R/estimate.lvm.R

Defines functions `estimate.lvm`

###{{{ estimate.lvm

##' Estimation of parameters in a Latent Variable Model (lvm)
##'
##' Estimate parameters. MLE, IV or user-defined estimator.
##'
##' A list of parameters controlling the estimation and optimization procedures
##' is parsed via the \code{control} argument. By default Maximum Likelihood is
##' used assuming multivariate normal distributed measurement errors. A list
##' with one or more of the following elements is expected:
##'
##' \describe{
##' \item{start:}{Starting value. The order of the parameters can be shown by
##' calling \code{coef} (with \code{mean=TRUE}) on the \code{lvm}-object or with
##' \code{plot(..., labels=TRUE)}. Note that this requires a check that it is
##' actual the model being estimated, as \code{estimate} might add additional
##' restriction to the model, e.g. through the \code{fix} and \code{exo.fix}
##' arguments. The \code{lvm}-object of a fitted model can be extracted with the
##' \code{Model}-function.}
##'
##' \item{starterfun:}{Starter-function with syntax
##' \code{function(lvm, S, mu)}.  Three builtin functions are available:
##' \code{startvalues}, \code{startvalues0}, \code{startvalues1}, ...}
##'
##' \item{estimator:}{ String defining which estimator to use (Defaults to
##' ``\code{gaussian}'')}
##'
##' \item{meanstructure}{Logical variable indicating
##' whether to fit model with meanstructure.}
##'
##' \item{method:}{ String pointing to
##' alternative optimizer (e.g. \code{optim} to use simulated annealing).}
##'
##' \item{control:}{ Parameters passed to the optimizer (default
##' \code{stats::nlminb}).}
##'
##' \item{tol:}{ Tolerance of optimization constraints on lower limit of
##' variance parameters.  } }
##'
##' @param x \code{lvm}-object
##' @param data \code{data.frame}
##' @param estimator String defining the estimator (see details below)
##' @param control control/optimization parameters (see details below)
##' @param missing Logical variable indiciating how to treat missing data.
##' Setting to FALSE leads to complete case analysis. In the other case
##' likelihood based inference is obtained by integrating out the missing data
##' under assumption the assumption that data is missing at random (MAR).
##' @param weights Optional weights to used by the chosen estimator.
##' @param weightsname Weights names (variable names of the model) in case
##' \code{weights} was given as a vector of column names of \code{data}
##' @param data2 Optional additional dataset used by the chosen
##' estimator.
##' @param id Vector (or name of column in \code{data}) that identifies
##' correlated groups of observations in the data leading to variance estimates
##' based on a sandwich estimator
##' @param fix Logical variable indicating whether parameter restriction
##' automatically should be imposed (e.g. intercepts of latent variables set to
##' 0 and at least one regression parameter of each measurement model fixed to
##' ensure identifiability.)
##' @param index For internal use only
##' @param graph For internal use only
##' @param messages Control how much information should be
##' printed during estimation (0: none)
##' @param quick If TRUE the parameter estimates are calculated but all
##' additional information such as standard errors are skipped
##' @param method Optimization method
##' @param param set parametrization (see \code{help(lava.options)})
##' @param cluster Obsolete. Alias for 'id'.
##' @param p Evaluate model in parameter 'p' (no optimization)
##' @param ... Additional arguments to be passed to lower-level functions
##' @return A \code{lvmfit}-object.
##' @author Klaus K. Holst
##' @seealso estimate.default score, information
##' @keywords models regression
##' @export
##' @method estimate lvm
##' @examples
##' dd <- read.table(header=TRUE,
##' text="x1 x2 x3
##'  0.0 -0.5 -2.5
##' -0.5 -2.0  0.0
##'  1.0  1.5  1.0
##'  0.0  0.5  0.0
##' -2.5 -1.5 -1.0")
##' e <- estimate(lvm(c(x1,x2,x3)~u),dd)
##'
##' ## Simulation example
##' m <- lvm(list(y~v1+v2+v3+v4,c(v1,v2,v3,v4)~x))
##' covariance(m) <- v1~v2+v3+v4
##' dd <- sim(m,10000) ## Simulate 10000 observations from model
##' e <- estimate(m, dd) ## Estimate parameters
##' e
##'
##' ## Using just sufficient statistics
##' n <- nrow(dd)
##' e0 <- estimate(m,data=list(S=cov(dd)*(n-1)/n,mu=colMeans(dd),n=n))
##' rm(dd)
##'
##' ## Multiple group analysis
##' m <- lvm()
##' regression(m) <- c(y1,y2,y3)~u
##' regression(m) <- u~x
##' d1 <- sim(m,100,p=c("u,u"=1,"u~x"=1))
##' d2 <- sim(m,100,p=c("u,u"=2,"u~x"=-1))
##'
##' mm <- baptize(m)
##' regression(mm,u~x) <- NA
##' covariance(mm,~u) <- NA
##' intercept(mm,~u) <- NA
##' ee <- estimate(list(mm,mm),list(d1,d2))
##'
##' ## Missing data
##' d0 <- makemissing(d1,cols=1:2)
##' e0 <- estimate(m,d0,missing=TRUE)
##' e0
`estimate.lvm` <-
    function(x, data=parent.frame(),
      estimator=NULL,
      control=list(),
      missing=FALSE,
      weights, weightsname,
      data2,
      id,
      fix,
      index=!quick,
      graph=FALSE,
      messages=lava.options()$messages,
      quick=FALSE,
      method,
      param,
      cluster,
      p,
      ...) {
        cl <- match.call()
        if (!base::missing(param)) {
            oldparam <- lava.options()$param
            lava.options(param=param)
            on.exit(lava.options(param=oldparam))
        }
        if (!base::missing(method)) {
            control["method"] <- list(method)
        }

        Optim <- list(
            iter.max=lava.options()$iter.max,
            trace=ifelse(lava.options()$debug,3,0),
            gamma=lava.options()$gamma,
            gamma2=1,
            ngamma=lava.options()$ngamma,
            backtrack=lava.options()$backtrack,
            lambda=0.05,
            abs.tol=1e-9,
            epsilon=1e-10,
            delta=1e-10,
            rel.tol=1e-10,
            S.tol=1e-5,
            stabil=FALSE,
            start=NULL,
            constrain=lava.options()$constrain,
            method=NULL,
            starterfun="startvalues0",
            information="E",
            meanstructure=TRUE,
            sparse=FALSE,
            tol=lava.options()$tol)

        defopt <- lava.options()[]
        defopt <- defopt[intersect(names(defopt),names(Optim))]
        Optim[names(defopt)] <- defopt
        if (length(control)>0) {
            Optim[names(control)] <- control
        }
        if (is.environment(data)) {
            innames <- intersect(ls(envir=data),vars(x))
            data <- as.data.frame(lapply(innames,function(x) get(x,envir=data)))
            names(data) <- innames
        }
        if (length(exogenous(x)>0)) {
            catx <- categorical2dummy(x,data)
            x <- catx$x; data <- catx$data
        }

        if (!lava.options()$exogenous) exogenous(x) <- NULL

        redvar <- intersect(intersect(parlabels(x),latent(x)),colnames(data))
        if (length(redvar)>0)
            warning(paste("Latent variable exists in dataset",redvar))
        ## Random-slopes:
        xfix <- setdiff(colnames(data)[(colnames(data)%in%parlabels(x,exo=TRUE))],latent(x))
        if (base::missing(fix)) {
            fix <- ifelse(length(xfix)>0,FALSE,TRUE)
        }
        Debug(list("start=",Optim$start))

        if (!base::missing(cluster)) id <- cluster

        ## Weights...
        if (!base::missing(weights)) {
            if (is.character(weights)) {
                weights <- data[,weights,drop=FALSE]
                if (!base::missing(weightsname)) {
                    colnames(weights) <- weightsname
                } else {
                    yvar <- index(x)$endogenous
                    nw <- seq_len(min(length(yvar),ncol(weights)))
                    colnames(weights)[nw] <- yvar[nw]
                }
            }
            weights <- cbind(weights)
        } else {
            weights <- NULL
        }
        if (!base::missing(data2)) {
            if (is.character(data2)) {
                data2 <- data[,data2]
            }
        } else {
            data2 <- NULL
        }
        ## Correlated clusters...
        if (!base::missing(id)) {
            if (is.character(id)) {
                id <- data[,id]
            }
        } else {
            id <- NULL
        }

        Debug("procdata")
        val <- try({
            dd <- procdata.lvm(x,data=data,missing=missing)
            S <- dd$S; mu <- dd$mu; n <- dd$n
            var.missing <- setdiff(vars(x),colnames(S))
        }, silent=TRUE)
        if (inherits(val,"try-error")) {
            var.missing <- setdiff(vars(x),colnames(data))
            S <- NULL; mu <- NULL; n <- nrow(data)
        }

        ##  if (fix) {
        if (length(var.missing)>0) {## Convert to latent:
            new.lat <- setdiff(var.missing,latent(x))
            if (length(new.lat)>0)
                x <- latent(x, new.lat)
        }
        ##}

        ## Run hooks (additional lava plugins)
        myhooks <- gethook()
        for (f in myhooks) {
            res <- do.call(f, list(x=x,data=data,weights=weights,data2=data2,estimator=estimator,optim=Optim))
            if (!is.null(res$x)) x <- res$x
            if (!is.null(res$data)) data <- res$data
            if (!is.null(res$weights)) weights <- res$weights
            if (!is.null(res$data2)) data2 <- res$data2
            if (!is.null(res$optim)) Optim <- res$optim
            if (!is.null(res$estimator)) estimator <- res$estimator
            rm(res)
        }
        if (is.null(estimator)) {
            if (!missing(weights) && !is.null(weights)) {
                estimator <- "normal"
            } else estimator <- "gaussian"
        }

        checkestimator <- function(x,...) {
            ffname <- paste0(x,c("_objective","_gradient"),".lvm")
            exists(ffname[1])||exists(ffname[2])
        }
        if (!checkestimator(estimator)) { ## Try down/up-case version
            estimator <- tolower(estimator)
            if (!checkestimator(estimator)) {
                estimator <- toupper(estimator)
            }
        }
        ObjectiveFun  <- paste0(estimator, "_objective", ".lvm")
        GradFun  <- paste0(estimator, "_gradient", ".lvm")
        if (!exists(ObjectiveFun) & !exists(GradFun))
            stop("Unknown estimator.")


        Method <-  paste0(estimator, "_method", ".lvm")
        if (!exists(Method)) {
            Method <- "nlminb1"
        } else {
            Method <- get(Method)
        }
        NoOptim <- "method"%in%names(control) && is.null(control$method)
        if (is.null(Optim$method) && !(NoOptim)) {
            Optim$method <- if (missing && Method!="nlminb0") "nlminb1" else Method
        }


        if (index) {
            ## Proces data and setup some matrices
            x <- fixsome(x, measurement.fix=fix, S=S, mu=mu, n=n,debug=messages>1)
            if (messages>1)
                message("Reindexing model...\n")
            if (length(xfix)>0) {
                index(x) <- reindex(x,sparse=Optim$sparse,zeroones=TRUE,deriv=TRUE)
            } else {
                x <- updatelvm(x,sparse=Optim$sparse,zeroones=TRUE,deriv=TRUE,mean=TRUE)
            }
        }
        if (is.null(estimator) || estimator==FALSE) {
            return(x)
        }

        if (length(index(x)$endogenous)==0) stop("No observed outcome variables. Check variable names in model and data.")
        if (!Optim$meanstructure) {
            mu <- NULL
        }

        nparall <- index(x)$npar + ifelse(Optim$meanstructure, index(x)$npar.mean+index(x)$npar.ex,0)
        ## Get starting values
        if (!missing(p)) {
            start <- p
            Optim$start <- p
        } else {
          myparnames <- coef(x,mean=TRUE)
          paragree <- FALSE
          paragree_pos <- c()
          if (!is.null(Optim$start)) {
            paragree <- myparnames%in%names(Optim$start)
            paragree_pos <- matrix(0, sum(paragree), 2)
            count <- 0
            for (i in which(paragree)) {
              count <- count + 1
              paragree_pos[count,] <-  c(i, which(myparnames[i]==names(Optim$start))[1])
            }
          }
          ## If starting values are given for all parameters and not named, then
          ## we just keep the provided starting values as they are.
          ## Otherwise, calculate starting values and place user-provided
          ## starting values in the right positions
          if (! (length(Optim$start)==length(myparnames) & sum(paragree)==0)) {
            if (is.null(Optim$starterfun) && lava.options()$param!="relative")
              Optim$starterfun <- startvalues0
            start <- suppressWarnings(do.call(Optim$starterfun, list(x=x,S=S,mu=mu,debug=lava.options()$debug,
                                                                     messages=messages,data=data,...)))
            if (!is.null(x$expar) && length(start)<nparall) {
              ii <- which(index(x)$e1==1)
              start <- c(start, structure(unlist(x$expar[ii]), names=names(x$expar)[ii]))
            }
            for (i in seq_len(NROW(paragree_pos))) {
              start[paragree_pos[i, 1]] <- Optim$start[paragree_pos[i, 2]]
            }
            Optim$start <- start
          }
        }
        ## Missing data
        if (missing) {
            return(estimate.MAR(x=x,data=data,fix=fix,control=Optim,debug=lava.options()$debug,
                                messages=messages,estimator=estimator,weights=weights,data2=data2,cluster=id,...))
        }
        coefname <- coef(x,mean=Optim$meanstructure,fix=FALSE);
        names(Optim$start) <- coefname

        ## Non-linear parameter constraints involving observed variables? (e.g. nonlinear regression)
        constr <- lapply(constrain(x), function(z)(attributes(z)$args))
        xconstrain <- intersect(unlist(constr), manifest(x))
        xconstrainM <- TRUE
        XconstrStdOpt <- TRUE
        if (length(xconstrain)>0) {
            constrainM <- names(constr)%in%unlist(x$mean)
            for (i in seq_len(length(constr))) {
                if (!constrainM[i]) {
                    if (any(constr[[i]]%in%xconstrain)) {
                        xconstrainM <- FALSE
                        break;
                    }
                }
            }
            if (xconstrainM & ( (is.null(control$method) || Optim$method=="nlminb0") & (lava.options()$test & estimator=="gaussian") ) ) {
                XconstrStdOpt <- FALSE
                Optim$method <- "nlminb0"
                if (is.null(control$constrain)) control$constrain <- TRUE
            }
        }

        ## Setup optimization constraints
        lowmin <- -Inf
        lower <- rep(lowmin,length(Optim$start))
        if (length(Optim$constrain)==1 & Optim$constrain)
            lower[variances(x)+index(x)$npar.mean] <- Optim$tol
        if (any(Optim$constrain)) {
            if (length(Optim$constrain)!=length(lower))
                constrained <- is.finite(lower)
            else
                constrained <- Optim$constrain
            lower[] <- -Inf
            Optim$constrain <- TRUE
            constrained <- which(constrained)
            nn <- names(Optim$start)
            CS <- Optim$start[constrained]
            CS[CS<0] <- 0.01
            Optim$start[constrained] <- log(CS)
            names(Optim$start) <- nn
        }
        ## Fix problems with starting values?
        Optim$start[is.nan(unlist(Optim$start))] <- 0

        ObjectiveFun  <- paste0(estimator, "_objective", ".lvm")
        GradFun  <- paste0(estimator, "_gradient", ".lvm")
        if (!exists(ObjectiveFun) & !exists(GradFun)) stop("Unknown estimator.")

        InformationFun <- paste0(estimator, "_hessian", ".lvm")

        mymodel <- x
        myclass <- "lvmfit"

        ## Random slopes?
        if (length(xfix)>0 | (length(xconstrain)>0 & XconstrStdOpt | !lava.options()$test)) { ## Yes
            x0 <- x

            if (length(xfix)>0) {
                myclass <- c("lvmfit.randomslope",myclass)
                nrow <- length(vars(x))
                xpos <- lapply(xfix,function(y) which(regfix(x)$labels==y))
                colpos <- lapply(xpos, function(y) ceiling(y/nrow))
                rowpos <- lapply(xpos, function(y) (y-1)%%nrow+1)
                myfix <- list(var=xfix, col=colpos, row=rowpos)
                x0 <- x
                for (i in seq_along(myfix$var))
                    for (j in seq_len(length(myfix$col[[i]])))
                        regfix(x0, from=vars(x0)[myfix$row[[i]][j]],
                               to=vars(x0)[myfix$col[[i]][j]]) <-
                            colMeans(data[,myfix$var[[i]],drop=FALSE])
                x0 <- updatelvm(x0,zeroones=TRUE,deriv=TRUE)
                x <- x0
                ## Alter start-values/constraints:
                new.par.idx <- which(coef(mymodel,mean=TRUE,fix=FALSE)%in%coef(x0,mean=TRUE,fix=FALSE))
                if (length(Optim$start)>length(new.par.idx))
                    Optim$start <- Optim$start[new.par.idx]
                lower <- lower[new.par.idx]
                if (Optim$constrain) {
                    constrained <- match(constrained,new.par.idx)
                }
            }
            mydata <- as.matrix(data[,manifest(x0)])

            myObj <- function(pp) {
                if (Optim$constrain) {
                    pp[constrained] <- exp(pp[constrained])
                }
                myfun <- function(ii) {
                    if (length(xfix)>0)
                        for (i in seq_along(myfix$var)) {
                            x0$fix[cbind(rowpos[[i]],colpos[[i]])] <- index(x0)$A[cbind(rowpos[[i]],colpos[[i]])] <- data[ii,xfix[i]]
                        }
                    if (is.list(data2)) {
                        res <- do.call(ObjectiveFun, list(x=x0, p=pp, data=mydata[ii,], n=1, weights=weights[ii,], data2=data2[ii,]))
                    } else {
                        res <- do.call(ObjectiveFun, list(x=x0, p=pp, data=mydata[ii,], n=1, weights=weights[ii,], data2=data2))
                    }
                    return(res)
                }
                sum(sapply(seq_len(nrow(data)),myfun))
            }

            myGrad <- function(pp) {
                if (Optim$constrain) {
                    pp[constrained] <- exp(pp[constrained])
                }
                myfun <- function(ii) {
                    if (length(xfix)>0)
                        for (i in seq_along(myfix$var)) {
                            x0$fix[cbind(rowpos[[i]],colpos[[i]])] <- index(x0)$A[cbind(rowpos[[i]],colpos[[i]])] <- data[ii,xfix[i]]
                        }
                    if (is.list(data2)) {
                        rr <- do.call(GradFun, list(x=x0, p=pp, data=mydata[ii,,drop=FALSE], n=1, weights=weights[ii,], data2=data2))
                    } else
                    {
                        rr <- do.call(GradFun, list(x=x0, p=pp, data=mydata[ii,,drop=FALSE], n=1, weights=weights[ii,], data2=data2[ii,]))
                    }
                    return(rr)
                }
                ss <- rowSums(rbind(sapply(seq_len(nrow(data)),myfun)))
                if (Optim$constrain) {
                    ss[constrained] <- ss[constrained]*pp[constrained]
                }
                return(ss)
            }


            myInfo <- function(pp) {
                myfun <- function(ii) {
                    if (length(xfix)>0)
                        for (i in seq_along(myfix$var)) {
                            x0$fix[cbind(rowpos[[i]],colpos[[i]])] <- index(x0)$A[cbind(rowpos[[i]],colpos[[i]])] <- data[ii,xfix[i]]
                        }
                    if (is.list(data2)) {
                        res <- do.call(InformationFun, list(p=pp, obj=myObj, x=x0, data=data[ii,],
                                                            n=1, weights=weights[ii,], data2=data2))
                    } else {
                        res <- do.call(InformationFun, list(p=pp, obj=myObj, x=x0, data=data[ii,],
                                                            n=1, weights=weights[ii,], data2=data2[ii,]))
                    }
                    return(res)
                }
                L <- lapply(seq_len(nrow(data)),function(x) myfun(x))
                val <- apply(array(unlist(L),dim=c(length(pp),length(pp),nrow(data))),c(1,2),sum)
                if (!is.null(attributes(L[[1]])$grad)) {
                    attributes(val)$grad <- colSums (
                                       matrix( unlist(lapply(L,function(i) attributes(i)$grad)) , ncol=length(pp), byrow=TRUE)
                                   )
                }
                return(val)
            }

            ##################################################
        } else { ## No, standard model

            ## Non-linear parameter constraints involving observed variables? (e.g. nonlinear regression)
            xconstrain <- c()
            for (i in seq_len(length(constrain(x)))) {
                z <- constrain(x)[[i]]
                xx <- intersect(attributes(z)$args,manifest(x))
                if (length(xx)>0) {
                    warg <- setdiff(attributes(z)$args,xx)
                    wargidx <- which(attributes(z)$args%in%warg)
                    exoidx <- which(attributes(z)$args%in%xx)
                    parname <- names(constrain(x))[i]
                    y <- names(which(unlist(lapply(intercept(x),function(x) x==parname))))
                    el <- list(i,y,parname,xx,exoidx,warg,wargidx,z)
                    names(el) <- c("idx","endo","parname","exo","exoidx","warg","wargidx","func")
                    xconstrain <- c(xconstrain,list(el))
                }
            }
            yconstrain <- unlist(lapply(xconstrain,function(x) x$endo))
            iconstrain <- unlist(lapply(xconstrain,function(x) x$idx))

            MkOffset <- function(pp,grad=FALSE) {
                if (length(xconstrain)>0) {
                    Mu <- matrix(0,nrow(data),length(vars(x))); colnames(Mu) <- vars(x)
                    M <- modelVar(x,p=pp,data=data)
                    M$parval <- c(M$parval,  x$mean[unlist(lapply(x$mean,is.numeric))])
                    for (i in seq_len(length(xconstrain))) {
                        pp <- unlist(M$parval[xconstrain[[i]]$warg]);
                        myidx <- with(xconstrain[[i]],order(c(wargidx,exoidx)))
                        D <- cbind(rbind(pp)%x%cbind(rep(1,nrow(Mu))),
                                   data[,xconstrain[[i]]$exo,drop=FALSE])[,myidx,drop=FALSE]
                        mu <- try(xconstrain[[i]]$func(D),silent=TRUE)
                        if (is.data.frame(mu)) mu <- mu[,1]
                        if (inherits(mu,"try-error") || NROW(mu)!=NROW(Mu)) {
                            ## mu1 <- with(xconstrain[[i]],
                            ##            apply(data[,exo,drop=FALSE],1,
                            ##                  function(x) func(unlist(c(pp,x))[myidx])))
                            mu <- apply(D,1,xconstrain[[i]]$func)
                        }
                        Mu[,xconstrain[[i]]$endo] <- mu
                    }
                    offsets <- Mu%*%t(M$IAi)[,endogenous(x)]
                    return(offsets)
                }
                return(NULL)
            }

            myObj <- function(pp) {
                if (Optim$constrain) {
                    pp[constrained] <- exp(pp[constrained])
                }
                offset <- MkOffset(pp)
                mu0 <- mu; S0 <- S; x0 <- x
                if (!is.null(offset)) {
                    x0$constrain[iconstrain] <- NULL
                    data0 <- data[,manifest(x0)]
                    data0[,endogenous(x)] <- data0[,endogenous(x)]-offset
                    pd <- procdata.lvm(x0,data=data0)
                    S0 <- pd$S; mu0 <- pd$mu
                    x0$mean[yconstrain] <- 0
                }
                do.call(ObjectiveFun, list(x=x0, p=pp, data=data, S=S0, mu=mu0, n=n, weights=weights
                                          ,data2=data2, offset=offset
                                           ))
            }

            myGrad <- function(pp) {
                if (Optim$constrain)
                    pp[constrained] <- exp(pp[constrained])
                S <- do.call(GradFun, list(x=x, p=pp, data=data, S=S, mu=mu, n=n, weights=weights
                                         , data2=data2##, offset=offset
                                           ))
                if (Optim$constrain) {
                    S[constrained] <- S[constrained]*pp[constrained]
                }
                if (is.null(mu) & index(x)$npar.mean>0) {
                    return(S[-c(seq_len(index(x)$npar.mean))])
                }
                if (length(S)<length(pp))  S <- c(S,rep(0,length(pp)-length(S)))

                return(S)
            }
            myInfo <- function(pp) {
                I <- do.call(InformationFun, list(p=pp,
                                                  obj=myObj,
                                                  x=x, data=data,
                                                  S=S, mu=mu,
                                                  n=n,
                                                  weights=weights, data2=data2,
                                                  type=Optim$information
                                                  ))
                if (is.null(mu) && index(x)$npar.mean>0) {
                    return(I[-seq_len(index(x)$npar.mean),-seq_len(index(x)$npar.mean)])
                }
                return(I)
            }

        }

        myHess <- function(pp) {
            p0 <- pp
            if (Optim$constrain)
                pp[constrained] <- exp(pp[constrained])
            I0 <- myInfo(pp)
            attributes(I0)$grad <- NULL
            D <- attributes(I0)$grad
            if (is.null(D)) {
                D <- myGrad(p0)
                attributes(I0)$grad <- D
            }
            if (Optim$constrain) {
                I0[constrained,-constrained] <- apply(I0[constrained,-constrained,drop=FALSE],2,function(x) x*pp[constrained]);
                I0[-constrained,constrained] <- t(I0[constrained,-constrained])
                if (sum(constrained)==1) {
                    I0[constrained,constrained] <- I0[constrained,constrained]*outer(pp[constrained],pp[constrained]) - D[constrained]
                } else {
                    I0[constrained,constrained] <- I0[constrained,constrained]*outer(pp[constrained],pp[constrained]) - diag(D[constrained],ncol=length(constrained))
                }
            }
            return(I0)
        }
        if (is.null(tryCatch(get(InformationFun),error = function (x) NULL)))
            myInfo <- myHess <- NULL
        if (is.null(tryCatch(get(GradFun),error = function (x) NULL)))
            myGrad <- NULL

        if (messages>1) message("Optimizing objective function...")
        if (Optim$trace>0 & (messages>1)) message("\n")
        ## Optimize with lower constraints on the variance-parameters
        if ((is.data.frame(data) | is.matrix(data)) && nrow(data)==0) stop("No observations")
        if (!missing(p)) {
            opt <- list(estimate=p)

        } else {
            if (!is.null(Optim$method)) {
                optarg <- list(start=Optim$start, objective=myObj, gradient=myGrad, hessian=myHess, lower=lower, control=Optim, debug=debug)
                if (length(Optim$method)>1) {
                    Optim$optimx.method <- Optim$method
                }
                if (!is.null(Optim$optimx.method)) {
                    Optim$method <- "optimx"
                }
                if (Optim$method%in%c("optimx","optim")) {
                    optimcontrolnames <-
                        c("trace",
                          "follow.on",
                          "save.failures",
                          "maximize",
                          "all.methods",
                          "kkt",
                          "kkttol",
                          "kkt2tol",
                          "starttests",
                          "dowarn",
                          "badval",
                          "usenumDeriv",
                          "fnscale",
                          "parscale",
                          "ndeps",
                          "maxit",
                          "abstol",
                          "reltol",
                          #"alpha","beta","gamma",
                          "REPORT",
                          "type",
                          "lmm",
                          "factr",
                          "pgtol")
                    if (!is.null(optarg$control)) {
                        optarg$control[names(optarg$control)%ni%optimcontrolnames] <- NULL
                    }
                    args <- names(formals(get(Optim$method)))
                    names(optarg)[1] <- "par"
                    if (is.null(optarg$upper)) optarg$upper <- Inf
                    if (!is.null(optarg[["objective"]])) names(optarg)[2] <- "fn"
                    if (!is.null(optarg[["gradient"]])) names(optarg)[3] <- "gr"
                    ##if (!is.null(optarg[["hessian"]])) names(optarg)[4] <- "hess"
                    optarg$hessian <- NULL
                    optarg[names(optarg)%ni%args] <- NULL
                }
                if (!is.null(Optim$optimx.method)) optarg$method <- Optim$optimx.method
                opt <- do.call(Optim$method,
                               optarg)
                if (inherits(opt,"optimx")) {
                    opt <- list(par=coef(opt)[1,])
                }
                if (is.null(opt$estimate))
                    opt$estimate <- opt$par
                if (Optim$constrain) {
                    opt$estimate[constrained] <- exp(opt$estimate[constrained])
                }

                if (XconstrStdOpt & !is.null(myGrad))
                    opt$gradient <- as.vector(myGrad(opt$par))
                else {
                    opt$gradient <- numDeriv::grad(myObj,opt$par)
                }
            } else {
                if (!NoOptim) {
                    opt <- do.call(ObjectiveFun, list(x=x,data=data,control=control,...))
                    opt$gradient <- rep(0,length(opt$estimate))
                } else {
                    opt <- list(estimate=Optim$start,
                                gradient=rep(0,length(Optim$start)))
                }
            }
        }
        if (!is.null(opt$convergence)) {
            if (opt$convergence != 0)
                warning("Lack of convergence. Increase number of iteration or change starting values.")
        } else if (!is.null(opt$gradient) && mean(opt$gradient)^2>1e-3)
            warning("Lack of convergence. Increase number of iteration or change starting values.")
        if (quick) {
            return(opt$estimate)
        }
        ## Calculate std.err:
        pp <- rep(NA,length(coefname)); names(pp) <- coefname
        pp.idx <- NULL
        if (!is.null(names(opt$estimate))) {
            pp[names(opt$estimate)] <- opt$estimate
            pp.idx <- na.omit(match(coefname,names(opt$estimate)))
        } else {
            pp[] <- opt$estimate
            pp.idx <- seq(length(pp))
        }
        ## TODO:
        ## if (length(pp.idx)!=length(pp)) {
        ##     pp <- rep(NA,length(coefname)); names(pp) <- coefname
        ##     pp[] <- opt$estimate
        ##     pp.idx <- seq(length(pp))
        ## }

        suppressWarnings(mom <- tryCatch(modelVar(x, pp, data=data),error=function(x)NULL))
        if (NoOptim) {
            asVar <- matrix(NA,ncol=length(pp),nrow=length(pp))
        } else {

            if (messages>1) message("\nCalculating asymptotic variance...\n")
            asVarFun  <- paste0(estimator, "_variance", ".lvm")

            if (!exists(asVarFun)) {
                if (is.null(myInfo)) {
                    if (!is.null(myGrad))
                        myInfo <- function(pp)
                            numDeriv::jacobian(myGrad,pp,method=lava.options()$Dmethod)
                    else
                        myInfo <- function(pp)
                            numDeriv::hessian(myObj,pp)
                }
                I <- myInfo(opt$estimate)
                asVar <- tryCatch(Inverse(I),
                                  error=function(e) matrix(NA, length(opt$estimate), length(opt$estimate)))
            } else {
                asVar <- tryCatch(do.call(asVarFun,
                                          list(x=x,p=opt$estimate,data=data,opt=opt)),
                                  error=function(e) matrix(NA, length(opt$estimate), length(opt$estimate)))
            }

            if (any(is.na(asVar))) {
                warning("Problems with asymptotic variance matrix. Possibly non-singular information matrix!")
            }
            if (!is.null(attributes(asVar)$pseudo) && attributes(asVar)$pseudo) {
                warning("Near-singular covariance matrix, using pseudo-inverse!")
            }
            diag(asVar)[diag(asVar)==0] <- NA
        }

        mycoef <- matrix(NA,nrow=nparall,ncol=4)
        mycoef[pp.idx,1] <- opt$estimate ## Will be finished during post.hooks

        res <- list(model=x, call=cl, coef=mycoef,
                    vcov=asVar, mu=mu, S=S, ##A=A, P=P,
                    model0=mymodel, ## Random slope hack
                    estimator=estimator, opt=opt,expar=x$expar,
                    data=list(model.frame=data, S=S, mu=mu,
                              C=mom$C, v=mom$v, n=n,
                              m=length(latent(x)), k=length(index(x)$manifest), data2=data2),
                    weights=weights, data2=data2,
                    cluster=id,
                    pp.idx=pp.idx,
                    graph=NULL, control=Optim)

        class(res) <- myclass

        myhooks <- gethook("post.hooks")
        for (f in myhooks) {
            res0 <- do.call(f,list(x=res))
            if (!is.null(res0))
                res <- res0
        }

        if(graph) {
            res <- edgelabels(res,type="est")
        }

        return(res)
    }

###}}} estimate.lvm
kkholst/lava documentation built on Sept. 6, 2021, 11:36 p.m.