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
|
##' Initialize new latent variable model
##'
##' Function that constructs a new latent variable model object
##'
##' @aliases lvm print.lvm summary.lvm
##' @param x Vector of variable names. Optional but gives control of the
##' sequence of appearance of the variables. The argument can be given as a
##' character vector or formula, e.g. \code{~y1+y2} is equivalent to
##' \code{c("y1","y2")}. Alternatively the argument can be a formula specifying
##' a linear model.
##' @param \dots Additional arguments to be passed to the low level functions
##' @param latent (optional) Latent variables
##' @param messages Controls what messages are printed (0: none)
##' @return Returns an object of class \code{lvm}.
##' @author Klaus K. Holst
##' @seealso \code{\link{regression}}, \code{\link{covariance}},
##' \code{\link{intercept}}, ...
##' @keywords models regression
##' @export
##' @examples
##'
##' m <- lvm() # Empty model
##' m1 <- lvm(y~x) # Simple linear regression
##' m2 <- lvm(~y1+y2) # Model with two independent variables (argument)
##' m3 <- lvm(list(c(y1,y2,y3)~u,u~x+z)) # SEM with three items
##'
lvm <- function(x=NULL, ..., latent=NULL, messages=lava.options()$messages) {
M <- C <- par <- fix <- numeric(); mu <- list()
noderender <- list(
fill=c(),
shape=c(),
label=c()
)
edgerender <- list(lty=c(),
lwd=c(),
col=c(),
textCol=c(),
est=c(),
arrowhead=c(),
dir=c(),
cex=c(),
futureinfo=list())
graphrender <- list(recipEdges="distinct")
graphdefault <- list(
"fill"="white",
"shape"="rectangle",
"label"=expression(NA),
"lty"=1,
"lwd"=1,
"col"="black",
"textCol"="black",
"est"=0,
"arrowhead"="open",
"dir"="forward",
"cex"=1.5,
"label"=expression(),
"futureinfo"=c())
modelattr <- list(
randomslope=list(),
survival=list(),
parameter=list(),
categorical=list(),
distribution=list(),
nonlinear=list(),
functional=list(),
label=list())
res <- list(M=M, par=par, cov=C, covpar=C, fix=fix, covfix=fix,latent=list(),
mean=mu, index=NULL, exogenous=NA,
constrain=list(), constrainY=list(),
attributes=modelattr, noderender=noderender,
edgerender=edgerender, graphrender=graphrender,
graphdef=graphdefault)
class(res) <- "lvm"
myhooks <- gethook("init.hooks")
for (f in myhooks) {
res <- do.call(f, list(x=res))
}
myvar <- NULL
if (!is.list(x)) x <- list(x,...)
for (myvar in x) {
if (inherits(myvar,"formula")) {
regression(res,messages=messages) <- myvar
}
if (is.character(myvar)) {
res <- addvar(res, myvar, messages=messages) }
}
if (!is.null(myvar)) {
index(res) <- reindex(res,zeroones=TRUE) }
if (!is.null(latent)) {
latent(res) <- latent
}
return(res)
}
|
