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### Path diagrams ###
#
# semPaths_MxRAMModel <- function(object,...){
# invisible(semPaths(semPlotModel(object),...))
# }
#
# semPaths_MxModel <- function(object,...){
# invisible(semPaths(semPlotModel(object),...))
# }
#
### EXTRACT MODEL ###
### SINGLE GROUP ###
semPlotModel_MxRAMModel <- function(object){
# Extract names:
varNames <- object@manifestVars
factNames <- object@latentVars
if (!(length(varNames) || length(factNames)))
stop(as.character(substitute(object)), '@manifestVars (and ',
as.character(substitute(object)), '@latentVars if the model has ',
'latent variables) must contain variable names. You can set them ',
'using the manifestVars= and latentVars= arguments in mxModel().')
# Standardized object:
std <- OpenMx::mxStandardizeRAMpaths(object, SE = TRUE)
# Extract directed paths:
# Dirpaths <- which(t(object@matrices$A@free | object@matrices$A@values!=0),arr.ind=TRUE)
# DirpathsFixed <- !t(object@matrices$A@free)[Dirpaths]
# DirpathsValues <- t(object@matrices$A@values)[Dirpaths]
# DirpathsLabels <- t(object@matrices$A@labels)[Dirpaths]
# Extract symmetric paths:
# Sympaths <- which(t(object@matrices$S@free | object@matrices$S@values!=0) & upper.tri(object@matrices$S@values,diag=TRUE),arr.ind=TRUE)
# SympathsFixed <- !t(object@matrices$S@free)[Sympaths]
# SympathsValues <- t(object@matrices$S@values)[Sympaths]
# SympathsLabels <- t(object@matrices$A@labels)[Sympaths]
# if (!is.null(object@matrices$M))
# {
# # Extract intercepts:
# Means <- which(object@matrices$M@free | object@matrices$M@values!=0)
# MeansFixed <- !object@matrices$M@free[Means]
# MeansValues <- object@matrices$M@values[Means]
# MeansLabels <- object@matrices$M@labels[Means]
# } else
# {
# Means <- numeric(0)
# MeansFixed <- logical(0)
# MeansValues <- numeric(0)
# MeansLabels <- character(0)
# }
#
# ## Standardized
# if (!length(object@output)==0)
# {
# # browser()
# # Function by Ryne Estabrook (http://openmx.psyc.virginia.edu/thread/718)
#
# standObj <- standardizeRAM(object,"model")
#
# # Extract directed paths:
# # DirpathsValuesStd <- t(standObj@matrices$A@values)[Dirpaths]
# # DirpathsValuesStd <- std$Std.Value[std$matrix=="A"]
#
# # Extract symmetric paths:
# SympathsValuesStd <- t(standObj@matrices$S@values)[Sympaths]
#
# # Extract means:
#
#if (!is.null(standObj@matrices$M))
# {
# MeansValuesStd <- standObj@matrices$S@values[Means]
# } else {
# MeansValuesStd <- numeric(0)
# }
# } else
# {
# DirpathsValuesStd <- rep(NA,nrow(Dirpaths))
# SympathsValuesStd <- rep(NA,nrow(Sympaths))
# MeansValuesStd <- rep(NA,length(Means))
# }
#
# Vars dataframe:
Vars <- data.frame(
name = c(varNames,factNames),
manifest = c(varNames,factNames)%in%varNames,
exogenous = NA,
stringsAsFactors=FALSE)
# standObj <- standardizeMx(object,free=T) # old semTools function, now in this file
Edges <- std
# Only edges in mats A and S:
corMats <- Edges$matrix %in% c("A","S")
# Define Pars:
Pars <- data.frame(
label = ifelse(is.na(Edges$label[corMats]),"",Edges$label[corMats]),
lhs = Edges$col[corMats],
edge = ifelse(Edges$matrix[corMats]=="A","->","<->"),
rhs = Edges$row[corMats],
est = Edges$Raw.Value[corMats],
std = Edges$Std.Value[corMats],
group = '',
fixed = Edges$Raw.SE[corMats]==0,
par = 0,
stringsAsFactors=FALSE)
# Maybe remove ints?
if (!is.null(object@matrices$M)) {
MeanStd <- c(object@matrices$M$values)
## in case labels are NA, use variable names
if (!is.null(colnames(object@matrices$M$values))) {
v.names <- colnames(object@matrices$M$values)
v.idx <- v.names
names(MeanStd) <- v.names
} else {
#FIXME? Warn users that this assumes order is {all manifest, all latent}
v.names <- c(varNames, factNames)
v.idx <- seq_along(v.names)
}
## extract rows of std corresponding to the M matrix
stdM <- std[std$matrix == "M", , drop = FALSE]
## loop over variable names that have a standardized estimate
## (only free parameters; assume others are fixed to zero)
for (v in seq_along(stdM$col)) {
MeanStd[ v.idx[v] ] <- stdM$Std.Value[stdM$col == v.idx[v] ]
}
## old method (using deprecated semTools function, now at the bottom of this script)
## standardizeMx(object,free=T)[which(names(standardizeMx(object,free=T))%in%object@matrices$M$labels)]
MeanEst <-data.frame(
label = c(object@matrices$M$labels),
##### or, if they are NA, replace with variable names?
# label = ifelse(!is.na(object@matrices$M$labels),
# yes = object@matrices$M$labels,
# no = v.names),
lhs = '',
rhs = v.names,
edge = 'int',
est = c(object@matrices$M$values),
std = MeanStd,
group = '',
fixed = c(!object@matrices$M$free),
par = 0,
stringsAsFactors = FALSE )
Pars <- rbind(Pars,MeanEst)
}
Pars$par[is.na(Pars$label)] <- seq_len(sum(is.na(Pars$label)))
for (lbl in unique(Pars$label[!is.na(Pars$label)]))
{
Pars$par[Pars$label==lbl] <- max(Pars$par)+1
}
#
# # Add standardized:
# for (i in 1:nrow(standPars))
# {
# if (standPars$matrix[i] == "A")
# {
# Pars$std[Pars$lhs == standPars$col[i] & Pars$rhs == standPars$row[i] & Pars$edge == "->"] <- standPars[["Std. Estimate"]][i]
# }
# if (standPars$matrix[i] == "S")
# {
# Pars$std[Pars$lhs == standPars$col[i] & Pars$rhs == standPars$row[i] & Pars$edge == "<->"] <- standPars[["Std. Estimate"]][i]
# }
# }
Pars$label[is.na(Pars$label)] <- ""
semModel <- new("semPlotModel")
semModel@Pars <- Pars
semModel@Vars <- Vars
semModel@Computed <- !length(object@output)==0
semModel@Original <- list(object)
if (!is.null(object@data))
{
if (object@data@type=="cov")
{
semModel@ObsCovs <- list(object@data@observed)
} else if (object@data@type=="raw")
{
semModel@ObsCovs <- list(cov(object@data@observed))
} else
{
semModel@ObsCovs <- list(NULL)
}
} else
{
semModel@ObsCovs <- list(NULL)
}
semModel@ImpCovs <- list(object@fitfunction@info$expCov)
return(semModel)
}
semPlotModel_MxModel <- function(object){
if (any(!"MxRAMModel"%in%sapply(object@submodels,class))) stop("Model or all submodels must be of class 'MxRAMModel'")
for (i in 1:length(object@submodels)) object@submodels[[i]]@output <- list(TRUE)
S4objects <- lapply(object@submodels,semPlotModel)
semModel <- new("semPlotModel")
semModel@Pars <- do.call("rbind",lapply(S4objects,slot,"Pars"))
semModel@Pars$par <- 0
semModel@Pars$par[semModel@Pars$label==""] <- seq_len(sum(semModel@Pars$label==""))
for (lbl in unique(semModel@Pars$label[semModel@Pars$label!=""]))
{
semModel@Pars$par[semModel@Pars$label==lbl] <- max(semModel@Pars$par)+1
}
semModel@Vars <- S4objects[[1]]@Vars
semModel@Computed <- !length(object@output)==0
semModel@Original <- list(object)
semModel@ObsCovs <- lapply(S4objects,function(x)x@ObsCovs[[1]])
names(semModel@ObsCovs) <- sapply(object@submodels,slot,"name")
semModel@ImpCovs <- lapply(S4objects,function(x)x@ImpCovs[[1]])
names(semModel@ImpCovs) <- sapply(object@submodels,slot,"name")
return(semModel)
}
## -----------------------------------------------------------------
## semTools function (no longer used, but can be borrowed if needed)
## -----------------------------------------------------------------
standardizeMx <- function(object, free = TRUE) {
.Deprecated(msg = c("The standardizeMx function is deprecated, and it will",
" cease to be included in future versions of semTools.",
" See help('semTools-deprecated) for details."))
# objectOrig <- object
multigroup <- length(object@submodels) > 0
if(multigroup) {
defVars <- lapply(object@submodels, findDefVars)
defVars <- do.call(c, defVars)
} else {
defVars <- findDefVars(object)
}
if(length(defVars) > 0) stop("The standardizeMx is not available for the model with definition variable.")
if(multigroup) {
object@submodels <- lapply(object@submodels, standardizeMxSingleGroup)
} else {
object <- standardizeMxSingleGroup(object)
}
vectorizeMx(object, free=free)
}
## Hidden functions
findDefVars <- function(object) {
## borrowed from OpenMx::imxIsDefinitionVariable
imxSeparatorChar <- "."
imxIsDefinitionVariable <- function (name) {
if (is.na(name)) {
return(FALSE)
}
components <- unlist(strsplit(name, imxSeparatorChar, fixed = TRUE))
if (length(components) == 2 && components[[1]] == "data") {
return(TRUE)
}
else if (length(components) > 2 && components[[2]] == "data") {
return(TRUE)
}
else {
return(FALSE)
}
}
## end borrowed code
mat <- lapply(object@matrices, slot, "labels")
defvars <- sapply(mat, function(x) x[apply(x, c(1,2), imxIsDefinitionVariable)])
Reduce("c", defvars)
}
vectorizeMx <- function(object, free = TRUE) {
multigroup <- length(object@submodels) > 0
if(multigroup) {
object <- object@submodels
} else {
object <- list(object)
}
result <- NULL
for(i in seq_along(object)) {
name <- ""
if(multigroup) name <- paste0(object[[i]]@name, ".")
mat <- object[[i]]@matrices
for(j in seq_along(mat)) {
tempname <- paste0(name, mat[[j]]@name)
lab <- mat[[j]]@labels
tempfree <- as.vector(mat[[j]]@free)
madeLab <- paste0(tempname, "[", row(lab), ",", col(lab), "]")
lab <- as.vector(lab)
madeLab[!is.na(lab)] <- lab[!is.na(lab)]
if(!free) tempfree <- rep(TRUE, length(tempfree))
temp <- mat[[j]]@values[tempfree]
names(temp) <- madeLab[tempfree]
result <- c(result, temp)
}
}
result[!duplicated(names(result))]
}
standardizeMxSingleGroup <- function(object) {
if (!is(object@expectation, "MxExpectationRAM"))
stop("The standardizeMx function is available for the MxExpectationRAM only.")
A <- object@matrices$A@values
I <- diag(nrow(A))
S <- object@matrices$S@values
# F <- object@matrices$F@values
Z <- solve(I - A)
impliedCov <- Z %*% S %*% t(Z)
temp <- sqrt(diag(impliedCov))
if (length(temp) == 1) {
ImpliedSd <- as.matrix(temp)
} else {
ImpliedSd <- diag(temp)
}
ImpliedInvSd <- solve(ImpliedSd)
object@matrices$S@values <- ImpliedInvSd %*% S %*% ImpliedInvSd
object@matrices$A@values <- ImpliedInvSd %*% A %*% ImpliedSd
if (!is.null(object@matrices$M)) {
M <- object@matrices$M@values
object@matrices$M@values <- M %*% ImpliedInvSd
}
object
}
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