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R/xmu_make_top_twin_models.R
In umx: Structural Equation Modeling and Twin Modeling in R
Defines functions
xmu_twin_add_WeightMatrices
xmu_starts
xmu_twin_make_def_means_mats_and_alg
xmuTwinUpgradeMeansToCovariateModel
xmuTwinSuper_CovCor
xmuTwinSuper_SomeBinary
xmuTwinSuper_NoBinary
xmuTwinSuper_Continuous
xmu_make_TwinSuperModel
Documented in
xmu_make_TwinSuperModel
xmu_starts
xmu_twin_add_WeightMatrices
xmu_twin_make_def_means_mats_and_alg
xmuTwinSuper_Continuous
xmuTwinSuper_NoBinary
xmuTwinUpgradeMeansToCovariateModel
#' Helper to make a basic top, MZ, and DZ model.
#'
#' @description
#' `xmu_make_TwinSuperModel` makes basic twin model containing `top`, `MZ`, and `DZ` models. It intelligently handles thresholds for
#' ordinal data, and means model for covariates matrices in the twin models if needed.
#'
#' It's the replacement for `xmu_assemble_twin_supermodel` approach.
#'
#' @details
#' `xmu_make_TwinSuperModel` is used in twin models (e.g.[umxCP()], [umxACE()] and [umxACEv()] and will be added to the other models: [umxGxE()], [umxIP()],
#' simplifying code maintenance.
#'
#' It takes `mzData` and `dzData`, a list of the `selDVs` to analyse and optional `selCovs` (as well as `sep` and `nSib`), along with other
#' relevant information such as whether the user wants to `equateMeans`.
#' It can also handle a `weightVar`.
#'
#' If covariates are passed in these are included in the means model (via a call to `xmuTwinUpgradeMeansToCovariateModel`.
#'
#'
#' **Modeling**
#'
#' **Matrices created**
#'
#' *top model*
#'
#' For raw and WLS data, `top` contains a `expMeans` matrix (if needed). For summary data, the top model contains only a name.
#'
#' For ordinal data, `top` gains `top.threshMat` (from a call to [umxThresholdMatrix()]).
#'
#' For covariates, top stores the `intercepts` matrix and a `betaDef` matrix. These are then used to make expMeans in `MZ` and `DZ`.
#'
#' *MZ and DZ models*
#'
#' `MZ` and `DZ` contain the data, and an expectation referencing `top.expCovMZ` and `top.expMean`, and, `vector = bVector`.
#' For continuous raw data, MZ and DZ contain [OpenMx::mxExpectationNormal()] and [OpenMx::mxFitFunctionML()].
#' For WLS these the fit function is switched to [OpenMx::mxFitFunctionWLS()] with appropriate `type` and `allContinuousMethod`.
#'
#'
#' For binary, a constraint and algebras are included to constrain `Vtot` (A+C+E) to 1.
#'
#' If a `weightVar` is detected, these columns are used to create a row-weighted MZ and DZ models.
#'
#' If `equateMeans` is `TRUE`, then the Twin-2 vars in the mean matrix are equated by label with Twin-1.
#'
#'
#' Decent starts are guessed from the data.
#' `varStarts` is computed as `sqrt(variance)/3` of the DVs and `meanStarts` as the variable means.
#' For raw data, a check is made for ordered variables. For Binary variables, means are fixed at 0 and
#' total variance (A+C+E) is fixed at 1. For ordinal variables, the first 2 thresholds are fixed.
#'
#' Where needed, e.g. continuous raw data, top adds a means matrix "expMean".
#' For ordinal data, top adds a [umxThresholdMatrix()].
#'
#' If binary variables are present, matrices and a constraint to hold `A+C+E == 1` are added to top.
#'
#' If a weight variable is offered up, an `mzWeightMatrix` will be added.
#'
#' **Data handling**
#'
#' In terms of data handling, `xmu_make_TwinSuperModel` was primarily designed to take
#' data.frames and process these into mxData.
#' It can also, however, handle cov and mxData input.
#'
#' It can process data into all the types supported by `mxData`.
#'
#' Raw data input with a target of `cov` or `cor` type requires the `numObsMZ` and `numObsDZ` to be set.
#'
#' Type "WLS", "DWLS", or "ULS", data remain raw, but are handled as WLS in the [OpenMx::mxFitFunctionWLS()].
#'
#' Unused columns are dropped.
#'
#' If you pass in raw data, you can't request type cov/cor yet. Will work on this if desired.
#'
#' @param name for the supermodel
#' @param mzData Dataframe containing the MZ data
#' @param dzData Dataframe containing the DZ data
#' @param selDVs List of manifest base names (e.g. BMI, NOT 'BMI_T1') (OR, you don't set "sep", the full variable names)
#' @param selCovs List of covariate base names (e.g. age, NOT 'age_T1') (OR, you don't set "sep", the full variable names)
#' @param sep string used to expand selDVs into selVars, i.e., "_T" to expand BMI into BMI_T1 and BMI_T2 (optional but STRONGLY encouraged)
#' @param type One of 'Auto','FIML','cov', 'cor', 'WLS','DWLS', or 'ULS'. Auto tries to react to the incoming mxData type (raw/cov).
#' @param allContinuousMethod "cumulants" or "marginals". Used in all-continuous WLS data to determine if a means model needed.
#' @param nSib Number of members per family (default = 2)
#' @param equateMeans Whether to equate T1 and T2 means (default = TRUE).
#' @param weightVar If provided, a vector objective will be used to weight the data. (default = NULL).
#' @param numObsMZ Number of MZ observations contributing (for summary data only)
#' @param numObsDZ Number of DZ observations contributing (for summary data only)
#' @param bVector Whether to compute row-wise likelihoods (defaults to FALSE).
#' @param dropMissingDef Whether to automatically drop missing def var rows for the user (default = TRUE). You get a polite note.
#' @param verbose (default = FALSE)
#' @return - [mxModel()]s for top, MZ and DZ.
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
# # TODO add tests with numObsMZ = NULL, numObsDZ = NULL, equateMeans = TRUE,
# # TODO add tests with weightVar = NULL, bVector = FALSE,
#
#' # ==============
#' # = Continuous =
#' # ==============
#' library(umx)
#' data(twinData)
#' twinData = umx_scale(twinData, varsToScale= c('ht1','ht2'))
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1= xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData, selDVs=c("wt","ht"), sep="", nSib=2)
#' names(m1) # "top" "MZ" "DZ"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionML"
#'
#' # ====================
#' # = With a covariate =
#' # ====================
#'
#' m1= xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData,
#' selDVs= "wt", selCovs= "age", sep="", nSib=2)
#' m1$top$intercept$labels
#' m1$MZ$expMean
#'
#' # ===============
#' # = WLS example =
#' # ===============
#' m1=xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData,selDVs=c("wt","ht"),sep="",type="WLS")
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#' m1$MZ$fitfunction$type =="WLS"
#' # Check default all-continuous method
#' m1$MZ$fitfunction$continuousType == "cumulants"
#'
#' # Choose non-default type (DWLS)
#' m1= xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData,
#' selDVs= c("wt","ht"), sep="", type="DWLS")
#' m1$MZ$fitfunction$type =="DWLS"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#'
#' # Switch WLS method
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= c("wt","ht"), sep= "",
#' type = "WLS", allContinuousMethod = "marginals")
#' m1$MZ$fitfunction$continuousType == "marginals"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#'
#'
#' # ============================================
#' # = Bivariate continuous and ordinal example =
#' # ============================================
#' data(twinData)
#' selDVs = c("wt", "obese")
#' # Cut BMI column to form ordinal obesity variables
#' ordDVs = c("obese1", "obese2")
#' obesityLevels = c('normal', 'overweight', 'obese')
#' cutPoints = quantile(twinData[, "bmi1"], probs = c(.5, .2), na.rm = TRUE)
#' twinData$obese1 = cut(twinData$bmi1, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' twinData$obese2 = cut(twinData$bmi2, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' # Make the ordinal variables into mxFactors (ensure ordered is TRUE, and require levels)
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= selDVs, sep="", nSib= 2)
#' names(m1) # "top" "MZ" "DZ"
#'
#' # ==============
#' # = One binary =
#' # ==============
#' data(twinData)
#' cutPoints = quantile(twinData[, "bmi1"], probs = .2, na.rm = TRUE)
#' obesityLevels = c('normal', 'obese')
#' twinData$obese1 = cut(twinData$bmi1, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' twinData$obese2 = cut(twinData$bmi2, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' ordDVs = c("obese1", "obese2")
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' selDVs = c("wt", "obese")
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= selDVs, sep= "", nSib= 2)
#'
#' # ========================================
#' # = Cov data (calls xmuTwinSuper_CovCor) =
#' # ========================================
#'
#' data(twinData)
#' mzData =cov(twinData[twinData$zygosity %in% "MZFF", tvars(c("wt","ht"), sep="")], use="complete")
#' dzData =cov(twinData[twinData$zygosity %in% "DZFF", tvars(c("wt","ht"), sep="")], use="complete")
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= "wt", sep= "",
#' nSib= 2, numObsMZ = 100, numObsDZ = 100, verbose=TRUE)
#' class(m1$MZ$fitfunction)[[1]] =="MxFitFunctionML"
#' dimnames(m1$MZ$data$observed)[[1]]==c("wt1", "wt2")
#'
xmu_make_TwinSuperModel <- function(name = "twin_super", mzData, dzData, selDVs, selCovs= NULL, sep = NULL, type = c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS"), allContinuousMethod = c("cumulants", "marginals"), numObsMZ = NULL, numObsDZ = NULL, nSib = 2, equateMeans = TRUE, weightVar = NULL, bVector = FALSE, dropMissingDef = TRUE, verbose= FALSE) {
# TODO for xmu_make_TwinSuperModel
# TODO: 1. xmu_make_TwinSuperModel Add selCovs
# TODO: 2. xmu_make_TwinSuperModel Add beta matrix for fixed covariates in means.
# TODO: 4. xmu_make_TwinSuperModel more tests in a test page
# *Note*: If dropping this into an existing model, all your code setting: nVar, selVars, used
# any code figuring out data-type, is not needed!
# ===================
# = match arguments =
# ===================
type = match.arg(type)
allContinuousMethod = match.arg(allContinuousMethod)
# ====================================================
# = Figure out selVars, nVar, usedVars, and dataType =
# ====================================================
if(is.null(sep)){
fullVars = selDVs
fullCovs = selCovs
message("Polite note: It's better to use 'sep'. This might become compulsory as it helps umx manage variable names in twin models.")
# stop("You MUST set 'sep'. Otherwise xmu_make_top can't reliably expand selDVs into full variable names")
}else{
fullVars = tvars(selDVs , sep = sep, suffixes= 1:nSib)
fullCovs = tvars(selCovs, sep = sep, suffixes= 1:nSib)
}
dataType = umx_is_cov(dzData, boolean = FALSE)
if(verbose){ umx_msg(dataType)}
if(type %in% c("cov", "cor") && !dataType %in% c("cov", "cor")){
stop("You've requested type= cov or cor, but the provided dataType is ", omxQuotes(dataType), " I don't support that yet. Please pass in cov data.")
}
if(dataType == "raw") {
if(!all(is.null(c(numObsMZ, numObsDZ)))){
stop("You should not be setting numObsMZ or numObsDZ with ", omxQuotes(dataType), " data...")
}
if(!is.null(weightVar)){
# Weight variable provided: check it exists in each frame.
mzWeightData = xmu_extract_column(mzData, weightVar)
dzWeightData = xmu_extract_column(dzData, weightVar)
bVector = TRUE
}
# ============================================
# = Make mxData, dropping any unused columns =
# ============================================
usedVars = c(fullVars, fullCovs)
if(verbose){
umx_msg(usedVars)
umx_msg(fullVars)
umx_msg(fullCovs)
umx_msg(type)
}
mzData = xmu_make_mxData(mzData, type = type, manifests = usedVars, fullCovs = fullCovs, numObs = numObsMZ, dropMissingDef = dropMissingDef)
dzData = xmu_make_mxData(dzData, type = type, manifests = usedVars, fullCovs = fullCovs, numObs = numObsDZ, dropMissingDef = dropMissingDef)
# ========================================================================
# = 3. Add mxExpectationNormal, means and var matrices to top, MZ and DZ =
# ========================================================================
# Find ordinal variables
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
if(colTypes$nFactors == 0){
model = xmuTwinSuper_Continuous(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib, type= type, allContinuousMethod= allContinuousMethod)
} else if(sum(colTypes$isBin) == 0){
model = xmuTwinSuper_NoBinary(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib)
} else if(sum(colTypes$isBin) > 0){
model = xmuTwinSuper_SomeBinary(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib, verbose = verbose)
} else {
stop("You appear to have something other than I expected in terms of WLS, or binary, ordinal and continuous variable mix")
}
# nb: means not yet equated across twins
} else if(dataType %in% c("cov", "cor")){
if(!is.null(weightVar)){
stop("You can't set weightVar when you give cov data - use cov.wt to create weighted cov matrices, or pass in raw data")
}
if(!is.null(fullCovs)){
stop("You can't set covariates when you have cov data: needs raw data to estimate on each row")
}
model = xmuTwinSuper_CovCor(name=name, fullVars = fullVars, mzData= mzData, dzData= dzData, type = dataType, numObsMZ = numObsMZ, numObsDZ = numObsDZ)
} else {
stop("Datatype \"", dataType, "\" not understood")
}
# ==============================
# = Add mxFitFunction to model =
# ==============================
if(type %in% c('WLS', 'DWLS', 'ULS')) {
message("Data treated as ", type)
# Still mxExpectationNormal (`top` is not affected - either has or lacks means matrix already).
# Replace the MZ and DZ model FitFunctions
model$MZ = mxModel(model$MZ, mxFitFunctionWLS(type = type, allContinuousMethod = allContinuousMethod) )
model$DZ = mxModel(model$DZ, mxFitFunctionWLS(type = type, allContinuousMethod = allContinuousMethod) )
}else{
model$MZ = mxModel(model$MZ, mxFitFunctionML(vector = bVector) )
model$DZ = mxModel(model$DZ, mxFitFunctionML(vector = bVector) )
}
if(!is.null(weightVar)){
model = xmu_twin_add_WeightMatrices(model, mzWeights = mzWeightData, dzWeights = dzWeightData)
}
return(model)
}
# =============================
# = raw twin-assembly helpers =
# =============================
#' Create core of twin model for all-continuous data.
#'
#' @description
#' Sets up top, MZ and DZ submodels with a means model, data, and expectation for all-continuous data.
#' called by [xmu_make_TwinSuperModel()].
#'
#' @param name The name of the supermodel
#' @param fullVars Full Variable names (wt_T1)
#' @param fullCovs Full Covariate names (age_T1)
#' @param mzData An mxData object containing the MZ data
#' @param dzData An mxData object containing the DZ data
#' @param type type
#' @param nSib nSib
#' @param sep default "_T"
#' @param allContinuousMethod allContinuousMethod
#' @param equateMeans Whether to equate the means across twins (default TRUE)
#' @return - A twin model
#' @export
#' @family xmu internal not for end user
#' @seealso - [xmu_make_TwinSuperModel()]
#' @md
#' @examples
#' \dontrun{
#' xmuTwinSuper_Continuous(name="twin_super", selVars = selVars, selCovs = selCovs,
#' mzData = mzData, dzData = dzData, equateMeans = TRUE, type = type,
#' allContinuousMethod = allContinuousMethod, nSib= nSib, sep = "_T" )
#' }
xmuTwinSuper_Continuous <- function(name= NULL, fullVars, fullCovs = NULL, sep, mzData, dzData, equateMeans, type, allContinuousMethod, nSib){
# ==============================
# = Handle all continuous case =
# ==============================
# expects fullVars and fullCovs to be expanded
# = Special case (for WLS, can affect mean or not)
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times nSib for a family)**
if(type %in% c('WLS', 'DWLS', 'ULS') & allContinuousMethod == "cumulants"){
# Raw data, mo means (WLS with cumulants
umx_check(is.null(fullCovs), "warning", "covariates not allowed in all-continuous WLS with method = cumulants. Switch to other method")
model = mxModel(name,
mxModel("top"),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
} else {
# Raw data or (WLS && allContinuousMethod != cumulants): Needs means and MZ and DZ a means model.
# ====================================================================
# = Figure out start values =
# = NOTE: fullVars is expanded by the time we get to here... no sep. =
# ====================================================================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
umxMatrix("expMean", "Full", nrow= 1, ncol= nVar*nSib, free= TRUE, values= starts$meanStarts, labels= starts$meanLabels, dimnames= list("means", fullVars))
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
}
return(model)
}
#' xmuTwinSuper_NoBinary
#'
#' @description
#' # Handle 1 or more ordinal variables (no binary)
#' Means ordinal, but no binary
#' Means: all free, start cont at the measured value, ordinals @0
#'
#' Notes: Ordinal requires:
#' 1. Variable set to mxFactor
#' 2. For Binary variables:
#' 1. Latent means of binary variables fixedAt 0 (or by data.def?)
#' 2. Latent variance (A + C + E) constrained == 1
#' 3. For Ordinal variables, first 2 thresholds fixed
#'
#' @param name = NULL
#' @param fullVars full names of variables
#' @param fullCovs full names of covariates
#' @param mzData mzData
#' @param dzData dzData
#' @param sep sep
#' @param nSib nSib
#' @param equateMeans T/F
#' @param verbose (Default FALSE)
#' @return - twin model
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' #
xmuTwinSuper_NoBinary <- function(name = NULL, fullVars, fullCovs = NULL, mzData, dzData, sep, nSib, equateMeans= TRUE, verbose=FALSE){
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times-2 for a family)**
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
# ===============
# = Inform user =
# ===============
message("Found ", (colTypes$nOrdVars/nSib), " pair(s) of ordinal variables:", omxQuotes(colTypes$ordVarNames), " (No binary)")
if(length(colTypes$contVarNames) > 0){
message(length(colTypes$contVarNames)/nSib, " pair(s) of continuous variables:", omxQuotes(colTypes$contVarNames[1:(length(colTypes$contVarNames)/nSib)]))
}
# ===========================
# = Figure out start values =
# ===========================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
umxMatrix("expMean", "Full" , nrow = 1, ncol = (nVar * nSib), free = TRUE, values = starts$meanStarts, labels = starts$meanLabels, dimnames = list("means", fullVars)),
umxThresholdMatrix(rbind(mzData$observed, dzData$observed), fullVarNames = fullVars, sep = sep, verbose = verbose)
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean", thresholds = "top.threshMat") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean", thresholds = "top.threshMat") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
return(model)
}
# xmuTwinSuper_SomeBinary(name=NULL, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, nSib, equateMeans= equateMeans, sep = "_T", verbose = verbose)
xmuTwinSuper_SomeBinary <- function(name = NULL, fullVars, fullCovs = NULL, mzData, dzData, sep, nSib, equateMeans= equateMeans, verbose = verbose){
# =============================================
# = Handle case of at least 1 binary variable =
# =============================================
# ===========================================================================
# = Means: bin fixed, others free, start cont at the measured value, ord @0 =
# ===========================================================================
# ===================================
# = Constrain Ordinal variance @1 =
# ===================================
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times-2 for a family)**
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
nBin = colTypes$nBinVars/nSib
# ===============
# = Inform user =
# ===============
message("Found ", nBin, " pairs of binary variables:", omxQuotes(colTypes$binVarNames))
message("\nI am fixing the latent means and variances of these variables to 0 and 1")
if(colTypes$nOrdVars > 0){
message("There were also ", colTypes$nOrdVars/nSib, " pair(s) of ordinal variables:", omxQuotes(colTypes$ordVarNames))
}
if(length(colTypes$contVarNames) > 0){
message("\nand ", length(colTypes$contVarNames)/nSib, " pair(s) of continuous variables first of each pair was: ", omxQuotes(colTypes$contVarNames[1:(length(colTypes$contVarNames)/nSib)]))
}else{
message("No continuous variables")
}
# Algebra to pick out the ordinal variables.
# TODO check using twin 1 to pick where the bin variables are is robust...
# Fill with zeros: default for ordinals and binary...
meansFree = !colTypes$isBin # Fix the binary variables at zero (umx_means did this)
the_bin_cols = which(colTypes$isBin)[1:nBin] # Columns in which the bin vars appear for T1, i.e., c(1,3,5)
binBracketLabels = paste0("Vtot[", the_bin_cols, ",", the_bin_cols, "]") # "Vtot[1,1]" "Vtot[3,3]"
# =============================
# = Figure out start values =
# = NOTE: fullVars is expanded by the time we get to here... no sep. =
# ===================================================================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
# means
umxMatrix("expMean", "Full" , nrow = 1, ncol = (nVar*nSib), free = meansFree, values = starts$meanStarts, labels = starts$meanLabels, dimnames = list("means", fullVars)),
# thresholds
umxThresholdMatrix(rbind(mzData$observed, dzData$observed), fullVarNames = fullVars, sep = sep, verbose = verbose),
# var-cov
# NOTE: Assumes A+C+E is Vtot (i.e., these are the three and only components forming expCov)
mxAlgebra(name = "Vtot", A + C + E), # Total variance (also added by models with std = TRUE, but is OK to add twice)
umxMatrix("binLabels" , "Full", nrow = nBin, ncol = 1, labels = binBracketLabels),
umxMatrix("Unit_nBinx1", "Unit", nrow = nBin, ncol = 1),
mxConstraint(name = "constrain_Bin_var_to_1", binLabels == Unit_nBinx1)
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean", thresholds = "top.threshMat") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean", thresholds = "top.threshMat") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
return(model)
}
# xmuTwinSuper_CovCor(name=name, fullVars = fullVars, mzData= mzData, dzData = dzData, numObsMZ = numObsMZ, numObsDZ = numObsDZ)
xmuTwinSuper_CovCor <- function(name=NULL, fullVars, mzData, dzData, type, numObsMZ, numObsDZ){
# Check the data and get it into shape
umx_check(!is.null(numObsMZ), "stop", paste0("You must set numObsMZ with summary data"))
umx_check(!is.null(numObsDZ), "stop", paste0("You must set numObsDZ with summary data"))
mzData = xmu_make_mxData(mzData, type = type, manifests = fullVars, numObs = numObsMZ)
dzData = xmu_make_mxData(dzData, type = type, manifests = fullVars, numObs = numObsDZ)
model = mxModel(name,
mxModel("top"),
mxModel("MZ", mxExpectationNormal("top.expCovMZ"), mzData),
mxModel("DZ", mxExpectationNormal("top.expCovDZ"), dzData),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
return(model)
}
#' Not for end-users: Add a means model with covariates to a twin model
#'
#' @description
#' Does the following to `model` (i.e., a umx top/MZ/DZ supermodel):
#'
#' 1. Change `top.expMeans` to `top.intercept`.
#' 2. Create `top.meansBetas` for beta weights in rows (of covariates) and columns for each variable.
#' 3. Add matrices for each twin's data.cov vars (matrixes are called `T1DefVars`).
#' 4. Switch `mxExpectationNormal` in each data group to point to the local `expMean`.
#' 5. Add "expMean" algebra to each data group.
#' * `grp.expMean` sums `top.intercept` and `grp.DefVars %*% top.meansBetas` for each twin.
#'
#' @details In umx models with no covariates, means live in `top$expMean`
#'
#'
#' @param model The [umxSuperModel()] we are modifying (must have `MZ` `DZ` and `top` submodels)
#' @param fullVars the FULL names of manifest variables
#' @param fullCovs the FULL names of definition variables
#' @param nSib How many siblings
#' @param sep How twin variable names have been expanded, e.g. "_T".
#' @return - model, now with means model extended to covariates.
#' @export
#' @family xmu internal not for end user
#' @seealso - called by [xmuTwinSuper_Continuous()]
#' @md
#' @examples
#' \dontrun{
#' data(twinData) # ?twinData from Australian twins.
#' twinData[, c("ht1", "ht2")] = twinData[, c("ht1", "ht2")] * 10
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#' # m1 = umxACE(selDVs= "ht", sep= "", dzData= dzData, mzData= mzData, autoRun= FALSE)
# # This example won't work, as umxACE drops the covs from the data...
#' # m2 = xmuTwinUpgradeMeansToCovariateModel(m1, fullVars = c("ht1", "ht2"),
#' # fullCovs = c("age1", "sex1", "age2", "sex2"), sep = "")
#'
#' }
#'
xmuTwinUpgradeMeansToCovariateModel <- function(model, fullVars, fullCovs, nSib, sep) {
# TODO Check the def vars are still in the dataset at this point...
umx_check(all(c("MZ", "DZ", "top") %in% names(model)), "stop", message= "need a model with top, MZ and DZ sub-models")
umx_check(!is.null(model$top$expMean), "stop", "Model must have $top$expMean for xmuTwinUpgradeMeansToCovariateModel to work.")
baseVars = umx_explode_twin_names(fullVars, sep = sep)$baseNames
baseCovs = umx_explode_twin_names(fullCovs, sep = sep)$baseNames
nVar = length(baseVars)
nCov = length(baseCovs)
# 1. Make `meansBetas` matrix
betaLabels = paste0(rep(baseCovs, times= nVar), "_b_Var", rep(1:nVar, each= nCov) )
meansBetas = umxMatrix("meansBetas", "Full", nrow = nCov, ncol = nVar, free = TRUE, labels= betaLabels, values = 0, lbound = -2, ubound = 2)
dimnames(meansBetas) = list(baseCovs, baseVars)
# age "age_b_Var1" "age_b_Var2" "age_b_Var3"
# sex "sex_b_Var1" "sex_b_Var2" "sex_b_Var3"
# =============================================================================================================
# = 1. Add the betaDef matrix to top and replace top.expMean with top.intercept
# =============================================================================================================
# (assumes expMean has correctly locked to zero for binary variables)
top = model$top
# Add meansBetas to top, and rename expMean to intercept
top = mxModel(top, meansBetas)
top = umxRenameMatrix(top, matrixName = "expMean", name="intercept")
# New expectation to point to expMean in the local data group
if(is.na(model$MZ$expectation$thresholds)){
# no thresholds found, don't add any
MZexpectation = mxExpectationNormal("top.expCovMZ", "expMean")
DZexpectation = mxExpectationNormal("top.expCovDZ", "expMean")
} else {
# had thresholds, keep them
MZexpectation = mxExpectationNormal("top.expCovMZ", "expMean", thresholds = model$MZ$expectation$thresholds) # "top.threshMat"
DZexpectation = mxExpectationNormal("top.expCovDZ", "expMean", thresholds = model$DZ$expectation$thresholds) # "top.threshMat"
}
# 2. Upgrade MZ and DZ groups with local Def Vars and new expMean algebra
matsAndAlg = xmu_twin_make_def_means_mats_and_alg(baseCovs= baseCovs, fullVars = fullVars, nSib = nSib, sep= sep) # "T1DefVars", "T2DefVars"... + "expMean" algebra
MZ = mxModel(model$MZ, MZexpectation, matsAndAlg)
DZ = mxModel(model$DZ, DZexpectation, matsAndAlg)
return(mxModel(model, top, MZ, DZ))
}
#' Make the matrices and algebras for definition-based means models
#'
#' @description
#' not-for-end-user helper for means in twin models. Returns matrices for
#' each definition variable, and an algebra to compute means.
#'
#' @param baseCovs base names of the DVs, e.g. "age"
#' @param fullVars full names of the DVs, e.g. "E_T1"
#' @param nSib how many siblings - typically 2
#' @param sep in twin variable, i.e., "_T"
#' @return matrices and an algebra
#' @export
#' @family xmu internal not for end user
#' @seealso - [xmuTwinUpgradeMeansToCovariateModel()]
#' @md
#' @examples
#' # xmu_twin_make_def_means_mats_and_alg(baseCovs= baseCovs,
#' # fullVars = fullVars, nSib = nSib, sep= sep)
xmu_twin_make_def_means_mats_and_alg <- function(baseCovs, fullVars, nSib, sep) {
nCov = length(baseCovs)
defMats = list()
for (i in 1:nSib) {
tmp = umxMatrix(paste0("T", i, "DefVars"), type= "Full", nrow= 1, ncol= nCov, free= FALSE, labels= paste0("data.", baseCovs, sep, i))
defMats[[i]] = tmp
}
# note: intercept is full width
if(nSib == 2){
tmpAlg = mxAlgebra(name= "expMean", top.intercept + cbind(T1DefVars %*% top.meansBetas, T2DefVars %*% top.meansBetas), dimnames= list("means", fullVars) )
} else if (nSib == 3){
tmpAlg = mxAlgebra(name= "expMean", top.intercept + cbind(
T1DefVars %*% top.meansBetas,
T2DefVars %*% top.meansBetas,
T3DefVars %*% top.meansBetas), dimnames= list("means", fullVars)
)
}else{
stop("I can't handle nSib = ", nSib, ", only 2 or 3 so far. post as an issue on github if you need this")
}
return(append(defMats, tmpAlg))
}
#' Helper providing boilerplate start values for means and variance in twin models
#'
#' @description
#' `xmu_starts` can handle several common/boilerplate situations in which means and variance start values
#' are used in twin models.
#'
#' @param mzData Data for MZ pairs.
#' @param dzData Data for DZ pairs.
#' @param selVars Variable names: If sep = NULL, then treated as full names for both sibs.
#' @param sep All the variables full names.
#' @param equateMeans (NULL)
#' @param nSib How many subjects in a family.
#' @param varForm currently just "Cholesky" style.
#' @param SD = TRUE (FALSE = variance, not SD).
#' @param divideBy = 3 (A,C,E) 1/3rd each. Use 1 to do this yourself post-hoc.
#' @return - varStarts and meanStarts
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' data(twinData)
#' selDVs = c("wt", "ht")
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#'
#' round(sqrt(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3),3)
#' xmu_starts(mzData, dzData, selVars=selDVs, nSib= 2, sep="", equateMeans=TRUE, varForm="Cholesky")
#'
#' # Variance instead of SD
#' round(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3,3)
#' xmu_starts(mzData, dzData, selVars = selDVs, nSib = 2, sep= "",
#' equateMeans= TRUE, varForm= "Cholesky", SD= FALSE)
#'
#' # one variable
#' xmu_starts(mzData, dzData, selVars= "wt", nSib = 2, sep="", equateMeans = TRUE)
#'
#' # Ordinal/continuous mix
#' data(twinData)
#' twinData= umx_scale_wide_twin_data(data=twinData,varsToScale="wt",sep= "")
#' # Cut BMI column to form ordinal obesity variables
#' cuts = quantile(twinData[, "bmi1"], probs = c(.5, .8), na.rm = TRUE)
#' obLevels = c('normal', 'overweight', 'obese')
#' twinData$obese1= cut(twinData$bmi1,breaks=c(-Inf,cuts,Inf),labels=obLevels)
#' twinData$obese2= cut(twinData$bmi2,breaks=c(-Inf,cuts,Inf),labels=obLevels)
#' # Make the ordinal variables into mxFactors
#' ordDVs = c("obese1", "obese2")
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' xmu_starts(mzData, dzData, selVars = c("wt","obese"), sep= "",
#' nSib= 2, equateMeans = TRUE, SD= FALSE)
#'
#' xmu_starts(mxData(mzData, type="raw"), mxData(mzData, type="raw"),
#' selVars = c("wt","obese"), sep= "", nSib= 2, equateMeans = TRUE, SD= FALSE)
#'
#' # ==============
#' # = Three sibs =
#' # ==============
#' data(twinData)
#' twinData$wt3 = twinData$wt2
#' twinData$ht3 = twinData$ht2
#' selDVs = c("wt", "ht")
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#'
#' xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=TRUE)
#' xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=FALSE)
xmu_starts <- function(mzData, dzData, selVars = selVars, sep = NULL, equateMeans= NULL, nSib, varForm = c("Cholesky"), SD= TRUE, divideBy = 3) {
if(!is.null(sep)){
selVars = umx_paste_names(selVars, sep = sep, suffixes = 1:nSib)
}
nVar = length(selVars)/nSib
dataType = umx_is_cov(dzData, boolean = FALSE)
if(dataType == "raw") {
if(is.null(equateMeans)){
stop("you have to tell xmu_starts whether to equate the means or not")
}
if(umx_is_MxData(mzData)){
allData = rbind(mzData$observed, dzData$observed)[,selVars]
}else{
allData = rbind(mzData, dzData)[,selVars]
}
# Uses sib 1 and 2 irrespective of nSib (doesn't matter here)
T1 = allData[, 1:nVar, drop = FALSE]
T2 = allData[, (nVar+1):(nVar*2), drop = FALSE];
names(T2) = names(T1)
longData = rbind(T1, T2)[, selVars[1:nVar], drop = FALSE]
# Mean starts (1 per variable)
meanStarts = umx_means(longData, ordVar = 0, na.rm = TRUE)
# Make nSib wide
meanStarts = rep(meanStarts, times = nSib)
if(equateMeans){
# e.g. "expMean_ht1" "expMean_wt1" "expMean_ht1"
meanLabels = rep(paste0("expMean_", selVars[1:nVar]), times = nSib)
} else {
meanLabels = paste0("expMean_", selVars)
}
varStarts = umx_var(longData, format= "diag", ordVar = 1, use = "pairwise.complete.obs", strict = FALSE)
} else if(dataType %in% c("cov", "cor")){
meanStarts = NA # Not used for summary data
meanLabels = NA
if(umx_is_MxData(mzData)){
het_mz = umx_reorder(mzData$observed, selVars)
het_dz = umx_reorder(dzData$observed, selVars)
}else{
het_mz = umx_reorder(mzData, selVars)
het_dz = umx_reorder(dzData, selVars)
}
varStarts = (diag(het_mz)[1:nVar]+ diag(het_dz)[1:nVar])/2
}else{
stop("xmu_starts can only handle raw and cov/cor data types. You gave me ", omxQuotes(dataType))
}
# Covariance matrix, 1/3 allocated to each of A=C=E.
varStarts = varStarts/divideBy
if(varForm == "Cholesky"){
if(SD){
# sqrt() to switch from var to path coefficient scale
# Sqrt to switch from var to path coefficient scale
varStarts = t(chol(diag(varStarts, length(varStarts))))
varStarts = matrix(varStarts, nVar, nVar)
} else {
varStarts = diag(varStarts, nVar, nVar)
}
} else {
stop("Not sure how to handle varForm = ", omxQuotes(varForm))
}
return(list(varStarts=varStarts, meanStarts= meanStarts, meanLabels= meanLabels))
}
#' Add weight matrices to twin models.
#'
#' @description
#' Add weight models (MZw, DZw) with matrices (e.g. mzWeightMatrix) to a twin model, and
#' update `mxFitFunctionMultigroup`. This yields a weighted model with vector objective.
#'
#' To weight objective functions in OpenMx, you specify a container model that applies the weights
#' m1 is the model with no weights, but with "vector = TRUE" option added to the FIML objective.
#' This option makes FIML return individual likelihoods for each row of the data (rather than a single
#' -2LL value for the model)
#' You then optimize weighted versions of these likelihoods by building additional models containing
#' weight data and an algebra that multiplies the likelihoods from the first model by the weight vector.
#'
#' @param model umx-style twin model
#' @param mzWeights data for MZ weights matrix
#' @param dzWeights data for DZ weights matrix
#' @return - model
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' tmp = umx_make_twin_data_nice(data=twinData, sep="", zygosity="zygosity", numbering= 1:2)
#' m1 = umxACE(selDVs = "wt", data = tmp, dzData = "DZFF", mzData = "MZFF", autoRun= FALSE)
#' m1$MZ$fitfunction$vector= TRUE
#'
#' tmp = xmu_twin_add_WeightMatrices(m1,
#' mzWeights= rnorm(nrow(m1$MZ$data$observed)),
#' dzWeights= rnorm(nrow(m1$DZ$data$observed))
#' )
#'
xmu_twin_add_WeightMatrices <- function(model, mzWeights = NULL, dzWeights = NULL) {
umx_check(model$MZ$fitfunction$vector, "stop", "xmu_twin_add_WeightMatrix: You need to set the fitFunction to vector mode... but it appears I haven't")
if(umx_is_MxMatrix(mzWeights)){
# just check the name
if(mzWeights$name != "mzWeightMatrix"){ stop(
paste0("xmu_twin_add_WeightMatrices expects name of mzWeights to be 'mzWeightMatrix'.\n", "It was ", omxQuotes(mzWeights$name))
)}
} else {
# make incoming raw data into matrices
# convert from dataframe if necessary
if(!is.null(dim(mzWeights)) ){ mzWeights = mzWeights[,1] }
if(!is.null(dim(dzWeights)) ){ dzWeights = dzWeights[,1] }
mzWeights = mxMatrix(name = "mzWeightMatrix", type = "Full", nrow = length(mzWeights), ncol = 1, free = FALSE, values = mzWeights)
dzWeights = mxMatrix(name = "dzWeightMatrix", type = "Full", nrow = length(dzWeights), ncol = 1, free = FALSE, values = dzWeights)
}
model = mxModel(model,
mxModel("MZw", mzWeights,
mxAlgebra(-2 * sum(mzWeightMatrix * log(MZ.objective) ), name = "mzWeightedCov"),
mxFitFunctionAlgebra("mzWeightedCov")
),
mxModel("DZw", dzWeights,
mxAlgebra(-2 * sum(dzWeightMatrix * log(DZ.objective) ), name = "dzWeightedCov"),
mxFitFunctionAlgebra("dzWeightedCov")
),
mxFitFunctionMultigroup(c("MZw", "DZw"))
)
return(model)
}
# one_by_nCov = umxMatrix("T1DefVars", "Full", nrow = 1, ncol = nCov, values = c(exp(1), pi))
# meansBetas = umxMatrix("meansBetas", "Full", nrow = nCov, ncol = nVar, free = TRUE, labels=betaLabels, values = 1:6, lbound = -2, ubound = 2)
# one_by_nCov = qm(1, 57) # qm(sex=1, age=57)
# # a beta for each covariate for each variable: nCov * nVar (product has nVar columns)
# meansBetas = qm(
# .1, .2, .3|
# .4, .5, .6)
# dimnames(meansBetas)=list(c("sex", "age"), c("var1","var2","var3"))
# var1 var2 var3
# sex 0.1 0.2 0.3
# age 0.4 0.5 0.6
# one_by_nCov %*% meansBetas
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Defines functions xmu_twin_add_WeightMatrices xmu_starts xmu_twin_make_def_means_mats_and_alg xmuTwinUpgradeMeansToCovariateModel xmuTwinSuper_CovCor xmuTwinSuper_SomeBinary xmuTwinSuper_NoBinary xmuTwinSuper_Continuous xmu_make_TwinSuperModel
Documented in xmu_make_TwinSuperModel xmu_starts xmu_twin_add_WeightMatrices xmu_twin_make_def_means_mats_and_alg xmuTwinSuper_Continuous xmuTwinSuper_NoBinary xmuTwinUpgradeMeansToCovariateModel
#' Helper to make a basic top, MZ, and DZ model.
#'
#' @description
#' `xmu_make_TwinSuperModel` makes basic twin model containing `top`, `MZ`, and `DZ` models. It intelligently handles thresholds for
#' ordinal data, and means model for covariates matrices in the twin models if needed.
#'
#' It's the replacement for `xmu_assemble_twin_supermodel` approach.
#'
#' @details
#' `xmu_make_TwinSuperModel` is used in twin models (e.g.[umxCP()], [umxACE()] and [umxACEv()] and will be added to the other models: [umxGxE()], [umxIP()],
#' simplifying code maintenance.
#'
#' It takes `mzData` and `dzData`, a list of the `selDVs` to analyse and optional `selCovs` (as well as `sep` and `nSib`), along with other
#' relevant information such as whether the user wants to `equateMeans`.
#' It can also handle a `weightVar`.
#'
#' If covariates are passed in these are included in the means model (via a call to `xmuTwinUpgradeMeansToCovariateModel`.
#'
#'
#' **Modeling**
#'
#' **Matrices created**
#'
#' *top model*
#'
#' For raw and WLS data, `top` contains a `expMeans` matrix (if needed). For summary data, the top model contains only a name.
#'
#' For ordinal data, `top` gains `top.threshMat` (from a call to [umxThresholdMatrix()]).
#'
#' For covariates, top stores the `intercepts` matrix and a `betaDef` matrix. These are then used to make expMeans in `MZ` and `DZ`.
#'
#' *MZ and DZ models*
#'
#' `MZ` and `DZ` contain the data, and an expectation referencing `top.expCovMZ` and `top.expMean`, and, `vector = bVector`.
#' For continuous raw data, MZ and DZ contain [OpenMx::mxExpectationNormal()] and [OpenMx::mxFitFunctionML()].
#' For WLS these the fit function is switched to [OpenMx::mxFitFunctionWLS()] with appropriate `type` and `allContinuousMethod`.
#'
#'
#' For binary, a constraint and algebras are included to constrain `Vtot` (A+C+E) to 1.
#'
#' If a `weightVar` is detected, these columns are used to create a row-weighted MZ and DZ models.
#'
#' If `equateMeans` is `TRUE`, then the Twin-2 vars in the mean matrix are equated by label with Twin-1.
#'
#'
#' Decent starts are guessed from the data.
#' `varStarts` is computed as `sqrt(variance)/3` of the DVs and `meanStarts` as the variable means.
#' For raw data, a check is made for ordered variables. For Binary variables, means are fixed at 0 and
#' total variance (A+C+E) is fixed at 1. For ordinal variables, the first 2 thresholds are fixed.
#'
#' Where needed, e.g. continuous raw data, top adds a means matrix "expMean".
#' For ordinal data, top adds a [umxThresholdMatrix()].
#'
#' If binary variables are present, matrices and a constraint to hold `A+C+E == 1` are added to top.
#'
#' If a weight variable is offered up, an `mzWeightMatrix` will be added.
#'
#' **Data handling**
#'
#' In terms of data handling, `xmu_make_TwinSuperModel` was primarily designed to take
#' data.frames and process these into mxData.
#' It can also, however, handle cov and mxData input.
#'
#' It can process data into all the types supported by `mxData`.
#'
#' Raw data input with a target of `cov` or `cor` type requires the `numObsMZ` and `numObsDZ` to be set.
#'
#' Type "WLS", "DWLS", or "ULS", data remain raw, but are handled as WLS in the [OpenMx::mxFitFunctionWLS()].
#'
#' Unused columns are dropped.
#'
#' If you pass in raw data, you can't request type cov/cor yet. Will work on this if desired.
#'
#' @param name for the supermodel
#' @param mzData Dataframe containing the MZ data
#' @param dzData Dataframe containing the DZ data
#' @param selDVs List of manifest base names (e.g. BMI, NOT 'BMI_T1') (OR, you don't set "sep", the full variable names)
#' @param selCovs List of covariate base names (e.g. age, NOT 'age_T1') (OR, you don't set "sep", the full variable names)
#' @param sep string used to expand selDVs into selVars, i.e., "_T" to expand BMI into BMI_T1 and BMI_T2 (optional but STRONGLY encouraged)
#' @param type One of 'Auto','FIML','cov', 'cor', 'WLS','DWLS', or 'ULS'. Auto tries to react to the incoming mxData type (raw/cov).
#' @param allContinuousMethod "cumulants" or "marginals". Used in all-continuous WLS data to determine if a means model needed.
#' @param nSib Number of members per family (default = 2)
#' @param equateMeans Whether to equate T1 and T2 means (default = TRUE).
#' @param weightVar If provided, a vector objective will be used to weight the data. (default = NULL).
#' @param numObsMZ Number of MZ observations contributing (for summary data only)
#' @param numObsDZ Number of DZ observations contributing (for summary data only)
#' @param bVector Whether to compute row-wise likelihoods (defaults to FALSE).
#' @param dropMissingDef Whether to automatically drop missing def var rows for the user (default = TRUE). You get a polite note.
#' @param verbose (default = FALSE)
#' @return - [mxModel()]s for top, MZ and DZ.
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
# # TODO add tests with numObsMZ = NULL, numObsDZ = NULL, equateMeans = TRUE,
# # TODO add tests with weightVar = NULL, bVector = FALSE,
#
#' # ==============
#' # = Continuous =
#' # ==============
#' library(umx)
#' data(twinData)
#' twinData = umx_scale(twinData, varsToScale= c('ht1','ht2'))
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1= xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData, selDVs=c("wt","ht"), sep="", nSib=2)
#' names(m1) # "top" "MZ" "DZ"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionML"
#'
#' # ====================
#' # = With a covariate =
#' # ====================
#'
#' m1= xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData,
#' selDVs= "wt", selCovs= "age", sep="", nSib=2)
#' m1$top$intercept$labels
#' m1$MZ$expMean
#'
#' # ===============
#' # = WLS example =
#' # ===============
#' m1=xmu_make_TwinSuperModel(mzData=mzData, dzData=dzData,selDVs=c("wt","ht"),sep="",type="WLS")
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#' m1$MZ$fitfunction$type =="WLS"
#' # Check default all-continuous method
#' m1$MZ$fitfunction$continuousType == "cumulants"
#'
#' # Choose non-default type (DWLS)
#' m1= xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData,
#' selDVs= c("wt","ht"), sep="", type="DWLS")
#' m1$MZ$fitfunction$type =="DWLS"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#'
#' # Switch WLS method
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= c("wt","ht"), sep= "",
#' type = "WLS", allContinuousMethod = "marginals")
#' m1$MZ$fitfunction$continuousType == "marginals"
#' class(m1$MZ$fitfunction)[[1]] == "MxFitFunctionWLS"
#'
#'
#' # ============================================
#' # = Bivariate continuous and ordinal example =
#' # ============================================
#' data(twinData)
#' selDVs = c("wt", "obese")
#' # Cut BMI column to form ordinal obesity variables
#' ordDVs = c("obese1", "obese2")
#' obesityLevels = c('normal', 'overweight', 'obese')
#' cutPoints = quantile(twinData[, "bmi1"], probs = c(.5, .2), na.rm = TRUE)
#' twinData$obese1 = cut(twinData$bmi1, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' twinData$obese2 = cut(twinData$bmi2, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' # Make the ordinal variables into mxFactors (ensure ordered is TRUE, and require levels)
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= selDVs, sep="", nSib= 2)
#' names(m1) # "top" "MZ" "DZ"
#'
#' # ==============
#' # = One binary =
#' # ==============
#' data(twinData)
#' cutPoints = quantile(twinData[, "bmi1"], probs = .2, na.rm = TRUE)
#' obesityLevels = c('normal', 'obese')
#' twinData$obese1 = cut(twinData$bmi1, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' twinData$obese2 = cut(twinData$bmi2, breaks = c(-Inf, cutPoints, Inf), labels = obesityLevels)
#' ordDVs = c("obese1", "obese2")
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' selDVs = c("wt", "obese")
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= selDVs, sep= "", nSib= 2)
#'
#' # ========================================
#' # = Cov data (calls xmuTwinSuper_CovCor) =
#' # ========================================
#'
#' data(twinData)
#' mzData =cov(twinData[twinData$zygosity %in% "MZFF", tvars(c("wt","ht"), sep="")], use="complete")
#' dzData =cov(twinData[twinData$zygosity %in% "DZFF", tvars(c("wt","ht"), sep="")], use="complete")
#' m1 = xmu_make_TwinSuperModel(mzData= mzData, dzData= dzData, selDVs= "wt", sep= "",
#' nSib= 2, numObsMZ = 100, numObsDZ = 100, verbose=TRUE)
#' class(m1$MZ$fitfunction)[[1]] =="MxFitFunctionML"
#' dimnames(m1$MZ$data$observed)[[1]]==c("wt1", "wt2")
#'
xmu_make_TwinSuperModel <- function(name = "twin_super", mzData, dzData, selDVs, selCovs= NULL, sep = NULL, type = c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS"), allContinuousMethod = c("cumulants", "marginals"), numObsMZ = NULL, numObsDZ = NULL, nSib = 2, equateMeans = TRUE, weightVar = NULL, bVector = FALSE, dropMissingDef = TRUE, verbose= FALSE) {
# TODO for xmu_make_TwinSuperModel
# TODO: 1. xmu_make_TwinSuperModel Add selCovs
# TODO: 2. xmu_make_TwinSuperModel Add beta matrix for fixed covariates in means.
# TODO: 4. xmu_make_TwinSuperModel more tests in a test page
# *Note*: If dropping this into an existing model, all your code setting: nVar, selVars, used
# any code figuring out data-type, is not needed!
# ===================
# = match arguments =
# ===================
type = match.arg(type)
allContinuousMethod = match.arg(allContinuousMethod)
# ====================================================
# = Figure out selVars, nVar, usedVars, and dataType =
# ====================================================
if(is.null(sep)){
fullVars = selDVs
fullCovs = selCovs
message("Polite note: It's better to use 'sep'. This might become compulsory as it helps umx manage variable names in twin models.")
# stop("You MUST set 'sep'. Otherwise xmu_make_top can't reliably expand selDVs into full variable names")
}else{
fullVars = tvars(selDVs , sep = sep, suffixes= 1:nSib)
fullCovs = tvars(selCovs, sep = sep, suffixes= 1:nSib)
}
dataType = umx_is_cov(dzData, boolean = FALSE)
if(verbose){ umx_msg(dataType)}
if(type %in% c("cov", "cor") && !dataType %in% c("cov", "cor")){
stop("You've requested type= cov or cor, but the provided dataType is ", omxQuotes(dataType), " I don't support that yet. Please pass in cov data.")
}
if(dataType == "raw") {
if(!all(is.null(c(numObsMZ, numObsDZ)))){
stop("You should not be setting numObsMZ or numObsDZ with ", omxQuotes(dataType), " data...")
}
if(!is.null(weightVar)){
# Weight variable provided: check it exists in each frame.
mzWeightData = xmu_extract_column(mzData, weightVar)
dzWeightData = xmu_extract_column(dzData, weightVar)
bVector = TRUE
}
# ============================================
# = Make mxData, dropping any unused columns =
# ============================================
usedVars = c(fullVars, fullCovs)
if(verbose){
umx_msg(usedVars)
umx_msg(fullVars)
umx_msg(fullCovs)
umx_msg(type)
}
mzData = xmu_make_mxData(mzData, type = type, manifests = usedVars, fullCovs = fullCovs, numObs = numObsMZ, dropMissingDef = dropMissingDef)
dzData = xmu_make_mxData(dzData, type = type, manifests = usedVars, fullCovs = fullCovs, numObs = numObsDZ, dropMissingDef = dropMissingDef)
# ========================================================================
# = 3. Add mxExpectationNormal, means and var matrices to top, MZ and DZ =
# ========================================================================
# Find ordinal variables
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
if(colTypes$nFactors == 0){
model = xmuTwinSuper_Continuous(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib, type= type, allContinuousMethod= allContinuousMethod)
} else if(sum(colTypes$isBin) == 0){
model = xmuTwinSuper_NoBinary(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib)
} else if(sum(colTypes$isBin) > 0){
model = xmuTwinSuper_SomeBinary(name= name, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, sep = sep, equateMeans= equateMeans, nSib = nSib, verbose = verbose)
} else {
stop("You appear to have something other than I expected in terms of WLS, or binary, ordinal and continuous variable mix")
}
# nb: means not yet equated across twins
} else if(dataType %in% c("cov", "cor")){
if(!is.null(weightVar)){
stop("You can't set weightVar when you give cov data - use cov.wt to create weighted cov matrices, or pass in raw data")
}
if(!is.null(fullCovs)){
stop("You can't set covariates when you have cov data: needs raw data to estimate on each row")
}
model = xmuTwinSuper_CovCor(name=name, fullVars = fullVars, mzData= mzData, dzData= dzData, type = dataType, numObsMZ = numObsMZ, numObsDZ = numObsDZ)
} else {
stop("Datatype \"", dataType, "\" not understood")
}
# ==============================
# = Add mxFitFunction to model =
# ==============================
if(type %in% c('WLS', 'DWLS', 'ULS')) {
message("Data treated as ", type)
# Still mxExpectationNormal (`top` is not affected - either has or lacks means matrix already).
# Replace the MZ and DZ model FitFunctions
model$MZ = mxModel(model$MZ, mxFitFunctionWLS(type = type, allContinuousMethod = allContinuousMethod) )
model$DZ = mxModel(model$DZ, mxFitFunctionWLS(type = type, allContinuousMethod = allContinuousMethod) )
}else{
model$MZ = mxModel(model$MZ, mxFitFunctionML(vector = bVector) )
model$DZ = mxModel(model$DZ, mxFitFunctionML(vector = bVector) )
}
if(!is.null(weightVar)){
model = xmu_twin_add_WeightMatrices(model, mzWeights = mzWeightData, dzWeights = dzWeightData)
}
return(model)
}
# =============================
# = raw twin-assembly helpers =
# =============================
#' Create core of twin model for all-continuous data.
#'
#' @description
#' Sets up top, MZ and DZ submodels with a means model, data, and expectation for all-continuous data.
#' called by [xmu_make_TwinSuperModel()].
#'
#' @param name The name of the supermodel
#' @param fullVars Full Variable names (wt_T1)
#' @param fullCovs Full Covariate names (age_T1)
#' @param mzData An mxData object containing the MZ data
#' @param dzData An mxData object containing the DZ data
#' @param type type
#' @param nSib nSib
#' @param sep default "_T"
#' @param allContinuousMethod allContinuousMethod
#' @param equateMeans Whether to equate the means across twins (default TRUE)
#' @return - A twin model
#' @export
#' @family xmu internal not for end user
#' @seealso - [xmu_make_TwinSuperModel()]
#' @md
#' @examples
#' \dontrun{
#' xmuTwinSuper_Continuous(name="twin_super", selVars = selVars, selCovs = selCovs,
#' mzData = mzData, dzData = dzData, equateMeans = TRUE, type = type,
#' allContinuousMethod = allContinuousMethod, nSib= nSib, sep = "_T" )
#' }
xmuTwinSuper_Continuous <- function(name= NULL, fullVars, fullCovs = NULL, sep, mzData, dzData, equateMeans, type, allContinuousMethod, nSib){
# ==============================
# = Handle all continuous case =
# ==============================
# expects fullVars and fullCovs to be expanded
# = Special case (for WLS, can affect mean or not)
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times nSib for a family)**
if(type %in% c('WLS', 'DWLS', 'ULS') & allContinuousMethod == "cumulants"){
# Raw data, mo means (WLS with cumulants
umx_check(is.null(fullCovs), "warning", "covariates not allowed in all-continuous WLS with method = cumulants. Switch to other method")
model = mxModel(name,
mxModel("top"),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
} else {
# Raw data or (WLS && allContinuousMethod != cumulants): Needs means and MZ and DZ a means model.
# ====================================================================
# = Figure out start values =
# = NOTE: fullVars is expanded by the time we get to here... no sep. =
# ====================================================================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
umxMatrix("expMean", "Full", nrow= 1, ncol= nVar*nSib, free= TRUE, values= starts$meanStarts, labels= starts$meanLabels, dimnames= list("means", fullVars))
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
}
return(model)
}
#' xmuTwinSuper_NoBinary
#'
#' @description
#' # Handle 1 or more ordinal variables (no binary)
#' Means ordinal, but no binary
#' Means: all free, start cont at the measured value, ordinals @0
#'
#' Notes: Ordinal requires:
#' 1. Variable set to mxFactor
#' 2. For Binary variables:
#' 1. Latent means of binary variables fixedAt 0 (or by data.def?)
#' 2. Latent variance (A + C + E) constrained == 1
#' 3. For Ordinal variables, first 2 thresholds fixed
#'
#' @param name = NULL
#' @param fullVars full names of variables
#' @param fullCovs full names of covariates
#' @param mzData mzData
#' @param dzData dzData
#' @param sep sep
#' @param nSib nSib
#' @param equateMeans T/F
#' @param verbose (Default FALSE)
#' @return - twin model
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' #
xmuTwinSuper_NoBinary <- function(name = NULL, fullVars, fullCovs = NULL, mzData, dzData, sep, nSib, equateMeans= TRUE, verbose=FALSE){
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times-2 for a family)**
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
# ===============
# = Inform user =
# ===============
message("Found ", (colTypes$nOrdVars/nSib), " pair(s) of ordinal variables:", omxQuotes(colTypes$ordVarNames), " (No binary)")
if(length(colTypes$contVarNames) > 0){
message(length(colTypes$contVarNames)/nSib, " pair(s) of continuous variables:", omxQuotes(colTypes$contVarNames[1:(length(colTypes$contVarNames)/nSib)]))
}
# ===========================
# = Figure out start values =
# ===========================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
umxMatrix("expMean", "Full" , nrow = 1, ncol = (nVar * nSib), free = TRUE, values = starts$meanStarts, labels = starts$meanLabels, dimnames = list("means", fullVars)),
umxThresholdMatrix(rbind(mzData$observed, dzData$observed), fullVarNames = fullVars, sep = sep, verbose = verbose)
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean", thresholds = "top.threshMat") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean", thresholds = "top.threshMat") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
return(model)
}
# xmuTwinSuper_SomeBinary(name=NULL, fullVars = fullVars, fullCovs = fullCovs, mzData = mzData, dzData = dzData, nSib, equateMeans= equateMeans, sep = "_T", verbose = verbose)
xmuTwinSuper_SomeBinary <- function(name = NULL, fullVars, fullCovs = NULL, mzData, dzData, sep, nSib, equateMeans= equateMeans, verbose = verbose){
# =============================================
# = Handle case of at least 1 binary variable =
# =============================================
# ===========================================================================
# = Means: bin fixed, others free, start cont at the measured value, ord @0 =
# ===========================================================================
# ===================================
# = Constrain Ordinal variance @1 =
# ===================================
umx_check(!is.null(name), "stop", "I need a name for the super model")
nVar = length(fullVars)/nSib; # Number of dependent variables ** per INDIVIDUAL ( so times-2 for a family)**
colTypes = umx_is_ordered(xmu_extract_column(mzData, fullVars), summaryObject= TRUE)
nBin = colTypes$nBinVars/nSib
# ===============
# = Inform user =
# ===============
message("Found ", nBin, " pairs of binary variables:", omxQuotes(colTypes$binVarNames))
message("\nI am fixing the latent means and variances of these variables to 0 and 1")
if(colTypes$nOrdVars > 0){
message("There were also ", colTypes$nOrdVars/nSib, " pair(s) of ordinal variables:", omxQuotes(colTypes$ordVarNames))
}
if(length(colTypes$contVarNames) > 0){
message("\nand ", length(colTypes$contVarNames)/nSib, " pair(s) of continuous variables first of each pair was: ", omxQuotes(colTypes$contVarNames[1:(length(colTypes$contVarNames)/nSib)]))
}else{
message("No continuous variables")
}
# Algebra to pick out the ordinal variables.
# TODO check using twin 1 to pick where the bin variables are is robust...
# Fill with zeros: default for ordinals and binary...
meansFree = !colTypes$isBin # Fix the binary variables at zero (umx_means did this)
the_bin_cols = which(colTypes$isBin)[1:nBin] # Columns in which the bin vars appear for T1, i.e., c(1,3,5)
binBracketLabels = paste0("Vtot[", the_bin_cols, ",", the_bin_cols, "]") # "Vtot[1,1]" "Vtot[3,3]"
# =============================
# = Figure out start values =
# = NOTE: fullVars is expanded by the time we get to here... no sep. =
# ===================================================================
starts = xmu_starts(mzData= mzData, dzData= dzData, selVars= fullVars, equateMeans= equateMeans, nSib= nSib, varForm= "Cholesky")
# Contains starts$varStarts; starts$meanStarts; starts$meanLabels # (Equated across twins if requested)
model = mxModel(name,
mxModel("top",
# means
umxMatrix("expMean", "Full" , nrow = 1, ncol = (nVar*nSib), free = meansFree, values = starts$meanStarts, labels = starts$meanLabels, dimnames = list("means", fullVars)),
# thresholds
umxThresholdMatrix(rbind(mzData$observed, dzData$observed), fullVarNames = fullVars, sep = sep, verbose = verbose),
# var-cov
# NOTE: Assumes A+C+E is Vtot (i.e., these are the three and only components forming expCov)
mxAlgebra(name = "Vtot", A + C + E), # Total variance (also added by models with std = TRUE, but is OK to add twice)
umxMatrix("binLabels" , "Full", nrow = nBin, ncol = 1, labels = binBracketLabels),
umxMatrix("Unit_nBinx1", "Unit", nrow = nBin, ncol = 1),
mxConstraint(name = "constrain_Bin_var_to_1", binLabels == Unit_nBinx1)
),
mxModel("MZ", mzData, mxExpectationNormal("top.expCovMZ", "top.expMean", thresholds = "top.threshMat") ),
mxModel("DZ", dzData, mxExpectationNormal("top.expCovDZ", "top.expMean", thresholds = "top.threshMat") ),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
if(!is.null(fullCovs)){
model = xmuTwinUpgradeMeansToCovariateModel(model, fullVars = fullVars, fullCovs = fullCovs, nSib = nSib, sep = sep)
}
return(model)
}
# xmuTwinSuper_CovCor(name=name, fullVars = fullVars, mzData= mzData, dzData = dzData, numObsMZ = numObsMZ, numObsDZ = numObsDZ)
xmuTwinSuper_CovCor <- function(name=NULL, fullVars, mzData, dzData, type, numObsMZ, numObsDZ){
# Check the data and get it into shape
umx_check(!is.null(numObsMZ), "stop", paste0("You must set numObsMZ with summary data"))
umx_check(!is.null(numObsDZ), "stop", paste0("You must set numObsDZ with summary data"))
mzData = xmu_make_mxData(mzData, type = type, manifests = fullVars, numObs = numObsMZ)
dzData = xmu_make_mxData(dzData, type = type, manifests = fullVars, numObs = numObsDZ)
model = mxModel(name,
mxModel("top"),
mxModel("MZ", mxExpectationNormal("top.expCovMZ"), mzData),
mxModel("DZ", mxExpectationNormal("top.expCovDZ"), dzData),
mxFitFunctionMultigroup(c("MZ", "DZ"))
)
return(model)
}
#' Not for end-users: Add a means model with covariates to a twin model
#'
#' @description
#' Does the following to `model` (i.e., a umx top/MZ/DZ supermodel):
#'
#' 1. Change `top.expMeans` to `top.intercept`.
#' 2. Create `top.meansBetas` for beta weights in rows (of covariates) and columns for each variable.
#' 3. Add matrices for each twin's data.cov vars (matrixes are called `T1DefVars`).
#' 4. Switch `mxExpectationNormal` in each data group to point to the local `expMean`.
#' 5. Add "expMean" algebra to each data group.
#' * `grp.expMean` sums `top.intercept` and `grp.DefVars %*% top.meansBetas` for each twin.
#'
#' @details In umx models with no covariates, means live in `top$expMean`
#'
#'
#' @param model The [umxSuperModel()] we are modifying (must have `MZ` `DZ` and `top` submodels)
#' @param fullVars the FULL names of manifest variables
#' @param fullCovs the FULL names of definition variables
#' @param nSib How many siblings
#' @param sep How twin variable names have been expanded, e.g. "_T".
#' @return - model, now with means model extended to covariates.
#' @export
#' @family xmu internal not for end user
#' @seealso - called by [xmuTwinSuper_Continuous()]
#' @md
#' @examples
#' \dontrun{
#' data(twinData) # ?twinData from Australian twins.
#' twinData[, c("ht1", "ht2")] = twinData[, c("ht1", "ht2")] * 10
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#' # m1 = umxACE(selDVs= "ht", sep= "", dzData= dzData, mzData= mzData, autoRun= FALSE)
# # This example won't work, as umxACE drops the covs from the data...
#' # m2 = xmuTwinUpgradeMeansToCovariateModel(m1, fullVars = c("ht1", "ht2"),
#' # fullCovs = c("age1", "sex1", "age2", "sex2"), sep = "")
#'
#' }
#'
xmuTwinUpgradeMeansToCovariateModel <- function(model, fullVars, fullCovs, nSib, sep) {
# TODO Check the def vars are still in the dataset at this point...
umx_check(all(c("MZ", "DZ", "top") %in% names(model)), "stop", message= "need a model with top, MZ and DZ sub-models")
umx_check(!is.null(model$top$expMean), "stop", "Model must have $top$expMean for xmuTwinUpgradeMeansToCovariateModel to work.")
baseVars = umx_explode_twin_names(fullVars, sep = sep)$baseNames
baseCovs = umx_explode_twin_names(fullCovs, sep = sep)$baseNames
nVar = length(baseVars)
nCov = length(baseCovs)
# 1. Make `meansBetas` matrix
betaLabels = paste0(rep(baseCovs, times= nVar), "_b_Var", rep(1:nVar, each= nCov) )
meansBetas = umxMatrix("meansBetas", "Full", nrow = nCov, ncol = nVar, free = TRUE, labels= betaLabels, values = 0, lbound = -2, ubound = 2)
dimnames(meansBetas) = list(baseCovs, baseVars)
# age "age_b_Var1" "age_b_Var2" "age_b_Var3"
# sex "sex_b_Var1" "sex_b_Var2" "sex_b_Var3"
# =============================================================================================================
# = 1. Add the betaDef matrix to top and replace top.expMean with top.intercept
# =============================================================================================================
# (assumes expMean has correctly locked to zero for binary variables)
top = model$top
# Add meansBetas to top, and rename expMean to intercept
top = mxModel(top, meansBetas)
top = umxRenameMatrix(top, matrixName = "expMean", name="intercept")
# New expectation to point to expMean in the local data group
if(is.na(model$MZ$expectation$thresholds)){
# no thresholds found, don't add any
MZexpectation = mxExpectationNormal("top.expCovMZ", "expMean")
DZexpectation = mxExpectationNormal("top.expCovDZ", "expMean")
} else {
# had thresholds, keep them
MZexpectation = mxExpectationNormal("top.expCovMZ", "expMean", thresholds = model$MZ$expectation$thresholds) # "top.threshMat"
DZexpectation = mxExpectationNormal("top.expCovDZ", "expMean", thresholds = model$DZ$expectation$thresholds) # "top.threshMat"
}
# 2. Upgrade MZ and DZ groups with local Def Vars and new expMean algebra
matsAndAlg = xmu_twin_make_def_means_mats_and_alg(baseCovs= baseCovs, fullVars = fullVars, nSib = nSib, sep= sep) # "T1DefVars", "T2DefVars"... + "expMean" algebra
MZ = mxModel(model$MZ, MZexpectation, matsAndAlg)
DZ = mxModel(model$DZ, DZexpectation, matsAndAlg)
return(mxModel(model, top, MZ, DZ))
}
#' Make the matrices and algebras for definition-based means models
#'
#' @description
#' not-for-end-user helper for means in twin models. Returns matrices for
#' each definition variable, and an algebra to compute means.
#'
#' @param baseCovs base names of the DVs, e.g. "age"
#' @param fullVars full names of the DVs, e.g. "E_T1"
#' @param nSib how many siblings - typically 2
#' @param sep in twin variable, i.e., "_T"
#' @return matrices and an algebra
#' @export
#' @family xmu internal not for end user
#' @seealso - [xmuTwinUpgradeMeansToCovariateModel()]
#' @md
#' @examples
#' # xmu_twin_make_def_means_mats_and_alg(baseCovs= baseCovs,
#' # fullVars = fullVars, nSib = nSib, sep= sep)
xmu_twin_make_def_means_mats_and_alg <- function(baseCovs, fullVars, nSib, sep) {
nCov = length(baseCovs)
defMats = list()
for (i in 1:nSib) {
tmp = umxMatrix(paste0("T", i, "DefVars"), type= "Full", nrow= 1, ncol= nCov, free= FALSE, labels= paste0("data.", baseCovs, sep, i))
defMats[[i]] = tmp
}
# note: intercept is full width
if(nSib == 2){
tmpAlg = mxAlgebra(name= "expMean", top.intercept + cbind(T1DefVars %*% top.meansBetas, T2DefVars %*% top.meansBetas), dimnames= list("means", fullVars) )
} else if (nSib == 3){
tmpAlg = mxAlgebra(name= "expMean", top.intercept + cbind(
T1DefVars %*% top.meansBetas,
T2DefVars %*% top.meansBetas,
T3DefVars %*% top.meansBetas), dimnames= list("means", fullVars)
)
}else{
stop("I can't handle nSib = ", nSib, ", only 2 or 3 so far. post as an issue on github if you need this")
}
return(append(defMats, tmpAlg))
}
#' Helper providing boilerplate start values for means and variance in twin models
#'
#' @description
#' `xmu_starts` can handle several common/boilerplate situations in which means and variance start values
#' are used in twin models.
#'
#' @param mzData Data for MZ pairs.
#' @param dzData Data for DZ pairs.
#' @param selVars Variable names: If sep = NULL, then treated as full names for both sibs.
#' @param sep All the variables full names.
#' @param equateMeans (NULL)
#' @param nSib How many subjects in a family.
#' @param varForm currently just "Cholesky" style.
#' @param SD = TRUE (FALSE = variance, not SD).
#' @param divideBy = 3 (A,C,E) 1/3rd each. Use 1 to do this yourself post-hoc.
#' @return - varStarts and meanStarts
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' data(twinData)
#' selDVs = c("wt", "ht")
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#'
#' round(sqrt(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3),3)
#' xmu_starts(mzData, dzData, selVars=selDVs, nSib= 2, sep="", equateMeans=TRUE, varForm="Cholesky")
#'
#' # Variance instead of SD
#' round(var(dzData[,tvars(selDVs, "")], na.rm=TRUE)/3,3)
#' xmu_starts(mzData, dzData, selVars = selDVs, nSib = 2, sep= "",
#' equateMeans= TRUE, varForm= "Cholesky", SD= FALSE)
#'
#' # one variable
#' xmu_starts(mzData, dzData, selVars= "wt", nSib = 2, sep="", equateMeans = TRUE)
#'
#' # Ordinal/continuous mix
#' data(twinData)
#' twinData= umx_scale_wide_twin_data(data=twinData,varsToScale="wt",sep= "")
#' # Cut BMI column to form ordinal obesity variables
#' cuts = quantile(twinData[, "bmi1"], probs = c(.5, .8), na.rm = TRUE)
#' obLevels = c('normal', 'overweight', 'obese')
#' twinData$obese1= cut(twinData$bmi1,breaks=c(-Inf,cuts,Inf),labels=obLevels)
#' twinData$obese2= cut(twinData$bmi2,breaks=c(-Inf,cuts,Inf),labels=obLevels)
#' # Make the ordinal variables into mxFactors
#' ordDVs = c("obese1", "obese2")
#' twinData[, ordDVs] = umxFactor(twinData[, ordDVs])
#' mzData = twinData[twinData$zygosity %in% "MZFF",]
#' dzData = twinData[twinData$zygosity %in% "DZFF",]
#' xmu_starts(mzData, dzData, selVars = c("wt","obese"), sep= "",
#' nSib= 2, equateMeans = TRUE, SD= FALSE)
#'
#' xmu_starts(mxData(mzData, type="raw"), mxData(mzData, type="raw"),
#' selVars = c("wt","obese"), sep= "", nSib= 2, equateMeans = TRUE, SD= FALSE)
#'
#' # ==============
#' # = Three sibs =
#' # ==============
#' data(twinData)
#' twinData$wt3 = twinData$wt2
#' twinData$ht3 = twinData$ht2
#' selDVs = c("wt", "ht")
#' mzData = twinData[twinData$zygosity %in% "MZFF", ]
#' dzData = twinData[twinData$zygosity %in% "DZFF", ]
#'
#' xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=TRUE)
#' xmu_starts(mzData, dzData, selVars=selDVs, sep="", nSib=3, equateMeans=FALSE)
xmu_starts <- function(mzData, dzData, selVars = selVars, sep = NULL, equateMeans= NULL, nSib, varForm = c("Cholesky"), SD= TRUE, divideBy = 3) {
if(!is.null(sep)){
selVars = umx_paste_names(selVars, sep = sep, suffixes = 1:nSib)
}
nVar = length(selVars)/nSib
dataType = umx_is_cov(dzData, boolean = FALSE)
if(dataType == "raw") {
if(is.null(equateMeans)){
stop("you have to tell xmu_starts whether to equate the means or not")
}
if(umx_is_MxData(mzData)){
allData = rbind(mzData$observed, dzData$observed)[,selVars]
}else{
allData = rbind(mzData, dzData)[,selVars]
}
# Uses sib 1 and 2 irrespective of nSib (doesn't matter here)
T1 = allData[, 1:nVar, drop = FALSE]
T2 = allData[, (nVar+1):(nVar*2), drop = FALSE];
names(T2) = names(T1)
longData = rbind(T1, T2)[, selVars[1:nVar], drop = FALSE]
# Mean starts (1 per variable)
meanStarts = umx_means(longData, ordVar = 0, na.rm = TRUE)
# Make nSib wide
meanStarts = rep(meanStarts, times = nSib)
if(equateMeans){
# e.g. "expMean_ht1" "expMean_wt1" "expMean_ht1"
meanLabels = rep(paste0("expMean_", selVars[1:nVar]), times = nSib)
} else {
meanLabels = paste0("expMean_", selVars)
}
varStarts = umx_var(longData, format= "diag", ordVar = 1, use = "pairwise.complete.obs", strict = FALSE)
} else if(dataType %in% c("cov", "cor")){
meanStarts = NA # Not used for summary data
meanLabels = NA
if(umx_is_MxData(mzData)){
het_mz = umx_reorder(mzData$observed, selVars)
het_dz = umx_reorder(dzData$observed, selVars)
}else{
het_mz = umx_reorder(mzData, selVars)
het_dz = umx_reorder(dzData, selVars)
}
varStarts = (diag(het_mz)[1:nVar]+ diag(het_dz)[1:nVar])/2
}else{
stop("xmu_starts can only handle raw and cov/cor data types. You gave me ", omxQuotes(dataType))
}
# Covariance matrix, 1/3 allocated to each of A=C=E.
varStarts = varStarts/divideBy
if(varForm == "Cholesky"){
if(SD){
# sqrt() to switch from var to path coefficient scale
# Sqrt to switch from var to path coefficient scale
varStarts = t(chol(diag(varStarts, length(varStarts))))
varStarts = matrix(varStarts, nVar, nVar)
} else {
varStarts = diag(varStarts, nVar, nVar)
}
} else {
stop("Not sure how to handle varForm = ", omxQuotes(varForm))
}
return(list(varStarts=varStarts, meanStarts= meanStarts, meanLabels= meanLabels))
}
#' Add weight matrices to twin models.
#'
#' @description
#' Add weight models (MZw, DZw) with matrices (e.g. mzWeightMatrix) to a twin model, and
#' update `mxFitFunctionMultigroup`. This yields a weighted model with vector objective.
#'
#' To weight objective functions in OpenMx, you specify a container model that applies the weights
#' m1 is the model with no weights, but with "vector = TRUE" option added to the FIML objective.
#' This option makes FIML return individual likelihoods for each row of the data (rather than a single
#' -2LL value for the model)
#' You then optimize weighted versions of these likelihoods by building additional models containing
#' weight data and an algebra that multiplies the likelihoods from the first model by the weight vector.
#'
#' @param model umx-style twin model
#' @param mzWeights data for MZ weights matrix
#' @param dzWeights data for DZ weights matrix
#' @return - model
#' @export
#' @family xmu internal not for end user
#' @md
#' @examples
#' tmp = umx_make_twin_data_nice(data=twinData, sep="", zygosity="zygosity", numbering= 1:2)
#' m1 = umxACE(selDVs = "wt", data = tmp, dzData = "DZFF", mzData = "MZFF", autoRun= FALSE)
#' m1$MZ$fitfunction$vector= TRUE
#'
#' tmp = xmu_twin_add_WeightMatrices(m1,
#' mzWeights= rnorm(nrow(m1$MZ$data$observed)),
#' dzWeights= rnorm(nrow(m1$DZ$data$observed))
#' )
#'
xmu_twin_add_WeightMatrices <- function(model, mzWeights = NULL, dzWeights = NULL) {
umx_check(model$MZ$fitfunction$vector, "stop", "xmu_twin_add_WeightMatrix: You need to set the fitFunction to vector mode... but it appears I haven't")
if(umx_is_MxMatrix(mzWeights)){
# just check the name
if(mzWeights$name != "mzWeightMatrix"){ stop(
paste0("xmu_twin_add_WeightMatrices expects name of mzWeights to be 'mzWeightMatrix'.\n", "It was ", omxQuotes(mzWeights$name))
)}
} else {
# make incoming raw data into matrices
# convert from dataframe if necessary
if(!is.null(dim(mzWeights)) ){ mzWeights = mzWeights[,1] }
if(!is.null(dim(dzWeights)) ){ dzWeights = dzWeights[,1] }
mzWeights = mxMatrix(name = "mzWeightMatrix", type = "Full", nrow = length(mzWeights), ncol = 1, free = FALSE, values = mzWeights)
dzWeights = mxMatrix(name = "dzWeightMatrix", type = "Full", nrow = length(dzWeights), ncol = 1, free = FALSE, values = dzWeights)
}
model = mxModel(model,
mxModel("MZw", mzWeights,
mxAlgebra(-2 * sum(mzWeightMatrix * log(MZ.objective) ), name = "mzWeightedCov"),
mxFitFunctionAlgebra("mzWeightedCov")
),
mxModel("DZw", dzWeights,
mxAlgebra(-2 * sum(dzWeightMatrix * log(DZ.objective) ), name = "dzWeightedCov"),
mxFitFunctionAlgebra("dzWeightedCov")
),
mxFitFunctionMultigroup(c("MZw", "DZw"))
)
return(model)
}
# one_by_nCov = umxMatrix("T1DefVars", "Full", nrow = 1, ncol = nCov, values = c(exp(1), pi))
# meansBetas = umxMatrix("meansBetas", "Full", nrow = nCov, ncol = nVar, free = TRUE, labels=betaLabels, values = 1:6, lbound = -2, ubound = 2)
# one_by_nCov = qm(1, 57) # qm(sex=1, age=57)
# # a beta for each covariate for each variable: nCov * nVar (product has nVar columns)
# meansBetas = qm(
# .1, .2, .3|
# .4, .5, .6)
# dimnames(meansBetas)=list(c("sex", "age"), c("var1","var2","var3"))
# var1 var2 var3
# sex 0.1 0.2 0.3
# age 0.4 0.5 0.6
# one_by_nCov %*% meansBetas
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