1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
#### Utility functions for testing covMCD()
#### -------------------------------------- ../tests/tmcd.R
repMCD <- function(x, nrep = 1, method = c("FASTMCD","MASS"))
{
stopifnot(length(nrep) == 1, nrep >= 1)
method <- match.arg(method)
if(method == "MASS") {
if(paste(R.version$major, R.version$minor, sep=".") < 2.3)
cov.rob <- MASS::cov.rob
for(i in 1:nrep) MASS::cov.mcd(x)
}
else for(i in 1:nrep) covMcd(x)
}
doMCDdata <- function(nrep = 1, time = nrep >= 3, short = time, full = !short,
method = c("FASTMCD", "MASS"))
{
##@bdescr
## Test the function covMcd() on the literature datasets:
##
## Call covMcd() for "all" regression datasets available in robustbase
## and print:
## - execution time (if time)
## - objective function
## - best subsample found (if not short)
## - outliers identified (with cutoff 0.975) (if not short)
## - estimated center and covariance matrix (if full)
##
##@edescr
##
##@in nrep : [integer] number of repetitions to use for estimating the
## (average) execution time
##@in time : [boolean] whether to evaluate the execution time
##@in short : [boolean] whether to do short output (i.e. only the
## objective function value). If short == FALSE,
## the best subsample and the identified outliers are
## printed. See also the parameter full below
##@in full : [boolean] whether to print the estimated center and covariance matrix
##@in method : [character] select a method: one of (FASTMCD, MASS)
domcd <- function(x, xname, nrep = 1)
{
n <- dim(x)[1]
p <- dim(x)[2]
if(method == "MASS") {
mcd <- MASS::cov.mcd(x)
quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2)
}
else {
mcd <- covMcd(x) # trace = FALSE
quan <- as.integer(mcd$quan)
}
crit <- if(method == "MASS") mcd$crit else log(mcd$crit)
xres <- sprintf("%*s %3d %3d %3d %12.6f", lname, xname, n, p, quan, crit)
if(time) {
xtime <- system.time(repMCD(x, nrep, method))[1]/nrep
xres <- sprintf("%s %10.1f", xres, 1000 * xtime)
}
cat(xres, "\n")
if(!short) {
cat("Best subsample: \n")
print(mcd$best)
ibad <- which(mcd$mcd.wt == 0)
names(ibad) <- NULL
nbad <- length(ibad)
cat("Outliers: ",nbad,"\n")
if(nbad > 0)
print(ibad)
if(full) {
cat("-------------\n")
print(mcd)
}
cat("--------------------------------------------------------\n")
}
}
lname <- 20
method <- match.arg(method)
if(method == "MASS" &&
paste(R.version$major, R.version$minor, sep=".") < 2.3)
cov.rob <- MASS::cov.rob
data(heart)
data(phosphor)
data(starsCYG)
data(stackloss)
data(coleman)
data(salinity)
data(wood)
data(hbk)
data(Animals, package = "MASS")
brain <- Animals[c(1:24, 26:25, 27:28),]
data(milk)
data(bushfire)
## data(x1000)
## data(x5000)
tmp <- sys.call()
cat("\nCall: ", deparse(substitute(tmp)),"\n")
cat("Data Set n p Half LOG(obj) Time [ms]\n")
cat("========================================================\n")
domcd(heart[, 1:2], data(heart), nrep)
domcd(data.matrix(subset(phosphor, select = -plant)),
data(phosphor), nrep)
domcd(starsCYG, data(starsCYG), nrep)
domcd(stack.x, data(stackloss), nrep)
domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep)
domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep)
domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep)
domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep)
domcd(brain, "Animals", nrep)
domcd(milk, data(milk), nrep)
domcd(bushfire, data(bushfire), nrep)
cat("========================================================\n")
## domcd(x1000$X,data(x1000), nrep)
## domcd(x5000$X,data(x5000), nrep)
}
if(FALSE){
data(mortality, package = "riv")
mm <- as.data.frame(lapply(mortality, signif, 3))
for(j in c(1,2,6,7))
mm[,j] <- mm[,j] * 10
mm[,5] <- mm[,5] * 1000
mm[,8] <- mm[,8] / 100
mort3 <- mm
dput(mort3)
}
mort3 <-
data.frame(MO70 = c(140, 101, 86, 102, 115, 121, 118, 76.6,
131, 112, 111, 112, 117, 118, 123, 122, 81.7, 108, 111, 109,
92.5, 83.9, 93.8, 135, 124, 126, 122, 120, 127, 115, 156, 95.1,
127, 129, 116, 82.3, 115, 106, 134, 94.9, 119, 111, 131, 85.6,
135, 126, 141, 152, 137, 151, 93.6, 84.2, 78, 50.2, 81.3, 112,
80.1, 125, 120, 143),
MAGE = c(297, 277, 275, 268, 296, 327, 314, 258, 342, 278, 278,
313, 284, 272, 296, 277, 271, 296, 286, 250, 280, 270, 246, 301,
279, 287, 293, 271, 291, 295, 314, 267, 275, 307, 259, 251, 324,
285, 288, 254, 278, 287, 316, 287, 326, 309, 334, 369, 321, 311,
261, 272, 260, 244, 248, 277, 240, 295, 319, 346),
CI68 = c(137, 137, 129, 129, 151, 157, 157, 157, 157, 202, 202, 202,
138, 160, 190, 191, 191, 191, 159, 159, 146, 146, 203, 203, 182, 166,
203, 203, 167, 167, 165, 153, 149, 149, 149, 157, 152, 183, 183, 183,
183, 183, 183, 111, 171, 148, 148, 148, 192, 160, 160, 172, 172,
172, 172, 101, 173, 173, 144, 181),
MDOC = c(142, 80.4, 148, 167, 230, 187, 240, 149, 240, 195, 327,
377, 203, 160, 161, 68.7, 141, 120, 176, 105, 128, 112, 98.9, 160,
209, 200, 153, 126, 157, 157, 145, 160, 158, 102, 195, 188, 250,
143, 157, 186, 114, 129, 129, 143, 186, 207, 144, 112, 157, 121,
168, 155, 144, 144, 120, 194, 93.6, 231, 185, 89.7),
DENS = c(37, 37, 27, 32, 17, 13, 23, 19, 27, 29, 15, 15, 48, 34,
26, 47, 17, 10, 10, 18, 11, 13, 26, 19, 55, 17, 16, 7, 10, 17,
44, 13, 18, 26, 40, 22, 29, 7, 28, 10, 15, 1, 11, 10, 8, 13, 13,
6, 10, 26, 49, 28, 32, 18, 62, 15, 21, 18, 10, 12),
NONW = c(4.22, 3.36, 0.67, 0.52, 2.51, 0.82, 4.07, 1.11,
2.86, 2.92, 2.74, 1.05, 7.23, 5.16, 3.44, 2.84, 1.84,
1.47, 0.62, 0.03, 0.96, 1.07, 1.74, 2.41, 0.45, 4.7, 4.45,
1.2, 0.64, 2.28, 4.13, 1.06, 4.02, 2.22, 5.6, 0.43, 2.34,
1.78, 2.81, 1.9, 3.09, 1.43, 2.58, 1.34, 0.78, 3.44, 2.07,
0.68, 1, 3.6, 3.92, 2.58, 2.66, 0.05, 0.86, 0.32, 3.02,
4.24, 1.26, 1.08),
EDUC = c(454, 516, 601, 631, 565, 620, 661, 653, 661, 591,
568, 499, 685, 534, 539, 536, 560, 542, 680, 546, 648,
632, 601, 469, 458, 446, 521, 540, 661, 601, 480, 627,
506, 363, 551, 662, 518, 556, 484, 607, 562, 517, 521,
582, 629, 506, 534, 433, 459, 476, 492, 548, 517, 517,
468, 685, 483, 471, 678, 528),
IN69 = c(86.9, 99.3, 113, 99.2, 104, 118, 113, 117, 125,
100, 104, 115, 122, 107, 135, 101, 123, 114, 114, 113,
108, 109, 100, 99.8, 102, 100, 110, 112, 111, 113, 92.7,
116, 86.3, 103, 86.4, 109, 116, 112, 104, 108, 103, 116,
99.3, 116, 114, 104, 105, 97, 102, 83.4, 101, 125, 117,
118, 90.3, 108, 92.4, 106, 126, 109))
|
