Add osmasem2 and related methods

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: metaSEM
 Type: Package
 Title: Meta-Analysis using Structural Equation Modeling
-Version: 1.4.3
-Date: 2024-07-13
+Version: 1.4.4
+Date: 2024-07-27
 Depends: R (>= 3.4.0), OpenMx
 Imports: Matrix, MASS, ellipse, graphics, stats, utils, mvtnorm, numDeriv, lavaan
 Suggests: metafor, semPlot, R.rsp, testthat, matrixcalc

NAMESPACE

@@ -29,6 +29,7 @@ S3method(summary, CorPop)
 S3method(summary, Cor3L)
 S3method(summary, bootuniR2)
 S3method(summary, osmasem)
+S3method(summary, osmasem2)
 S3method(summary, mxRAMmodel)
 # S3method(summary, osmasem3L)
 # S3method(summary, tssemRobust1)
@@ -38,6 +39,7 @@ S3method(anova, meta3LFIML)
 S3method(anova, wls)
 S3method(anova, reml)
 S3method(anova, osmasem)
+S3method(anova, osmasem2)
 S3method(anova, mxRAMmodel)
 # S3method(anova, osmasem3L)
 
@@ -50,6 +52,7 @@ S3method(coef, meta)
 S3method(coef, meta3LFIML)
 S3method(coef, reml)
 S3method(coef, osmasem)
+S3method(coef, osmasem2)
 S3method(coef, mxRAMmodel)
 # S3method(coef, osmasem3L)
 # S3method(coef, tssemRobust1)
@@ -63,6 +66,7 @@ S3method(vcov, meta)
 S3method(vcov, meta3LFIML)
 S3method(vcov, reml)
 S3method(vcov, osmasem)
+S3method(vcov, osmasem2)
 S3method(vcov, mxRAMmodel)
 # S3method(vcov, osmasem3L)
 # S3method(vcov, tssemRobust1)

NEWS

@@ -1,3 +1,8 @@
+Release 1.4.4 (Jul 27, 2024)
+====================================
+* Add osmasem2() and friends.
+* Add mean data in issp05.
+
 Release 1.4.3 (Jul 13, 2024)
 ====================================
 * Support means in Cor2DataFrame().

R/Cor2DataFrame.R

@@ -2,7 +2,9 @@ Cor2DataFrame <- function(x, n, v.na.replace=TRUE, cor.analysis=TRUE,
  acov=c("weighted", "individual", "unweighted"),
  Means, row.names.unique=FALSE, append.vars=TRUE,
  asyCovOld=FALSE, ...) {
-
+
+ acov <- match.arg(acov, c("weighted", "individual", "unweighted"))
+
  ## x is a list of "data", "n", ...
  if (all(c("data", "n") %in% names(x))) {
  my.cov <- x$data
@@ -24,16 +26,34 @@ Cor2DataFrame <- function(x, n, v.na.replace=TRUE, cor.analysis=TRUE,
  my.df <- list2matrix(x=my.cov, diag=TRUE)
  }
 
+ ## NA is not allowed in definition variables
+ ## Check if there are NAs in my.cov and replace them with the weighted or unweighted averages
+ if (any(sapply(my.cov, is.na)) & v.na.replace) {
+ ## Replace NA with 0 before calculations
+ my.x <- lapply(my.cov, function(z) {z[is.na(z)] <- 0; z} )
+ if (acov=="unweighted") {
+ ## x: original covariance matrices in the input
+ ## Unweighted means = sum of r/(no. of studies)
+ cov.mean <- Reduce("+", my.x)/pattern.na(x, show.na = FALSE)
+ } else if (acov=="weighted") {
+ my.x <- mapply("*", my.x, n, SIMPLIFY = FALSE)
+ ## Weighted means = Cummulative sum of r*n/(sum of n)
+ cov.mean <- Reduce("+", my.x)/pattern.n(x, n)
+ }
+
+ my.cov <- lapply(my.cov,
+ function(z) {
+ na.index <- is.na(z)
+ z[na.index] <- cov.mean[na.index]
+ z})
+ }
+
  if (asyCovOld) {
  acovR <- asyCovOld(x=my.cov, n=n, cor.analysis=cor.analysis, acov=acov, ...)
  } else {
  acovR <- asyCov(x=my.cov, n=n, cor.analysis=cor.analysis, acov=acov, ...)
  }
 
- ## NA is not allowed in definition variables
- ## They are replaced by 1e10
- if (v.na.replace) acovR[is.na(acovR)] <- 1e10
-
  ## x is a list of "data", "n", and moderators, and append
  ## Append the moderators x[-c(1,2)] into data
  if (all(c(c("data", "n") %in% names(x), length(names(x))>2, append.vars))) {
@@ -56,20 +76,16 @@ Cor2DataFrame <- function(x, n, v.na.replace=TRUE, cor.analysis=TRUE,
  stop("Number of columns of 'Means' and covariance matrices are different.\n")
  }
  if (!identical(colnames(my.cov[[1]]), colnames(Means))) {
- stop("The variable names are not in the same order in 'x' and 'Means'. The results are likely incorrect unless this is what you want.\n")
+ stop("The variable names are not in the same order in 'x' and 'Means'.
+The results are likely incorrect unless this is what you want.\n")
  }
- 
+
  ## Sampling covariance matrices of the means: covariance matrices/n
- ## NA are replaced with 10^5
- acov_mean <- mapply(function(x, y) {
- out <- x/y
- out[is.na(out)] <- 1e10
- out},
- my.cov, n, SIMPLIFY=FALSE)
+ acov_mean <- mapply(function(x, y) {x/y}, my.cov, n, SIMPLIFY=FALSE)
  acov_mean <- t(sapply(acov_mean, function(x) vech(x)))
 
  ## Variable names of p (sampling covariance matrix of the means
- pCovNames <- matrix(paste("M(",
+ pCovNames <- matrix(paste("C(",
  outer(obslabels, obslabels, paste, sep = " "), 
  ")", sep=""),
  nrow=length(obslabels), ncol=length(obslabels))
@@ -80,12 +96,22 @@ Cor2DataFrame <- function(x, n, v.na.replace=TRUE, cor.analysis=TRUE,
  rownames(acov_mean) <- make.names(rownames(acov_mean), unique=TRUE) 
  }
 
- list(data=cbind(data, Means, acov_mean), n=n, obslabels=obslabels,
- ylabels=colnames(my.df), vlabels=colnames(acovR),
- Meanvlabels=pCovNames)
+ out <- list(data=cbind(data, Means, acov_mean), n=n, obslabels=obslabels,
+  ylabels=colnames(my.df), vlabels=colnames(acovR),
+  vMlabels=pCovNames, VyMlabels=NULL)
  } else {
  ## Without the means
- list(data=data, n=n, obslabels=obslabels, ylabels=colnames(my.df),
- vlabels=colnames(acovR))
+ out <- list(data=data, n=n, obslabels=obslabels, ylabels=colnames(my.df),
+  vlabels=colnames(acovR), VyMlabels=NULL)
  }
+
+ ## obslabels: labels of the means (Means) and dimnames in the
+ ## correlation/covariance matrices (Cov)
+ ## ylabels: vech(Cov) generated from list2matrix()
+ ## vlabels: Acov of vech(Cov) generated from asyCov()
+ ## Mlabels: labels of the means (not included as they are identical to obslabels)
+ ## vMlabels: Acov of Means
+ ## VyMlabels: Acov of Cov and Means (not included as Cov and Means are independent)
+
+ out
 }

R/create.mxModel.R

@@ -157,12 +157,10 @@ create.mxModel <- function(model.name="mxModel", RAM=NULL, data=NULL,
  mx.model <- mxModel(mx.model, M$mxalgebra, M$parameters, M$list)
  }
 
- mx.model <- mxModel(mx.model, Fmatrix, Id, Id_A, expCov, expMean,
- mxCI(c("Amatrix", "Smatrix", "Mmatrix")))
+ mx.model <- mxModel(mx.model, Fmatrix, Id, Id_A, expCov, expMean)
  } else {
  ## No mean structure
- mx.model <- mxModel(mx.model, Fmatrix, Id, Id_A, expCov, 
- mxCI(c("Amatrix", "Smatrix")))
+ mx.model <- mxModel(mx.model, Fmatrix, Id, Id_A, expCov)
  }
 
  ## Add additional arguments to mxModel
@@ -172,6 +170,14 @@ create.mxModel <- function(model.name="mxModel", RAM=NULL, data=NULL,
  }
  }
 
+ ## New parameter labels including those in constraints
+ new.para.labels <- unique(c(A, S, M))
+ ## Get the variable names
+ new.para.labels <- all.vars(parse(text=new.para.labels))
+ ## Drop the definition variables
+ new.para.labels <- new.para.labels[!grepl("data.", new.para.labels)]
+ mx.model <- mxModel(mx.model, mxCI(new.para.labels))
+
  ## A list of mxalgebras required SE or CI
  mxalgebras.ci <- NULL