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
|
## Calculate prediction and Bayesian SE from ssllrm objects
predict.ssllrm <- function (object,x,y=object$qd.pt,odds=NULL,se.odds=FALSE,...)
{
if (class(object)!="ssllrm")
stop("gss error in predict.ssllrm: not a ssllrm object")
if ("random"%in%colnames(x)) {
zz <- x$random
x$random <- NULL
}
else zz <- NULL
if (!all(sort(object$xnames)==sort(colnames(x))))
stop("gss error in predict.ssllrm: mismatched x variable names")
if (!all(sort(object$ynames)==sort(colnames(y))))
stop("gss error in predict.ssllrm: mismatched y variable names")
mf <- object$mf
term <- object$term
qd.pt <- object$qd.pt
nmesh <- dim(qd.pt)[1]
y.id <- NULL
for (i in 1:dim(y)[1]) {
if (!sum(duplicated(rbind(qd.pt,y[i,object$ynames,drop=FALSE]))))
stop("gss error in predict.ssllrm: y value is out of range")
wk <- FALSE
for (j in 1:nmesh) {
if (sum(duplicated(rbind(qd.pt[j,],y[i,object$ynames])))) y.id <- c(y.id,j)
}
}
if (!is.null(odds)) {
if (length(y.id)-length(odds))
stop("gss error in predict.ssllrm: odds is of wrong length")
if (!max(odds)|sum(odds))
stop("gss error in predict.ssllrm: odds is not a contrast")
if (sum(duplicated(y.id)))
stop("gss error in predict.ssllrm: duplicated y in contrast")
qd.pt <- qd.pt[y.id,,drop=FALSE]
}
## Generate s, and r
nobs <- dim(x)[1]
nmesh <- dim(qd.pt)[1]
nbasis <- length(object$id.basis)
nnull <- length(object$d)
nZ <- length(object$b)
s <- NULL
r <- array(0,c(nmesh,nbasis,nobs))
nu <- nq <- 0
for (label in term$labels) {
vlist <- term[[label]]$vlist
x.list <- object$xnames[object$xnames%in%vlist]
y.list <- object$ynames[object$ynames%in%vlist]
xy.basis <- mf[object$id.basis,vlist]
qd.xy <- data.frame(matrix(0,nmesh,length(vlist)))
names(qd.xy) <- vlist
qd.xy[,y.list] <- qd.pt[,y.list]
if (length(x.list)) xx <- x[,x.list,drop=FALSE]
else xx <- NULL
nphi <- term[[label]]$nphi
nrk <- term[[label]]$nrk
if (nphi) {
phi <- term[[label]]$phi
for (i in 1:nphi) {
nu <- nu+1
if (is.null(xx)) {
s.wk <- phi$fun(qd.xy[,,drop=TRUE],nu=i,env=phi$env)
wk <- matrix(s.wk,nmesh,nobs)
}
else {
wk <- NULL
for (j in 1:nobs) {
qd.xy[,x.list] <- xx[rep(j,nmesh),]
wk <- cbind(wk,phi$fun(qd.xy,i,phi$env))
}
}
s <- array(c(s,wk),c(nmesh,nobs,nu))
}
}
if (nrk) {
rk <- term[[label]]$rk
for (i in 1:nrk) {
nq <- nq+1
if (is.null(xx)) {
r.wk <- rk$fun(qd.xy[,,drop=TRUE],xy.basis,nu=i,env=rk$env,out=TRUE)
r <- r + as.vector(10^object$theta[nq]*r.wk)
}
else {
wk <- NULL
for (j in 1:nobs) {
qd.xy[,x.list] <- xx[rep(j,nmesh),]
wk <- array(c(wk,rk$fun(qd.xy,xy.basis,i,rk$env,TRUE)),
c(nmesh,nbasis,j))
}
r <- r + 10^object$theta[nq]*wk
}
}
}
}
## random effects
if (nZ) {
nz <- object$Random$sigma$env$nz
if (is.null(zz)) z.wk <- matrix(0,nobs,nz)
else z.wk <- as.matrix(zz)
if (dim(z.wk)[2]!=nz)
stop("gss error in predict.ssllrm: x$random is of wrong dimension")
z <- nlvl <- NULL
for (ylab in object$ynames) {
y.wk <- mf[,ylab]
lvl.wk <- levels(y.wk)
nlvl.wk <- length(lvl.wk)
nlvl <- c(nlvl,nlvl.wk)
z.aux <- diag(1,nlvl.wk-1)
z.aux <- rbind(z.aux,rep(-1,nlvl.wk-1))
rownames(z.aux) <- lvl.wk
pt.wk <- qd.pt[,ylab]
for (i in 1:(nlvl.wk-1)) {
for (j in 1:nmesh) {
z <- cbind(z,z.aux[pt.wk[j],i]*z.wk)
}
}
}
z <- aperm(array(z,c(nobs,nz,nmesh,nZ/nz)),c(3,2,4,1))
z <- array(z,c(nmesh,nZ,nobs))
}
## return
if (is.null(odds)) {
pdf <- NULL
for (j in 1:nobs) {
wk <- matrix(r[,,j],nmesh,nbasis)%*%object$c
if (nnull) wk <- wk + matrix(s[,j,],nmesh,nnull)%*%object$d
if (nZ) wk <- wk + matrix(z[,,j],nmesh,nZ)%*%object$b
wk <- exp(wk)
pdf <- cbind(pdf,wk/sum(wk))
}
return(t(pdf[y.id,]))
}
else {
s.wk <- r.wk <- z.wk <- 0
for (i in 1:length(odds)) {
r.wk <- r.wk + odds[i]*r[i,,]
if (nnull) s.wk <- s.wk + odds[i]*s[i,,]
if (nZ) z.wk <- z.wk + odds[i]*z[i,,]
}
s.wk <- matrix(s.wk,nobs,nnull)
r.wk <- t(matrix(r.wk,nbasis,nobs))
z.wk <- t(matrix(z.wk,nZ,nobs))
rs <- cbind(r.wk,z.wk,s.wk)
if (!se.odds) as.vector(rs%*%c(object$c,object$b,object$d))
else {
fit <- as.vector(rs%*%c(object$c,object$b,object$d))
se.fit <- .Fortran("hzdaux2",
as.double(object$se.aux$v), as.integer(dim(rs)[2]),
as.integer(object$se.aux$jpvt),
as.double(t(rs)), as.integer(dim(rs)[1]),
se=double(dim(rs)[1]), PACKAGE="gss")[["se"]]
return(list(fit=fit,se.fit=se.fit))
}
}
}
|
