R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for a HTML browser interface to help. Type 'q()' to quit R. > ### *
> ### > 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("adlift-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('adlift') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "AdaptNeigh" > > ### * AdaptNeigh > > flush(stderr()); flush(stdout()) > > ### Name: AdaptNeigh > ### Title: AdaptNeigh > ### Aliases: AdaptNeigh > ### Keywords: regression > > ### ** Examples > > # > # Generate some doppler data: 500 observations. > # > tx <- runif(500) > ty<-make.signal2("doppler",x=tx) > # > # Compute the neighbours of point 173 (2 neighbours on each side) > # > out<-getnbrs(tx,173,order(tx),2,FALSE) > > # > # Perform the adaptive lifting step > # > an<-AdaptNeigh(order(tx),tx,ty,out$nbrs,173,FALSE,2) > # > an[[1]][[8]] [1] "Quad" > > an[[2]][[3]] [1] 230 325 52 > > #shows best prediction when removing point 173, with the neighbours used > > > > > cleanEx(); ..nameEx <- "AdaptNeighmp" > > ### * AdaptNeighmp > > flush(stderr()); flush(stdout()) > > ### Name: AdaptNeighmp > ### Title: AdaptNeighmp > ### Aliases: AdaptNeighmp > ### Keywords: regression > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > AdaptNeighmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) $results $results[[1]] [,1] [1,] 16.6 [2,] 16.8 [3,] 16.8 [4,] 16.8 [5,] 17.8 [6,] 17.8 $results[[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.009453518 0.009567416 0.009567416 0.009567416 0.01013691 0.01013691 $results[[3]] [,1] [1,] -4.914884 $results[[4]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.1663819 0.1683865 0.1683865 0.1683865 0.1784095 0.1784095 $results[[5]] [1] -86.50196 $results[[6]] [,1] [1,] 2.4000000 [2,] 2.6000000 [3,] 3.2000000 [4,] 3.6000000 [5,] 4.0000000 [6,] 6.2000000 [7,] 6.6000000 [8,] 6.8000000 [9,] 7.8000000 [10,] 8.2000000 [11,] 8.8000000 [12,] 9.6000000 [13,] 10.0000000 [14,] 10.2000000 [15,] 10.6000000 [16,] 11.0000000 [17,] 11.4000000 [18,] 13.2000000 [19,] 13.6000000 [20,] 13.8000000 [21,] 14.6000000 [22,] 14.8000000 [23,] 15.4000000 [24,] 15.6000000 [25,] 15.8000000 [26,] 16.0000000 [27,] 16.2000000 [28,] 16.4000000 [29,] 16.6000000 [30,] 16.8000000 [31,] -0.5230387 [32,] 17.8000000 [33,] 18.6000000 [34,] 19.2000000 [35,] 19.4000000 [36,] 19.6000000 [37,] 20.2000000 [38,] 20.4000000 [39,] 21.2000000 [40,] 21.4000000 [41,] 21.8000000 [42,] 22.0000000 [43,] 23.2000000 [44,] 23.4000000 [45,] 24.0000000 [46,] 24.2000000 [47,] 24.6000000 [48,] 25.0000000 [49,] 25.4000000 [50,] 25.6000000 [51,] 26.0000000 [52,] 26.2000000 [53,] 26.4000000 [54,] 27.0000000 [55,] 27.2000000 [56,] 27.6000000 [57,] 28.2000000 [58,] 28.4000000 [59,] 28.6000000 [60,] 29.4000000 [61,] 30.2000000 [62,] 31.0000000 [63,] 31.2000000 [64,] 32.0000000 [65,] 32.8000000 [66,] 33.4000000 [67,] 33.8000000 [68,] 34.4000000 [69,] 34.8000000 [70,] 35.2000000 [71,] 35.4000000 [72,] 35.6000000 [73,] 36.2000000 [74,] 38.0000000 [75,] 39.2000000 [76,] 39.4000000 [77,] 40.0000000 [78,] 40.4000000 [79,] 41.6000000 [80,] 42.4000000 [81,] 42.8000000 [82,] 43.0000000 [83,] 44.0000000 [84,] 44.4000000 [85,] 45.0000000 [86,] 46.6000000 [87,] 47.8000000 [88,] 48.8000000 [89,] 50.6000000 [90,] 52.0000000 [91,] 53.2000000 [92,] 55.0000000 [93,] 55.4000000 [94,] 57.6000000 $results[[7]] [1] FALSE $results[[8]] [1] "Linear" $results[[9]] [1] -0.5230387 10.1060925 10.1060925 10.3616667 21.4083516 21.4083516 $results[[10]] [1] 1 $newinfo $newinfo[[1]] [1] TRUE $newinfo[[2]] [1] 5 $newinfo[[3]] [1] 29 30 32 $newinfo[[4]] [1] 29 30 32 $newinfo[[5]] [1] 3.9345677 -2.8926923 14.7250000 3.9345677 -0.5230387 -2.8926923 $newinfo[[6]] [1] 1 4 1 1 1 4 > > > > > cleanEx(); ..nameEx <- "AdaptPred" > > ### * AdaptPred > > flush(stderr()); flush(stdout()) > > ### Name: AdaptPred > ### Title: AdaptPred > ### Aliases: AdaptPred > ### Keywords: regression > > ### ** Examples > > > # > # Generate some doppler data: 500 observations. > # > tx <- runif(500) > ty<-make.signal2("doppler",x=tx) > # > # Compute the neighbours of point 173 (2 neighbours on each side) > # > out<-getnbrs(tx,173,order(tx),2,FALSE) > > # > # Perform the adaptive lifting step > # > ap<-AdaptPred(order(tx),tx,ty,out$nbrs,173,FALSE,2) > # > ap[[10]] [1] 3 > > #this corresponds to no intercept and highest order regression (Cubic)... > # > #and let's check it... > ap[[7]] [1] FALSE > > ap[[8]] [1] "Cubic" > > > > > > cleanEx(); ..nameEx <- "AdaptPredmp" > > ### * AdaptPredmp > > flush(stderr()); flush(stdout()) > > ### Name: AdaptPredmp > ### Title: AdaptPredmp > ### Aliases: AdaptPredmp > ### Keywords: regression > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > AdaptPredmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) [[1]] [,1] [1,] 16.8 [2,] 16.8 [3,] 16.8 [4,] 17.8 [5,] 17.8 [[2]] [,1] [,2] [,3] [,4] [,5] [1,] 0.01134828 0.01134828 0.01134828 0.01202378 0.01202378 [[3]] [,1] [1,] -5.168157 [[4]] [,1] [,2] [,3] [,4] [,5] [1,] 0.1997298 0.1997298 0.1997298 0.2116185 0.2116185 [[5]] [1] -90.95957 [[6]] [,1] [1,] 2.400000 [2,] 2.600000 [3,] 3.200000 [4,] 3.600000 [5,] 4.000000 [6,] 6.200000 [7,] 6.600000 [8,] 6.800000 [9,] 7.800000 [10,] 8.200000 [11,] 8.800000 [12,] 9.600000 [13,] 10.000000 [14,] 10.200000 [15,] 10.600000 [16,] 11.000000 [17,] 11.400000 [18,] 13.200000 [19,] 13.600000 [20,] 13.800000 [21,] 14.600000 [22,] 14.800000 [23,] 15.400000 [24,] 15.600000 [25,] 15.800000 [26,] 16.000000 [27,] 16.200000 [28,] 16.400000 [29,] 16.600000 [30,] 16.800000 [31,] 3.934568 [32,] 17.800000 [33,] 18.600000 [34,] 19.200000 [35,] 19.400000 [36,] 19.600000 [37,] 20.200000 [38,] 20.400000 [39,] 21.200000 [40,] 21.400000 [41,] 21.800000 [42,] 22.000000 [43,] 23.200000 [44,] 23.400000 [45,] 24.000000 [46,] 24.200000 [47,] 24.600000 [48,] 25.000000 [49,] 25.400000 [50,] 25.600000 [51,] 26.000000 [52,] 26.200000 [53,] 26.400000 [54,] 27.000000 [55,] 27.200000 [56,] 27.600000 [57,] 28.200000 [58,] 28.400000 [59,] 28.600000 [60,] 29.400000 [61,] 30.200000 [62,] 31.000000 [63,] 31.200000 [64,] 32.000000 [65,] 32.800000 [66,] 33.400000 [67,] 33.800000 [68,] 34.400000 [69,] 34.800000 [70,] 35.200000 [71,] 35.400000 [72,] 35.600000 [73,] 36.200000 [74,] 38.000000 [75,] 39.200000 [76,] 39.400000 [77,] 40.000000 [78,] 40.400000 [79,] 41.600000 [80,] 42.400000 [81,] 42.800000 [82,] 43.000000 [83,] 44.000000 [84,] 44.400000 [85,] 45.000000 [86,] 46.600000 [87,] 47.800000 [88,] 48.800000 [89,] 50.600000 [90,] 52.000000 [91,] 53.200000 [92,] 55.000000 [93,] 55.400000 [94,] 57.600000 [[7]] [1] FALSE [[8]] [1] "Linear" [[9]] [1] 3.934568 3.934568 3.934568 10.361667 10.361667 10.361667 [[10]] [1] 1 > > > > > cleanEx(); ..nameEx <- "Amatdual" > > ### * Amatdual > > flush(stderr()); flush(stdout()) > > ### Name: Amatdual > ### Title: Amatdual > ### Aliases: Amatdual > ### Keywords: array > > ### ** Examples > > # > x<-runif(256) > y<-make.signal2("doppler",x=x) > a<-fwtnp(x,y,LocalPred=AdaptNeigh,neighbours=2) > # > A<-Amatdual(90,a$pointsin,a$removelist,a$neighbrs[[90]],a$gamlist[[90]],a$alphalist[[90]]) > # > A$Adual [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [1,] 1 0 0 0 0 0.00000000 0 0 0 0 0 0 [2,] 0 1 0 0 0 0.00000000 0 0 0 0 0 0 [3,] 0 0 1 0 0 0.00000000 0 0 0 0 0 0 [4,] 0 0 0 1 0 0.00000000 0 0 0 0 0 0 [5,] 0 0 0 0 1 0.00000000 0 0 0 0 0 0 [6,] 0 0 0 0 0 0.94892281 0 0 0 0 0 0 [7,] 0 0 0 0 0 0.00000000 1 0 0 0 0 0 [8,] 0 0 0 0 0 0.00000000 0 1 0 0 0 0 [9,] 0 0 0 0 0 0.00000000 0 0 1 0 0 0 [10,] 0 0 0 0 0 0.00000000 0 0 0 1 0 0 [11,] 0 0 0 0 0 0.00000000 0 0 0 0 1 0 [12,] 0 0 0 0 0 0.00000000 0 0 0 0 0 1 [13,] 0 0 0 0 0 0.00000000 0 0 0 0 0 0 [14,] 0 0 0 0 0 0.00000000 0 0 0 0 0 0 [15,] 0 0 0 0 0 0.00000000 0 0 0 0 0 0 [16,] 0 0 0 0 0 0.00000000 0 0 0 0 0 0 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[20,] 0 0.00000000 [21,] 0 0.00000000 [22,] 0 0.00000000 [23,] 0 0.00000000 [24,] 0 0.00000000 [25,] 0 0.00000000 [26,] 0 0.00000000 [27,] 0 0.00000000 [28,] 0 0.00000000 [29,] 0 0.00000000 [30,] 0 0.00000000 [31,] 0 0.00000000 [32,] 0 0.00000000 [33,] 0 0.00000000 [34,] 0 0.00000000 [35,] 0 0.00000000 [36,] 0 0.00000000 [37,] 0 0.00000000 [38,] 0 0.00000000 [39,] 0 0.00000000 [40,] 0 0.00000000 [41,] 0 0.00000000 [42,] 0 0.00000000 [43,] 0 0.00000000 [44,] 0 0.00000000 [45,] 0 0.00000000 [46,] 0 0.00000000 [47,] 0 0.00000000 [48,] 0 0.00000000 [49,] 0 0.00000000 [50,] 0 0.00000000 [51,] 0 0.00000000 [52,] 0 0.14305907 [53,] 0 0.00000000 [54,] 0 0.00000000 [55,] 0 0.00000000 [56,] 0 0.00000000 [57,] 0 0.00000000 [58,] 0 0.00000000 [59,] 0 0.00000000 [60,] 0 0.00000000 [61,] 0 0.00000000 [62,] 0 0.00000000 [63,] 0 0.00000000 [64,] 0 0.00000000 [65,] 0 0.00000000 [66,] 0 0.00000000 [67,] 0 0.00000000 [68,] 0 0.00000000 [69,] 0 0.00000000 [70,] 0 0.00000000 [71,] 0 0.00000000 [72,] 0 0.00000000 [73,] 0 0.00000000 [74,] 0 0.00000000 [75,] 0 0.00000000 [76,] 0 0.00000000 [77,] 0 0.00000000 [78,] 0 0.00000000 [79,] 0 0.00000000 [80,] 0 0.00000000 [81,] 0 0.00000000 [82,] 0 0.00000000 [83,] 0 0.00000000 [84,] 0 0.00000000 [85,] 0 0.00000000 [86,] 0 0.00000000 [87,] 0 0.00000000 [88,] 0 0.00000000 [89,] 0 0.00000000 [90,] 0 0.00000000 [91,] 0 0.00000000 [92,] 0 0.00000000 [93,] 0 0.00000000 [94,] 0 0.00000000 [95,] 0 0.00000000 [96,] 0 0.00000000 [97,] 0 0.00000000 [98,] 0 0.00000000 [99,] 0 0.00000000 [100,] 0 0.00000000 [101,] 0 0.00000000 [102,] 0 0.00000000 [103,] 0 0.00000000 [104,] 0 0.00000000 [105,] 0 0.00000000 [106,] 0 0.00000000 [107,] 0 0.00000000 [108,] 0 0.09920097 [109,] 0 0.09192251 [110,] 0 0.00000000 [111,] 0 0.00000000 [112,] 0 0.00000000 [113,] 0 0.00000000 [114,] 0 0.00000000 [115,] 0 0.00000000 [116,] 0 0.00000000 [117,] 0 0.00000000 [118,] 0 0.00000000 [119,] 0 0.00000000 [120,] 0 0.00000000 [121,] 0 0.00000000 [122,] 0 0.00000000 [123,] 0 0.00000000 [124,] 0 0.00000000 [125,] 0 0.00000000 [126,] 0 0.00000000 [127,] 0 0.00000000 [128,] 0 0.00000000 [129,] 0 0.00000000 [130,] 0 0.00000000 [131,] 0 0.00000000 [132,] 0 0.00000000 [133,] 0 0.00000000 [134,] 0 0.00000000 [135,] 0 0.00000000 [136,] 0 0.00000000 [137,] 0 0.00000000 [138,] 0 0.00000000 [139,] 0 0.00000000 [140,] 0 0.00000000 [141,] 0 0.00000000 [142,] 0 0.00000000 [143,] 0 0.00000000 [144,] 0 0.00000000 [145,] 0 0.00000000 [146,] 0 0.00000000 [147,] 0 0.00000000 [148,] 0 0.00000000 [149,] 0 0.00000000 [150,] 0 0.00000000 [151,] 0 0.00000000 [152,] 0 0.00000000 [153,] 0 0.00000000 [154,] 0 0.00000000 [155,] 0 0.00000000 [156,] 0 0.00000000 [157,] 0 0.00000000 [158,] 0 0.00000000 [159,] 0 0.00000000 [160,] 0 0.00000000 [161,] 0 0.00000000 [162,] 0 0.00000000 [163,] 0 0.00000000 [164,] 0 0.00000000 [165,] 0 0.00000000 [166,] 1 0.00000000 [167,] 0 1.00000000 > # > #the 90th refinement matrix for the transform above. > # > > > > cleanEx(); ..nameEx <- "CubicPred" > > ### * CubicPred > > flush(stderr()); flush(stdout()) > > ### Name: CubicPred > ### Title: CubicPred > ### Aliases: CubicPred > ### Keywords: regression > > ### ** Examples > > # > # Generate some doppler data: 500 observations. > # > tx <- runif(500) > ty<-make.signal2("doppler",x=tx) > # > # Compute the neighbours of point 173 (2 neighbours on each side) > # > out<-getnbrs(tx,173,order(tx),2,FALSE) > > # > # Perform cubic prediction based on the neighbours (without intercept) > # > cp<-CubicPred(order(tx),tx,ty,out$nbrs,173,FALSE,2) > # > cp$bhat [,1] [1,] 1.3345166 [2,] 0.8721676 [3,] -2.3695632 > > # > #the coefficients which define the cubic regression curve > # > cp$pred [,1] [1,] 0.294859 > > # > #the predicted value from the regression curve > # > > > > cleanEx(); ..nameEx <- "CubicPredmp" > > ### * CubicPredmp > > flush(stderr()); flush(stdout()) > > ### Name: CubicPredmp > ### Title: CubicPredmp > ### Aliases: CubicPredmp > ### Keywords: regression > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > CubicPredmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) $Xneighbours [,1] [,2] [1,] 1 16.8 [2,] 1 16.8 [3,] 1 16.8 [4,] 1 17.8 [5,] 1 17.8 $mm [,1] [,2] [,3] [,4] [,5] [1,] 5.9333333 5.9333333 5.9333333 -8.4 -8.4 [2,] -0.3333333 -0.3333333 -0.3333333 0.5 0.5 $bhat [,1] [1,] 286.58667 [2,] -21.81667 $weights [,1] [,2] [,3] [,4] [,5] [1,] 0.06666667 0.06666667 0.06666667 0.4 0.4 $pred [,1] [1,] -97.38667 $coeff [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 2.4 2.6 3.2 3.6 4 6.2 6.6 6.8 7.8 8.2 8.8 9.6 10 10.2 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 10.6 11 11.4 13.2 13.6 13.8 14.6 14.8 15.4 15.6 15.8 16 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [1,] 16.2 16.4 16.6 16.8 17.6 17.8 18.6 19.2 19.4 19.6 20.2 20.4 [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 21.2 21.4 21.8 22 23.2 23.4 24 24.2 24.6 25 25.4 25.6 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 26 26.2 26.4 27 27.2 27.6 28.2 28.4 28.6 29.4 30.2 31 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [1,] 31.2 32 32.8 33.4 33.8 34.4 34.8 35.2 35.4 35.6 36.2 38 [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [1,] 39.2 39.4 40 40.4 41.6 42.4 42.8 43 44 44.4 45 46.6 [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [1,] 47.8 48.8 50.6 52 53.2 55 55.4 57.6 > > > > > cleanEx(); ..nameEx <- "LinearPred" > > ### * LinearPred > > flush(stderr()); flush(stdout()) > > ### Name: LinearPred > ### Title: LinearPred > ### Aliases: LinearPred > ### Keywords: regression > > ### ** Examples > > # > # Generate some doppler data: 500 observations. > # > tx <- runif(500) > ty<-make.signal2("doppler",x=tx) > # > # Compute the neighbours of point 173 (2 neighbours on each side) > # > out<-getnbrs(tx,173,order(tx),2,FALSE) > # > # Perform linear regression based on the neighbours (without intercept) > # > lp<-LinearPred(order(tx),tx,ty,out$nbrs,173,FALSE,2) > # > # > lp $Xneighbours [,1] [1,] 0.8540015 [2,] 0.8550824 [3,] 0.8612095 [4,] 0.8636306 $mm [,1] [,2] [,3] [,4] [1,] 0.2896861 0.2900527 0.2921311 0.2929524 $bhat [,1] [1,] 0.3367515 $weights [,1] [,2] [,3] [,4] [1,] 0.2480094 0.2483233 0.2501027 0.2508058 $pred [,1] [1,] 0.2883036 $coeff [1] 0.389676156 0.038162996 -0.454949700 0.163483601 0.353944329 [6] 0.188373711 0.078278444 0.067552110 -0.138060426 0.150036048 [11] 0.241738615 -0.285378102 0.211424401 0.237585432 0.413109395 [16] -0.248788699 0.332251393 0.004371709 0.173967031 0.413011670 [21] 0.099974852 0.014008939 0.010710316 -0.039403986 0.411435343 [26] 0.266971335 -0.045010632 0.211395628 0.261649335 -0.440443718 [31] -0.083164567 -0.327401735 -0.207810975 0.131740294 0.356970817 [36] 0.112928141 0.403941561 -0.248665506 0.349687078 0.485360596 [41] 0.368564828 -0.018990126 0.411333760 -0.496417441 -0.462643623 [46] 0.407758431 0.137147417 -0.026536394 0.370555708 0.238086648 [51] -0.031144423 0.282625358 0.403604261 -0.162716654 -0.244122072 [56] 0.046821578 -0.345464776 -0.410534799 0.074853021 0.468658502 [61] 0.151834351 0.156548796 0.190994510 -0.470911786 0.005579771 [66] 0.236983134 -0.042056176 0.412232594 -0.250266047 0.247446419 [71] -0.449036612 0.332690611 -0.379585114 -0.469731606 -0.016109301 [76] 0.204236092 0.274953969 0.319275365 0.413034456 0.047148399 [81] 0.428992031 0.315936375 0.424218128 -0.447904752 0.407034531 [86] 0.332847982 0.311211084 0.217133369 -0.141802739 0.145494706 [91] -0.302444458 -0.180349344 -0.050230800 0.245039198 0.412690907 [96] 0.401082104 0.234625293 0.481821854 0.384534280 -0.295281960 [101] 0.030017796 -0.291007627 0.436950767 0.003755753 -0.108731050 [106] -0.027673085 -0.266798838 -0.037026696 0.124600736 -0.332079366 [111] 0.022174213 0.369415156 -0.234944410 0.449204707 0.339687551 [116] -0.090276288 0.325877976 -0.240959433 0.330431796 -0.064690221 [121] 0.004426597 -0.228358420 -0.108999913 -0.359504391 0.405091002 [126] 0.250104592 -0.362038091 0.188541551 -0.417268264 -0.349431269 [131] -0.447765832 0.265730818 0.182884818 -0.046888867 0.113915215 [136] -0.335934801 -0.486755686 -0.447950054 0.010947985 -0.335468843 [141] 0.190492160 -0.315761083 -0.319610631 0.240828502 0.363763798 [146] 0.264742435 -0.325972621 0.395875776 -0.302843303 0.274447289 [151] -0.233898604 -0.492552626 -0.465256462 0.258576655 -0.274349832 [156] -0.114531441 -0.462320631 0.178774348 0.428995100 -0.007810746 [161] 0.340943826 0.196807553 0.330915735 0.412397578 0.233947370 [166] 0.489518689 0.241643804 -0.465788924 0.349727277 -0.457194064 [171] -0.129351351 0.330174218 0.294862332 0.336293523 0.181393263 [176] 0.195907261 -0.036900900 0.387381902 -0.293012569 0.176425172 [181] 0.153535592 0.315030369 0.218997770 -0.300029384 0.243034211 [186] 0.250130321 0.407818911 0.351780499 0.080280227 -0.497258144 [191] 0.313336863 0.305244846 -0.075830107 0.116981204 0.366096998 [196] -0.377217558 -0.092591033 0.330405675 -0.370561969 0.411368124 [201] 0.414571746 -0.224751875 -0.399587175 0.428030547 -0.100803169 [206] -0.410198484 -0.483050887 -0.257350686 0.192691966 0.333212588 [211] 0.045735374 -0.012382119 0.410968085 0.071464517 0.372477896 [216] -0.195127609 -0.002703021 0.060724016 0.059992695 -0.443070179 [221] 0.338518504 -0.265330185 -0.421184955 -0.354410534 0.124852901 [226] -0.360520352 0.226652022 -0.138954397 0.492887649 0.299909017 [231] -0.373047247 -0.328811974 0.079990397 -0.122611421 0.320410156 [236] 0.223816014 0.222810143 -0.495006223 0.459353582 0.263105442 [241] -0.154062154 -0.434274516 0.158069003 0.104709451 0.492133570 [246] 0.061624672 0.463724422 -0.339195541 0.178907467 0.081908015 [251] 0.410322244 0.104034782 0.051964801 -0.303473687 -0.116197718 [256] -0.201233501 -0.072142815 -0.256515028 0.051396729 0.100006029 [261] -0.438683868 -0.358740681 0.427398985 0.408939444 0.279255233 [266] -0.248800892 0.217888635 0.404984893 -0.196098225 -0.367735438 [271] 0.188871243 -0.187227650 0.200883073 -0.364541044 0.033311104 [276] 0.339051281 0.214635391 -0.097900284 -0.027526570 0.413237120 [281] 0.001674801 -0.453357257 0.234714297 0.054888392 -0.209575485 [286] -0.140229610 -0.414209093 0.257652239 -0.337775154 -0.302310547 [291] -0.398726068 -0.328378312 0.015074714 -0.457795713 -0.329908270 [296] 0.183641202 0.072494134 0.316573445 0.062326719 0.112431262 [301] 0.142492167 0.293005228 -0.198145606 0.161709979 0.092726958 [306] 0.005042891 -0.291845991 0.380743992 -0.189671670 0.427884004 [311] 0.014802792 0.361936315 0.328581845 0.339080937 0.411339386 [316] -0.222494921 -0.178646683 -0.280467220 0.032595224 -0.167487358 [321] 0.090459566 -0.280311560 0.462826212 0.001472611 0.297355158 [326] 0.060349586 0.382438125 0.411639911 0.418385634 0.410418898 [331] 0.009632244 0.156497127 0.418869869 0.382694969 0.266507063 [336] -0.114037048 0.365430389 0.174087718 -0.417829474 0.031091110 [341] 0.006464962 -0.288689926 -0.492911511 0.240575988 0.155865822 [346] 0.430672176 0.069210877 -0.375300378 -0.014655142 0.354142970 [351] -0.129125969 0.241744408 0.410595350 0.492668084 0.015750455 [356] 0.371324147 0.314078241 -0.100885498 0.218977722 0.036857103 [361] -0.182589753 -0.470364538 0.336968159 0.435068501 -0.395146544 [366] 0.338396983 -0.222629224 0.280500515 0.264507052 0.132956174 [371] 0.412608952 -0.484300235 0.114222441 -0.420808140 -0.315177448 [376] 0.490785919 -0.470084335 0.273799229 -0.264150535 -0.205546155 [381] 0.458893173 -0.479777580 0.039021429 0.018924294 -0.415354519 [386] -0.273756509 0.401545097 0.348672804 0.149812886 0.042398800 [391] 0.319522378 0.163525497 0.121859766 -0.330674423 0.256889447 [396] -0.359723463 0.061604781 -0.266644056 -0.315109058 0.332295630 [401] 0.055827030 0.080531941 0.058747646 0.189756767 0.080338925 [406] 0.349632940 0.006443095 0.412067356 0.092338218 0.087594938 [411] 0.002964970 -0.223671655 0.397121184 0.405072452 0.292321223 [416] -0.009846380 -0.219032508 -0.189908728 0.248225838 0.365338022 [421] 0.428592418 -0.386512938 0.080892771 0.137637094 -0.403893856 [426] 0.463223694 -0.278803585 0.104803669 0.334932773 0.236977401 [431] 0.097716397 -0.116510715 0.153008048 -0.485776511 -0.303692527 [436] 0.396414953 0.384925120 0.218621315 -0.084191879 0.133124858 [441] 0.035109269 0.147297641 -0.278063204 -0.265287202 0.370351083 [446] 0.484953535 -0.062265905 0.082870219 0.090042997 -0.255173496 [451] 0.040669159 0.366175862 0.004915800 -0.373590133 0.106188737 [456] 0.049361658 -0.435053633 0.008808316 -0.302235149 0.184252851 [461] -0.113264849 -0.004752858 0.043059851 0.375544133 0.306655295 [466] 0.091730310 0.326722042 -0.162324271 -0.014168073 0.226751155 [471] 0.169486831 0.057275829 0.316021576 0.399056412 0.321587710 [476] -0.290913871 0.276699220 0.419685364 -0.250397045 0.234442429 [481] 0.409341812 0.234326413 0.310481168 0.073821356 -0.388257875 [486] -0.290550255 0.270192205 0.042790210 0.241708972 -0.326064687 [491] 0.059436959 0.090146240 0.172002177 -0.219572455 0.416750217 [496] 0.185027861 0.433243235 -0.400889612 0.317776524 -0.022372925 > # > #the regression curve details > > > > > cleanEx(); ..nameEx <- "LinearPredmp" > > ### * LinearPredmp > > flush(stderr()); flush(stdout()) > > ### Name: LinearPredmp > ### Title: LinearPredmp > ### Aliases: LinearPredmp > ### Keywords: regression > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > LinearPredmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) $Xneighbours [,1] [,2] [1,] 1 16.8 [2,] 1 16.8 [3,] 1 16.8 [4,] 1 17.8 [5,] 1 17.8 $mm [,1] [,2] [,3] [,4] [,5] [1,] 5.9333333 5.9333333 5.9333333 -8.4 -8.4 [2,] -0.3333333 -0.3333333 -0.3333333 0.5 0.5 $bhat [,1] [1,] 286.58667 [2,] -21.81667 $weights [,1] [,2] [,3] [,4] [,5] [1,] 0.06666667 0.06666667 0.06666667 0.4 0.4 $pred [,1] [1,] -97.38667 $coeff [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 2.4 2.6 3.2 3.6 4 6.2 6.6 6.8 7.8 8.2 8.8 9.6 10 10.2 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 10.6 11 11.4 13.2 13.6 13.8 14.6 14.8 15.4 15.6 15.8 16 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [1,] 16.2 16.4 16.6 16.8 17.6 17.8 18.6 19.2 19.4 19.6 20.2 20.4 [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 21.2 21.4 21.8 22 23.2 23.4 24 24.2 24.6 25 25.4 25.6 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 26 26.2 26.4 27 27.2 27.6 28.2 28.4 28.6 29.4 30.2 31 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [1,] 31.2 32 32.8 33.4 33.8 34.4 34.8 35.2 35.4 35.6 36.2 38 [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [1,] 39.2 39.4 40 40.4 41.6 42.4 42.8 43 44 44.4 45 46.6 [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [1,] 47.8 48.8 50.6 52 53.2 55 55.4 57.6 > > > > > cleanEx(); ..nameEx <- "PointsUpdate" > > ### * PointsUpdate > > flush(stderr()); flush(stdout()) > > ### Name: PointsUpdate > ### Title: PointsUpdate > ### Aliases: PointsUpdate > ### Keywords: arith > > ### ** Examples > > # > # Generate some blocks data: 100 observations. > # > x <- runif(100) > y <-make.signal2("blocks",x=x) > # > #find initial interval lengths... > # > I<-intervals(x,"reflect") > l<-lengthintervals(x,I$intervals,neighbours=2,closest=FALSE) > lengths<-l$lengths > # > #perform prediction step... > p<-AdaptNeigh(order(x),x,y,32,5,TRUE,2) > # > # > u<-PointsUpdate(x,p$results[[6]],p$newinfo[[3]],p$newinfo[[4]],5,order(x),p$results[[4]], + lengths,"add") > # > #and here are the updated coefficients... > u$coeff [1] 3.000000e+00 3.000000e+00 9.000000e-01 -8.881784e-16 0.000000e+00 [6] -8.881784e-16 -8.881784e-16 5.200000e+00 9.000000e-01 0.000000e+00 [11] 2.000000e+00 2.000000e+00 5.200000e+00 3.000000e+00 2.100000e+00 [16] 9.000000e-01 5.200000e+00 -8.881784e-16 3.000000e+00 2.100000e+00 [21] -8.881784e-16 2.000000e+00 5.200000e+00 4.000000e+00 3.000000e+00 [26] 3.000000e+00 0.000000e+00 3.000000e+00 -8.881784e-16 3.000000e+00 [31] 9.000000e-01 9.000000e-01 9.000000e-01 2.000000e+00 -8.881784e-16 [36] 5.200000e+00 4.200000e+00 4.000000e+00 5.200000e+00 -1.200000e+00 [41] -8.881784e-16 9.000000e-01 4.200000e+00 9.000000e-01 9.000000e-01 [46] 4.200000e+00 0.000000e+00 9.000000e-01 5.200000e+00 5.200000e+00 [51] 9.000000e-01 -8.881784e-16 -1.200000e+00 -2.000000e+00 0.000000e+00 [56] 0.000000e+00 3.000000e+00 9.000000e-01 5.200000e+00 -1.200000e+00 [61] -8.881784e-16 3.000000e+00 9.000000e-01 3.000000e+00 5.200000e+00 [66] 3.000000e+00 9.000000e-01 2.100000e+00 0.000000e+00 -8.881784e-16 [71] 3.000000e+00 -8.881784e-16 3.000000e+00 3.000000e+00 9.000000e-01 [76] -8.881784e-16 -8.881784e-16 3.000000e+00 2.100000e+00 -8.881784e-16 [81] -1.200000e+00 5.200000e+00 3.000000e+00 3.000000e+00 5.200000e+00 [86] 2.000000e+00 5.200000e+00 4.000000e+00 -2.000000e+00 -1.000000e+00 [91] -2.000000e+00 0.000000e+00 9.000000e-01 -8.881784e-16 2.100000e+00 [96] 4.200000e+00 9.000000e-01 -1.200000e+00 -8.881784e-16 9.000000e-01 > # > > > > > cleanEx(); ..nameEx <- "PointsUpdatemp" > > ### * PointsUpdatemp > > flush(stderr()); flush(stdout()) > > ### Name: PointsUpdatemp > ### Title: PointsUpdatemp > ### Aliases: PointsUpdatemp > ### Keywords: arith > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > I<-intervals(X,"reflect") > l<-lengthintervals(X,I$intervals,neighbours=2,closest=TRUE) > lengths<-l$lengths > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > p<-AdaptNeighmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) > > nbrs<-p$newinfo[[3]] > nbrs [1] 29 30 32 > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > newnbrs [1] 29 30 30 30 32 32 > > coefflist[[31]]<-p$results[[6]][31] > > u<-PointsUpdatemp(X,coefflist,p$newinfo[[3]],newnbrs,p$newinfo[[4]],31,order(X),p$results[[4]] + ,lengths,"add") > # > #and here is the updated coefficient list... > u$coeff [[1]] [1] 0 [[2]] [1] -1.3 [[3]] [1] -2.7 [[4]] [1] 0 [[5]] [1] -2.7 [[6]] [1] -2.7 [[7]] [1] -2.7 [[8]] [1] -1.3 [[9]] [1] -2.7 [[10]] [1] -2.7 [[11]] [1] -1.3 -2.7 [[12]] [1] -2.7 [[13]] [1] -2.7 [[14]] [1] -5.4 [[15]] [1] -2.7 [[16]] [1] -5.4 [[17]] [1] 0 [[18]] [1] -2.7 [[19]] [1] -2.7 [[20]] [1] 0 [[21]] [1] -13.3 -5.4 -5.4 -9.3 -16.0 -22.8 [[22]] [1] -2.7 [[23]] [1] -22.8 -32.1 -53.5 -54.9 [[24]] [1] -40.2 -21.5 [[25]] [1] -21.5 -50.8 [[26]] [1] -42.9 -26.8 [[27]] [1] -21.5 -50.8 -61.7 [[28]] [1] -5.4 -80.4 [[29]] [1] -59.09635 [[30]] [1] -71.19876 -91.29876 -77.89876 [[31]] [1] -0.5230387 [[32]] [1] -99.30047 -104.60047 [[33]] [1] -112.5 -50.8 [[34]] [1] -123.1 [[35]] [1] -85.6 -72.3 [[36]] [1] -127.2 [[37]] [1] -123.1 [[38]] [1] -117.9 [[39]] [1] -134 [[40]] [1] -101.9 [[41]] [1] -108.4 [[42]] [1] -123.1 [[43]] [1] -123.1 [[44]] [1] -128.5 [[45]] [1] -112.5 [[46]] [1] -95.1 -81.8 [[47]] [1] -53.5 [[48]] [1] -64.4 -57.6 [[49]] [1] -72.3 -44.3 [[50]] [1] -26.8 [[51]] [1] -5.4 [[52]] [1] -107.1 -21.5 [[53]] [1] -65.6 [[54]] [1] -16 [[55]] [1] -45.6 -24.2 9.5 [[56]] [1] 4 [[57]] [1] 12 [[58]] [1] -21.5 37.5 [[59]] [1] 46.9 [[60]] [1] -17.4 [[61]] [1] 36.2 [[62]] [1] 75 [[63]] [1] 8.1 [[64]] [1] 54.9 48.2 [[65]] [1] 46.9 [[66]] [1] 16 [[67]] [1] 45.6 [[68]] [1] 1.3 [[69]] [1] 75 [[70]] [1] -16.0 -54.9 [[71]] [1] 69.6 [[72]] [1] 34.8 32.1 [[73]] [1] -37.5 22.8 [[74]] [1] 46.9 10.7 [[75]] [1] 5.4 [[76]] [1] -1.3 [[77]] [1] -21.5 [[78]] [1] -13.3 [[79]] [1] 30.8 -10.7 [[80]] [1] 29.4 [[81]] [1] 0.0 -10.7 [[82]] [1] 14.7 [[83]] [1] -1.3 [[84]] [1] 0 [[85]] [1] 10.7 [[86]] [1] 10.7 [[87]] [1] -26.8 -14.7 [[88]] [1] -13.3 [[89]] [1] 0 [[90]] [1] 10.7 [[91]] [1] -14.7 [[92]] [1] -2.7 10.7 [[93]] [1] -2.7 [[94]] [1] 10.7 > > > > cleanEx(); ..nameEx <- "QuadPred" > > ### * QuadPred > > flush(stderr()); flush(stdout()) > > ### Name: QuadPred > ### Title: QuadPred > ### Aliases: QuadPred > ### Keywords: regression > > ### ** Examples > > # > # Generate some doppler data: 500 observations. > # > tx <- runif(500) > ty<-make.signal2("doppler",x=tx) > # > # Compute the neighbours of point 173 (2 neighbours on each side) > # > out<-getnbrs(tx,173,order(tx),2,FALSE) > # > # Perform quadratic prediction based on the neighbours (without intercept) > # > qp<-QuadPred(order(tx),tx,ty,out$nbrs,173,FALSE,2) > # > # > qp[3:5] $bhat [,1] [1,] 3.081697 [2,] -3.197303 $weights [,1] [,2] [,3] [,4] [1,] 0.4110259 0.3724951 0.1522062 0.06428614 $pred [,1] [1,] 0.294839 > # > #the regression curve details > > > > cleanEx(); ..nameEx <- "QuadPredmp" > > ### * QuadPredmp > > flush(stderr()); flush(stdout()) > > ### Name: QuadPredmp > ### Title: QuadPredmp > ### Aliases: QuadPredmp > ### Keywords: regression > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > QuadPredmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) $Xneighbours [,1] [,2] [1,] 1 16.8 [2,] 1 16.8 [3,] 1 16.8 [4,] 1 17.8 [5,] 1 17.8 $mm [,1] [,2] [,3] [,4] [,5] [1,] 5.9333333 5.9333333 5.9333333 -8.4 -8.4 [2,] -0.3333333 -0.3333333 -0.3333333 0.5 0.5 $bhat [,1] [1,] 286.58667 [2,] -21.81667 $weights [,1] [,2] [,3] [,4] [,5] [1,] 0.06666667 0.06666667 0.06666667 0.4 0.4 $pred [,1] [1,] -97.38667 $coeff [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 2.4 2.6 3.2 3.6 4 6.2 6.6 6.8 7.8 8.2 8.8 9.6 10 10.2 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 10.6 11 11.4 13.2 13.6 13.8 14.6 14.8 15.4 15.6 15.8 16 [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [1,] 16.2 16.4 16.6 16.8 17.6 17.8 18.6 19.2 19.4 19.6 20.2 20.4 [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 21.2 21.4 21.8 22 23.2 23.4 24 24.2 24.6 25 25.4 25.6 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [,61] [,62] [1,] 26 26.2 26.4 27 27.2 27.6 28.2 28.4 28.6 29.4 30.2 31 [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [,74] [1,] 31.2 32 32.8 33.4 33.8 34.4 34.8 35.2 35.4 35.6 36.2 38 [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84] [,85] [,86] [1,] 39.2 39.4 40 40.4 41.6 42.4 42.8 43 44 44.4 45 46.6 [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [1,] 47.8 48.8 50.6 52 53.2 55 55.4 57.6 > > > > > cleanEx(); ..nameEx <- "Rmatsolve" > > ### * Rmatsolve > > flush(stderr()); flush(stdout()) > > ### Name: Rmatsolve > ### Title: Rmatsolve > ### Aliases: Rmatsolve > ### Keywords: array > > ### ** Examples > > # > #create a 4x4 matrix > m<-matrix(runif(16),4,4) > > temp<-crossprod(m) > > #i.e. temp is t(m) > > inv<-Rmatsolve(temp) > > > > > cleanEx(); ..nameEx <- "UndoPointsUpdate" > > ### * UndoPointsUpdate > > flush(stderr()); flush(stdout()) > > ### Name: UndoPointsUpdate > ### Title: UndoPointsUpdate > ### Aliases: UndoPointsUpdate > ### Keywords: arith > > ### ** Examples > > # > # Generate some blocks data: 100 observations. > # > x <- runif(100) > y <-make.signal2("blocks",x=x) > # > #find initial interval lengths... > # > I<-intervals(x,"reflect") > l<-lengthintervals(x,I$intervals,neighbours=2,closest=FALSE) > lengths<-l$lengths > # > #perform prediction step... > p<-AdaptNeigh(order(x),x,y,32,5,TRUE,2) > # > # > u<-PointsUpdate(x,p$results[[6]],p$newinfo[[3]],p$newinfo[[4]],5,order(x),p$results[[4]], + lengths,"add") > # > p2<-setdiff(order(x),5) > a<-which(order(x)==5) > l2<-lengths[setdiff(1:100, a)] > # > #remove the lifted coefficient > # > #now undo the update step... > # > undo<-UndoPointsUpdate(x,u$coeff,p$newinfo[[3]],p$newinfo[[4]],5,a,99,p2,p$results[[4]],l2, + lengths[a]) > # > > > > > cleanEx(); ..nameEx <- "UndoPointsUpdatemp" > > ### * UndoPointsUpdatemp > > flush(stderr()); flush(stdout()) > > ### Name: UndoPointsUpdatemp > ### Title: UndoPointsUpdatemp > ### Aliases: UndoPointsUpdatemp > ### Keywords: arith > > ### ** Examples > > #read in data with multiple values... > > mcdata() > > short<-adjustx(times,accel,"mean") > X<-short$sepx > coeff<-short$sepx > g<-short$g > > coefflist<-list() > for (i in 1:length(g)){ + coefflist[[i]]<-accel[g[[i]]] + } > > I<-intervals(X,"reflect") > l<-lengthintervals(X,I$intervals,neighbours=2,closest=TRUE) > lengths<-l$lengths > > #work out neighbours of point to be removed (31) > > out<-getnbrs(X,31,order(X),2,TRUE) > nbrs<-out$n > > nbrs [1] 30 32 > > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > > #work out repeated neighbours using g... > newnbrs [1] 30 30 30 32 32 > > p<-AdaptNeighmp(order(X),X,coefflist,coeff,nbrs,newnbrs,31,TRUE,2,"ave",g) > > nbrs<-p$newinfo[[3]] > newnbrs<-NULL > for (i in 1:length(nbrs)){ + newnbrs<-c(newnbrs,rep(nbrs[i],times=length(g[[nbrs[i]]]))) + } > coefflist[[31]]<-p$results[[6]][31] > > u<-PointsUpdatemp(X,coefflist,p$newinfo[[3]],newnbrs,p$newinfo[[4]],31,order(X),p$results[[4]], + lengths,"add") > > p2<-setdiff(order(X),31) > a<-which(order(X)==31) > l2<-lengths[setdiff(1:length(X), a)] > # > #remove the lifted coefficient > # > #now undo the update step... > # > undo<-UndoPointsUpdatemp(X,coeff,newnbrs,p$newinfo[[3]],p$newinfo[[4]],31,a,length(X)-1,p2, + p$results[[4]],l2,lengths[a]) Warning: number of items to replace is not a multiple of replacement length > # > > > > > cleanEx(); ..nameEx <- "adjustx" > > ### * adjustx > > flush(stderr()); flush(stdout()) > > ### Name: adjustx > ### Title: adjustx > ### Aliases: adjustx > ### Keywords: manip arith > > ### ** Examples > > #read in the motorcycle crash data > # > mcdata() > > # > dim(mcdata) NULL > > #check data. > # > a<-adjustx(times,accel,"mean") > # > #note the repeated values in the original grid data > # > #display new data vectors > a$sepx [,1] [1,] 2.4 [2,] 2.6 [3,] 3.2 [4,] 3.6 [5,] 4.0 [6,] 6.2 [7,] 6.6 [8,] 6.8 [9,] 7.8 [10,] 8.2 [11,] 8.8 [12,] 9.6 [13,] 10.0 [14,] 10.2 [15,] 10.6 [16,] 11.0 [17,] 11.4 [18,] 13.2 [19,] 13.6 [20,] 13.8 [21,] 14.6 [22,] 14.8 [23,] 15.4 [24,] 15.6 [25,] 15.8 [26,] 16.0 [27,] 16.2 [28,] 16.4 [29,] 16.6 [30,] 16.8 [31,] 17.6 [32,] 17.8 [33,] 18.6 [34,] 19.2 [35,] 19.4 [36,] 19.6 [37,] 20.2 [38,] 20.4 [39,] 21.2 [40,] 21.4 [41,] 21.8 [42,] 22.0 [43,] 23.2 [44,] 23.4 [45,] 24.0 [46,] 24.2 [47,] 24.6 [48,] 25.0 [49,] 25.4 [50,] 25.6 [51,] 26.0 [52,] 26.2 [53,] 26.4 [54,] 27.0 [55,] 27.2 [56,] 27.6 [57,] 28.2 [58,] 28.4 [59,] 28.6 [60,] 29.4 [61,] 30.2 [62,] 31.0 [63,] 31.2 [64,] 32.0 [65,] 32.8 [66,] 33.4 [67,] 33.8 [68,] 34.4 [69,] 34.8 [70,] 35.2 [71,] 35.4 [72,] 35.6 [73,] 36.2 [74,] 38.0 [75,] 39.2 [76,] 39.4 [77,] 40.0 [78,] 40.4 [79,] 41.6 [80,] 42.4 [81,] 42.8 [82,] 43.0 [83,] 44.0 [84,] 44.4 [85,] 45.0 [86,] 46.6 [87,] 47.8 [88,] 48.8 [89,] 50.6 [90,] 52.0 [91,] 53.2 [92,] 55.0 [93,] 55.4 [94,] 57.6 > # > a$sepf [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 0 -1.3 -2.7 0 -2.7 -2.7 -2.7 -1.3 -2.7 -2.7 -2 -2.7 -2.7 -5.4 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [1,] -2.7 -5.4 0 -2.7 -2.7 0 -12.03333 -2.7 -40.825 -30.85 -36.15 [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [1,] -34.85 -44.66667 -42.9 -59 -79.93333 -87.025 -101.75 -81.65 -123.1 [,35] [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [1,] -78.95 -127.2 -123.1 -117.9 -134 -101.9 -108.4 -123.1 -123.1 -128.5 [,45] [,46] [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [1,] -112.5 -88.45 -53.5 -61 -58.3 -26.8 -5.4 -64.3 -65.6 -16 -20.1 4 [,57] [,58] [,59] [,60] [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [1,] 12 8 46.9 -17.4 36.2 75 8.1 51.55 46.9 16 45.6 1.3 [,69] [,70] [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 75 -35.45 69.6 33.45 -7.35 28.8 5.4 -1.3 -21.5 -13.3 10.05 29.4 [,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [1,] -5.35 14.7 -1.3 0 10.7 10.7 -20.75 -13.3 0 10.7 -14.7 4 [,93] [,94] [1,] -2.7 10.7 > # > #and now the new adjusted data has length 94. > # > > > > cleanEx(); ..nameEx <- "artlev1" > > ### * artlev1 > > flush(stderr()); flush(stdout()) > > ### Name: artlev1 > ### Title: artlev1 > ### Aliases: artlev1 > ### Keywords: arith > > ### ** Examples > > #create test signal data > # > x<-runif(100) > y<-make.signal2("blocks",x=x) > # > #perform forward transform... > # > out<-fwtnp(x,y,LocalPred=AdaptNeigh,neighbours=2) > # > al<-artlev1(out$lengthsremove,out$removelist) > # > # > # the indices of removelist split into levels: > al [[1]] [1] 48 20 51 14 86 98 65 95 54 26 82 71 70 74 67 59 37 77 42 1 11 60 19 43 29 [26] 17 10 15 78 30 4 31 64 33 6 41 83 46 23 88 68 89 27 66 99 53 97 38 93 [[2]] [1] 50 36 87 34 7 55 100 69 84 58 52 39 22 76 2 75 72 16 61 [20] 79 44 40 80 12 [[3]] [1] 90 62 91 85 8 96 9 18 47 28 57 24 [[4]] [1] 63 56 3 35 81 13 25 92 21 45 49 73 94 > # > > > > cleanEx(); ..nameEx <- "as.column" > > ### * as.column > > flush(stderr()); flush(stdout()) > > ### Name: as.column > ### Title: as.column > ### Aliases: as.column > ### Keywords: manip > > ### ** Examples > > vector<-1:8 > # > vector [1] 1 2 3 4 5 6 7 8 > # > #...vector has no dimension attributes > # > as.column(vector) [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 [5,] 5 [6,] 6 [7,] 7 [8,] 8 > # > #...gives output dimension of (8,1) > # > F<-matrix(c(6,2,2,10,6,17),3,2) > # > # > as.column(F) [,1] [,2] [1,] 6 10 [2,] 2 6 [3,] 2 17 > > # > #the function has no effect on F > # > F<-t(F) > F [,1] [,2] [,3] [1,] 6 2 2 [2,] 10 6 17 > #now has dimension (2,3)... > # > as.column(F) [,1] [,2] [1,] 6 10 [2,] 2 6 [3,] 2 17 > # > #the output is made to have more rows than columns > > > > > cleanEx(); ..nameEx <- "as.row" > > ### * as.row > > flush(stderr()); flush(stdout()) > > ### Name: as.row > ### Title: as.row > ### Aliases: as.row > ### Keywords: manip > > ### ** Examples > > X<-0:5 > # > X [1] 0 1 2 3 4 5 > # > as.row(X) [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0 1 2 3 4 5 > # > #puts input into row (matrix) > # > Y<-matrix(0:5,6,1) > # > Y [,1] [1,] 0 [2,] 1 [3,] 2 [4,] 3 [5,] 4 [6,] 5 > # > as.row(Y) [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0 1 2 3 4 5 > # > #input forced into a row. > # > > > > cleanEx(); ..nameEx <- "basisfns" > > ### * basisfns > > flush(stderr()); flush(stdout()) > > ### Name: basisfns > ### Title: basisfns > ### Aliases: basisfns > ### Keywords: graphs > > ### ** Examples > > #create test signal data > # > x<-runif(100) > y<-make.signal2("blocks",x=x) > # > #perform procedure... > # > a<-basisfns(x,y,AdaptNeigh,2,TRUE,TRUE,2,FALSE,c(1,14,15),FALSE) > # > #this produces plots of three basis functions all on one graph. > > > > cleanEx(); ..nameEx <- "condno" > > ### * condno > > flush(stderr()); flush(stdout()) > > ### Name: condno > ### Title: condno > ### Aliases: condno > ### Keywords: array algebra > > ### ** Examples > > #create test signal data > # > x<-runif(100) > y<-make.signal2("blocks",x=x) > # > a<-transmatdual(x,y,Pred=AdaptNeigh,neigh=2) > # > #computes the transition matrix for the specified options > # > W<-a$Wnew > # > condno(W,"F") [1] 698563 > # > condno(W,"l1") [1] 499227 > # > condno(W,"1") [1] 53.17046 > # > > > > cleanEx(); ..nameEx <- "denoise" > > ### * denoise > > flush(stderr()); flush(stdout()) > > ### Name: denoise > ### Title: denoise > ### Aliases: denoise > ### Keywords: smooth regression > > ### ** Examples > > x1<-runif(256) > y1<-make.signal2("doppler",x=x1) > n1<-rnorm(256,0,.1) > z1<-y1+n1 > # > est1<-denoise(x1,z1,AdaptNeigh,1,TRUE,TRUE,2) > sum(abs(y1-est1$fhat$coeff)) [1] 13.39469 > # > #the error between the true signal and the denoised version. > > > > > cleanEx(); ..nameEx <- "denoisehetero" > > ### * denoisehetero > > flush(stderr()); flush(stdout()) > > ### Name: denoisehetero > ### Title: denoisehetero > ### Aliases: denoisehetero > ### Keywords: smooth regression > > ### ** Examples > > x1<-runif(256) > y1<-make.signal2("doppler",x=x1) > n1<-rnorm(256,0,.1) > z1<-y1+n1 > # > est1<-denoisehetero(x1,z1,AdaptNeigh,1,TRUE,TRUE,2) [1] 256 [1] 2 > sum(abs(y1-est1$fhat$coeff)) [1] 23.26214 > # > #the error between the true signal and the denoised version. > > > > > cleanEx(); ..nameEx <- "denoiseheteromp" > > ### * denoiseheteromp > > flush(stderr()); flush(stdout()) > > ### Name: denoiseheteromp > ### Title: denoiseheteromp > ### Aliases: denoiseheteromp > ### Keywords: smooth regression > > ### ** Examples > > mcdata() > # > est1<-denoiseheteromp(times,accel,AdaptNeighmp,1,TRUE,TRUE,2,"median","ave") > # > #the estimate of the underlying curve. > > > > > cleanEx(); ..nameEx <- "denoiseheteroprop" > > ### * denoiseheteroprop > > flush(stderr()); flush(stdout()) > > ### Name: denoiseheteroprop > ### Title: denoiseheteroprop > ### Aliases: denoiseheteroprop > ### Keywords: smooth regression > > ### ** Examples > > x1<-runif(256) > y1<-make.signal2("doppler",x=x1) > n1<-rnorm(256,0,.1) > z1<-y1+n1 > gvec<-c(rep(.4,times=100),rep(.7,times=100),rep(.3,times=56)) > # > est1<-denoiseheteroprop(x1,z1,AdaptNeigh,1,TRUE,TRUE,2,"median",gvec) > sum(abs(y1-est1$fhat$coeff)) [1] 14.09568 > # > #the error between the true signal and the denoised version. > > > > > cleanEx(); ..nameEx <- "dojitter" > > ### * dojitter > > flush(stderr()); flush(stdout()) > > ### Name: dojitter > ### Title: dojitter > ### Aliases: dojitter > ### Keywords: manip arith > > ### ** Examples > > #create grid vector > # > xgrid<-seq(0,1,length=51) > # > xgrid [1] 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 [16] 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 [31] 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 [46] 0.90 0.92 0.94 0.96 0.98 1.00 > # > #a regularly-spaced grid > # > dojitter(xgrid,.01) [1] -0.004689827 0.017442478 0.041457067 0.068164156 0.074033639 [6] 0.107967794 0.128893505 0.143215956 0.162582281 0.171235725 [11] 0.194119491 0.213531135 0.243740457 0.257682074 0.285396828 [16] 0.299953985 0.324352370 0.349838122 0.357600704 0.385548904 [21] 0.408694105 0.414242850 0.443033475 0.452511102 0.475344413 [26] 0.497722282 0.510267807 0.537647759 0.567393817 0.576806980 [31] 0.599641602 0.621991317 0.639870826 0.653724352 0.686547466 [36] 0.703369335 0.725884797 0.732158873 0.764474219 0.778225489 [41] 0.806418926 0.822941204 0.845658655 0.861060726 0.880594392 [46] 0.905787125 0.910466624 0.939544601 0.964646275 0.983854631 [51] 0.999552392 > # > #a jittered grid. > # > > > > cleanEx(); ..nameEx <- "fwtnp" > > ### * fwtnp > > flush(stderr()); flush(stdout()) > > ### Name: fwtnp > ### Title: fwtnp > ### Aliases: fwtnp > ### Keywords: methods > > ### ** Examples > > # > # Generate some one-dimensional data: 100 observations. > # > input <- runif(100) > f <- input^2 - 3*input > # > # Compute fwtnp function on this data > # > out <- fwtnp(input,f,LocalPred=AdaptPred,neighbours=2,closest=TRUE) > # > # That's it. > # > > > > cleanEx(); ..nameEx <- "fwtnpmp" > > ### * fwtnpmp > > flush(stderr()); flush(stdout()) > > ### Name: fwtnpmp > ### Title: fwtnpmp > ### Aliases: fwtnpmp > ### Keywords: methods > > ### ** Examples > > #read in multiple point data... > > mcdata() > > out<-fwtnpmp(times,accel,LocalPredmp=AdaptPredmp,neighbours=2) > out$coeff [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 1.3 1.682001 -0.9376169 1.948371 1.134820 0.211524 -0.2664273 0.892828 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [1,] 15.60448 -0.1486263 -0.4918032 -2.104793 0.4248921 -1.941503 -2.354328 [,16] [,17] [,18] [,19] [,20] [,21] [,22] [1,] -3.411133 -2.226912 23.09288 -0.4953838 -0.4207613 24.69959 16.44612 [,23] [,24] [,25] [,26] [,27] [,28] [,29] [1,] -0.005477965 3.83259 0.7146921 15.64645 -2.372222 2.664765 5.13837 [,30] [,31] [,32] [,33] [,34] [,35] [,36] [1,] 0.4766045 1.024598 -18.35626 7.636405 -13.32409 29.74428 -27.09452 [,37] [,38] [,39] [,40] [,41] [,42] [,43] [1,] -1.919507 -102.9493 -8.968098 20.41954 8.591751 5.631246 -27.73577 [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] -4.277547 -23.05167 -0.046159 1.380886 0.1198506 -0.02951088 8.130375 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [1,] 41.53786 -24.31499 -13.74733 0.1532602 2.075565 48.11153 5.11187 -3.209659 [,59] [,60] [,61] [,62] [,63] [,64] [,65] [1,] 44.25081 -21.4516 20.46771 46.17309 -21.27504 -5.465673 0.9899074 [,66] [,67] [,68] [,69] [,70] [,71] [,72] [,73] [1,] -17.25414 36.53638 -19.70535 45.98041 -58.1618 58.25373 14.19854 -11.41633 [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 26.01551 -6.226602 -1.256818 -13.29017 -22.51966 3.257269 5.891675 [,81] [,82] [,83] [,84] [,85] [,86] [,87] [1,] -16.78450 9.054863 -12.89772 -3.879037 -7.044817 8.669776 -16.80695 [,88] [,89] [,90] [,91] [,92] [,93] [,94] [1,] -30.2127 0.7721344 6.867571 -14.05476 2.438420 -4.241383 -1.194541 > > #these are the detail coefficients of the transform. > > > > > cleanEx(); ..nameEx <- "getnbrs" > > ### * getnbrs > > flush(stderr()); flush(stdout()) > > ### Name: getnbrs > ### Title: getnbrs > ### Aliases: getnbrs > ### Keywords: arith > > ### ** Examples > > x1<-runif(20) > # > x1 [1] 0.26550866 0.37212390 0.57285336 0.90820779 0.20168193 0.89838968 [7] 0.94467527 0.66079779 0.62911404 0.06178627 0.20597457 0.17655675 [13] 0.68702285 0.38410372 0.76984142 0.49769924 0.71761851 0.99190609 [19] 0.38003518 0.77744522 > # > y1<-make.signal2("bumps",x=x1) > # > y1 [1] 1.321133e-01 3.040252e-01 2.823905e-03 1.370533e-04 2.459658e-02 [6] 1.751622e-04 6.874120e-05 2.304130e-01 4.806491e-02 1.133469e-03 [11] 3.742294e-02 8.289078e-03 9.401857e-03 7.679934e-01 2.329853e-01 [16] 1.387711e-02 2.687376e-03 3.774162e-05 5.469323e-01 1.692392e+00 > # > order(x1) [1] 10 12 5 11 1 2 19 14 16 3 9 8 13 17 15 20 6 4 7 18 > # > # shows where the points lie in relation to each other. > # > neigh<-getnbrs(x1,3,order(x1),4,TRUE) > # > neigh$nbrs [1] 16 9 8 13 > # > # these are the indices of the 4 closest neighbours to point 3. > # > > > > cleanEx(); ..nameEx <- "heterovar" > > ### * heterovar > > flush(stderr()); flush(stdout()) > > ### Name: heterovar > ### Title: heterovar > ### Aliases: heterovar > ### Keywords: arith > > ### ** Examples > > x1<-runif(256) > # > y1<-make.signal2("doppler",x=x1) > # > fwd<-fwtnp(x1,y1,LocalPred=AdaptNeigh,neighbours=2) > # > y<-fwd$lengthsremove > rem<-fwd$removelist > al<-artlev1(y,rem) > # > yrem<-x1[sort(rem)] > detail<-fwd$coeff[sort(rem)] > # > h<-heterovar(yrem,detail,al) > # > h$varvec[1:10] [1] 1.118423e-04 2.988191e-05 9.199889e-06 8.280890e-06 5.685900e-04 [6] 8.280890e-06 8.280890e-06 7.199464e-06 6.705784e-06 2.975945e-02 > # > #the first ten coefficient variances to be used in the normalisation of the detail > #coefficients > > > > cleanEx(); ..nameEx <- "intervals" > > ### * intervals > > flush(stderr()); flush(stdout()) > > ### Name: intervals > ### Title: intervals > ### Aliases: intervals > ### Keywords: arith > > ### ** Examples > > x2<-runif(50) > x2 [1] 0.26550866 0.37212390 0.57285336 0.90820779 0.20168193 0.89838968 [7] 0.94467527 0.66079779 0.62911404 0.06178627 0.20597457 0.17655675 [13] 0.68702285 0.38410372 0.76984142 0.49769924 0.71761851 0.99190609 [19] 0.38003518 0.77744522 0.93470523 0.21214252 0.65167377 0.12555510 [25] 0.26722067 0.38611409 0.01339033 0.38238796 0.86969085 0.34034900 [31] 0.48208012 0.59956583 0.49354131 0.18621760 0.82737332 0.66846674 [37] 0.79423986 0.10794363 0.72371095 0.41127443 0.82094629 0.64706019 [43] 0.78293276 0.55303631 0.52971958 0.78935623 0.02333120 0.47723007 [49] 0.73231374 0.69273156 > # > intervals(x2,"reflect") $intervals [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.008419899 0.01836077 0.04255874 0.08486495 0.1167494 0.1510559 0.1813872 [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 0.1939498 0.2038283 0.2090585 0.2388256 0.2663647 0.3037848 0.3562364 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [1,] 0.3760795 0.3812116 0.3832458 0.3851089 0.3986943 0.4442522 0.4796551 [,22] [,23] [,24] [,25] [,26] [,27] [,28] [1,] 0.4878107 0.4956203 0.5137094 0.541378 0.5629448 0.5862096 0.6143399 [,29] [,30] [,31] [,32] [,33] [,34] [,35] [1,] 0.6380871 0.649367 0.6562358 0.6646323 0.6777448 0.6898772 0.705175 [,36] [,37] [,38] [,39] [,40] [,41] [,42] [1,] 0.7206647 0.7280123 0.7510776 0.7736433 0.780189 0.7861445 0.791798 [,43] [,44] [,45] [,46] [,47] [,48] [,49] [1,] 0.807593 0.8241598 0.848532 0.8840403 0.9032987 0.9214565 0.9396902 [,50] [,51] [1,] 0.9682907 1.015522 $order [1] 27 47 10 38 24 12 34 5 11 22 1 25 30 2 19 28 14 26 40 48 31 33 16 45 44 [26] 3 32 9 42 23 8 36 13 50 17 39 49 15 20 43 46 37 41 35 29 6 4 21 7 18 > # > #check that the gridpoints are between the interval vector entries... > # > > > > cleanEx(); ..nameEx <- "invtnp" > > ### * invtnp > > flush(stderr()); flush(stdout()) > > ### Name: invtnp > ### Title: invtnp > ### Aliases: invtnp > ### Keywords: methods > > ### ** Examples > > # > # Generate some one-dimensional data: 500 observations. > x2<-runif(500) > f2<-make.signal2("bumps",x=x2) > # > # perform the forward transform... > out<-fwtnp(x2,f2,LocalPred=AdaptPred) > # > # and now invert using the information from out... > # > fhat<-invtnp(x2,out$coeff,out$lengths,out$lengthsremove,out$pointsin,out$removelist, + out$neighbrs,out$schemehist,out$interhist,LocalPred=AdaptPred) > # > # Now compare the original signal with the reconstruction. > sum(abs(f2-fhat$coeff)) [1] 1.371372e-12 > # > > > > cleanEx(); ..nameEx <- "invtnpmp" > > ### * invtnpmp > > flush(stderr()); flush(stdout()) > > ### Name: invtnpmp > ### Title: invtnpmp > ### Aliases: invtnpmp > ### Keywords: methods > > ### ** Examples > > #read in multiple point data... > > mcdata() > shortf<-adjustx(times,accel)$sepf > > out<-fwtnpmp(times,accel,LocalPredmp=CubicPredmp,neighbours=2) > > inv<-invtnpmp(times, out$coefflist, out$coeff, out$lengths, out$lengthsremove, out$pointsin, + out$removelist,out$neighbrs,out$newneighbrs,out$schemehist,out$interhist, neighbours = 2, + LocalPredmp = CubicPredmp) > > sum(abs(shortf-inv$coeff)) [1] 1.702367e-07 > > > > cleanEx(); ..nameEx <- "lengthintervals" > > ### * lengthintervals > > flush(stderr()); flush(stdout()) > > ### Name: lengthintervals > ### Title: lengthintervals > ### Aliases: lengthintervals > ### Keywords: arith > > ### ** Examples > > input<-runif(10) > #gridpoint vector > # > I<-intervals(input,"reflect") > #create the interval endpoint vector using the input > # > lengthintervals(input,I,"average",3,TRUE) $lengths [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.1398957 0.1247215 0.1247215 0.1592622 0.1149782 0.1149782 0.1190734 [,8] [,9] [,10] [1,] 0.09789853 0.09789853 0.03646748 $initialnbrs [,1] [,2] [,3] [,4] [1,] 10 5 5 5 [2,] 5 10 1 2 [3,] 1 10 5 2 [4,] 2 5 1 3 [5,] 3 2 9 8 [6,] 9 2 3 8 [7,] 8 3 9 6 [8,] 6 8 4 7 [9,] 4 8 6 7 [10,] 7 4 4 4 $initialindex [,1] [,2] [,3] [1,] 2 2 2 [2,] 1 3 4 [3,] 1 2 4 [4,] 2 3 5 [5,] 4 6 7 [6,] 4 5 7 [7,] 5 6 8 [8,] 7 9 10 [9,] 7 8 10 [10,] 9 9 9 > # > #computes 'intervals' based on 3 closest neighbour distance averages > # > > > > cleanEx(); ..nameEx <- "make.signal2" > > ### * make.signal2 > > flush(stderr()); flush(stdout()) > > ### Name: make.signal2 > ### Title: make.signal2 > ### Aliases: make.signal2 > ### Keywords: datagen > > ### ** Examples > > #create grid vector > # > xgrid<-rnorm(50) > xgrid [1] -0.62645381 0.18364332 -0.83562861 1.59528080 0.32950777 -0.82046838 [7] 0.48742905 0.73832471 0.57578135 -0.30538839 1.51178117 0.38984324 [13] -0.62124058 -2.21469989 1.12493092 -0.04493361 -0.01619026 0.94383621 [19] 0.82122120 0.59390132 0.91897737 0.78213630 0.07456498 -1.98935170 [25] 0.61982575 -0.05612874 -0.15579551 -1.47075238 -0.47815006 0.41794156 [31] 1.35867955 -0.10278773 0.38767161 -0.05380504 -1.37705956 -0.41499456 [37] -0.39428995 -0.05931340 1.10002537 0.76317575 -0.16452360 -0.25336168 [43] 0.69696338 0.55666320 -0.68875569 -0.70749516 0.36458196 0.76853292 [49] -0.11234621 0.88110773 > # > pp<-make.signal2("ppoly",x=xgrid) > # > #piecewise polynomial data vector > # > plot(sort(xgrid),pp[order(xgrid)],type="l") > # > > > > cleanEx(); ..nameEx <- "matcond" > > ### * matcond > > flush(stderr()); flush(stdout()) > > ### Name: matcond > ### Title: matcond > ### Aliases: matcond > ### Keywords: array algebra > > ### ** Examples > > x1<-runif(256) > y1<-make.signal2("doppler",x=x1) > # > m<-matcond(x1,y1,AdaptNeigh,2,TRUE,TRUE,2) > # > m$cno [1] 24532574 > # > m$v [1] 30.06742 > # shows the two different condition number measures for the matrix associated > # to the transform performed. > # > > > > cleanEx(); ..nameEx <- "mcdata" > > ### * mcdata > > flush(stderr()); flush(stdout()) > > ### Name: mcdata > ### Title: mcdata > ### Aliases: mcdata > ### Keywords: datasets > > ### ** Examples > > mcdata() > # > time function (x, ...) UseMethod("time") > accel [,1] [1,] 0.0 [2,] -1.3 [3,] -2.7 [4,] 0.0 [5,] -2.7 [6,] -2.7 [7,] -2.7 [8,] -1.3 [9,] -2.7 [10,] -2.7 [11,] -1.3 [12,] -2.7 [13,] -2.7 [14,] -2.7 [15,] -5.4 [16,] -2.7 [17,] -5.4 [18,] 0.0 [19,] -2.7 [20,] -2.7 [21,] 0.0 [22,] -13.3 [23,] -5.4 [24,] -5.4 [25,] -9.3 [26,] -16.0 [27,] -22.8 [28,] -2.7 [29,] -22.8 [30,] -32.1 [31,] -53.5 [32,] -54.9 [33,] -40.2 [34,] -21.5 [35,] -21.5 [36,] -50.8 [37,] -42.9 [38,] -26.8 [39,] -21.5 [40,] -50.8 [41,] -61.7 [42,] -5.4 [43,] -80.4 [44,] -59.0 [45,] -71.0 [46,] -91.1 [47,] -77.7 [48,] -37.5 [49,] -85.6 [50,] -123.1 [51,] -101.9 [52,] -99.1 [53,] -104.4 [54,] -112.5 [55,] -50.8 [56,] -123.1 [57,] -85.6 [58,] -72.3 [59,] -127.2 [60,] -123.1 [61,] -117.9 [62,] -134.0 [63,] -101.9 [64,] -108.4 [65,] -123.1 [66,] -123.1 [67,] -128.5 [68,] -112.5 [69,] -95.1 [70,] -81.8 [71,] -53.5 [72,] -64.4 [73,] -57.6 [74,] -72.3 [75,] -44.3 [76,] -26.8 [77,] -5.4 [78,] -107.1 [79,] -21.5 [80,] -65.6 [81,] -16.0 [82,] -45.6 [83,] -24.2 [84,] 9.5 [85,] 4.0 [86,] 12.0 [87,] -21.5 [88,] 37.5 [89,] 46.9 [90,] -17.4 [91,] 36.2 [92,] 75.0 [93,] 8.1 [94,] 54.9 [95,] 48.2 [96,] 46.9 [97,] 16.0 [98,] 45.6 [99,] 1.3 [100,] 75.0 [101,] -16.0 [102,] -54.9 [103,] 69.6 [104,] 34.8 [105,] 32.1 [106,] -37.5 [107,] 22.8 [108,] 46.9 [109,] 10.7 [110,] 5.4 [111,] -1.3 [112,] -21.5 [113,] -13.3 [114,] 30.8 [115,] -10.7 [116,] 29.4 [117,] 0.0 [118,] -10.7 [119,] 14.7 [120,] -1.3 [121,] 0.0 [122,] 10.7 [123,] 10.7 [124,] -26.8 [125,] -14.7 [126,] -13.3 [127,] 0.0 [128,] 10.7 [129,] -14.7 [130,] -2.7 [131,] 10.7 [132,] -2.7 [133,] 10.7 > # > > > > cleanEx(); ..nameEx <- "modjitter" > > ### * modjitter > > flush(stderr()); flush(stdout()) > > ### Name: modjitter > ### Title: modjitter > ### Aliases: modjitter > ### Keywords: arith manip > > ### ** Examples > > #create grid vector > # > xgrid<-seq(0,1,length=51) > # > xgrid [1] 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 [16] 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 [31] 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 [46] 0.90 0.92 0.94 0.96 0.98 1.00 > # > #a regularly-spaced grid on [0,1] > # > modjitter(xgrid,1) [1] 0.00000000 0.03444838 0.04291413 0.07632831 0.06806728 0.11593559 [7] 0.13778701 0.14643191 0.16516456 0.16247145 0.18823898 0.20706227 [13] 0.24748091 0.25536415 0.29079366 0.29990797 0.32870474 0.35967624 [19] 0.35520141 0.39109781 0.41738821 0.40848570 0.44606695 0.44502220 [25] 0.47068883 0.49544456 0.50053561 0.53529552 0.57478763 0.57361396 [31] 0.59928320 0.62398263 0.63974165 0.64744870 0.69309493 0.70673867 [37] 0.73176959 0.72431775 0.76894844 0.77645098 0.81283785 0.82588241 [43] 0.85131731 0.86212145 0.88118878 0.91157425 0.90093325 0.93908920 [49] 0.96929255 0.97752388 1.00000000 > # > #jitters xgrid with a maximum change of .02, keeping endpoints of zero and one > > > > cleanEx(); ..nameEx <- "pts" > > ### * pts > > flush(stderr()); flush(stdout()) > > ### Name: pts > ### Title: pts > ### Aliases: pts > ### Keywords: arith > > ### ** Examples > > y<-runif(20) > y [1] 0.26550866 0.37212390 0.57285336 0.90820779 0.20168193 0.89838968 [7] 0.94467527 0.66079779 0.62911404 0.06178627 0.20597457 0.17655675 [13] 0.68702285 0.38410372 0.76984142 0.49769924 0.71761851 0.99190609 [19] 0.38003518 0.77744522 > # > y1<-c(0,y) > y2<-c(NA,y) > # > # > pts(y1)$X [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0 0.2655087 0.6376326 1.210486 2.118694 2.320376 3.218765 4.163441 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] 4.824238 5.453352 5.515139 5.721113 5.89767 6.584693 6.968797 7.738638 [,17] [,18] [,19] [,20] [,21] [1,] 8.236337 8.953956 9.945862 10.32590 11.10334 > #the gridpoints, with specified startpoint of zero. > # > # > pts(y2)$X [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0.5287306 0.7942392 1.166363 1.739217 2.647424 2.849106 3.747496 4.692171 [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16] [1,] 5.352969 5.982083 6.043869 6.249844 6.4264 7.113423 7.497527 8.267369 [,17] [,18] [,19] [,20] [,21] [1,] 8.765068 9.482686 10.47459 10.85463 11.63207 > #the grid vector with unspecified startpoint. > # > > > > > cleanEx(); ..nameEx <- "transmatdual" > > ### * transmatdual > > flush(stderr()); flush(stdout()) > > ### Name: transmatdual > ### Title: transmatdual > ### Aliases: transmatdual > ### Keywords: array > > ### ** Examples > > x1<-runif(10) > y1<-make.signal2("doppler",x=x1) > # > a<-transmatdual(x1,y1,AdaptNeigh,2,TRUE,TRUE,2) > # > a$Wnew [,1] [,2] [,3] [,4] [,5] [1,] 1.000000000 0.00000000 0.000000000 0.000000000 -1.000000000 [2,] -0.039187891 0.95880930 0.017203328 -0.026311961 -0.046839702 [3,] 0.039618363 -0.50148790 1.010806086 0.005813656 0.047354227 [4,] 0.000000000 0.00000000 0.000000000 1.000000000 0.000000000 [5,] 0.260238587 0.09405221 0.185700747 -0.004015209 0.311052660 [6,] -0.001852657 0.04777536 -0.009822396 0.125973988 -0.002214406 [7,] 0.000000000 0.00000000 0.000000000 -0.106470122 0.000000000 [8,] -0.006997774 0.21868059 0.116857490 0.044139431 -0.008364156 [9,] 0.000000000 -0.25917273 -0.398974569 0.000000000 0.000000000 [10,] -0.455526999 0.00000000 0.000000000 0.000000000 -0.544473001 [,6] [,7] [,8] [,9] [,10] [1,] 0.00000000 0.000000000 0.0000000 0.000000000 0.000000000 [2,] -0.14064514 -0.041461328 -0.3681481 0.070213992 -0.064329414 [3,] 0.03107569 0.009160925 -0.6770864 -0.027084648 0.065036060 [4,] -0.78787985 -0.212120152 0.0000000 0.000000000 0.000000000 [5,] -0.02146247 -0.006327004 -0.1930302 0.008626833 0.427198176 [6,] 0.67336785 0.198504732 -0.9251436 -0.092587821 -0.003041254 [7,] -0.91611434 1.022584458 0.0000000 0.000000000 0.000000000 [8,] 0.23593818 0.069553136 0.3613825 0.073845825 -0.011487290 [9,] 0.00000000 0.000000000 -0.4602251 1.000000000 0.000000000 [10,] 0.00000000 0.000000000 0.0000000 0.000000000 1.000000000 > # > #the transform matrix for this adaptive lifting scheme > > > > > ### *