-01 5.432153e-02 4.521211e-01 5.756158e-01 [11] 1.061669e-01 1.177202e-01 1.971591e-01 2.407896e-01 2.967994e-01 [16] 2.680718e-01 -1.326749e-02 -2.731234e-02 9.408317e-03 4.924811e-02 [21] 2.233523e-02 1.953303e-02 1.153432e-02 4.541287e-02 9.998871e-02 [26] 1.201414e-01 1.358071e-01 9.037262e-02 1.268346e-01 1.497639e-01 init converged (remaining method = "M") -> coef= (Intercept) a I(a^2) tf I(tf^2) 1.075654e+01 -1.154462e-02 1.106946e-04 3.571354e-04 -1.297327e-08 A I(A^2) tb r21 r31 7.327823e-01 -3.256861e-01 5.432153e-02 4.521211e-01 5.756158e-01 r41 r51 r61 r71 r81 1.061669e-01 1.177202e-01 1.971591e-01 2.407896e-01 2.967994e-01 r91 t011 t021 t031 t041 2.680718e-01 -1.326749e-02 -2.731234e-02 9.408317e-03 4.924811e-02 t051 t061 t071 t081 t091 2.233523e-02 1.953303e-02 1.153432e-02 4.541287e-02 9.998871e-02 t101 t111 t121 t131 t141 1.201414e-01 1.358071e-01 9.037262e-02 1.268346e-01 1.497639e-01 lmrob_MM(): rwls(): rwls() used 22 it.; last ||b0 - b1||_1 = 9.29659e-07, L(b1) = 0.152580120415; convergence lmrob..MM..fit(*, obj) --> updating .. qr(x * rweights) -> rank= 30, outlierStats() step "M" -> new coef= (Intercept) a I(a^2) tf I(tf^2) 1.052071e+01 -1.016172e-02 8.111234e-05 4.551547e-04 -2.761075e-08 A I(A^2) tb r21 r31 5.829592e-01 -1.886155e-01 6.396563e-02 5.126003e-01 6.765112e-01 r41 r51 r61 r71 r81 2.036129e-01 2.390444e-01 2.682150e-01 3.467074e-01 3.959954e-01 r91 t011 t021 t031 t041 3.581571e-01 -8.591738e-04 -2.056299e-02 2.945871e-02 6.693598e-02 t051 t061 t071 t081 t091 2.995238e-02 4.368286e-02 2.705788e-02 6.034512e-02 7.243219e-02 t101 t111 t121 t131 t141 9.472191e-02 1.561789e-01 1.211456e-01 1.768558e-01 1.772017e-01 > showProc.time()# 2.77 Time (user system elapsed): 1.48 0.01 1.5 > > if(doExtras) withAutoprint({##--------------------------------------------------------- + + ## Q: does it change to use numeric instead of binary factors ? + ## A: not really .. + d1k.n <- d1k27 + d1k.n[-(1:5)] <- lapply(d1k27[,-(1:5)], as.numeric) + + fm1.n <- lmrob(y ~ a + I(a^2) + tf + I(tf^2) + A + I(A^2) + . , data = d1k.n) + fm2.n <- lmrob(y ~ a + I(a^2) + tf + I(tf^2) + A + I(A^2) + . , data = d1k.n, + cov = ".vcov.w", trace = 2) + + summary(weights(fm1, type="robustness")) + hist(weights(fm1, type="robustness"), main="robustness weights of fm1") + rug(weights(fm1, type="robustness")) + showProc.time()## 2.88 + + ## + fmc <- lm (y ~ poly(a,2)-a + poly(tf, 2)-tf + poly(A, 2)-A + . , data = d1k27) + summary(fmc) + ## -> has NA's for 'a, tf, A' --- bad that it did *not* work to remove them + + nform <- update(formula(fm1), ~ . + +poly(A,2) -A -I(A^2) + +poly(a,2) -a -I(a^2) + +poly(tf,2) -tf -I(tf^2)) + + fm1. <- lmrob(nform, data = d1k27)# now w/o warning !? !! + fm2. <- lmrob(nform, data = d1k27, cov = ".vcov.w", trace = TRUE) + + ## now lmrob takes care of NA coefficients automatically + lmrob(y ~ poly(a,2)-a + poly(tf, 2)-tf + poly(A, 2)-A + . , data = d1k27) + showProc.time() ## 4.24 + }) ## only if(doExtras) ----------------------------------------------------- > > ## test exact fit property > set.seed(20) > data <- data.frame(y=c(rep.int(0, 20), round(100*rnorm(5))), + group=rep(letters[1:5], each=5)) > x <- model.matrix(y ~ group, data) > (ini <- lmrob.S(x, data$y, lmrob.control())) S-estimator lmrob.S(): Exact fit detected Coefficients: (Intercept) groupb groupc groupd groupe 0 0 0 0 116 scale = 0 ; did NOT converge in 200 refinement steps Algorithmic parameters: tuning.chi bb tuning.psi refine.tol rel.tol scale.tol solve.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 1.000e-07 1.000e-10 1.000e-07 zero.tol 1.000e-10 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" seed : int(0) Warning message: In lmrob.S(x, data$y, lmrob.control()) : S-estimated scale == 0: Probably exact fit; check your data > (ret <- lmrob(y ~ group, data)) Call: lmrob(formula = y ~ group, data = data) \--> method = "S" Exact fit detected Coefficients: (Intercept) groupb groupc groupd groupe 0 0 0 0 -45 Warning messages: 1: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 2: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged > summary(ret) Call: lmrob(formula = y ~ group, data = data) \--> method = "S" Residuals: Min 1Q Median 3Q Max -88 0 0 0 224 Exact fit detected Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0 0 NA NA groupb 0 0 NA NA groupc 0 0 NA NA groupd 0 0 NA NA groupe -45 0 NA NA Robustness weights: 4 observations c(21,22,23,24) are outliers with |weight| = 0 ( < 0.004); 21 weights are ~= 1. Algorithmic parameters: tuning.chi bb tuning.psi refine.tol 1.548e+00 5.000e-01 4.685e+00 1.000e-07 rel.tol scale.tol solve.tol zero.tol 1.000e-07 1.000e-10 1.000e-07 1.000e-10 eps.outlier eps.x warn.limit.reject warn.limit.meanrw 4.000e-03 1.819e-12 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 500 50 2 1 200 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 psi subsampling cov "bisquare" "nonsingular" ".vcov.avar1" compute.outlier.stats "SM" seed : int(0) > showProc.time() ## 4.24 Time (user system elapsed): 0.02 0 0.02 > > ##--- continuous x -- exact fit -- inspired by Thomas Mang's real data example > mkD9 <- function(iN, dN = 1:m) { + stopifnot((length(iN) -> m) == length(dN), 1 <= m, m <= 5, + iN == as.integer(iN), is.numeric(dN), !is.na(dN)) + x <- c(-3:0,0:1,1:3) # {n=9; sorted; x= 0, 1 are "doubled"} + y <- x+5 + y[iN] <- y[iN] + dN + data.frame(x,y) + } > > mkRS <- function(...) { set.seed(...); .Random.seed } > > d <- mkD9(c(1L,3:4, 7L)) > rs2 <- mkRS(2) > Se <- tryCatch(error = identity, + with(d, lmrob.S(cbind(1,x), y, lmrob.control("KS2014", seed=rs2)))) Warning message: In lmrob.S(cbind(1, x), y, lmrob.control("KS2014", seed = rs2)) : S-estimated scale == 0: Probably exact fit; check your data > ## gave DGELS rank error {for lmrob.c+wg..} > > if(inherits(Se, "error")) { + cat("Caught ") + print(Se) + } else withAutoprint({ ## no error + coef(Se) + stopifnot(coef(Se) == c(5, 1)) # was (0 0) + residuals(Se) # was == y ---- FIXME + }) > coef(Se) x 5 1 > stopifnot(coef(Se) == c(5, 1)) > residuals(Se) [1] 1 0 2 3 0 0 4 0 0 > > ## try 100 different seeds > repS <- lapply(1:100, function(ii) tryCatch(error = identity, + with(d, lmrob.S(cbind(1,x), y, lmrob.control("KS2014", seed = mkRS(ii)))))) There were 100 warnings (use warnings() to see them) > if(FALSE) + ## was + str(unique(repS))## ==> 100 times the same error > ## now completely different: *all* returned properly > str(cfS <- t(sapply(repS, coef))) # all numeric -- not *one* error -- num [1:100, 1:2] 5 5 5 5 5 5 5 5 5 5 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "" "x" > ## even all the *same* (5 1) solution: > (ucfS <- unique(cfS)) x [1,] 5 1 > stopifnot(identical(ucfS, array(c(5, 1), dim = 1:2, dimnames = list(NULL, c("", "x"))))) > > ## *Not* "KS2014" but the defaults works *all the time* (!) > repS0 <- lapply(1:100, function(ii) tryCatch(error = identity, + with(d, lmrob.S(cbind(1,x), y, lmrob.control(seed = mkRS(ii)))))) There were 100 warnings (use warnings() to see them) > summary(warnings()) 100 identical warnings: In lmrob.S(cbind(1, x), y, lmrob.control(seed = mkRS(ii))) : S-estimated scale == 0: Probably exact fit; check your data > ## 100 identical warnings: > ## In lmrob.S(cbind(1, x), y, lmrob.control(seed = mkRS(ii))) : > ## S-estimated scale == 0: Probably exact fit; check your data > > str(cfS0 <- t(sapply(repS0, coef))) # all numeric -- not *one* error num [1:100, 1:2] 5 5 5 5 5 5 5 5 5 5 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "" "x" > ## even all the same *and* the same as "KS2014" > (ucfS0 <- unique(cfS0)) x [1,] 5 1 > stopifnot(nrow(ucfS0) == 1L, + ucfS0 == c(5,1)) > > > > d9L <- list( + mkD9(c(1L,3L, 5L, 7L)) + , mkD9(c(1L,3L, 8:9)) + , mkD9(2L*(1:4)) + ) > > if(doExtras) { + sfsmisc::mult.fig(length(d9L)); invisible(lapply(d9L, function(d) plot(y ~ x, data=d))) + } > > dorob <- function(dat, control=lmrob.control(...), meth = c("S", "MM"), + doPl=interactive(), cex=1, ...) { + meth <- match.arg(meth) + stopifnot(is.data.frame(dat), c("x","y") %in% names(dat), is.list(control)) + if(doPl) plot(y ~ x, data=dat) ## with(dat, n.plot(x, y, cex=cex)) + ans <- tryCatch(error = identity, + switch(meth + , "S" = with(dat, lmrob.S(cbind(1,x), y, control)) + , "MM"= lmrob(y ~ x, data = dat, control=control) + , stop("invalid 'meth'"))) + if(!doPl) + return(ans) + ## else + if(!inherits(ans, "error")) { + abline(coef(ans)) + } else { # error + mtext(paste(paste0("lmrob.", meth), "Error:", conditionMessage(ans))) + } + invisible(ans) + } > > ## a bad case -- much better new robustbase >= 0.99-0 > Se <- dorob(d9L[[1]], lmrob.control("KS2014", mkRS(2), trace.lev=4)) Assigning .Random.seed to .GlobalEnv: int [1:626] 10403 624 -1619336578 -714750745 -765106180 158863565 -1093870294 242367651 -1691232888 -1538791959 ... lmrob_S(n = 9, nRes = 1000): fast_s() [non-large n]: Optimal block size for DGELS: 66 Subsampling 1000 times to find candidate betas: Sample[ 0]: idc = 1 5 b^[] = 5 1 refine_fast_s(s0=-1, convChk=FALSE): beta_cand= 5 1 |{i; |R_i| <= 6.222e-10 ~= 0}| = 5 zeroes (zero_tol=1e-10, s_y=6.22222); too many zeroes -> scale=0 & quit refinement after refine_(*, conv=F): beta_ref : 5 1 with ||beta_ref - beta_cand|| = 0, --> sc = 0 |s|=0: Have 5 (too many) exact zeroes -> leaving refinement! lmrob.S(): scale = 0; coeff.= [1] 5 1 Warning message: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data > ## was really bad -- ended returning coef = (0 0); fitted == 0, residuals == 0 !! > > if(doExtras) sfsmisc::mult.fig(length(d9L)) > r0 <- lapply(d9L, dorob, seed=rs2, doPl=doExtras) # 3 x ".. exact fit" warning Warning messages: 1: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data 2: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data 3: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data > if(doExtras) print(r0) > ## back to 3 identical fits: (5 1) > (cf0 <- sapply(r0, coef)) [,1] [,2] [,3] 5 5 5 x 1 1 1 > stopifnot(cf0 == c(5,1)) > > if(doExtras) sfsmisc::mult.fig(length(d9L)) > ### Here, all 3 were "0-models" > r14 <- lapply(d9L, dorob, control=lmrob.control("KS2014", seed=rs2), doPl=doExtras) Warning messages: 1: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data 2: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data 3: In lmrob.S(cbind(1, x), y, control) : S-estimated scale == 0: Probably exact fit; check your data > ## --> 3 (identical) warnings: In lmrob.S(cbind(1, x), y, control) :# > ## S-estimated scale == 0: Probably exact fit; check your data > ## now *does* plot > if(doExtras) print(r14) > ## all 3 are "identical" > (cf14 <- sapply(r14, coef)) [,1] [,2] [,3] 5 5 5 x 1 1 1 > identical(cf0, cf14) # see TRUE; test a bit less: [1] TRUE > stopifnot(all.equal(cf0, cf14, tol=1e-15)) > > ## use "large n" > ctrl.LRG.n <- lmrob.control("KS2014", seed=rs2, trace.lev = if(doExtras) 2 else 1, # 3: too much (for now), + nResample = 60, + fast.s.large.n = 7, n.group = 3, groups = 2) > rLrg.n <- lapply(d9L, \(d) lmrob.S(cbind(1,d$x), d$y, ctrl.LRG.n)) Assigning .Random.seed to .GlobalEnv: int [1:626] 10403 624 -1619336578 -714750745 -765106180 158863565 -1093870294 242367651 -1691232888 -1538791959 ... lmrob_S(n = 9, nRes = 60): fast_s_large_n(): Subsampling to find candidate betas in group 0: * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 Subsampling to find candidate betas in group 1: * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 Now refine() to convergence for 20 very best ones: Final best[0]: convergence (Exact fit! 5 zeroes; scale=0, sc=0 improved scale to 0 = 0 ==> finish lmrob.S(): scale = 0; coeff.= [1] 5 1 Assigning .Random.seed to .GlobalEnv: int [1:626] 10403 624 -1619336578 -714750745 -765106180 158863565 -1093870294 242367651 -1691232888 -1538791959 ... lmrob_S(n = 9, nRes = 60): fast_s_large_n(): Subsampling to find candidate betas in group 0: * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 * exact fit! 3 zero residuals; scale = 0 Subsampling to find candidate betas in group 1: * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 Now refine() to convergence for 20 very best ones: Final best[0]: convergence (Exact fit! 5 zeroes; scale=0, sc=0 improved scale to 0 = 0 ==> finish lmrob.S(): scale = 0; coeff.= [1] 5 1 Assigning .Random.seed to .GlobalEnv: int [1:626] 10403 624 -1619336578 -714750745 -765106180 158863565 -1093870294 242367651 -1691232888 -1538791959 ... lmrob_S(n = 9, nRes = 60): fast_s_large_n(): Subsampling to find candidate betas in group 0: * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 Subsampling to find candidate betas in group 1: * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 * exact fit! 2 zero residuals; scale = 0 Now refine() to convergence for 20 very best ones: Final best[0]: convergence (Exact fit! 2 zeroes; scale=0, sc=0 improved scale to 0 = 0 ==> finish lmrob.S(): scale = 0; coeff.= [1] 7.333333 1.666667 There were 186 warnings (use warnings() to see them) > summary(warnings()) Summary of (a total of 186) warning messages: 3x : In lmrob.S(cbind(1, d$x), d$y, ctrl.LRG.n) : 'control$n.group' is not much larger than 'p', probably too small 180x : In lmrob.S(cbind(1, d$x), d$y, ctrl.LRG.n) : find_scale(*, initial_scale = 0 <= 0) -> final scale = 0 3x : In lmrob.S(cbind(1, d$x), d$y, ctrl.LRG.n) : S-estimated scale == 0: Probably exact fit; check your data > sapply(rLrg.n, coef) [,1] [,2] [,3] [1,] 5 5 7.333333 [2,] 1 1 1.666667 > ## currently ... .... really would want always (5 1) > ## [,1] [,2] [,3] > ## [1,] 5 5 7.333333 > ## [2,] 1 1 1.666667 > > > ## ==> use lmrob() instead of lmrob.S(): > > mm0 <- lapply(d9L, dorob, meth = "MM", seed=rs2, doPl=doExtras) # looks all fine -- no longer: error in [[3]] Warning messages: 1: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 2: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged 3: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 4: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged 5: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 6: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged > if(doExtras) print(mm0) > ## now, the 3rd one errors (on Linux, not on M1 mac!) > (cm0 <- sapply(mm0, function(.) if(inherits(.,"error")) noquote(paste("Caught", as.character(.))) else coef(.))) [,1] [,2] [,3] (Intercept) 5 5 5 x 1 1 1 > > ## no longer needed > c0.12 <- rbind(`(Intercept)` = c(5.7640215, 6.0267156), + x = c(0.85175883, 1.3823841)) > if(is.list(cm0)) { ## after error {was on Linux+Win, not on M1 mac}: + ## NB: This does *not* happen on Macbuilder -- there the result it cf = (5 1) !! + stopifnot(all.equal(tol = 1e-8, # seen 4.4376e-9 + c0.12, simplify2array(cm0[1:2]))) + print(cm0[[3]]) + ## FIXME?: Caught Error in eigen(ret, symmetric = TRUE): infinite or missing values in 'x'\n + } else if(is.matrix(cm0)) { # when no error happened + k <- ncol(cm0) + stopifnot(all.equal(tol = 1e-8, rbind(`(Intercept)` = rep(5,k), "x" = rep(1,k)), cm0)) + } else warning("not yet encountered this case {and it should not happen}") > > > se3 <- lmrob(y ~ x, data=d9L[[3]], init = r0[[3]], seed=rs2, trace.lev=6) init *NOT* converged; init$scale = 0, init$coef: x 5 1 Warning message: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged > > > if(doExtras) sfsmisc::mult.fig(length(d9L)) > ### Here, all 3 were "0-models" > ## now, have 3 *different* cases {with this seed} > ## [1] : init fails (-> r14[[1]] above) > ## [2] : init s=0, b=(5,1) .. but residuals(),fitted() wrong > ## [3] : init s=0, b=(5,1) ..*and* residuals(),fitted() are good > > cm14 <- lapply(d9L, dorob, meth = "MM", control=lmrob.control("KS2014", seed=rs2), doPl=doExtras) Warning messages: 1: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 2: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged 3: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 4: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged 5: In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 6: In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged > ## now, first is error; for others, coef = (5, 1) are correct: > stopifnot(exprs = { + sapply(cm14[-1], coef) == c(5,1) + sapply(cm14[-1], sigma) == 0 + }) > > m2 <- cm14[[2]] > summary(m2) # prints quite nicely; and this is perfect (for scale=0), too: Call: lmrob(formula = y ~ x, data = dat, control = control) \--> method = "S" Residuals: Min 1Q Median 3Q Max 0 0 0 2 4 Exact fit detected Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5 0 NA NA x 1 0 NA NA Robustness weights: [1] 0 1 0 1 1 1 1 0 0 Algorithmic parameters: tuning.chi1 tuning.chi2 tuning.chi3 tuning.chi4 -5.000e-01 1.500e+00 NA 5.000e-01 bb tuning.psi1 tuning.psi2 tuning.psi3 5.000e-01 -5.000e-01 1.500e+00 9.500e-01 tuning.psi4 refine.tol rel.tol scale.tol NA 1.000e-07 1.000e-07 1.000e-10 solve.tol zero.tol eps.outlier eps.x 1.000e-07 1.000e-10 1.111e-02 5.457e-12 warn.limit.reject warn.limit.meanrw 5.000e-01 5.000e-01 nResample max.it best.r.s k.fast.s k.max 1000 500 20 2 2000 maxit.scale trace.lev mts compute.rd fast.s.large.n 200 0 1000 0 2000 setting psi subsampling "KS2014" "lqq" "nonsingular" cov compute.outlier.stats ".vcov.w" "SMDM" seed : int [1:626] 10403 624 -1619336578 -714750745 -765106180 ... > ## {residual != 0 <==> weights = 0}: > cbind(rwgt = weights(m2, "rob"), res = residuals(m2), fit = fitted(m2), y = d9L[[2]][,"y"]) rwgt res fit y 1 0 1 2 3 2 1 0 3 3 3 0 2 4 6 4 1 0 5 5 5 1 0 5 5 6 1 0 6 6 7 1 0 6 6 8 0 3 7 10 9 0 4 8 12 > > sapply(cm14, residuals) ## now, [2] is good; [3] still wrong - FIXME [,1] [,2] [,3] 1 1 1 0 2 0 0 1 3 2 2 0 4 0 0 2 5 3 0 0 6 0 0 3 7 4 0 0 8 0 3 4 9 0 4 0 > sapply(cm14, fitted) [,1] [,2] [,3] 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 6 6 6 6 7 6 6 6 8 7 7 7 9 8 8 8 > sapply(cm14, weights, "robust")## [2]: 0 1 0 1 1 1 1 0 0; [3]: all 0 [,1] [,2] [,3] [1,] 0 0 1 [2,] 1 1 0 [3,] 0 0 1 [4,] 1 1 0 [5,] 0 1 1 [6,] 1 1 0 [7,] 0 1 1 [8,] 1 0 0 [9,] 1 0 1 > > ## (unfinished ... do *test* once we've checked platform consistency) > > summary(warnings()) Summary of (a total of 6) warning messages: 3x : In lmrob.S(x, y, control = control) : S-estimated scale == 0: Probably exact fit; check your data 3x : In lmrob.fit(x, y, control, init = init) : initial estim. 'init' not converged -- will be return()ed basically unchanged > showProc.time() Time (user system elapsed): 0.2 0.02 0.22 > > > proc.time() user system elapsed 4.12 0.20 4.31