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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("ncomplete-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('ncomplete') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "ncomplete" > > ### * ncomplete > > flush(stderr()); flush(stdout()) > > ### Name: ncomplete.for > ### Title: Minimal number of misclassifications based on an affine > ### hyperplane in binary regression (complete separation) > ### Aliases: ncomplete.for ncomplete > ### Keywords: regression robust > > ### ** Examples > > data(Z2) > Z2 [,1] [,2] [,3] [1,] -1.5 0 0 [2,] -1.0 3 1 [3,] 0.0 1 0 [4,] 0.0 2 0 [5,] 1.0 2 0 [6,] 1.0 4 0 [7,] 2.0 2 1 [8,] 3.0 1 1 [9,] 3.0 3 1 [10,] 3.5 4 1 > ncomplete.for(Z2) [1] 1 > ncomplete.for(Z2,NDIR=100000) [1] 1 > # X11() > postscript(file="tmp1.ps") > par(mfrow=c(2,1)) > ncomplete.for(Z2,NDIR=10000,PLOT=TRUE) [1] 1 > tmp <- ncomplete.for(Z2) [1] 1 > tmp$NCOMPLETE [1] 1 > tmp$COEFFICIENTS [1] 0.6507914 -0.2169305 > tmp$NSIN [1] 0 > tmp$DETAILS id y xu' [1,] 2 1 -1.3015827 [2,] 1 0 -0.9761871 [3,] 4 0 -0.4338609 [4,] 3 0 -0.2169305 [5,] 6 0 -0.2169305 [6,] 5 0 0.2169305 [7,] 7 1 0.8677218 [8,] 9 1 1.3015827 [9,] 10 1 1.4100480 [10,] 8 1 1.7354437 > Z3 <- as.data.frame(Z2) > names(Z3) <- c("x1","x2","y") > plot(x2 ~x1, data=Z3,pch=as.character(y),main="Scatterplot") > abline(c(0,1.5),col="blue") > points(Z3[2,1],Z3[2,2],pch=as.character(Z3[2,3]),col="red") > dev.off() postscript 2 > > # COMPLETE SEPARATION: maximum likelihood estimates do NOT exist > data(Z1) > Z1 [,1] [,2] [1,] 1 0 [2,] 2 0 [3,] 3 0 [4,] 4 0 [5,] 5 0 [6,] 6 0 [7,] 7 1 [8,] 8 1 [9,] 9 1 [10,] 10 1 > # X11() > postscript(file="tmp2.ps") > ncomplete.for(Z1) --> Because NP=1, NDIR is set to 1 [1] 1 [1] 0 > tmp <- ncomplete.for(Z1) --> Because NP=1, NDIR is set to 1 [1] 1 [1] 0 > tmp$NCOMPLETE [1] 0 > tmp$COEFFICIENTS [1] 0.4 > tmp$NSIN [1] 1 > tmp$DETAILS id y xu' [1,] 1 0 0.4 [2,] 2 0 0.8 [3,] 3 0 1.2 [4,] 4 0 1.6 [5,] 5 0 2.0 [6,] 6 0 2.4 [7,] 7 1 2.8 [8,] 8 1 3.2 [9,] 9 1 3.6 [10,] 10 1 4.0 > Z3 <- as.data.frame(Z1) > names(Z3) <- c("x1","y") > plot(y ~ x1, data=Z3,pch=as.character(y),main="Scatterplot") > summary(glm(y ~ x1, data=Z3, family=binomial(link=logit), trace=TRUE, maxit=30)) Deviance = 4.481401 Iterations - 1 Deviance = 2.709808 Iterations - 2 Deviance = 1.657551 Iterations - 3 Deviance = 0.9380147 Iterations - 4 Deviance = 0.4277694 Iterations - 5 Deviance = 0.1585970 Iterations - 6 Deviance = 0.05729967 Iterations - 7 Deviance = 0.02089573 Iterations - 8 Deviance = 0.007660696 Iterations - 9 Deviance = 0.00281457 Iterations - 10 Deviance = 0.001034926 Iterations - 11 Deviance = 0.0003806608 Iterations - 12 Deviance = 0.0001400282 Iterations - 13 Deviance = 5.151225e-05 Iterations - 14 Deviance = 1.895013e-05 Iterations - 15 Deviance = 6.971341e-06 Iterations - 16 Deviance = 2.56461e-06 Iterations - 17 Deviance = 9.43467e-07 Iterations - 18 Deviance = 3.470821e-07 Iterations - 19 Deviance = 1.276844e-07 Iterations - 20 Deviance = 4.697252e-08 Iterations - 21 Deviance = 1.728028e-08 Iterations - 22 Deviance = 6.357113e-09 Iterations - 23 Deviance = 2.338706e-09 Iterations - 24 Deviance = 8.604162e-10 Iterations - 25 Deviance = 3.165836e-10 Iterations - 26 Warning in glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : fitted probabilities numerically 0 or 1 occurred Call: glm(formula = y ~ x1, family = binomial(link = logit), data = Z3, trace = TRUE, maxit = 30) Deviance Residuals: Min 1Q Median 3Q Max -1.277e-05 -2.107e-08 -2.107e-08 2.107e-08 1.239e-05 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -302.35 627387.28 -0.000482 1 x1 46.52 96460.61 0.000482 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1.3460e+01 on 9 degrees of freedom Residual deviance: 3.1658e-10 on 8 degrees of freedom AIC: 4 Number of Fisher Scoring iterations: 26 > dev.off() postscript 2 > > # COMPLETE SEPARATION: maximum likelihood estimates in the logistic regression model > # do NOT exist for the banknotes data set due to complete separation > data(Banknotes) > Banknotes [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 214.8 131.0 131.1 9.0 9.7 141.0 0 [2,] 214.6 129.7 129.7 8.1 9.5 141.7 0 [3,] 214.8 129.7 129.7 8.7 9.6 142.2 0 [4,] 214.8 129.7 129.6 7.5 10.4 142.0 0 [5,] 215.0 129.6 129.7 10.4 7.7 141.8 0 [6,] 215.7 130.8 130.5 9.0 10.1 141.4 0 [7,] 215.5 129.5 129.7 7.9 9.6 141.6 0 [8,] 214.5 129.6 129.2 7.2 10.7 141.7 0 [9,] 214.9 129.4 129.7 8.2 11.0 141.9 0 [10,] 215.2 130.4 130.3 9.2 10.0 140.7 0 [11,] 215.3 130.4 130.3 7.9 11.7 141.8 0 [12,] 215.1 129.5 129.6 7.7 10.5 142.2 0 [13,] 215.2 130.8 129.6 7.9 10.8 141.4 0 [14,] 214.7 129.7 129.7 7.7 10.9 141.7 0 [15,] 215.1 129.9 129.7 7.7 10.8 141.8 0 [16,] 214.5 129.8 129.8 9.3 8.5 141.6 0 [17,] 214.6 129.9 130.1 8.2 9.8 141.7 0 [18,] 215.0 129.9 129.7 9.0 9.0 141.9 0 [19,] 215.2 129.6 129.6 7.4 11.5 141.5 0 [20,] 214.7 130.2 129.9 8.6 10.0 141.9 0 [21,] 215.0 129.9 129.3 8.4 10.0 141.4 0 [22,] 215.6 130.5 130.0 8.1 10.3 141.6 0 [23,] 215.3 130.6 130.0 8.4 10.8 141.5 0 [24,] 215.7 130.2 130.0 8.7 10.0 141.6 0 [25,] 215.1 129.7 129.9 7.4 10.8 141.1 0 [26,] 215.3 130.4 130.4 8.0 11.0 142.3 0 [27,] 215.5 130.2 130.1 8.9 9.8 142.4 0 [28,] 215.1 130.3 130.3 9.8 9.5 141.9 0 [29,] 215.1 130.0 130.0 7.4 10.5 141.8 0 [30,] 214.8 129.7 129.3 8.3 9.0 142.0 0 [31,] 215.2 130.1 129.8 7.9 10.7 141.8 0 [32,] 214.8 129.7 129.7 8.6 9.1 142.3 0 [33,] 215.0 130.0 129.6 7.7 10.5 140.7 0 [34,] 215.6 130.4 130.1 8.4 10.3 141.0 0 [35,] 215.9 130.4 130.0 8.9 10.6 141.4 0 [36,] 214.6 130.2 130.2 9.4 9.7 141.8 0 [37,] 215.5 130.3 130.0 8.4 9.7 141.8 0 [38,] 215.3 129.9 129.4 7.9 10.0 142.0 0 [39,] 215.3 130.3 130.1 8.5 9.3 142.1 0 [40,] 213.9 130.3 129.0 8.1 9.7 141.3 0 [41,] 214.4 129.8 129.2 8.9 9.4 142.3 0 [42,] 214.8 130.1 129.6 8.8 9.9 140.9 0 [43,] 214.9 129.6 129.4 9.3 9.0 141.7 0 [44,] 214.9 130.4 129.7 9.0 9.8 140.9 0 [45,] 214.8 129.4 129.1 8.2 10.2 141.0 0 [46,] 214.3 129.5 129.4 8.3 10.2 141.8 0 [47,] 214.8 129.9 129.7 8.3 10.2 141.5 0 [48,] 214.8 129.9 129.7 7.3 10.9 142.0 0 [49,] 214.6 129.7 129.8 7.9 10.3 141.1 0 [50,] 214.5 129.0 129.6 7.8 9.8 142.0 0 [51,] 214.6 129.8 129.4 7.2 10.0 141.3 0 [52,] 215.3 130.6 130.0 9.5 9.7 141.1 0 [53,] 214.5 130.1 130.0 7.8 10.9 140.9 0 [54,] 215.4 130.2 130.2 7.6 10.9 141.6 0 [55,] 214.5 129.4 129.5 7.9 10.0 141.4 0 [56,] 215.2 129.7 129.4 9.2 9.4 142.0 0 [57,] 215.7 130.0 129.4 9.2 10.4 141.2 0 [58,] 215.0 129.6 129.4 8.8 9.0 141.1 0 [59,] 215.1 130.1 129.9 7.9 11.0 141.3 0 [60,] 215.1 130.0 129.8 8.2 10.3 141.4 0 [61,] 215.1 129.6 129.3 8.3 9.9 141.6 0 [62,] 215.3 129.7 129.4 7.5 10.5 141.5 0 [63,] 215.4 129.8 129.4 8.0 10.6 141.5 0 [64,] 214.5 130.0 129.5 8.0 10.8 141.4 0 [65,] 215.0 130.0 129.8 8.6 10.6 141.5 0 [66,] 215.2 130.6 130.0 8.8 10.6 140.8 0 [67,] 214.6 129.5 129.2 7.7 10.3 141.3 0 [68,] 214.8 129.7 129.3 9.1 9.5 141.5 0 [69,] 215.1 129.6 129.8 8.6 9.8 141.8 0 [70,] 214.9 130.2 130.2 8.0 11.2 139.6 0 [71,] 213.8 129.8 129.5 8.4 11.1 140.9 0 [72,] 215.2 129.9 129.5 8.2 10.3 141.4 0 [73,] 215.0 129.6 130.2 8.7 10.0 141.2 0 [74,] 214.4 129.9 129.6 7.5 10.5 141.8 0 [75,] 215.2 129.9 129.7 7.2 10.6 142.1 0 [76,] 214.1 129.6 129.3 7.6 10.7 141.7 0 [77,] 214.9 129.9 130.1 8.8 10.0 141.2 0 [78,] 214.6 129.8 129.4 7.4 10.6 141.0 0 [79,] 215.2 130.5 129.8 7.9 10.9 140.9 0 [80,] 214.6 129.9 129.4 7.9 10.0 141.8 0 [81,] 215.1 129.7 129.7 8.6 10.3 140.6 0 [82,] 214.9 129.8 129.6 7.5 10.3 141.0 0 [83,] 215.2 129.7 129.1 9.0 9.7 141.9 0 [84,] 215.2 130.1 129.9 7.9 10.8 141.3 0 [85,] 215.4 130.7 130.2 9.0 11.1 141.2 0 [86,] 215.1 129.9 129.6 8.9 10.2 141.5 0 [87,] 215.2 129.9 129.7 8.7 9.5 141.6 0 [88,] 215.0 129.6 129.2 8.4 10.2 142.1 0 [89,] 214.9 130.3 129.9 7.4 11.2 141.5 0 [90,] 215.0 129.9 129.7 8.0 10.5 142.0 0 [91,] 214.7 129.7 129.3 8.6 9.6 141.6 0 [92,] 215.4 130.0 129.9 8.5 9.7 141.4 0 [93,] 214.9 129.4 129.5 8.2 9.9 141.5 0 [94,] 214.5 129.5 129.3 7.4 10.7 141.5 0 [95,] 214.7 129.6 129.5 8.3 10.0 142.0 0 [96,] 215.6 129.9 129.9 9.0 9.5 141.7 0 [97,] 215.0 130.4 130.3 9.1 10.2 141.1 0 [98,] 214.4 129.7 129.5 8.0 10.3 141.2 0 [99,] 215.1 130.0 129.8 9.1 10.2 141.5 0 [100,] 214.7 130.0 129.4 7.8 10.0 141.2 0 [101,] 214.4 130.1 130.3 9.7 11.7 139.8 1 [102,] 214.9 130.5 130.2 11.0 11.5 139.5 1 [103,] 214.9 130.3 130.1 8.7 11.7 140.2 1 [104,] 215.0 130.4 130.6 9.9 10.9 140.3 1 [105,] 214.7 130.2 130.3 11.8 10.9 139.7 1 [106,] 215.0 130.2 130.2 10.6 10.7 139.9 1 [107,] 215.3 130.3 130.1 9.3 12.1 140.2 1 [108,] 214.8 130.1 130.4 9.8 11.5 139.9 1 [109,] 215.0 130.2 129.9 10.0 11.9 139.4 1 [110,] 215.2 130.6 130.8 10.4 11.2 140.3 1 [111,] 215.2 130.4 130.3 8.0 11.5 139.2 1 [112,] 215.1 130.5 130.3 10.6 11.5 140.1 1 [113,] 215.4 130.7 131.1 9.7 11.8 140.6 1 [114,] 214.9 130.4 129.9 11.4 11.0 139.9 1 [115,] 215.1 130.3 130.0 10.6 10.8 139.7 1 [116,] 215.5 130.4 130.0 8.2 11.2 139.2 1 [117,] 214.7 130.6 130.1 11.8 10.5 139.8 1 [118,] 214.7 130.4 130.1 12.1 10.4 139.9 1 [119,] 214.8 130.5 130.2 11.0 11.0 140.0 1 [120,] 214.4 130.2 129.9 10.1 12.0 139.2 1 [121,] 214.8 130.3 130.4 10.1 12.1 139.6 1 [122,] 215.1 130.6 130.3 12.3 10.2 139.6 1 [123,] 215.3 130.8 131.1 11.6 10.6 140.2 1 [124,] 215.1 130.7 130.4 10.5 11.2 139.7 1 [125,] 214.7 130.5 130.5 9.9 10.3 140.1 1 [126,] 214.9 130.0 130.3 10.2 11.4 139.6 1 [127,] 215.0 130.4 130.4 9.4 11.6 140.2 1 [128,] 215.5 130.7 130.3 10.2 11.8 140.0 1 [129,] 215.1 130.2 130.2 10.1 11.3 140.3 1 [130,] 214.5 130.2 130.6 9.8 12.1 139.9 1 [131,] 214.3 130.2 130.0 10.7 10.5 139.8 1 [132,] 214.5 130.2 129.8 12.3 11.2 139.2 1 [133,] 214.9 130.5 130.2 10.6 11.5 139.9 1 [134,] 214.6 130.2 130.4 10.5 11.8 139.7 1 [135,] 214.2 130.0 130.2 11.0 11.2 139.5 1 [136,] 214.8 130.1 130.1 11.9 11.1 139.5 1 [137,] 214.6 129.8 130.2 10.7 11.1 139.4 1 [138,] 214.9 130.7 130.3 9.3 11.2 138.3 1 [139,] 214.6 130.4 130.4 11.3 10.8 139.8 1 [140,] 214.5 130.5 130.2 11.8 10.2 139.6 1 [141,] 214.8 130.2 130.3 10.0 11.9 139.3 1 [142,] 214.7 130.0 129.4 10.2 11.0 139.2 1 [143,] 214.6 130.2 130.4 11.2 10.7 139.9 1 [144,] 215.0 130.5 130.4 10.6 11.1 139.9 1 [145,] 214.5 129.8 129.8 11.4 10.0 139.3 1 [146,] 214.9 130.6 130.4 11.9 10.5 139.8 1 [147,] 215.0 130.5 130.4 11.4 10.7 139.9 1 [148,] 215.3 130.6 130.3 9.3 11.3 138.1 1 [149,] 214.7 130.2 130.1 10.7 11.0 139.4 1 [150,] 214.9 129.9 130.0 9.9 12.3 139.4 1 [151,] 214.9 130.3 129.9 11.9 10.6 139.8 1 [152,] 214.6 129.9 129.7 11.9 10.1 139.0 1 [153,] 214.6 129.7 129.3 10.4 11.0 139.3 1 [154,] 214.5 130.1 130.1 12.1 10.3 139.4 1 [155,] 214.5 130.3 130.0 11.0 11.5 139.5 1 [156,] 215.1 130.0 130.3 11.6 10.5 139.7 1 [157,] 214.2 129.7 129.6 10.3 11.4 139.5 1 [158,] 214.4 130.1 130.0 11.3 10.7 139.2 1 [159,] 214.8 130.4 130.6 12.5 10.0 139.3 1 [160,] 214.6 130.6 130.1 8.1 12.1 137.9 1 [161,] 215.6 130.1 129.7 7.4 12.2 138.4 1 [162,] 214.9 130.5 130.1 9.9 10.2 138.1 1 [163,] 214.6 130.1 130.0 11.5 10.6 139.5 1 [164,] 214.7 130.1 130.2 11.6 10.9 139.1 1 [165,] 214.3 130.3 130.0 11.4 10.5 139.8 1 [166,] 215.1 130.3 130.6 10.3 12.0 139.7 1 [167,] 216.3 130.7 130.4 10.0 10.1 138.8 1 [168,] 215.6 130.4 130.1 9.6 11.2 138.6 1 [169,] 214.8 129.9 129.8 9.6 12.0 139.6 1 [170,] 214.9 130.0 129.9 11.4 10.9 139.7 1 [171,] 213.9 130.7 130.5 8.7 11.5 137.8 1 [172,] 214.2 130.6 130.4 12.0 10.2 139.6 1 [173,] 214.8 130.5 130.3 11.8 10.5 139.4 1 [174,] 214.8 129.6 130.0 10.4 11.6 139.2 1 [175,] 214.8 130.1 130.0 11.4 10.5 139.6 1 [176,] 214.9 130.4 130.2 11.9 10.7 139.0 1 [177,] 214.3 130.1 130.1 11.6 10.5 139.7 1 [178,] 214.5 130.4 130.0 9.9 12.0 139.6 1 [179,] 214.8 130.5 130.3 10.2 12.1 139.1 1 [180,] 214.5 130.2 130.4 8.2 11.8 137.8 1 [181,] 215.0 130.4 130.1 11.4 10.7 139.1 1 [182,] 214.8 130.6 130.6 8.0 11.4 138.7 1 [183,] 215.0 130.5 130.1 11.0 11.4 139.3 1 [184,] 214.6 130.5 130.4 10.1 11.4 139.3 1 [185,] 214.7 130.2 130.1 10.7 11.1 139.5 1 [186,] 214.7 130.4 130.0 11.5 10.7 139.4 1 [187,] 214.5 130.4 130.0 8.0 12.2 138.5 1 [188,] 214.8 130.0 129.7 11.4 10.6 139.2 1 [189,] 214.8 129.9 130.2 9.6 11.9 139.4 1 [190,] 214.6 130.3 130.2 12.7 9.1 139.2 1 [191,] 215.1 130.2 129.8 10.2 12.0 139.4 1 [192,] 215.4 130.5 130.6 8.8 11.0 138.6 1 [193,] 214.7 130.3 130.2 10.8 11.1 139.2 1 [194,] 215.0 130.5 130.3 9.6 11.0 138.5 1 [195,] 214.9 130.3 130.5 11.6 10.6 139.8 1 [196,] 215.0 130.4 130.3 9.9 12.1 139.6 1 [197,] 215.1 130.3 129.9 10.3 11.5 139.7 1 [198,] 214.8 130.3 130.4 10.6 11.1 140.0 1 [199,] 214.7 130.7 130.8 11.2 11.2 139.4 1 [200,] 214.3 129.9 129.9 10.2 11.5 139.6 1 > # X11() > postscript(file="tmp3.ps") > tmp <- ncomplete.for(Banknotes,PLOT=TRUE) [1] 0 > dev.off() postscript 2 > tmp$NCOMPLETE [1] 0 > tmp$COEFFICIENTS [1] 0.4938776 -0.5066471 -0.4544666 -0.6946073 -0.6153259 0.4552016 > Z3 <- as.data.frame(Banknotes) > names(Z3) <- c("x1","x2", "x3", "x4", "x5", "x6","y") > summary(glm(y ~ x1+x2+x3+x4+x5+x6, data=Z3, family=binomial(link=logit), + trace=TRUE, maxit=30)) Deviance = 48.50263 Iterations - 1 Deviance = 21.63948 Iterations - 2 Deviance = 11.04281 Iterations - 3 Deviance = 6.413452 Iterations - 4 Deviance = 4.021488 Iterations - 5 Deviance = 2.392665 Iterations - 6 Deviance = 1.145208 Iterations - 7 Deviance = 0.4579331 Iterations - 8 Deviance = 0.1746516 Iterations - 9 Deviance = 0.06575025 Iterations - 10 Deviance = 0.02452953 Iterations - 11 Deviance = 0.009084874 Iterations - 12 Deviance = 0.003349305 Iterations - 13 Deviance = 0.001232677 Iterations - 14 Deviance = 0.0004535161 Iterations - 15 Deviance = 0.0001668441 Iterations - 16 Deviance = 6.137939e-05 Iterations - 17 Deviance = 2.258040e-05 Iterations - 18 Deviance = 8.306902e-06 Iterations - 19 Deviance = 3.055947e-06 Iterations - 20 Deviance = 1.124223e-06 Iterations - 21 Deviance = 4.135801e-07 Iterations - 22 Deviance = 1.521490e-07 Iterations - 23 Deviance = 5.59739e-08 Iterations - 24 Deviance = 2.059304e-08 Iterations - 25 Deviance = 7.57714e-09 Iterations - 26 Deviance = 2.788862e-09 Iterations - 27 Deviance = 1.027350e-09 Iterations - 28 Deviance = 3.793246e-10 Iterations - 29 Warning in glm.fit(x = X, y = Y, weights = weights, start = start, etastart = etastart, : fitted probabilities numerically 0 or 1 occurred Call: glm(formula = y ~ x1 + x2 + x3 + x4 + x5 + x6, family = binomial(link = logit), data = Z3, trace = TRUE, maxit = 30) Deviance Residuals: Min 1Q Median 3Q Max -1.115e-05 -2.107e-08 0.000e+00 2.107e-08 9.823e-06 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.873e+03 2.244e+08 -1.28e-05 1 x1 3.890e+01 1.221e+06 3.19e-05 1 x2 -1.656e+01 2.383e+06 -6.95e-06 1 x3 1.614e+01 1.861e+06 8.67e-06 1 x4 7.081e+01 3.193e+05 2.22e-04 1 x5 6.031e+01 2.740e+05 2.20e-04 1 x6 -4.797e+01 2.684e+05 -1.79e-04 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 2.7726e+02 on 199 degrees of freedom Residual deviance: 3.7932e-10 on 193 degrees of freedom AIC: 14 Number of Fisher Scoring iterations: 29 > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) Warning: calling par(new=) with no plot > cleanEx(); ..nameEx <- "ncomplete1" > > ### * ncomplete1 > > flush(stderr()); flush(stdout()) > > ### Name: ncomplete1.for > ### Title: Function called by ncomplete.for > ### Aliases: ncomplete1.for ncomplete1 > ### Keywords: regression robust > > ### ** Examples > > > > ### *