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
|
#############################################################################
## Copyright (c) 2010-2022 Rune Haubo Bojesen Christensen
##
## This file is part of the ordinal package for R (*ordinal*)
##
## *ordinal* is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 2 of the License, or
## (at your option) any later version.
##
## *ordinal* is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## A copy of the GNU General Public License is available at
## <https://www.r-project.org/Licenses/> and/or
## <http://www.gnu.org/licenses/>.
#############################################################################
## This file contains:
## The main clm function and some auxiliary functions to generate the
## model frame and handle the model environment.
checkArgs.clm <- function(mc) {
nm <- names(as.list(mc))
if(!"formula" %in% nm) stop("Model needs a formula", call.=FALSE)
if("offset" %in% nm)
stop("offset argument not allowed: ",
"specify 'offset' in formula or scale arguments instead")
invisible()
}
clm <-
function(formula, scale, nominal, data, weights, start, subset,
doFit = TRUE, na.action, contrasts, model = TRUE,
control = list(),
link = c("logit", "probit", "cloglog", "loglog", "cauchit",
"Aranda-Ordaz", "log-gamma"),
threshold = c("flexible", "symmetric", "symmetric2", "equidistant"), ...)
{
mc <- match.call(expand.dots = FALSE)
link <- match.arg(link)
threshold <- match.arg(threshold)
if(missing(contrasts)) contrasts <- NULL
if(missing(start)) start <- NULL
checkArgs.clm(mc=match.call())
## set control parameters: ## getControl.clm
control <- do.call(clm.control, c(control, list(...)))
## Extract and process formulas:
call.envir <- parent.frame(n=1)
formulas <- get_clmFormulas(mc, call.envir)
## Get full model.frame and terms.objects:
fullmf <- get_clm.mf(mc, formulas$fullForm, attr(formulas, "envir"),
call.envir)
if(control$method == "model.frame") return(fullmf)
terms.list <-
if(any(c("scale", "nominal") %in% names(formulas)))
get_clmTerms(mc, formulas, call.envir) else list(formula=terms(fullmf))
## Get y, X, weights, off etc.:
design <- get_clmDesign(fullmf, terms.list, contrasts)
lst <- namedList(doFit, control, link, threshold, start, formulas)
if(control$method == "design") return(c(design, lst))
## Get clm.struct:
design <- c(design, makeThresholds(design$y.levels, threshold))
design <- drop.cols(design, silent=TRUE, drop.scale=FALSE)
clm.struct <- c(design, lst)
## Fit model, check convergence, or return a model environment:
fit <- clm.fit.default(clm.struct)
if(doFit == FALSE) return(fit)
## Format output, prepare result:
keep <- c("terms", "contrasts", "xlevels", # formula
"S.terms", "S.contrasts", "S.xlevels", # scale
"nom.terms", "nom.contrasts", "nom.xlevels", # nominal
"na.action", "y", "y.levels",
"control", "link", "threshold", "start", "formulas")
res <- c(fit, clm.struct[match(keep, names(clm.struct), 0L)],
list(formula=lst$formulas$formula, call=match.call()))
## res$tJac <- format_tJac(res$tJac, res$y.levels, clm.struct$alpha.names)
res$info=get_clmInfoTab(res)
if(model) res$model <- fullmf
res <- res[sort(names(res))]
class(res) <- "clm"
res
}
clm.newRho <-
function(parent=parent.frame(), y, X, NOM=NULL, S=NULL, weights,
offset, S.offset=NULL, tJac, control=clm.control(), ...)
### Setting variables in rho: B1, B2, o1, o2, weights.
{
## Make B1, B2, o1, o2 based on y, X and tJac:
keep <- weights > 0
y[!keep] <- NA
y <- droplevels(y)
ntheta <- nlevels(y) - 1
y <- c(unclass(y))
y[is.na(y)] <- 0
n <- sum(keep)
B2 <- 1 * (col(matrix(0, nrow(X), ntheta + 1)) == y)
o1 <- c(1e5 * B2[keep, ntheta + 1]) - offset[keep]
o2 <- c(-1e5 * B2[keep, 1]) - offset[keep]
B1 <- B2[keep, -(ntheta + 1), drop = FALSE]
B2 <- B2[keep, -1, drop = FALSE]
## adjust B1 and B2 for structured thresholds:
B1 <- B1 %*% tJac
B2 <- B2 %*% tJac
## update B1 and B2 with nominal effects:
if(NCOL(NOM) > 1) { ## !is.null(NOM) && ncol(NOM) > 1) {
## if !is.null(NOM) and NOM is more than an intercept:
if(control$sign.nominal == "negative") NOM[, -1] <- -NOM[, -1]
LL1 <- lapply(1:ncol(NOM), function(x) B1 * NOM[keep, x])
B1 <- do.call(cbind, LL1)
LL2 <- lapply(1:ncol(NOM), function(x) B2 * NOM[keep, x])
B2 <- do.call(cbind, LL2)
}
## update B1 and B2 with location effects (X):
nbeta <- NCOL(X) - 1
if(nbeta > 0) {
if(control$sign.location == "negative") X <- -X
B1 <- cbind(B1, X[keep, -1, drop = FALSE])
B2 <- cbind(B2, X[keep, -1, drop = FALSE])
}
dimnames(B1) <- NULL
dimnames(B2) <- NULL
n.psi <- ncol(B1) ## no. linear model parameters
## there may be scale offset without scale predictors:
sigma <- Soff <-
if(is.null(S.offset)) rep(1, n) else exp(S.offset[keep])
## save scale model matrix:
k <- 0
if(!is.null(S)) {
S <- S[keep, -1, drop=FALSE]
dimnames(S) <- NULL
k <- ncol(S) ## no. scale parameters
}
has.scale <- ## TRUE if scale has to be considered.
(!is.null(S) || any(S.offset != 0))
## initialize fitted values and weights:
fitted <- numeric(length = n)
wts <- weights[keep]
lst <- namedList(B1, B2, o1, o2, n.psi, S, Soff, k, sigma, has.scale, fitted,
wts, clm.nll, clm.grad, clm.hess)
list2env(x=lst, parent=parent)
}
|
