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
|
lmrob.lar <- function(x, y, control = lmrob.control(), ...)
{
## LAR : Least Absolute Residuals -- i.e. L_1 M-estimate
## this function is identical to lmRob.lar of the robust package
## '...': to be called as 'init(**, mf)' from lmrob()
x <- as.matrix(x)
p <- ncol(x)
n <- nrow(x)
stopifnot(p > 0, n >= p, length(y) == n, is.numeric(control$rel.tol))
storage.mode(x) <- "double"
storage.mode(y) <- "double"
bet0 <- 0.773372647623 ## bet0 = pnorm(0.75); only for normalizing scale=SIGMA
tmpn <- double(n)
tmpp <- double(p)
z1 <- .Fortran(rllarsbi, ##-> ../src/rllarsbi.f
x,
y,
as.integer(n),
as.integer(p),
as.integer(n),
as.integer(n),
as.double(control$rel.tol),
NIT=integer(1),
K=integer(1),
KODE=integer(1),
SIGMA=double(1),
THETA=tmpn,
RS=tmpn,
SC1=tmpn,
SC2=tmpp,
SC3=tmpp,
SC4=tmpp,
BET0=as.double(bet0))[c("THETA","SIGMA","RS","NIT","KODE")]
if (z1[5] > 1)
stop("calculations stopped prematurely in rllarsbi\n",
"(probably because of rounding errors).")
names(z1) <- c("coefficients", "scale", "residuals", "iter", "status")
## c("THETA", "SIGMA", "RS", "NIT", "KODE")
z1$converged <- TRUE
length(z1$coefficients) <- p
z1
}
splitFrame <- function(mf, x = model.matrix(mt, mf),
type = c("f","fi", "fii"))
{
mt <- attr(mf, "terms")
type <- match.arg(type)
x <- as.matrix(x)
p <- ncol(x)
## --- split categorical and interactions of categorical vars.
## from continuous variables
factors <- attr(mt, "factors")
factor.idx <- attr(mt, "dataClasses") %in% c("factor", "character")
if (!any(factor.idx)) ## There are no factors
return(list(x1.idx = rep.int(FALSE, p), x1=matrix(NA_real_,nrow(x),0L), x2=x))
switch(type,
## --- include interactions cat * cont in x1:
fi = { factor.asgn <- which(factor.idx %*% factors > 0) },
## --- include also continuous variables that interact with factors in x1:
## make sure to include interactions of continuous variables
## interacting with categorical variables, too
fii = { factor.asgn <- numeric(0)
factors.cat <- factors
factors.cat[factors.cat > 0] <- 1L ## fix triple+ interactions
factors.cat[, factor.idx %*% factors == 0] <- 0L
for (i in 1:ncol(factors)) {
comp <- factors[,i] > 0
## if any of the components is a factor: include in x1 and continue
if (any(factor.idx[comp])) {
factor.asgn <- c(factor.asgn, i)
} else {
## if there is an interaction of this term with a categorical var.
tmp <- colSums(factors.cat[comp,,drop=FALSE]) >= sum(comp)
if (any(tmp)) {
## if no other continuous variables are involved
## include in x1 and continue
## if (identical(factors[!comp, tmp], factors.cat[!comp, tmp]))
if (!all(colSums(factors[!factor.idx & !comp, tmp, drop=FALSE]) > 0))
factor.asgn <- c(factor.asgn, i)
}
}
} },
## --- do not include interactions cat * cont in x1:
f = { factor.asgn <- which(factor.idx %*% factors & !(!factor.idx) %*% factors) },
stop("unknown split type"))
x1.idx <- attr(x, "assign") %in% c(0, factor.asgn) ## also include intercept
names(x1.idx) <- colnames(x)
## x1: factors and (depending on type) interactions of / with factors
## x2: continuous variables
list(x1 = x[, x1.idx, drop=FALSE],
x1.idx = x1.idx,
x2 = x[, !x1.idx, drop=FALSE])
}
##' Compute M-S-estimator for linear regression ---> ../man/lmrob.M.S.Rd
lmrob.M.S <- function(x, y, control, mf, split = splitFrame(mf, x, control$split.type)) {
if (ncol(split$x1) == 0) {
warning("No categorical variables found in model. Reverting to S-estimator.")
return(lmrob.S(x, y, control))
}
if (ncol(split$x2) == 0) {
warning("No continuous variables found in model. Reverting to L1-estimator.")
return(lmrob.lar(x, y, control))
}
## this is the same as in lmrob.S():
if (length(seed <- control$seed) > 0) { # not by default
if(length(seed) < 3L || seed[1L] < 100L)
stop("invalid 'seed'. Must be compatible with .Random.seed !")
if(!is.null(seed.keep <- get0(".Random.seed", envir = .GlobalEnv, inherits = FALSE)))
on.exit(assign(".Random.seed", seed.keep, envir = .GlobalEnv))
assign(".Random.seed", seed, envir = .GlobalEnv)
}
x1 <- split$x1
x2 <- split$x2
storage.mode(x1) <- "double"
storage.mode(x2) <- "double"
storage.mode(y) <- "double"
c.chi <- .psi.conv.cc(control$psi, control$tuning.chi)
traceLev <- as.integer(control$trace.lev)
z <- .C(R_lmrob_M_S, ## NB: If you change this, adapt ../inst/xtraR/m-s_fns.R
x1,
x2,
y,
res = double(length(y)),
n = length(y),
p1 = ncol(x1),
p2 = ncol(x2),
nResample = as.integer(control$nResample),
max_it_scale= as.integer(control$maxit.scale),
scale = double(1),
b1 = double(ncol(x1)),
b2 = double(ncol(x2)),
tuning_chi = as.double(c.chi),
ipsi = .psi2ipsi(control$psi),
bb = as.double(control$bb),
K_m_s = as.integer(control$k.m_s),
max_k = as.integer(control$k.max),
rel_tol = as.double(control$rel.tol),
inv_tol = as.double(control$solve.tol),
scale_tol = as.double(control$scale.tol),
zero.tol = as.double(control$zero.tol),
converged = logical(1),
trace_lev = traceLev,
## well, these 3 are for the experts ... still why not arguments?
orthogonalize=TRUE,
subsample=TRUE,
descent=TRUE,
mts = as.integer(control$mts),
ss = .convSs(control$subsampling)
)[c("b1","b2", "res","scale", "converged")]
conv <- z$converged
## FIXME? warning in any case if 'conv' is not ok ??
if(!conv && traceLev) warning("M-S estimator did *not* converge")
## coefficients :
idx <- split$x1.idx
cf <- numeric(length(idx))
cf[ idx] <- z$b1
cf[!idx] <- z$b2
## set method argument in control
control$method <- 'M-S'
obj <- list(coefficients = cf, scale = z$scale, residuals = z$res,
rweights = lmrob.rweights(z$res, z$scale, control$tuning.chi, control$psi),
## ../src/lmrob.c : m_s_descent() notes that convergence is *not* guaranteed
converged = TRUE,
descent.conv = conv, # the real truth ..
control = control)
if (control$method %in% control$compute.outlier.stats)
obj$ostats <- outlierStats(obj, x, control)
obj
}
|
