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### SINGLE GROUP MODEL ###
ramModel <- function(A,S,F,M,manNames,latNames,Names,ObsCovs,ImpCovs,modelLabels = FALSE)
{
# Check if meanstructure is included:
meanstructure <- !missing(M)
# Input matrices either in matrix form or list containing 'est', 'std', ; fixed', and 'par' or 'parSpec' matrices. If 'stdComp' is in the list it overwrites 'std' (compatibility with 'lisrelToR' package):
# Or a list of such lists for each group.
# Check input, replace matrices with list:
mats <- c("A","S","F", "M")
for (m in mats)
{
if (!do.call(missing,list(m)))
{
assign(m,fixMatrix(get(m)))
} else {
assign(m,list())
}
}
### Fix matrices:
matList <- list(A,S,F)
Ng <- max(sapply(matList,length))
Nvar <- max(sapply(matList,function(x)sapply(x,function(y)ncol(y$est))))
if (length(F)>0 && !is.null(F[[1]]$est))
{
Nman <- max(sapply(F,function(y)nrow(y$est)))
} else
{
if (!missing(manNames)) Nman <- length(manNames) else Nman <- Nvar
}
if (!missing(manNames) & !missing(latNames))
{
if (Nvar!=length(c(manNames,latNames))) stop("Number of variables in model not equal to given number of names")
}
if (!missing(manNames))
{
if (Nman!=length(manNames)) stop("Number of manifest variables in model not equal to given number of names")
}
# Fix A:
if (length(A)==0)
{
A <- lapply(seq_len(Ng),function(x)list(est=matrix(0,Nvar,Nvar)))
} else if (length(A) < Ng) A <- rep(A,length=Ng)
# Fix S
if (length(S)==0)
{
S <- lapply(seq_len(Ng),function(x)list(est=matrix(0,Nvar,Nvar)))
} else if (length(S) < Ng) S <- rep(S,length=Ng)
# Fix F:
if (length(F)==0)
{
F <- lapply(seq_len(Ng),function(x)list(est=cbind(diag(1,Nman,Nman),matrix(0,Nman,Nvar-Nman))))
} else if (length(F) < Ng) F <- rep(F,length=Ng)
# Fix M:
if (length(M)==0)
{
M <- lapply(seq_len(Ng),function(x)list(est=rep(0,Nvar)))
} else if (length(M) < Ng) M <- rep(M,length=Ng)
### NAMES ###
# If names missing, set default::
if (missing(manNames))
{
if (length(F)>0 && !is.null(F[[1]]$est))
{
if (!is.null(colnames(F[[1]]$est)) && !modelLabels)
{
manNames <- colnames(F[[1]]$est)[colSums(F[[1]]$est)>0]
} else manNames <- paste0(rep("m",Nman),seq_len(Nman))
} else manNames <- paste0(rep("m",Nman),seq_len(Nman))
}
if (missing(latNames))
{
if (length(F)>0 && !is.null(F[[1]]$est))
{
if (!is.null(colnames(F[[1]]$est)) && !modelLabels)
{
latNames <- colnames(F[[1]]$est)[colSums(F[[1]]$est)==0]
} else latNames <- paste0(rep("l",Nvar-Nman),seq_len(Nvar-Nman))
} else latNames <- paste0(rep("l",Nvar-Nman),seq_len(Nvar-Nman))
}
if (missing(Names))
{
if (length(F)>0 && !is.null(F[[1]]$est))
{
if (!is.null(colnames(F[[1]]$est)) && !modelLabels)
{
Names <- colnames(F[[1]]$est)
} else Names <- c(manNames,latNames)
} else Names <- c(manNames,latNames)
}
Parss <- list()
dumPars <- data.frame(
label = character(0),
lhs = character(0),
edge = character(0),
rhs = character(0),
est = numeric(0),
std = numeric(0),
group = character(0),
fixed = logical(0),
par = numeric(0),
stringsAsFactors=FALSE)
if (missing(ImpCovs))
{
modCovs <- list()
}
for (g in 1:Ng)
{
# Compute model implied covariance matrix and standardized matrices:
# M is matrix list:
Mod <- list(A=A[[g]]$est, S=S[[g]]$est, F=F[[g]]$est)
IminAinv <- InvEmp(diag(1,nrow(Mod$A),ncol(Mod$A)) - Mod$A)
if (missing(ImpCovs))
{
modCovs[[g]] <- with(Mod, F %*% IminAinv %*% S %*% t(IminAinv) %*% t(F))
rownames(modCovs[[g]]) <- colnames(modCovs[[g]]) <- manNames
}
Mstd <- Mod
## Standardize matrices
I <- diag(nrow(Mod$S))
expCov <- IminAinv %*% Mod$S %*% t(IminAinv)
invSDs <- 1/sqrt(diag(expCov))
diag(I) <- invSDs
# standardize the A, S and M matrices
# A paths are value*sd(from)/sd(to) = I %*% A %*% solve(I)
# S paths are value/(sd(from*sd(to))) = I %*% S %*% I
Mstd$A <- I %*% Mod$A %*% solve(I)
Mstd$S <- I %*% Mod$S %*% I
# Store matrices:
if (length(A) > 0 && !is.null(A[[g]]$est) && is.null(A[[g]]$std)) A[[g]]$std <- Mstd$A
if (length(S) > 0 && !is.null(S[[g]]$est) && is.null(S[[g]]$std)) S[[g]]$std <- Mstd$S
# Extract matrices:
if (length(A)>0) APars <- modMat2Pars(A[[g]],"->","A",symmetric=FALSE,vec=FALSE,Names,Names,group=paste("Group",g),exprsup="") else APars <- dumPars
if (length(S)>0) SPars <- modMat2Pars(S[[g]],"<->","S",symmetric=TRUE,vec=FALSE,Names,Names,group=paste("Group",g),exprsup="") else SPars <- dumPars
if (length(M)>0) MPars <- modMat2Pars(M[[g]],"int","M",symmetric=FALSE,vec=TRUE,"",Names,group=paste("Group",g),exprsup="") else Mpars <- dumPars
# Combine ParsS:
Parss[[g]] <- rbind(APars,SPars,MPars)
# Remove zeroes:
Parss[[g]] <- Parss[[g]][Parss[[g]]$est!=0,]
}
Pars <- do.call(rbind,Parss)
# Variable dataframe:
Vars <- data.frame(
name = c(manNames,latNames),
manifest = c(manNames,latNames)%in%manNames,
exogenous = NA,
stringsAsFactors=FALSE)
# Remove duplicates plus factor loadings betwen mans and lats of same name:
Vars <- Vars[!duplicated(Vars$name),]
Pars <- Pars[!(Pars$lhs==Pars$rhs&Pars$edge!="<->"),]
semModel <- new("semPlotModel")
semModel@Pars <- Pars
semModel@Vars <- Vars
semModel@Original <- list()
if (!missing(ObsCovs))
{
semModel@ObsCovs <- list(ObsCovs)
} else {
semModel@ObsCovs <- list()
}
if (!missing(ImpCovs))
{
semModel@ImpCovs <- list(ImpCovs)
} else {
semModel@ImpCovs <- modCovs
}
semModel@Computed <- length(semModel@ImpCovs) > 0
return(semModel)
}
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