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
|
mclogit.fit <- function(
y,
s,
w,
X,
dispersion=FALSE,
start=NULL,
offset=NULL,
control=mclogit.control()
){
nvar <- ncol(X)
nobs <- length(y)
if(!length(offset))
offset <- rep.int(0, nobs)
if(length(start)){
stopifnot(length(start)==ncol(X))
eta <- c(X%*%start) + offset
}
else
eta <- mclogitLinkInv(y,s,w)
pi <- mclogitP(eta,s)
dev.resids <- ifelse(y>0,
2*w*y*(log(y)-log(pi)),
0)
deviance <- sum(dev.resids)
if(length(start))
coef <- start
else coef <- NULL
converged <- FALSE
for(iter in 1:control$maxit){
y.star <- eta - offset + (y-pi)/pi
yP.star <- y.star - rowsum(pi*y.star,s)[s]
XP <- X - as.matrix(rowsum(pi*X,s))[s,,drop=FALSE]
ww <- w*pi
good <- ww > 0 & is.finite(yP.star)
wlsFit <- lm.wfit(x=XP[good,,drop=FALSE],y=yP.star[good],w=ww[good])
last.coef <- coef
coef <- wlsFit$coefficients
eta <- c(X%*%coef) + offset
pi <- mclogitP(eta,s)
last.deviance <- deviance
dev.resids <- ifelse(y>0,
2*w*y*(log(y)-log(pi)),
0)
deviance <- sum(dev.resids)
## check for divergence
boundary <- FALSE
if(!is.finite(deviance) || deviance > last.deviance && iter > 1){
if(is.null(last.coef))
stop("no valid set of coefficients has been found: please supply starting values", call. = FALSE)
warning("step size truncated due to divergence", call. = FALSE)
ii <- 1
while (!is.finite(deviance) || deviance > last.deviance){
if(ii > control$maxit)
stop("inner loop; cannot correct step size")
ii <- ii + 1
coef <- (coef + last.coef)/2
eta <- c(X %*% coef) + offset
pi <- mclogitP(eta,s)
dev.resids <- ifelse(y>0,2*w*y*(log(y)-log(pi)),0)
deviance <- sum(dev.resids)
}
boundary <- TRUE
if (control$trace)
cat("Step halved: new deviance =", deviance, "\n")
} ## inner loop
crit <- abs(deviance-last.deviance)/abs(0.1+deviance)
if(control$trace)
cat("\nIteration",iter,"- deviance =",deviance,"- criterion =",crit)
if(crit < control$eps){
converged <- TRUE
if(control$trace)
cat("\nconverged\n")
break
}
}
if (!converged) warning("algorithm did not converge",call.=FALSE)
if (boundary) warning("algorithm stopped at boundary value",call.=FALSE)
eps <- 10*.Machine$double.eps
if (any(pi < eps) || any(1-pi < eps))
warning("fitted probabilities numerically 0 occurred",call.=FALSE)
XP <- X - as.matrix(rowsum(pi*X,s))[s,,drop=FALSE]
ww <- w*pi
XWX <- crossprod(XP,ww*XP)
ntot <- length(y)
pi0 <- mclogitP(offset,s)
null.deviance <- sum(ifelse(y>0,
2*w*y*(log(y)-log(pi0)),
0))
resid.df <- length(y)-length(unique(s))
model.df <- ncol(X)
resid.df <- resid.df - model.df
ll <- mclogit.logLik(y,pi,w)
if(!isFALSE(dispersion)){
if(isTRUE(dispersion))
odisp.method <- "Afroz"
else
odisp.method <- match.arg(dispersion,
c("Afroz",
"Fletcher",
"Pearson",
"Deviance"))
phi <- mclogit.dispersion(y,w,s,pi,coef,
method=odisp.method)
}
else phi <- 1
return(list(
coefficients = drop(coef),
phi = phi,
linear.predictors = eta,
working.residuals = (y-pi)/pi,
response.residuals = y-pi,
df.residual = resid.df,
model.df = model.df,
fitted.values = pi,
deviance=deviance,
ll=ll,
deviance.residuals=dev.resids,
null.deviance=null.deviance,
iter = iter,
y = y,
s = s,
offset = offset,
converged = converged,
control=control,
information.matrix=XWX
))
}
mclogit.control <- function(
epsilon = 1e-08,
maxit = 25,
trace=TRUE
) {
if (!is.numeric(epsilon) || epsilon <= 0)
stop("value of epsilon must be > 0")
if (!is.numeric(maxit) || maxit <= 0)
stop("maximum number of iterations must be > 0")
list(epsilon = epsilon, maxit = maxit, trace = trace)
}
log.Det <- function(x) determinant(x,logarithm=TRUE)$modulus
mclogitP <- function(eta,s){
expeta <- exp(eta)
sum.expeta <- rowsum(expeta,s)
expeta/sum.expeta[s]
}
# mclogit.dev.resids <- function(y,p,w)
# ifelse(y>0,
# 2*w*y*(log(y)-log(p)),
# 0)
mclogit.logLik <- function(y,p,w) sum(w*y*log(p))
mclogitLinkInv <- function(y,s,w){
#n.alt <- tapply(y,s,length)
#c(log(sqrt(w)*y+1/n.alt[s])-log(w)/2)
n <- w*y+0.5
f <- n/(rowsum(n,s)[s])
log(f) - ave(log(f),s)
}
|
