R/msm.R

Defines functions onLoad model.matrix.msm model.frame.msm expand.data msm.center.covs model.matrix.wrap msm.form.mm.icov msm.form.mm.hcov msm.form.mm.mcov msm.form.mm.cov.agg msm.form.mm.cov msm.form.hmm.agg msm.aggregate.data msm.obs.to.fromto msm.form.mf.agg na.find.msmdata na.fail.msmdata na.omit.msmdata msm.form.cri msm.check.covlist msm.pci crudeinits.msm statetable.msm msm.form.houtput msm.form.output information.msm deriv.msm lik.msm Ccall.msm msm.initprobs2mat msm.dmninvlogit msm.form.dh msm.add.hmmcovs msm.form.dq msm.add.qcovs msm.rep.constraints msm.unfixallparams msm.form.params msm.inv.transform msm.transform msm.mninvlogit.transform msm.mnlogit.transform msm.form.cmodel msm.form.dmodel msm.form.covmodel.byrate msm.form.covmodel msm.check.covinits msm.check.constraint msm.check.model msm.impute.censored msm.form.obstrue msm.form.obstype msm.check.times msm.check.state msm.form.emodel msm.check.ematrix msm.form.qmodel msm.fixdiag.ematrix msm.fixdiag.qmatrix msm.check.qmatrix msm

Documented in crudeinits.msm model.frame.msm model.matrix.msm msm statetable.msm

msm <- function(formula, subject=NULL, data=list(), qmatrix, gen.inits=FALSE,
                ematrix=NULL,  hmodel=NULL, obstype=NULL, obstrue=NULL,
                covariates = NULL, covinits = NULL, constraint = NULL,
                misccovariates = NULL, misccovinits = NULL, miscconstraint = NULL,
                hcovariates = NULL, hcovinits = NULL, hconstraint = NULL, hranges=NULL,
                qconstraint=NULL, econstraint=NULL, initprobs = NULL,
                est.initprobs=FALSE, initcovariates = NULL, initcovinits = NULL,
                deathexact = NULL, death = NULL, exacttimes = FALSE, censor=NULL,
                censor.states=NULL, pci=NULL, phase.states=NULL,
                phase.inits = NULL, # TODO merge with inits eventually
                cl = 0.95, fixedpars = NULL, center=TRUE,
                opt.method="optim", hessian=NULL, use.deriv=TRUE,
                use.expm=TRUE, analyticp=TRUE, na.action=na.omit, ...)
{
    call <- match.call()
    if (missing(formula)) stop("state ~ time formula not given")
    if (missing(data)) data <- environment(formula)

### MODEL FOR TRANSITION INTENSITIES
    if (gen.inits) {
        if (is.null(hmodel) && is.null(ematrix)) {
            subj <- eval(substitute(subject), data, parent.frame())
            qmatrix <- crudeinits.msm(formula, subj, qmatrix, data, censor, censor.states)
        }
        else warning("gen.inits not supported for hidden Markov models, ignoring")
    }
    qmodel <- qmodel.orig <- msm.form.qmodel(qmatrix, qconstraint, analyticp, use.expm, phase.states)

    if (!is.null(phase.states)) {
        qmodel <- msm.phase2qmodel(qmodel, phase.states, phase.inits, qconstraint, analyticp, use.expm)
    } 
### MISCLASSIFICATION MODEL
    if (!is.null(ematrix)) {
        msm.check.ematrix(ematrix, qmodel.orig$nstates)
        if (!is.null(phase.states)){
            stop("phase-type models with additional misclassification must be specified through \"hmodel\" with hmmCat() or hmmIdent() constructors, or as HMMs by hand") 
        }
        emodel <- msm.form.emodel(ematrix, econstraint, initprobs, est.initprobs, qmodel)
    }
    else emodel <- list(misc=FALSE, npars=0, ndpars=0)

### GENERAL HIDDEN MARKOV MODEL
    if (!is.null(hmodel)) {
        msm.check.hmodel(hmodel, qmodel.orig$nstates)
        if (!is.null(phase.states)){
            hmodel.orig <- hmodel
            hmodel <- rep(hmodel, qmodel$phase.reps)
        }
        hmodel <- msm.form.hmodel(hmodel, hconstraint, initprobs, est.initprobs)
    }
    else {
        if (!is.null(hcovariates)) stop("hcovariates have been specified, but no hmodel")
        if (!is.null(phase.states)){
            hmodel <- msm.phase2hmodel(qmodel, hmodel)
        }
        else hmodel <- list(hidden=FALSE, models=rep(0, qmodel$nstates), nipars=0, nicoveffs=0, totpars=0, ncoveffs=0) # might change later if misc
    }
### CONVERT OLD STYLE MISCLASSIFICATION MODEL TO NEW GENERAL HIDDEN MARKOV MODEL
    if (emodel$misc) {
        hmodel <- msm.emodel2hmodel(emodel, qmodel)
    }
    else {
        emodel <- list(misc=FALSE, npars=0, ndpars=0, nipars=0, nicoveffs=0)
        hmodel$ematrix <- FALSE
    }

### EXACT DEATH TIMES. Logical values allowed for backwards compatibility (TRUE means final state has exact death time, FALSE means no states with exact death times)
    if (!is.null(deathexact)) death <- deathexact
    dmodel <- msm.form.dmodel(death, qmodel, hmodel)  # returns death, ndeath,
    if (dmodel$ndeath > 0 && exacttimes) warning("Ignoring death argument, as all states have exact entry times")

### CENSORING MODEL
    cmodel <- msm.form.cmodel(censor, censor.states, qmodel$qmatrix)

### SOME CHECKS    
    if (!inherits(formula, "formula")) stop("formula is not a formula")
    if (!is.null(covariates) && (!(is.list(covariates) || inherits(covariates, "formula"))))
        stop(deparse(substitute(covariates)), " should be a formula or list of formulae")
    if (!is.null(misccovariates) && (!inherits(misccovariates, "formula")))
        stop(deparse(substitute(misccovariates)), " should be a formula")
    if (is.list(covariates)) {  # different covariates on each intensity
        covlist <- covariates
        msm.check.covlist(covlist, qmodel)
        ter <- lapply(covlist, function(x)attr(terms(x),"term.labels"))
        covariates <- reformulate(unique(unlist(ter))) # merge the formulae into one
    } else covlist <- NULL # parameters will be constrained later, see msm.form.cri
    if (is.null(covariates)) covariates <- ~1
    if (emodel$misc && is.null(misccovariates)) misccovariates <- ~1
    if (hmodel$hidden && !is.null(hcovariates)) msm.check.hcovariates(hcovariates, qmodel)
    
### BUILD MODEL FRAME containing all data required for model fit,
### using all variables found in formulae.
    ## Names include factor() around covariate names, and interactions,
    ## if specified.  Need to build and evaluate a call, instead of
    ## running model.frame() directly, to find subject and other
    ## extras. Not sure why.
    indx <- match(c("data", "subject", "obstrue"), names(call), nomatch = 0)
    temp <- call[c(1, indx)]
    temp[[1]] <- as.name("model.frame")
    temp[["state"]] <- as.name(all.vars(formula[[2]]))
    temp[["time"]] <- as.name(all.vars(formula[[3]]))
    varnames <- function(x){if(is.null(x)) NULL else attr(terms(x), "term.labels")}
    forms <- c(covariates, misccovariates, hcovariates, initcovariates)
    covnames <- unique(unlist(lapply(forms, varnames)))
    temp[["formula"]] <- if (length(covnames) > 0) reformulate(covnames) else ~1
    temp[["na.action"]] <- na.pass # run na.action later so we can pass aux info to it
    temp[["data"]] <- data
    mf <- eval(temp, parent.frame())
    ## remember user-specified names for later (e.g. bootstrap/cross validation)
    attr(mf, "usernames") <- c(state=all.vars(formula[[2]]), time=all.vars(formula[[3]]), subject=as.character(temp$subject), obstype=as.character(substitute(obstype)), obstrue=as.character(temp$obstrue))
    if (is.factor(mf$"(state)")){
        if (!all(grepl("^[[:digit:]]+$", as.character(mf$"(state)"))))
            stop("state variable should be numeric or a factor with ordinal numbers as levels")
        else mf$"(state)" <- as.numeric(as.character(mf$"(state)"))
    }
    msm.check.state(qmodel$nstates, mf$"(state)", cmodel$censor, hmodel)
    if (is.null(mf$"(subject)")) mf$"(subject)" <- rep(1, nrow(mf))
    msm.check.times(mf$"(time)", mf$"(subject)", mf$"(state)")
    obstype <- if (missing(obstype)) NULL else eval(substitute(obstype), data, parent.frame()) # handle separately to allow passing a scalar (1, 2 or 3)
    mf$"(obstype)" <- msm.form.obstype(mf, obstype, dmodel, exacttimes)
    mf$"(obstrue)" <- msm.form.obstrue(mf, hmodel, cmodel)
    mf$"(obs)" <- seq_len(nrow(mf)) # row numbers before NAs omitted, for reporting in msm.check.*
    basenames <- c("(state)","(time)","(subject)","(obstype)","(obstrue)","(obs)")
    attr(mf, "covnames") <- setdiff(names(mf), basenames)
    attr(mf, "covnames.q") <- colnames(attr(terms(covariates), "factors")) # names as in data, plus factor() if user
    if (emodel$misc) attr(mf, "covnames.e") <- colnames(attr(terms(misccovariates), "factors"))
    attr(mf, "ncovs") <- length(attr(mf, "covnames"))


### HANDLE MISSING DATA
    ## Find which cols of model frame correspond to covs which appear only on initprobs
    ## Pass through to na.action as attribute
    ic <- all.vars(initcovariates)
    others <- c(covariates,misccovariates,hcovariates)
    oic <- ic[!ic %in% unlist(lapply(others, all.vars))]
    attr(mf, "icovi") <- match(oic, colnames(mf))
    if (missing(na.action) || identical(na.action, na.omit) || (identical(na.action,"na.omit")))
        mf <- na.omit.msmdata(mf)
    else if (identical(na.action, na.fail) || (identical(na.action,"na.fail")))
        mf <- na.fail.msmdata(mf)
    else stop ("na.action should be \"na.omit\" or \"na.fail\"")
    attr(mf, "npts") <- length(unique(mf$"(subject)"))

### UTILITY FOR PIECEWISE-CONSTANT INTENSITIES.  Insert censored
    ## observations into the model frame, add a factor "timeperiod" to
    ## the data, and add the corresponding term to the formula.
    ## NOTE pci kept pre-imputed data as msmdata.obs.orig
    if (!is.null(pci)) {
        tdmodel <- msm.pci(pci, mf, qmodel, cmodel, covariates)
        if (!is.null(tdmodel)) {
            mf <- tdmodel$mf; covariates <- tdmodel$covariates; cmodel <- tdmodel$cmodel
            pci <- tdmodel$tcut # was returned in msm object
        } else {pci <- NULL; mf$"(pci.imp)" <- 0}
    }

### CALCULATE COVARIATE MEANS from data by transition, including means of 0/1 contrasts.
    forms <- c(covariates, misccovariates, hcovariates, initcovariates) # covariates may have been updated to include timeperiod for pci
    covnames <- unique(unlist(lapply(forms, varnames)))
    if (length(covnames) > 0) {
        mm.mean <- model.matrix(reformulate(covnames), mf)
        cm <- colMeans(mm.mean[duplicated(mf$"(subject)",fromLast=TRUE),,drop=FALSE])
        cm["(Intercept)"] <- 0
    } else cm <- NULL
    attr(mf, "covmeans") <- cm

### MAKE AGGREGATE DATA for computing likelihood of non-hidden models efficiently
### This can also be used externally to extract aggregate data from model frame in msm object
    mf.agg <- msm.form.mf.agg(list(data=list(mf=mf), qmodel=qmodel, hmodel=hmodel, cmodel=cmodel))
### Make indicator for which distinct P matrix each observation corresponds to. Only used in HMMs.
    mf$"(pcomb)" <- msm.form.hmm.agg(mf);

### CONVERT misclassification covariate formula to hidden covariate formula
    if (inherits(misccovariates, "formula")){
        if (!emodel$misc) stop("misccovariates supplied but no ematrix")
        hcovariates <- lapply(ifelse(rowSums(emodel$imatrix)>0, deparse(misccovariates), deparse(~1)), as.formula)
    }

### FORM DESIGN MATRICES FOR COVARIATE MODELS
### These can also be used externally to extract design matrices from model frame in msm object
    mm.cov <- msm.form.mm.cov(list(data=list(mf=mf), covariates=covariates, center=center))
    mm.cov.agg <- msm.form.mm.cov.agg(list(data=list(mf.agg=mf.agg), covariates=covariates, hmodel=hmodel, cmodel=cmodel, center=center))
    mm.mcov <- msm.form.mm.mcov(list(data=list(mf=mf), misccovariates=misccovariates, emodel=emodel, center=center))
    mm.hcov <- msm.form.mm.hcov(list(data=list(mf=mf), hcovariates=hcovariates, qmodel=qmodel, hmodel=hmodel, center=center))
    mm.icov <- msm.form.mm.icov(list(data=list(mf=mf), initcovariates=initcovariates, hmodel=hmodel, center=center))

### MODEL FOR COVARIATES ON INTENSITIES
    if (!is.null(covlist)) {
        cri <- msm.form.cri(covlist, qmodel, mf, mm.cov)
    } else cri <- NULL
    qcmodel <-
        if (ncol(mm.cov) > 1)
            msm.form.covmodel(mf, mm.cov, constraint, covinits, cm, qmodel$npars, cri)
        else {
            if (!is.null(constraint)) warning("constraint specified but no covariates")
            list(npars=0, ncovs=0, ndpars=0)
        }
### MODEL FOR COVARIATES ON MISCLASSIFICATION PROBABILITIES
    if (!emodel$misc || is.null(misccovariates))
        ecmodel <- list(npars=0, ncovs=0)
    if (!is.null(misccovariates)) {
        if (!emodel$misc) {
            warning("misccovariates have been specified, but misc is FALSE. Ignoring misccovariates.")
        }
        else {
            ecmodel <- msm.form.covmodel(mf, mm.mcov, miscconstraint, misccovinits, cm, emodel$npars, cri=NULL)
            hcovariates <- msm.misccov2hcov(misccovariates, emodel)
            hcovinits <- msm.misccovinits2hcovinits(misccovinits, hcovariates, emodel, ecmodel)
        }
    }
### MODEL FOR COVARIATES ON GENERAL HIDDEN PARAMETERS
    if (!is.null(hcovariates)) {
        if (hmodel$mv) stop("hcovariates not supported for multivariate hidden Markov models")
        hmodel <- msm.form.hcmodel(hmodel, mm.hcov, hcovinits, hconstraint)
        if (emodel$misc)
            hmodel$covconstr <- msm.form.hcovconstraint(miscconstraint, hmodel)
    }
    else if (hmodel$hidden) {
        npars <- if(hmodel$mv) colSums(hmodel$npars) else hmodel$npars
        hmodel <- c(hmodel, list(ncovs=rep(rep(0, hmodel$nstates), npars), ncoveffs=0))
        class(hmodel) <- "hmodel"
    }
    if (!is.null(initcovariates)) {
        if (hmodel$hidden)
            hmodel <- msm.form.icmodel(hmodel, mm.icov, initcovinits)
        else warning("initprobs and initcovariates ignored for non-hidden Markov models")
    }
    else if (hmodel$hidden) {
        hmodel <- c(hmodel, list(nicovs=rep(0, hmodel$nstates-1), nicoveffs=0, cri=ecmodel$cri))
        class(hmodel) <- "hmodel"
    }
    if (hmodel$hidden && !emodel$misc) {
        hmodel$constr <- msm.form.hconstraint(hconstraint, hmodel)
        hmodel$covconstr <- msm.form.hcovconstraint(hconstraint, hmodel)
    }
    if (hmodel$hidden) hmodel$ranges <- msm.form.hranges(hranges, hmodel)
### INITIAL STATE OCCUPANCY PROBABILITIES IN HMMS
    if (hmodel$hidden) hmodel <- msm.form.initprobs(hmodel, initprobs, mf)
### FORM LIST OF INITIAL PARAMETERS, MATCHING PROVIDED INITS WITH SPECIFIED MODEL, FIXING SOME PARS IF REQUIRED
    p <- msm.form.params(qmodel, qcmodel, emodel, hmodel, fixedpars)

    msmdata <- list(mf=mf, mf.agg=mf.agg, mm.cov=mm.cov, mm.cov.agg=mm.cov.agg,
                    mm.mcov=mm.mcov, mm.hcov=mm.hcov, mm.icov=mm.icov)

### CALCULATE LIKELIHOOD AT INITIAL VALUES OR DO OPTIMISATION (see optim.R)
    if (p$fixed) opt.method <- "fixed"
    if (is.null(hessian)) hessian <- !p$fixed
    p <- msm.optim(opt.method, p, hessian, use.deriv, msmdata, qmodel, qcmodel, cmodel, hmodel, ...)
    if (p$fixed) {
        p$foundse <- FALSE
        p$covmat <- NULL
    } else {
        p$params <- msm.rep.constraints(p$params, p, hmodel)
        hess <- if (hessian) p$opt$hessian else p$information
        if (!is.null(hess) &&
            all(!is.na(hess)) && all(!is.nan(hess)) && all(is.finite(hess)) && all(eigen(hess)$values > 0))
        {
            p$foundse <- TRUE
            p$covmat <- matrix(0, nrow=p$npars, ncol=p$npars)
            p$covmat[p$optpars,p$optpars] <- solve(0.5 * hess)
            p$covmat <- p$covmat[!duplicated(abs(p$constr)),!duplicated(abs(p$constr)), drop=FALSE][abs(p$constr),abs(p$constr), drop=FALSE]
            p$ci <- cbind(p$params - qnorm(1 - 0.5*(1-cl))*sqrt(diag(p$covmat)),
                          p$params + qnorm(1 - 0.5*(1-cl))*sqrt(diag(p$covmat)))
            p$ci[p$fixedpars,] <- NA
            for (i in 1:2)
                p$ci[,i] <- gexpit(p$ci[,i], p$ranges[,"lower",drop=FALSE], p$ranges[,"upper",drop=FALSE])
        }
        else {
            p$foundse <- FALSE
            p$covmat <- p$ci <- NULL
            if (!is.null(hess))
                warning("Optimisation has probably not converged to the maximum likelihood - Hessian is not positive definite.")
        }
    }
    p$estimates.t <- p$params  # Calculate estimates and CIs on natural scale
    p$estimates.t <- msm.inv.transform(p$params, hmodel, p$ranges)
    ## calculate CIs for misclassification probabilities (needs multivariate transform and delta method)
    if (any(p$plabs=="p") && p$foundse){
        p.se <- p.se.msm(x=list(data=msmdata,qmodel=qmodel,emodel=emodel,hmodel=hmodel,
                         qcmodel=qcmodel,ecmodel=ecmodel,paramdata=p,center=center),
                         covariates = if(center) "mean" else 0)
        p$ci[p$plabs %in% c("p","pbase"),] <- as.numeric(unlist(p.se[,c("LCL","UCL")]))
    }
    ## calculate CIs for initial state probabilities in HMMs (using normal simulation method)
    if (p$foundse && any(p$plabs=="initp"))  p <- initp.ci.msm(p, cl)

### FORM A MSM OBJECT FROM THE RESULTS
    msmobject <- list (
                       call = match.call(),
                       minus2loglik = p$lik,
                       deriv = p$deriv,
                       estimates = p$params,
                       estimates.t = p$estimates.t,
                       fixedpars = p$fixedpars,
                       center = center,
                       covmat = p$covmat,
                       ci = p$ci,
                       opt = p$opt,
                       foundse = p$foundse,
                       data = msmdata,
                       qmodel = qmodel,
                       emodel = emodel,
                       qcmodel = qcmodel,
                       ecmodel = ecmodel,
                       hmodel = hmodel,
                       cmodel = cmodel,
                       pci = pci,
                       paramdata=p,
                       cl=cl,
        covariates=covariates,
        misccovariates=misccovariates,
        hcovariates=hcovariates,
        initcovariates=initcovariates
                       )
    attr(msmobject, "fixed") <- p$fixed
    class(msmobject) <- "msm"

### Form lists of matrices from parameter estimates 
    msmobject <- msm.form.output(msmobject, "intens")
   
### Include intensity and misclassification matrices on natural scales
    q <- qmatrix.msm(msmobject, covariates=(if(center) "mean" else 0))
    msmobject$Qmatrices$baseline <- q$estimates
    msmobject$QmatricesSE$baseline <- q$SE
    msmobject$QmatricesL$baseline <- q$L
    msmobject$QmatricesU$baseline <- q$U

    if (hmodel$hidden) {
        msmobject$hmodel <- msm.form.houtput(hmodel, p, msmdata)
    }
    if (emodel$misc) {
        msmobject <- msm.form.output(msmobject, "misc")
        e <- ematrix.msm(msmobject, covariates=(if(center) "mean" else 0))
        msmobject$Ematrices$baseline <- e$estimates
        msmobject$EmatricesSE$baseline <- e$SE
        msmobject$EmatricesL$baseline <- e$L
        msmobject$EmatricesU$baseline <- e$U
    }
    msmobject$msmdata[!names(msmobject$msmdata)=="mf"] <- NULL # only keep model frame in returned data.  drop at last minute, as might be needed in msm.form.houtput.
### Include mean sojourn times
    msmobject$sojourn <- sojourn.msm(msmobject, covariates=(if(center) "mean" else 0))
    msmobject
}

msm.check.qmatrix <- function(qmatrix)
{
    if (!is.numeric(qmatrix) || ! is.matrix(qmatrix))
        stop("qmatrix should be a numeric matrix")
    if (nrow(qmatrix) != ncol(qmatrix))
        stop("Number of rows and columns of qmatrix should be equal")
    q2 <- qmatrix; diag(q2) <- 0
    if (any(q2 < 0))
        stop("off-diagonal entries of qmatrix should not be negative")
    invisible()
}

msm.fixdiag.qmatrix <- function(qmatrix)
{
    diag(qmatrix) <- 0
    diag(qmatrix) <- - rowSums(qmatrix)
    qmatrix
}

msm.fixdiag.ematrix <- function(ematrix)
{
    diag(ematrix) <- 0
    diag(ematrix) <- 1 - rowSums(ematrix)
    ematrix
}

msm.form.qmodel <- function(qmatrix, qconstraint=NULL, analyticp=TRUE, use.expm=FALSE, phase.states=NULL)
{
    msm.check.qmatrix(qmatrix)
    nstates <- dim(qmatrix)[1]
    qmatrix <- msm.fixdiag.qmatrix(qmatrix)
    if (is.null(rownames(qmatrix)))
        rownames(qmatrix) <- colnames(qmatrix) <- paste("State", seq(nstates))
    else if (is.null(colnames(qmatrix))) colnames(qmatrix) <- rownames(qmatrix)
    imatrix <- ifelse(qmatrix > 0, 1, 0)
    inits <- t(qmatrix)[t(imatrix)==1]
    npars <- sum(imatrix)
    ## for phase-type models, leave processing qconstraint until after phased Q matrix has been formed in msm.phase2qmodel
    if (!is.null(qconstraint) && is.null(phase.states)) { 
        if (!is.numeric(qconstraint)) stop("qconstraint should be numeric")
        if (length(qconstraint) != npars)
            stop("baseline intensity constraint of length " ,length(qconstraint), ", should be ", npars)
        constr <- match(qconstraint, unique(qconstraint))
    }
    else
        constr <- 1:npars
    ndpars <- max(constr)
    ipars <- t(imatrix)[t(lower.tri(imatrix) | upper.tri(imatrix))]
    graphid <- paste(which(ipars==1), collapse="-")
    if (analyticp && graphid %in% names(.msm.graphs[[paste(nstates)]])) {
        ## analytic P matrix is implemented for this particular intensity matrix
        iso <- .msm.graphs[[paste(nstates)]][[graphid]]$iso
        perm <- .msm.graphs[[paste(nstates)]][[graphid]]$perm
        qperm <- order(perm) # diff def in 1.2.3, indexes q matrices not vectors
    }
    else {
        iso <- 0
        perm <- qperm <- NA
    }
    qmodel <- list(nstates=nstates, iso=iso, perm=perm, qperm=qperm,
                   npars=npars, imatrix=imatrix, qmatrix=qmatrix, inits=inits,
                   constr=constr, ndpars=ndpars, expm=as.numeric(use.expm))
    class(qmodel) <- "msmqmodel"
    qmodel
}

msm.check.ematrix <- function(ematrix, nstates)
{
    if (!is.numeric(ematrix) || ! is.matrix(ematrix))
        stop("ematrix should be a numeric matrix")
    if (nrow(ematrix) != ncol(ematrix))
        stop("Number of rows and columns of ematrix should be equal")
    if (!all(dim(ematrix) == nstates))
        stop("Dimensions of qmatrix and ematrix should be the same")
    if (!all ( ematrix >= 0 | ematrix <= 1) )
        stop("Not all elements of ematrix are between 0 and 1")
    invisible()
}

msm.form.emodel <- function(ematrix, econstraint=NULL, initprobs=NULL, est.initprobs, qmodel)
{
    diag(ematrix) <- 0
    imatrix <- ifelse(ematrix > 0 & ematrix < 1, 1, 0) # don't count as parameters if perfect misclassification (1.4.2 bug fix)
    diag(ematrix) <- 1 - rowSums(ematrix)
    if (is.null(rownames(ematrix)))
        rownames(ematrix) <- colnames(ematrix) <- paste("State", seq(qmodel$nstates))
    else if (is.null(colnames(ematrix))) colnames(ematrix) <- rownames(ematrix)
    dimnames(imatrix) <- dimnames(ematrix)
    npars <- sum(imatrix)
    nstates <- nrow(ematrix)
    inits <- t(ematrix)[t(imatrix)==1]
    if (is.null(initprobs)) {
        initprobs <- if (est.initprobs) rep(1/qmodel$nstates, qmodel$nstates) else c(1, rep(0, qmodel$nstates-1))
    }
    else {
        if (!is.numeric(initprobs)) stop("initprobs should be numeric")
        if (is.matrix(initprobs)) {
            if (ncol(initprobs) != qmodel$nstates) stop("initprobs matrix has ", ncol(initprobs), " columns, should be number of states = ", qmodel$nstates)
            if (est.initprobs) { warning("Not estimating initial state occupancy probabilities since supplied as a matrix") }
            initprobs <- initprobs / rowSums(initprobs)
            est.initprobs <- FALSE
        }
        else {
            if (length(initprobs) != qmodel$nstates) stop("initprobs vector of length ", length(initprobs), ", should be vector of length ", qmodel$nstates, " or a matrix")
            initprobs <- initprobs / sum(initprobs)
            if (est.initprobs && any(initprobs==1)) {
                est.initprobs <- FALSE
                warning("Not estimating initial state occupancy probabilities, since some are fixed to 1")
            }
        }
    }
    nipars <- if (est.initprobs) qmodel$nstates else 0
    if (!is.null(econstraint)) {
        if (!is.numeric(econstraint)) stop("econstraint should be numeric")
        if (length(econstraint) != npars)
            stop("baseline misclassification constraint of length " ,length(econstraint), ", should be ", npars)
        constr <- match(econstraint, unique(econstraint))
    }
    else
        constr <- seq(length=npars)
    ndpars <- if(npars>0) max(constr) else 0

    nomisc <- isTRUE(all.equal(as.vector(diag(ematrix)),rep(1,nrow(ematrix)))) # degenerate ematrix with no misclassification: all 1 on diagonal
    if (nomisc)
        emodel <- list(misc=FALSE, npars=0, ndpars=0)
    else                                     
        emodel <- list(misc=TRUE, npars=npars, nstates=nstates, imatrix=imatrix, ematrix=ematrix, inits=inits,
                       constr=constr, ndpars=ndpars, nipars=nipars, initprobs=initprobs, est.initprobs=est.initprobs)
    class(emodel) <- "msmemodel"
    emodel
}

### Check elements of state vector. For simple models and misc models specified with ematrix
### No check is performed for hidden models

msm.check.state <- function(nstates, state, censor, hmodel)
{
    if (hmodel$hidden){
        if (!is.null(ncol(state))) {
            pl1 <- if (ncol(state) > 1) "s" else ""
            pl2 <- if (max(hmodel$nout) > 1) "s" else ""
            if ((ncol(state) != max(hmodel$nout)) && (max(hmodel$nout) > 1))
                stop(sprintf("outcome matrix in data has %d column%s, but outcome models have a maximum of %d dimension%s", ncol(state), pl1, max(hmodel$nout), pl2))
        }
    }  else { 
        states <- c(1:nstates, censor)
        state <- na.omit(state) # NOTE added in 1.4
        if (!is.null(ncol(state)) && ncol(state) > 1) stop("Matrix outcomes only allowed for hidden Markov models")
        if (!is.null(state)) {
            statelist <- if (nstates==2) "1, 2" else if (nstates==3) "1, 2, 3" else paste("1, 2, ... ,",nstates)
            if (length(setdiff(unique(state), states)) > 0)
                stop("State vector contains elements not in ",statelist)
            miss.state <- setdiff(states, unique(state))
            ## Don't do this for misclassification models: it's OK to have particular state not observed by chance
            if (length(miss.state) > 0 && !hmodel$ematrix)
                warning("State vector doesn't contain observations of ",paste(miss.state, collapse=","))
        }
    }
    invisible()
}

msm.check.times <- function(time, subject, state=NULL)
{
    final.rows <- !is.na(subject) & !is.na(time)
    if (!is.null(state)) {
        nas <- if (is.matrix(state)) apply(state, 1, function(x)all(is.na(x))) else is.na(state)
        final.rows <- final.rows & !nas
        state <- if (is.matrix(state)) state[final.rows, ,drop=FALSE] else state[final.rows]
    }
    final.rows <- which(final.rows)
    time <- time[final.rows]; subject <- subject[final.rows]
### Check if any individuals have only one observation (after excluding missing data)
### Note this shouldn't happen after 1.2
    subj.num <- match(subject,unique(subject)) # avoid problems with factor subjects with empty levels
    nobspt <- table(subj.num)
    if (any (nobspt == 1)) {
        badsubjs <- unique(subject)[ nobspt == 1 ]
        andothers <- if (length(badsubjs)>3) " and others" else ""
        if (length(badsubjs)>3) badsubjs <- badsubjs[1:3]
        badlist <- paste(badsubjs, collapse=", ")
        plural <- if (length(badsubjs)==1) "" else "s"
        has <-  if (length(badsubjs)==1) "has" else "have"
        warning ("Subject", plural, " ", badlist, andothers, " only ", has, " one complete observation")
    }
### Check if observations within a subject are adjacent
    ind <- tapply(seq_along(subj.num), subj.num, length)
    imin <- tapply(seq_along(subj.num), subj.num, min)
    imax <- tapply(seq_along(subj.num), subj.num, max)
    adjacent <- (ind == imax-imin+1)
    if (any (!adjacent)) {
        badsubjs <- unique(subject)[ !adjacent ]
        andothers <- if (length(badsubjs)>3) " and others" else ""
        if (length(badsubjs)>3) badsubjs <- badsubjs[1:3]
        badlist <- paste(badsubjs, collapse=", ")
        plural <- if (length(badsubjs)==1) "" else "s"
        stop ("Observations within subject", plural, " ", badlist, andothers, " are not adjacent in the data")
    }
### Check if observations are ordered in time within subject
    orderedpt <- ! tapply(time, subj.num, is.unsorted)
    if (any (!orderedpt)) {
        badsubjs <- unique(subject)[ !orderedpt ]
        andothers <- if (length(badsubjs)>3) " and others" else ""
        if (length(badsubjs)>3) badsubjs <- badsubjs[1:3]
        badlist <- paste(badsubjs, collapse=", ")
        plural <- if (length(badsubjs)==1) "" else "s"
        stop ("Observations within subject", plural, " ", badlist, andothers, " are not ordered by time")
    }
### Check if any consecutive observations are made at the same time, but with different states
    if (!is.null(state)){
        if (is.matrix(state)) state <- apply(state, 1, paste, collapse=",")
        prevsubj <- c(-Inf, subj.num[seq_along(subj.num)-1])
        prevtime <- c(-Inf, time[1:length(time)-1])
        prevstate <- c(-Inf, state[1:length(state)-1])
        sametime <- final.rows[subj.num==prevsubj & prevtime==time & prevstate!=state]
        badlist <- paste(paste(sametime-1, sametime, sep=" and "), collapse=", ")
        if (any(sametime))
            warning("Different states observed at the same time on the same subject at observations ", badlist)
    }
    invisible()
}

msm.form.obstype <- function(mf, obstype, dmodel, exacttimes)
{
    if (!is.null(obstype)) {
        if (!is.numeric(obstype)) stop("obstype should be numeric")
        if (length(obstype) == 1) obstype <- rep(obstype, nrow(mf))
        else if (length(obstype)!=nrow(mf)) stop("obstype of length ", length(obstype), ", should be length 1 or ",nrow(mf))
        if (any(! obstype[duplicated(mf$"(subject)")] %in% 1:3)) stop("elements of obstype should be 1, 2, or 3") # ignore obstypes at subject's first observation
    }
    else if (!is.null(exacttimes) && exacttimes)
        obstype <- rep(2, nrow(mf))
    else {
        obstype <- rep(1, nrow(mf))
        if (dmodel$ndeath > 0)
            obstype[mf$"(state)" %in% dmodel$obs] <- 3
    }
    obstype
}

### On exit, obstrue will contain the true state (if known) or 0 (if unknown)
### Any NAs should be replaced by 0 - logically if you don't know whether the state is known or not, that means you don't know the state

### TODO For cases where true state is known, 
### distinguish between cases where the state data contains the true state or a misclassified version of the true state

msm.form.obstrue <- function(mf, hmodel, cmodel) {
    obstrue <- mf$"(obstrue)"
    if (!is.null(obstrue)) {
        if (!hmodel$hidden) {
            warning("Specified obstrue for a non-hidden model, ignoring.")
            obstrue <- rep(1, nrow(mf))
        }
        else if (!is.numeric(obstrue) && !is.logical(obstrue)) stop("obstrue should be logical or numeric")
        else {
            if (is.logical(obstrue) || (all(na.omit(obstrue) %in% 0:1) && !any(is.na(obstrue)))){
                ## obstrue is an indicator.  Actual state should then be supplied in the outcome vector
                ## (typically misclassification models)
                ## interpret presence of NAs as indicating true state supplied here
                if (!is.null(ncol(mf$"(state)")) && ncol(mf$"(state)") > 1)
                    stop("obstrue must contain NA or the true state for a multiple outcome HMM, not an 0/1 indicator")
                obstrue <- ifelse(obstrue, mf$"(state)", 0)
            } else {
                ## obstrue contains the actual state (used when we have another outcome conditionally on this)
                if (!all(na.omit(obstrue) %in% 0:hmodel$nstates)){
                    stop("Interpreting \"obstrue\" as containing true states, but it contains values not in 0,1,...,", hmodel$nstates)
                }
                obstrue[is.na(obstrue)] <- 0 # true state assumed unknown if NA

                ## If misclassification model specified through "ematrix", interpret the state data as the true state.

                ## If specified through hmodel (including hmmCat), interpret the state as a misclassified/HMM outcome generated conditionally on the true state.

                ## Though that doesn't allow a mixture of true/misclassified states in the same model

                ## If we know the true state, we might sometimes want
                ## to model a misclassified version of it to
                ## strengthen estimation of misclassification probs.

                ## In theory we could put NA in the state then to
                ## indicate that true state is passed through from
                ## obstrue, and misclassified version isn't known.
                ## But don't do this until someone asks for it.
            }
        }
    }
    else if (hmodel$hidden) obstrue <- rep(0, nrow(mf))
    else obstrue <- mf$"(state)"
    if (cmodel$ncens > 0){
        ## If censoring and obstrue, put the first of the possible states into obstrue
        ## Used in Viterbi
        for (i in seq_along(cmodel$censor))
            obstrue[obstrue==cmodel$censor[i] & obstrue > 0] <- cmodel$states[cmodel$index[i]]
    }
    obstrue
}

## Replace censored states by state with highest probability that they
## could represent. Used in msm.check.model to check consistency of
## data with transition parameters

msm.impute.censored <- function(fromstate, tostate, Pmat, cmodel)
{
    ## e.g. cmodel$censor 99,999;  cmodel$states 1,2,1,2,3;  cmodel$index 1, 3, 6
    ## Both from and to are censored
    wb <- which ( fromstate %in% cmodel$censor & tostate %in% cmodel$censor)
    for (i in wb) {
        si <- which(cmodel$censor==fromstate[i])
        fc <- cmodel$states[(cmodel$index[si]) : (cmodel$index[si+1]-1)]
        ti <- which(cmodel$censor==tostate[i])
        tc <- cmodel$states[(cmodel$index[ti]) : (cmodel$index[ti+1]-1)]
        mp <- which.max(Pmat[fc, tc])
        fromstate[i] <- fc[row(Pmat[fc, tc])[mp]]
        tostate[i] <- tc[col(Pmat[fc, tc])[mp]]
    }
    ## Only from is censored
    wb <- which(fromstate %in% cmodel$censor)
    for (i in wb) {
        si <- which(cmodel$censor==fromstate[i])
        fc <- cmodel$states[(cmodel$index[si]) : (cmodel$index[si+1]-1)]
        fromstate[i] <- fc[which.max(Pmat[fc, tostate[i]])]
    }
    ## Only to is censored
    wb <- which(tostate %in% cmodel$censor)
    for (i in wb) {
        si <- which(cmodel$censor==tostate[i])
        tc <- cmodel$states[(cmodel$index[si]) : (cmodel$index[si+1]-1)]
        tostate[i] <- tc[which.max(Pmat[fromstate[i], tc])]
    }
    list(fromstate=fromstate, tostate=tostate)
}

### CHECK THAT TRANSITION PROBABILITIES FOR DATA ARE ALL NON-ZERO
### (e.g. check for backwards transitions when the model is irreversible)
### obstype 1 must have unitprob > 0
### obstype 2 must have qunit != 0, and unitprob > 0.
### obstype 3 must have unitprob > 0

msm.check.model <- function(fromstate, tostate, obs, subject, obstype=NULL, qmatrix, cmodel)
{
    n <- length(fromstate)
    qmatrix <- qmatrix / mean(qmatrix[qmatrix>0]) # rescale to avoid false warnings with small rates
    Pmat <- MatrixExp(qmatrix)
    Pmat[Pmat < .Machine$double.eps] <- 0
    imputed <- msm.impute.censored(fromstate, tostate, Pmat, cmodel)
    fs <- imputed$fromstate; ts <- imputed$tostate
    unitprob <- apply(cbind(fs, ts), 1, function(x) { Pmat[x[1], x[2]] } )
    qunit <- apply(cbind(fs, ts), 1, function(x) { qmatrix[x[1], x[2]] } )

    if (identical(all.equal(min(unitprob, na.rm=TRUE), 0),  TRUE))
    {
        badobs <- which.min(unitprob)
        warning ("Data may be inconsistent with transition matrix for model without misclassification:\n",
                 "individual ", if(is.null(subject)) "" else subject[badobs], " moves from state ", fromstate[badobs],
                 " to state ", tostate[badobs], " at observation ", obs[badobs], "\n")
    }
    if (any(qunit[obstype==2]==0)) {
        badobs <- min (obs[qunit==0 & obstype==2], na.rm = TRUE)
        warning ("Data may be inconsistent with intensity matrix for observations with exact transition times and no misclassification:\n",
                 "individual ", if(is.null(subject)) "" else subject[obs==badobs], " moves from state ", fromstate[obs==badobs],
                 " to state ", tostate[obs==badobs], " at observation ", badobs)
    }
    absorbing <- absorbing.msm(qmatrix=qmatrix)
    absabs <- (fromstate %in% absorbing) & (tostate %in% absorbing)
    if (any(absabs)) {
        badobs <- min( obs[absabs] )
        warning("Absorbing - absorbing transition at observation ", badobs)
    }
    invisible()
}

msm.check.constraint <- function(constraint, mm){
    if (is.null(constraint)) return(invisible())
    covlabels <- colnames(mm)[-1]
    aa <- attr(mm, "assign")
    covfactor <- rep(table(aa), table(aa)) > 1
    if (!is.list(constraint)) stop(deparse(substitute(constraint)), " should be a list")
    if (!all(sapply(constraint, is.numeric)))
        stop(deparse(substitute(constraint)), " should be a list of numeric vectors")
    ## check and parse the list of constraints on covariates
    for (i in names(constraint))
        if (!(is.element(i, covlabels))){
            factor.warn <- if ((i %in% names(covfactor)) && covfactor[i])
                "\n\tFor factor covariates, specify constraints using covnameCOVVALUE = c(...)"
            else ""
            stop("Covariate \"", i, "\" in constraint statement not in model.", factor.warn)
        }
}

msm.check.covinits <- function(covinits, covlabels){
    if (!is.list(covinits)) warning(deparse(substitute(covinits)), " should be a list")
    else if (!all(sapply(covinits, is.numeric)))
        warning(deparse(substitute(covinits)), " should be a list of numeric vectors")
    else {
        notin <- setdiff(names(covinits), covlabels)
        if (length(notin) > 0){
            plural <- if (length(notin) > 1) "s " else " "
            warning("covariate", plural, paste(notin, collapse=", "), " in ", deparse(substitute(covinits)), " unknown")
        }
    }
}

### Process covariates constraints, in preparation for being passed to the likelihood optimiser
### This function is called for both sets of covariates (transition rates and the misclassification probs)

msm.form.covmodel <- function(mf,
                              mm,
                              constraint,
                              covinits,
                              covmeans,
                              nmatrix,     # number of transition intensities / misclassification probs
                              cri
                              )
{
    if (!is.null(cri))
        return(msm.form.covmodel.byrate(mf, mm, constraint, covinits, covmeans, nmatrix, cri))
    ncovs <- ncol(mm) - 1
    covlabels <- colnames(mm)[-1]
    if (is.null(constraint)) {
        constraint <- rep(list(1:nmatrix), ncovs)
        names(constraint) <- covlabels
        constr <- 1:(nmatrix*ncovs)
    }
    else {
        msm.check.constraint(constraint, mm)
        constr <- inits <- numeric()
        maxc <- 0
        for (i in seq(along=covlabels)){
            ## build complete vectorised list of constraints for covariates in covariates statement
            ## so. e.g. constraints = (x1=c(3,3,4,4,5), x2 = (0.1,0.2,0.3,0.4,0.4))
            ##     turns into constr = c(1,1,2,2,3,4,5,6,7,7) with seven distinct covariate effects
            ## Allow constraints such as: some elements are minus others. Use negative elements of constr to do this.
            ## e.g. constr = c(1,1,-1,-1,2,3,4,5)
            ## obtained by match(abs(x), unique(abs(x))) * sign(x)
            if (is.element(covlabels[i], names(constraint))) {
                if (length(constraint[[covlabels[i]]]) != nmatrix)
                    stop("\"",covlabels[i],"\" constraint of length ",
                         length(constraint[[covlabels[i]]]),", should be ",nmatrix)
            }
            else
                constraint[[covlabels[i]]] <- seq(nmatrix)
            constr <- c(constr, (maxc + match(abs(constraint[[covlabels[i]]]),
                                             unique(abs(constraint[[covlabels[i]]]))))*sign(constraint[[covlabels[i]]]) )
            maxc <- max(abs(constr))
        }
    }
    inits <- numeric()
    if (!is.null(covinits))
        msm.check.covinits(covinits, covlabels)
    for (i in seq(along=covlabels)) {
        if (!is.null(covinits) && is.element(covlabels[i], names(covinits))) {
            thisinit <- covinits[[covlabels[i]]]
            if (!is.numeric(thisinit)) {
                warning("initial values for covariates should be numeric, ignoring")
                thisinit <- rep(0, nmatrix)
            }
            if (length(thisinit) != nmatrix) {
                warning("\"", covlabels[i], "\" initial values of length ", length(thisinit), ", should be ", nmatrix, ", ignoring")
                thisinit <- rep(0, nmatrix)
            }
            inits <- c(inits, thisinit)
        }
        else {
            inits <- c(inits, rep(0, nmatrix))
        }
    }
    npars <- ncovs*nmatrix
    ndpars <- max(unique(abs(constr)))
    list(npars=npars,
         ndpars=ndpars,    # number of distinct covariate effect parameters
         ncovs=ncovs,
         constr=constr,
         covlabels=covlabels, # factors as separate contrasts
         inits = inits,
         covmeans = attr(mm, "means")
         )
}

## Process constraints and initial values for covariates supplied as a
## list of transition-specific formulae.  Convert to form needed for a
## single covariates formula common to all transitions, which can be
## processed with msm.form.covmodel.

msm.form.covmodel.byrate <- function(mf, mm,
                                     constraint, # as supplied by user
                                     covinits,  # as supplied by user
                                     covmeans,
                                     nmatrix,
                                     cri
                                     ){
    covs <- colnames(mm)[-1]
    ## Convert short form constraints to long form
    msm.check.constraint(constraint, mm)
    constr <- inits <- numeric()
    for (i in seq(along=covs)){
        if (covs[i] %in% names(constraint)){
            if (length(constraint[[covs[i]]]) != sum(cri[,i]))
                stop("\"",covs[i],"\" constraint of length ",
                     length(constraint[[covs[i]]]),", should be ",sum(cri[,i]))
            con <- match(constraint[[covs[i]]], unique(constraint[[covs[i]]])) + 1
            constraint[[covs[i]]] <- rep(1, nmatrix)
            constraint[[covs[i]]][cri[,i]==1] <- con
        }
        else constraint[[covs[i]]] <- seq(length=nmatrix)
    }
    ## convert short to long initial values in the same way
    if (!is.null(covinits)) msm.check.covinits(covinits, covs)
    for (i in seq(along=covs)) {
        if (!is.null(covinits) && (covs[i] %in% names(covinits))) {
            if (!is.numeric(covinits[[covs[i]]])) {
                warning("initial values for covariates should be numeric, ignoring")
                covinits[[covs[i]]] <- rep(0, nmatrix)
            }
            thisinit <- rep(0, nmatrix)
            if (length(covinits[[covs[i]]]) != sum(cri[,i])) {
                warning("\"", covs[i], "\" initial values of length ", length(covinits[[covs[i]]]), ", should be ", sum(cri[,i]), ", ignoring")
                covinits[[covs[i]]] <- rep(0, nmatrix)
            }
            else thisinit[cri[,i]==1] <- covinits[[covs[i]]]
            covinits[[covs[i]]] <- thisinit
        }
    }
    qcmodel <- msm.form.covmodel(mf, mm, constraint, covinits, covmeans, nmatrix, cri=NULL)
    qcmodel$cri <- cri
    qcmodel
}


msm.form.dmodel <- function(death, qmodel, hmodel)
{
    nstates <- qmodel$nstates
    statelist <- if (nstates==2) "1, 2" else if (nstates==3) "1, 2, 3" else paste("1, 2, ... ,",nstates)
    if (is.null(death)) death <- FALSE
    if (is.logical(death) && death==TRUE)
        states <- nstates
    else if (is.logical(death) && death==FALSE)
        states <- numeric(0) ## Will be changed to -1 when passing to C
    else if (!is.numeric(death)) stop("Death states indicator must be numeric")
    else if (length(setdiff(death, 1:nstates)) > 0)
        stop("Death states indicator contains states not in ",statelist)
    else states <- death
    ndeath <- length(states)
    if (hmodel$hidden) {
        ## Form death state info from hmmIdent parameters.
        ## Special observations in outcome data which denote death states
        ## are given as the parameter to hmmIdent()
        if (!all(hmodel$models[states] == match("identity", .msm.HMODELS)))
            stop("Death states should have the identity hidden distribution hmmIdent()")
        obs <- ifelse(hmodel$npars[states]>0, hmodel$pars[hmodel$parstate %in% states], states)
    }
    else obs <- states
    if (any (states %in% transient.msm(qmatrix=qmodel$qmatrix)))
        stop("Not all the \"death\" states are absorbing states")
    list(ndeath=ndeath, states=states, obs=obs)
}

msm.form.cmodel <- function(censor=NULL, censor.states=NULL, qmatrix)
{
    if (is.null(censor)) {
        ncens <- 0
        if (!is.null(censor.states)) warning("censor.states supplied but censor not supplied")
    }
    else {
        if (!is.numeric(censor)) stop("censor must be numeric")
        if (any(censor %in% 1:nrow(qmatrix))) warning("some censoring indicators are the same as actual states")
        ncens <- length(censor)
        if (is.null(censor.states)) {
            if (ncens > 1) {
                warning("more than one type of censoring given, but censor.states not supplied. Assuming only one type of censoring")
                ncens <- 1; censor <- censor[1]
            }
            censor.states <- transient.msm(qmatrix=qmatrix)
            states.index <- c(1, length(censor.states)+1)
        }
        else {
            if (ncens == 1) {
                if (!is.vector(censor.states) ||
                    (is.list(censor.states) && (length(censor.states) > 1)) )
                    stop("if one type of censoring, censor.states should be a vector, or a list with one vector element")
                if (!is.numeric(unlist(censor.states))) stop("censor.states should be all numeric")
                states.index <- c(1, length(unlist(censor.states))+1)
            }
            else {
                if (!is.list(censor.states)) stop("censor.states should be a list")
                if (length(censor.states) != ncens) stop("expected ", ncens, " elements in censor.states list, found ", length(censor.states))
                states.index <- cumsum(c(0, lapply(censor.states, length))) + 1
            }
            censor.states <- unlist(censor.states)
        }
    }
    if (ncens==0) censor <- censor.states <- states.index <- NULL
    ## Censoring information to be passed to C
    list(ncens = ncens, # number of censoring states
         censor = censor, # vector of their labels in the data
         states = censor.states, # possible true states that the censoring represents
         index = states.index # index into censor.states for the start of each true-state set, including an extra length(censor.states)+1
         )
}

### Transform set of sets of probs {prs} to {log(prs/pr1)}
msm.mnlogit.transform <- function(pars, plabs, states){
    res <- pars
    if (any(plabs=="p")) {
        whichst <- match(states[plabs == "p"], unique(states[plabs == "p"]))
        for (i in unique(whichst)) # recalculate baseline prob if necessary, e.g. if constraints applied
            res[plabs=="pbase"][i] <- 1 - sum(res[plabs=="p"][whichst==i])
        res[plabs == "p"] <- log(pars[plabs=="p"] / res[plabs=="pbase"][whichst])
    }
    res
}

### Transform set of sets of murs = {log(prs/pr1)} to probs {prs}
### ie psum = sum(exp(mus)),  pr1 = 1 / (1 + psum),  prs = exp(mus) / (1 + psum)
msm.mninvlogit.transform <- function(pars, plabs, states){
    res <- pars
    if (any(plabs=="p")) {
        whichst <- match(states[plabs=="p"], unique(states[plabs=="p"]))
        if (is.matrix(pars)) {# will be used when applying covariates
            for (i in unique(whichst)) {
                psum <- colSums(exp(pars[which(plabs=="p")[whichst==i],,drop=FALSE]))
                res[which(plabs=="pbase")[i],] <- 1 / (1 + psum)
                res[which(plabs=="p")[whichst==i],] <-
                    exp(pars[plabs=="p",,drop=FALSE][whichst==i,]) /
                        rep(1 + psum, each=sum(whichst==i))
            }
        }
        else {
            psum <- tapply(exp(pars[plabs=="p"]), states[plabs=="p"], sum)
            res[plabs=="pbase"][unique(states[plabs=="p"])] <- 1 / (1 + psum) # don't transform pbase if no p's for this state, i.e. if no/perfect misclassification
            res[plabs=="p"] <- exp(pars[plabs=="p"]) / (1 + psum[whichst])
        }
    }
    res
}

## transform parameters from natural scale to real-line optimisation scale

msm.transform <- function(pars, hmodel, ranges){
    labs <- names(pars)
    pars <- glogit(pars, ranges[,"lower"], ranges[,"upper"])
    hpinds <- which(!(labs %in% c("qbase","qcov","hcov","initpbase","initp","initp0","initpcov")))
    hpars <- pars[hpinds]
    hpars <- msm.mnlogit.transform(hpars, hmodel$plabs, hmodel$parstate)
    pars[hpinds] <- hpars
    pars[labs=="initp"] <- log(pars[labs=="initp"] / pars[labs=="initpbase"])
    pars
}

## transform parameters from real-line optimisation scale to natural scale

msm.inv.transform <- function(pars, hmodel, ranges){
    labs <- names(pars)
    pars <- gexpit(pars, ranges[,"lower"], ranges[,"upper"])
    hpinds <- which(!(labs %in% c("qbase","qcov","hcov","initp","initp0","initpcov")))
    hpars <- pars[hpinds]
    hpars <- msm.mninvlogit.transform(hpars, hmodel$plabs, hmodel$parstate)
    pars[hpinds] <- hpars
    ep <- exp(pars[labs=="initp"])
    pars[labs=="initp"] <- ep / (1 + sum(ep))
    pars[labs=="initpbase"] <- 1 / (1 + sum(ep))
    pars
}

## Collect all model parameters together ready for optimisation
## Handle parameters fixed at initial values or constrained to equal other parameters

msm.form.params <- function(qmodel, qcmodel, emodel, hmodel, fixedpars)
{
    ## Transition intensities
    ni <- qmodel$npars
    ## Covariates on transition intensities
    nc <- qcmodel$npars
    ## HMM response parameters
    nh <- sum(hmodel$npars)
    ## Covariates on HMM response distribution
    nhc <- sum(hmodel$ncoveffs)
    ## Initial state occupancy probabilities in HMM
    nip <- hmodel$nipars
    ## Covariates on initial state occupancy probabilities.
    nipc <- hmodel$nicoveffs
    npars <- ni + nc + nh + nhc + nip + nipc
    inits <- as.numeric(c(qmodel$inits, qcmodel$inits, hmodel$pars, unlist(hmodel$coveffect)))
    plabs <- c(rep("qbase",ni), rep("qcov", nc), hmodel$plabs, rep("hcov", nhc))
    if (nip > 0) {
        inits <- c(inits, hmodel$initprobs)
        initplabs <- c("initpbase", rep("initp",nip-1))
        initplabs[hmodel$initprobs==0] <- "initp0" # those initialised to zero will be fixed at zero
        plabs <- c(plabs, initplabs)
        if (nipc > 0) {
            inits <- c(inits, unlist(hmodel$icoveffect))
            plabs <- c(plabs, rep("initpcov",nipc))
        }
    }
    ## store indicator for which parameters are HMM location parameters (not HMM cov effects or initial state probs)
    hmmpars <- which(!(plabs %in% c("qbase","qcov","hcov","initpbase","initp","initp0","initpcov")))
    hmmparscov <- which(!(plabs %in% c("qbase","qcov","initpbase","initp","initp0","initpcov")))
    names(inits) <- plabs
    ranges <- .msm.PARRANGES[plabs,,drop=FALSE]
    if (!is.null(hmodel$ranges)) ranges[hmmparscov,] <- hmodel$ranges
    inits <- msm.transform(inits, hmodel, ranges)
    ## Form constraint vector for complete set of parameters
    ## No constraints allowed on initprobs and their covs for the moment
    constr <- c(qmodel$constr,
                if(is.null(qcmodel$constr)) NULL else (ni + abs(qcmodel$constr))*sign(qcmodel$constr),
                ni + nc + hmodel$constr,
                ni + nc + nh + hmodel$covconstr,
                ni + nc + nh + nhc + seq(length=nip),
                ni + nc + nh + nhc + nip + seq(length=nipc))
    constr <- match(abs(constr), unique(abs(constr)))*sign(constr)
    ## parameters which are always fixed and not included in user-supplied fixedpars
    auxpars <- which(plabs %in% .msm.AUXPARS)
    duppars <- which(duplicated(abs(constr)))
    realpars <- setdiff(seq(npars), union(auxpars, duppars))
    nrealpars <- npars - length(auxpars) - length(duppars)
    ## if transition-specific covariates, then fixedpars indices generally smaller
    nshortpars <- nrealpars - sum(qcmodel$cri[!duplicated(qcmodel$constr)]==0)
    if (is.logical(fixedpars))
        fixedpars <- if (fixedpars == TRUE) seq(nshortpars) else numeric()
    if (any(! (fixedpars %in% seq(length=nshortpars))))
        stop ( "Elements of fixedpars should be in 1, ..., ", nshortpars)
    if (!is.null(qcmodel$cri)) {
        ## Convert user-supplied fixedpars indexing transition-specific covariates
        ## to fixedpars indexing transition-common covariates
        inds <- rep(1, nrealpars)
        inds[qmodel$ndpars + qcmodel$constr[!duplicated(qcmodel$constr)]] <-
            qcmodel$cri[!duplicated(qcmodel$constr)]
        inds[inds==1] <- seq(length=nshortpars)
        fixedpars <- match(fixedpars, inds)
        ## fix covariate effects not included in model to zero
        fixedpars <- sort(c(fixedpars, which(inds==0)))
    }
    notfixed <- realpars[setdiff(seq_along(realpars),fixedpars)]
    fixedpars <- sort(c(realpars[fixedpars], auxpars)) # change fixedpars to index unconstrained pars
    allinits <- inits
    optpars <- intersect(notfixed, which(!duplicated(abs(constr))))
    inits <- inits[optpars]
    fixed <- (length(fixedpars) + length(duppars) == npars) # TRUE if all parameters are fixed, then no optimisation needed, just evaluate likelihood
    names(allinits) <- plabs; names(fixedpars) <- plabs[fixedpars]; names(plabs) <- NULL
    paramdata <- list(inits=inits, plabs=plabs, allinits=allinits, hmmpars=hmmpars,
                      fixed=fixed, fixedpars=fixedpars,
                      optpars=optpars, auxpars=auxpars,
                      constr=constr, npars=npars, duppars=duppars,
                      nfix=length(fixedpars), nopt=length(optpars), ndup=length(duppars),
                      ranges=ranges)
    paramdata
}

## Unfix all fixed parameters in a paramdata object p (as
## returned by msm.form.params).  Used for calculating deriv /
## information over all parameters for models with fixed parameters.
## Don't unfix auxiliary pars such as binomial denominators
## Don't unconstrain constraints

msm.unfixallparams <- function(p) {
    npars <- length(p$allinits)
    p$fixed <- FALSE
    p$optpars <- setdiff(1:npars, union(p$auxpars,p$duppars))
    p$fixedpars <- p$auxpars
    p$nopt <- length(p$optpars)
    p$nfix <- npars - length(p$optpars)
    p$inits <- p$allinits[p$optpars]
    p
}

msm.rep.constraints <- function(pars, # transformed pars
                                paramdata,
                                hmodel){
    plabs <- names(pars)
    p <- paramdata
    ## Handle constraints on misclassification probs / HMM cat probs separately
    ## This is fiddly. First replicate within states, on log(pr/pbase) scale
    if (any(hmodel$plabs=="p")) {
        for (i in 1:hmodel$nstates){
            inds <- (hmodel$parstate==i & hmodel$plabs=="p")
            hpc <- hmodel$constr[inds]
            pars[p$hmmpars][inds] <- pars[p$hmmpars][inds][match(hpc, unique(hpc))]
        }
    }
    ## ...then replicate them between states, on pr scale, so that constraint applies
    ## to pr not log(pr/pbase). After, transform back
    pars[p$hmmpars] <- msm.mninvlogit.transform(pars[p$hmmpars], hmodel$plabs, hmodel$parstate)
    plabs <- plabs[!duplicated(abs(p$constr))][abs(p$constr)]
    pars <- pars[!duplicated(abs(p$constr))][abs(p$constr)]*sign(p$constr)
    pars[p$hmmpars] <- msm.mnlogit.transform(pars[p$hmmpars], hmodel$plabs, hmodel$parstate)
    names(pars) <- plabs
    pars
}

## Apply covariates to transition intensities. Parameters enter this
## function already log transformed and replicated, and exit on
## natural scale.

msm.add.qcovs <- function(qmodel, pars, mm){
    labs <- names(pars)
    beta <- rbind(pars[labs=="qbase"],
                  matrix(pars[labs=="qcov"], ncol=qmodel$npars, byrow=TRUE))
    qvec <- exp(mm %*% beta)
    imat <- t(qmodel$imatrix); row <- col(imat)[imat==1]; col <- row(imat)[imat==1]
    qmat <- array(0, dim=c(qmodel$nstates, qmodel$nstates, nrow(mm)))
    for (i in 1:qmodel$npars) {
        qmat[row[i],col[i],] <- qvec[,i]
    }
    for (i in 1:qmodel$nstates)
        ## qmat[i,i,] <- -apply(qmat[i,,,drop=FALSE], 3, sum)
        qmat[i,i,] <- -colSums(qmat[i,,,drop=FALSE], , 2)
    qmat
}

## Derivatives of intensity matrix Q wrt unique log q and beta, after
## applying constraints.  By observation with covariates applied.
## e.g. qmodel$constr 1 1 2, qcmodel$constr 1 -1 2 3 3 -3
## returns derivs w.r.t pars named p1 p2 p3 p4 p5
## i.e. with baseline q on the log scale
## qo = exp(p1 + p3x1 + p5x2)
## q1 = exp(p1 + -p3x1 + p5x2)
## q2 = exp(p2 + p4x1  - p5x2)

msm.form.dq <- function(qmodel, qcmodel, pars, paramdata, mm){
    labs <- names(pars)
    q0 <- pars[labs=="qbase"]
    beta <- rbind(q0, matrix(pars[labs=="qcov"], ncol=qmodel$npars, byrow=TRUE))
    qvec <- exp(mm %*% beta)
    covs <- mm[,-1,drop=FALSE]
    qrvec <- exp(covs %*% beta[-1,,drop=FALSE])
    nopt <- qmodel$ndpars + qcmodel$ndpars # after constraint but before omitting fixed
    dqvec <- array(pars[labs=="qbase"],
                   dim=c(nrow(mm), qmodel$npars, nopt))
    for (i in seq_len(qmodel$npars)) {
        ind <- rep(0, qmodel$ndpars); ind[qmodel$constr[i]] <- 1
        cind <- 1:qmodel$ndpars
#        dqvec[,i,cind] = qrvec[,i] * rep(ind, each=nrow(mm))
        dqvec[,i,cind] = qrvec[,i] * rep(ind, each=nrow(mm)) * exp(q0[i])
        if (qcmodel$npars > 0)
            for (k in 1:qcmodel$ncovs) {
                con <- qcmodel$constr[(k-1)*qmodel$npars + 1:qmodel$npars]
                ucon <- match(abs(con), unique(abs(con)))
                ind <- rep(0, max(ucon)); ind[ucon[i]] <- sign(con[i])
                cind <- max(cind) + unique(ucon)
                dqvec[,i,cind] <- covs[,k] * qvec[,i] * rep(ind, each=nrow(mm))
            }
    }
    dqmat <- array(0, dim=c(qmodel$nstates, qmodel$nstates, nopt, nrow(mm)))
    imat <- t(qmodel$imatrix); row <- col(imat)[imat==1]; col <- row(imat)[imat==1]
    for (i in 1:qmodel$npars)
        dqmat[row[i],col[i],,] <- t(dqvec[,i,])
    for (i in 1:qmodel$nstates)
        ## dqmat[i,i,,] <- -apply(dqmat[i,,,,drop=FALSE], c(3,4), sum)
        dqmat[i,i,,] <- -colSums(dqmat[i,,,,drop=FALSE], , 2)
    p <- paramdata # leave fixed parameters out
    fixed <- p$fixedpars[p$plabs[p$fixedpars] %in% c("qbase","qcov")]
    con <- abs(p$constr[p$plabs %in% c("qbase","qcov")])
    ## FIXME
    if (any(fixed)) dqmat <- dqmat[,,-con[fixed],,drop=FALSE]
    dqmat
}


## Apply covariates to HMM location parameters
## Parameters enter transformed, and exit on natural scale

msm.add.hmmcovs <- function(hmodel, pars, mml){
    labs <- names(pars)
    hpinds <- which(!(labs %in% c("qbase","qcov","hcov","initpbase","initp","initp0","initpcov")))
    n <- nrow(mml[[1]])
    hpars <- matrix(rep(pars[hpinds], n), ncol=n,  dimnames=list(labs[hpinds], NULL))
    ito <- 0
    for (i in which(hmodel$ncovs > 0)) {
        mm <- mml[[hmodel$parstate[i]]]
        ## TODO is this cleaner with hmodel$coveffstate ?
        ## TODO is this correct for misc covs: two matrices
        ifrom <- ito + 1; ito <- ito + hmodel$ncovs[i]
        beta <- pars[names(pars)=="hcov"][ifrom:ito]
        hpars[i,] <- hpars[i,] + mm[,-1,drop=FALSE] %*% beta
    }
    for (i in seq_along(hpinds)){
        hpars[i,] <- gexpit(hpars[i,], hmodel$ranges[i,"lower"], hmodel$ranges[i,"upper"])
    }
    hpars <- msm.mninvlogit.transform(hpars, hmodel$plabs, hmodel$parstate)
    hpars
}

### Derivatives of HMM pars w.r.t HMM baseline pars (on transformed,
### e.g. log, scale) and covariate effects
## Account for parameter constraints, e.g.
## hmodel$constr 1 2 3 2 4, hmodel$covconstr 1 2 1 3
## mu0 = g(p1 + p5x1 + p6x2);  g is id for mean, g'(p)=1, or exp for sigma
## sd0 = g(p2)
## mu1 = g(p3 + p5x1 + p7x2)
## sd1 = g(p2)
## id1 = g(p4)
## want deriv wrt p1,p2,...,p7

msm.form.dh <- function(hmodel,
                        pars, # before adding covariates, and on transformed scale
                        newpars, # after adding covariates, and on natural scale
                        paramdata,
                        mml){
    labs <- names(pars)
    hpinds <- which(!(labs %in% c("qbase","qcov","hcov","initpbase","initp","initp0","initpcov"))) # baseline HMM parameters
    n <- nrow(mml[[1]])
    hpars <- matrix(rep(pars[hpinds], n), ncol=n,  dimnames=list(labs[hpinds], NULL))
    nh <- sum(hmodel$npars)
    nopt <- length(unique(hmodel$constr)) + length(unique(hmodel$covconstr)) # TODO store in hmodel
    dh <- array(0, dim=c(nh, nopt, n))
    for (i in 1:nh){
        ind <- rep(0, length(unique(hmodel$constr)))
        ind[hmodel$constr[i]] <- 1
        cind <- 1:length(unique(hmodel$constr))
        if (labs[hpinds][i] != "p") {
            a <- hmodel$ranges[i,"lower"]; b <- hmodel$ranges[i,"upper"]
            gdash <- dgexpit(glogit(newpars[i,], a, b), a, b)
            dh[i,cind,] <- rep(ind, n)*rep(gdash, each=length(cind))
            if (hmodel$ncovs[i] > 0) {
                mm <- mml[[hmodel$parstate[i]]][,-1,drop=FALSE]
                con <- hmodel$covconstr[hmodel$coveffstate == hmodel$parstate[i]]
                cind <- max(cind) + unique(con)
                dh[i,cind,] <- t(mm) * rep(gdash, each=length(cind))
            }
        }
    }
    dh <- msm.dmninvlogit(hmodel, hpars, mml, pars[names(pars)=="hcov"], dh)
    p <- paramdata # leave fixed parameters out
    dhinds <- which(!(labs %in% c("qbase","qcov","initpbase","initp","initp0","initpcov")))
    fixed <- intersect(p$fixedpars, dhinds) # index into full par vec
    fixed <- match(fixed, setdiff(dhinds, p$duppars)) # index into constrained hmm par vec
    if (any(fixed)) dh <- dh[,-fixed,,drop=FALSE]
    dh
}

### Derivatives of outcome probabilities w.r.t baseline log odds and covariate effects
### in a HMM categorical outcome distribution
### Constraints on probabilities not supported, seems too fiddly.
### Could generalize for use in est.initprobs, if ever support derivatives for that

### log(p_r/pbase) = lp_r + beta_r ' x
### p_r / pbase  =  exp(lp_r + beta_r'x)
### (p1 +...+ pR) / pbase  = 1/pbase = sum_allR(exp()), so
### pbase = 1/sum_allR(exp())
### p_r = exp(lp_r + beta_r'x) / sum_all(exp(lp_s + beta_s'x)), where pars 0 for s=pbase
### d/dlp_s pbase    = -exp(lp_s + beta_s'x)/ (sum_all())^2
### d/dbeta_sk pbase = (-x_sk * exp(lp_s + beta_s'x)/ (sum_all())^2

### for r!=s, d/dlp_s p_r  =  exp(lp_r + beta_r'x) * -exp(lp_s + beta_s'x)/ (sum_all())^2
### for r=s,  d/dlp_s p_r  =  exp(lp_r + beta_r'x) / sum_all()  +
###                                   exp(lp_r + beta_r'x) * -exp(lp_r + beta_r'x)/ (sum_all())^2
### for r!=s, d/dbeta_sk p_r  =  exp(lp_r + beta_r'x) * (-x_sk * exp(lp_s + beta_s'x)/ (sum_all())^2
### for r=s,  d/dbeta_sk p_r  =  x_sk exp(lp_r + beta_r'x) / sum_all()  +
###                                   exp(lp_r + beta_r'x) * (-x_sk * exp(lp_r + beta_r'x)/ (sum_all())^2

### if no covs:  p_r = exp(lp_r) / sum_all(exp(lp_s)).   lp_r = log(p_r/pbase)
### d/dlp_s p_r =
### for r!=s,  exp(lp_r) * (-exp(lp_s)/ (sum_all())^2
### for r=s,  d/dlp_s p_r  =  exp(lp_r) / sum_all() - exp(lp_r)^2 / (sum_all())^2

msm.dmninvlogit <- function(hmodel, pars, mml, hcov, dh){
    plabs <- hmodel$plabs
    states <- hmodel$parstate
    n <- nrow(mml[[1]])
    nh <- sum(hmodel$npars)
    if (any(plabs=="p")) {
        whichst <- match(states[plabs=="p"], unique(states[plabs=="p"]))
        for (i in unique(whichst)) {
            labsi <- plabs[states==i]
            ppars <- which(labsi %in% c("p","pbase","p0"))
            lp <- matrix(pars[states==i ,1][ppars], nrow=n, ncol=length(ppars),
                         byrow=TRUE, dimnames=list(NULL,labsi[ppars]))
            ncovs <- hmodel$ncovs[states==i][ppars]
            beta <- hcov[hmodel$coveffstate==i]
            X <- mml[[states[i]]][,-1,drop=FALSE]
            ito <- 0
            for (j in which(ncovs > 0)){
                ifrom <- ito + 1; ito <- ito + ncovs[j]
                betaj <- beta[ifrom:ito]
                lp[,j] <- lp[,j] + X %*% betaj
            }
            elp <- lp
            elp[,labsi[ppars]=="p"] <- exp(elp[,labsi[ppars]=="p"])
            psum <- 1 + rowSums(elp[,labsi[ppars]=="p",drop=FALSE])
            pil <- which(states==i & plabs=="p")
            pis <- which(colnames(elp) == "p")
            for (r in seq_along(pil)){
                ito <- 0
                for (s in seq_along(pil)){
                    dh[pil[r],pil[s],] <-
                        if (r == s)
                            elp[,pis[r]] / psum * (1 - elp[,pis[r]] / psum)
                        else
                            - elp[,pis[r]] * elp[,pis[s]] / psum^2

                    if (ncovs[pis[s]] > 0){
                        ifrom <- ito + 1; ito <- ito + ncovs[pis[s]]
                        bil <- nh + which(hmodel$coveffstate==i)[ifrom:ito]
                        for (k in seq_along(bil))
                            dh[pil[r],bil[k],] <-
                                if (r == s)
                                    X[,k] * elp[,pis[r]] / psum * (1 - elp[,pis[r]] / psum)
                                else
                                    - X[,k] *  elp[,pis[r]] * elp[,pis[s]] / psum^2
                    }
                }
            }
            pib <- which(states==i & plabs=="pbase")
            ito <- 0
            for (s in seq_along(pil)){
                dh[pib,pil[s],] <- - elp[,pis[s]] / psum^2
                if (ncovs[pis[s]] > 0){
                    ifrom <- ito + 1; ito <- ito + ncovs[pis[s]]
                    bil <- nh + which(hmodel$coveffstate==i)[ifrom:ito]
                    for (k in seq_along(bil))
                        dh[pib,bil[k],] <- - X[,k] * elp[,pis[s]] / psum^2
                }
            }
        }
    }
    dh
}


msm.initprobs2mat <- function(hmodel, pars, mm, mf){
    npts <- attr(mf, "npts")
    ## Convert vector initial state occupancy probs to matrix by patient
    if (!hmodel$hidden) return(0)
    if (hmodel$est.initprobs) {
        initp <- pars[names(pars) %in% c("initpbase","initp","initp0")]
        initp <- matrix(rep(initp, each=npts), nrow=npts,
                        dimnames=list(NULL,
                        names(pars)[names(pars) %in% c("initpbase","initp","initp0")]))
        ## Multiply baselines (entering on mnlogit scale) by current covariate
        ## effects, giving matrix of patient-specific initprobs
        est <- which(colnames(initp)=="initp")
        ip <- initp[,est,drop=FALSE]
        if (hmodel$nicoveffs > 0) {
            ## cov effs ordered by states (excluding state 1) within covariates
            coveffs <- pars[names(pars)=="initpcov"]
            coveffs <- matrix(coveffs, nrow=max(hmodel$nicovs), byrow=TRUE)
            ip <- ip + as.matrix(mm[,-1,drop=FALSE]) %*% coveffs
        }
        initp[,est] <- exp(ip) / (1 + rowSums(exp(ip)))
        initp[,"initpbase"] <- 1 / (1 + rowSums(exp(ip)))
    }
    else if (!is.matrix(hmodel$initprobs))
        initp <- matrix(rep(hmodel$initprobs,each=npts),nrow=npts)
    else initp <- hmodel$initprobs
    initp
}

## Entry point to C code for calculating the likelihood and related quantities

Ccall.msm <- function(params, do.what="lik", msmdata, qmodel, qcmodel, cmodel, hmodel, paramdata)
{
    p <- paramdata
    pars <- p$allinits
    pars[p$optpars] <- params
    pars <- msm.rep.constraints(pars, paramdata, hmodel)
    agg <- if (!hmodel$hidden && cmodel$ncens==0 &&
               do.what %in% c("lik","deriv","info")) TRUE else FALSE # data as aggregate transition counts, as opposed to individual observations
    ## Add covariates to hpars and q here. Inverse-transformed to natural scale on exit
    mm.cov <- if (agg) msmdata$mm.cov.agg else msmdata$mm.cov
    Q <- msm.add.qcovs(qmodel, pars, mm.cov)
    DQ <- if (do.what %in% c("deriv","info","deriv.subj","dpmat")) msm.form.dq(qmodel, qcmodel, pars, p, mm.cov) else NULL
    H <- if (hmodel$hidden) msm.add.hmmcovs(hmodel, pars, msmdata$mm.hcov) else NULL
    DH <- if (hmodel$hidden && (do.what %in% c("deriv","info","deriv.subj"))) msm.form.dh(hmodel, pars, H, paramdata, msmdata$mm.hcov) else NULL
    initprobs <- msm.initprobs2mat(hmodel, pars, msmdata$mm.icov, msmdata$mf)

   mf <- msmdata$mf; mf.agg <- msmdata$mf.agg
   ## In R, ordinal variables indexed from 1.  In C, these are indexed from 0.
   mf.agg$"(fromstate)" <- mf.agg$"(fromstate)" - 1
   mf.agg$"(tostate)" <- mf.agg$"(tostate)" - 1
   firstobs <- c(which(!duplicated(model.extract(mf, "subject"))), nrow(mf)+1) - 1
   mf$"(subject)" <- match(mf$"(subject)", unique(mf$"(subject)"))
   ntrans <- sum(duplicated(model.extract(mf, "subject")))
   hmodel$models <- hmodel$models - 1
   nagg <- if(is.null(mf.agg)) 0 else nrow(mf.agg)
   mf$"(pcomb)" <- mf$"(pcomb)" - 1
   npcombs <- length(unique(na.omit(model.extract(mf, "pcomb"))))
   qmodel$nopt <- if (is.null(DQ)) 0 else dim(DQ)[3]
   hmodel$nopt <- if (is.null(DH)) 0 else dim(DH)[2]
   nopt <- qmodel$nopt + hmodel$nopt

   ## coerce types here to avoid PROTECT faff with doing this in C
   mfac <- list("(fromstate)" = as.integer(mf.agg$"(fromstate)"),
                  "(tostate)" = as.integer(mf.agg$"(tostate)"),
                  "(timelag)" = as.double(mf.agg$"(timelag)"),
                  "(nocc)" = as.integer(mf.agg$"(nocc)"),
                  "(noccsum)" = as.integer(mf.agg$"(noccsum)"),
                  "(whicha)" = as.integer(mf.agg$"(whicha)"),
                  "(obstype)" = as.integer(mf.agg$"(obstype)"))
   mfc <- list("(subject)" = as.integer(mf$"(subject)"),  "(time)" = as.double(mf$"(time)"),
               ## supply matrix outcomes to C by row so multivariate outcomes are together
               ## Also, state data for misclassification HMMs are indexed from 1 not 0 in C
               "(state)" = as.double(t(mf$"(state)")), 
               "(obstype)" = as.integer(mf$"(obstype)"),
              "(obstrue)" = as.integer(mf$"(obstrue)"), "(pcomb)" = as.integer(mf$"(pcomb)"))
    auxdata <- list(nagg=as.integer(nagg),n=as.integer(nrow(mf)),npts=as.integer(attr(mf,"npts")),
                   ntrans=as.integer(ntrans), npcombs=as.integer(npcombs),
                   nout = as.integer(if(is.null(ncol(mf$"(state)"))) 1 else ncol(mf$"(state)")),
                   nliks=as.integer(get("nliks",msm.globals)),firstobs=as.integer(firstobs))
   qmodel <- list(nstates=as.integer(qmodel$nstates), npars=as.integer(qmodel$npars),
                  nopt=as.integer(qmodel$nopt), iso=as.integer(qmodel$iso),
                  perm=as.integer(qmodel$perm), qperm=as.integer(qmodel$qperm),
                  expm=as.integer(qmodel$expm))
   cmodel <- list(ncens=as.integer(cmodel$ncens), censor=as.integer(cmodel$censor),
                  states=as.integer(cmodel$states), index=as.integer(cmodel$index - 1))
   hmodel <- list(hidden=as.integer(hmodel$hidden), mv=as.integer(hmodel$mv),
                  models=as.integer(hmodel$models),
                  totpars=as.integer(hmodel$totpars), firstpar=as.integer(hmodel$firstpar),
                  npars=as.integer(hmodel$npars), nopt=as.integer(hmodel$nopt),
                  ematrix=as.integer(hmodel$ematrix))
    pars <- list(Q=as.double(Q),DQ=as.double(DQ),H=as.double(H),DH=as.double(DH),
                initprobs=as.double(initprobs),nopt=as.integer(nopt))
    .Call("msmCEntry",  as.integer(match(do.what, .msm.CTASKS) - 1),
         mfac, mfc, auxdata, qmodel, cmodel, hmodel, pars, PACKAGE="msm")
}


lik.msm <- function(params, ...)
{
    ## number of likelihood evaluations so far including this one
    ## used for error message for iffy initial values in HMMs
    assign("nliks", get("nliks",msm.globals) + 1, envir=msm.globals)
    Ccall.msm(params, do.what="lik", ...)
}

deriv.msm <- function(params, ...)
{
    Ccall.msm(params, do.what="deriv", ...)
}

information.msm <- function(params, ...)
{
    Ccall.msm(params, do.what="info", ...)
}

## Convert vector of MLEs into matrices and append them to the model object

msm.form.output <- function(x, whichp)
{
    model <- if (whichp=="intens") x$qmodel else x$emodel
    cmodel <- if (whichp=="intens") x$qcmodel else x$ecmodel
    p <- x$paramdata
    Matrices <- MatricesSE <- MatricesL <- MatricesU <- MatricesFixed <- list()
    basename <- if (whichp=="intens") "logbaseline" else "logitbaseline"
    fixedpars.logical <- p$constr %in% p$constr[p$fixedpars]
    for (i in 0:cmodel$ncovs) {
        matrixname <- if (i==0) basename else cmodel$covlabels[i] # name of the current output matrix.
        mat <- t(model$imatrix) # state matrices filled by row, while R fills them by column.
        if (whichp=="intens")
            parinds <- if (i==0) which(p$plabs=="qbase") else which(p$plabs=="qcov")[(i-1)*model$npars + 1:model$npars]
        if (whichp=="misc")
            parinds <- if (i==0) which(p$plabs=="p") else which(p$plabs=="hcov")[i + cmodel$ncovs*(1:model$npars - 1)]
        if (any(parinds)) mat[t(model$imatrix)==1] <- p$params[parinds]
        else mat[mat==1] <- Inf ## if no parinds are "p", then there are off-diag 1s in ematrix
        mat <- t(mat)
        dimnames(mat) <- dimnames(model$imatrix)
        fixed <- array(FALSE, dim=dim(model$imatrix))
        if (p$foundse && !p$fixed){
            intenscov <- p$covmat[parinds, parinds]
            intensse <- sqrt(diag(as.matrix(intenscov)))
            semat <- lmat <- umat <- t(model$imatrix)
            if (any(parinds)){
                semat[t(model$imatrix)==1] <- intensse
                lmat[t(model$imatrix)==1] <- p$ci[parinds,1]
                umat[t(model$imatrix)==1] <- p$ci[parinds,2]
                fixed[t(model$imatrix)==1] <- fixedpars.logical[parinds]
            }
            else semat[semat==1] <- lmat[lmat==1] <- umat[umat==1] <- Inf
            semat <- t(semat); lmat <- t(lmat); umat <- t(umat); fixed <- t(fixed)
            diag(semat) <- diag(lmat) <- diag(umat) <- 0
            for (i in 1:nrow(fixed)){
                foff <- fixed[i,-i][model$imatrix[i,-i]==1]
                fixed[i,i] <- (length(foff)>1) && all(foff)
            }
            if (whichp=="misc")
                fixed[which(x$hmodel$model==match("identity", .msm.HMODELS)),] <- TRUE
           
            dimnames(semat)  <- dimnames(mat)
        }
        else {
            semat <- lmat <- umat <- NULL
        }
        Matrices[[matrixname]] <- mat
        MatricesSE[[matrixname]] <- semat
        MatricesL[[matrixname]] <- lmat
        MatricesU[[matrixname]] <- umat
        MatricesFixed[[matrixname]] <- fixed
    }
    nam <- if(whichp=="intens") "Qmatrices" else "Ematrices"
    x[[nam]] <- Matrices; x[[paste0(nam, "SE")]] <- MatricesSE; x[[paste0(nam, "L")]] <- MatricesL
    x[[paste0(nam, "U")]] <- MatricesU; x[[paste0(nam, "Fixed")]] <- MatricesFixed
    x
}

## Format hidden Markov model estimates and CIs

msm.form.houtput <- function(hmodel, p, msmdata)
{
    hmodel$pars <- p$estimates.t[!(p$plabs %in% c("qbase","qcov","hcov","initp","initpbase","initp0","initpcov"))]
    hmodel$coveffect <- p$estimates.t[p$plabs == "hcov"]
    hmodel$fitted <- !p$fixed
    hmodel$foundse <- p$foundse
    if (hmodel$nip > 0) {
        iplabs <- p$plabs[p$plabs %in% c("initp","initp0")]
        whichst <- which(iplabs == "initp") + 1  # init probs for which states have covs on them (not the zero probs)
        if (hmodel$foundse) {
            hmodel$initprobs <- rbind(cbind(p$estimates.t[p$plabs %in% c("initpbase","initp","initp0")],
                                            p$ci[p$plabs %in% c("initpbase","initp","initp0"),,drop=FALSE]))
            rownames(hmodel$initprobs) <- paste("State",1:hmodel$nstates)
            colnames(hmodel$initprobs) <- c("Estimate", "LCL", "UCL")
            if (any(hmodel$nicovs > 0)) {
                covnames <- names(hmodel$icoveffect)
                hmodel$icoveffect <- cbind(p$estimates.t[p$plabs == "initpcov"],  p$ci[p$plabs == "initpcov",,drop=FALSE])
                rownames(hmodel$icoveffect) <- paste(covnames, paste("State",whichst), sep=", ")
                colnames(hmodel$icoveffect) <- c("Estimate", "LCL", "UCL")
            }
        }
        else {
            hmodel$initprobs <- c(1 - sum(p$estimates.t[p$plabs == "initp"]),
                                  p$estimates.t[p$plabs %in% c("initp","initp0")])
            names(hmodel$initprobs) <- paste("State", 1:hmodel$nstates)
            if (any(hmodel$nicovs > 0)) {
                covnames <- names(hmodel$icoveffect)
                hmodel$icoveffect <- p$estimates.t[p$plabs == "initpcov"]
#                names(hmodel$icoveffect) <- paste(covnames, paste("State",2:hmodel$nstates), sep=", ")
                names(hmodel$icoveffect) <- paste(covnames, paste("State",whichst), sep=", ")
            }
        }
    }
    hmodel$initpmat <- msm.initprobs2mat(hmodel, p$estimates, msmdata$mm.icov, msmdata$mf)
    if (hmodel$foundse) {
        hmodel$ci <- p$ci[!(p$plabs %in% c("qbase","qcov","hcov","initpbase","initp","initp0","initpcov")), , drop=FALSE]
        hmodel$covci <- p$ci[p$plabs %in% c("hcov"), ]
    }
    names(hmodel$pars) <- hmodel$plabs
    hmodel
}



## Table of 'transitions': previous state versus current state

statetable.msm <- function(state, subject, data=NULL)
{
    if(!is.null(data)) {
        data <- as.data.frame(data)
        state <- eval(substitute(state), data, parent.frame())
    }
    n <- length(state)
    if (!is.null(data))
        subject <-
            if(missing(subject)) rep(1,n) else eval(substitute(subject), data, parent.frame())
    subject <- match(subject, unique(subject))
    prevsubj <- c(NA, subject[1:(n-1)])
    previous <- c(NA, state[1:(n-1)])
    previous[prevsubj!=subject] <- NA
    ntrans <- table(previous, state)
# or simpler as
# ntrans <- table(state[duplicated(subject,fromLast=TRUE)], state[duplicated(subject)])
    names(dimnames(ntrans)) <- c("from", "to")
    ntrans
}

## Calculate crude initial values for transition intensities by assuming observations represent the exact transition times

crudeinits.msm <- function(formula, subject, qmatrix, data=NULL, censor=NULL, censor.states=NULL)
{
    cens <- msm.form.cmodel(censor, censor.states, qmatrix)
    mf <- model.frame(formula, data=data, na.action=NULL)
    state <- mf[,1]
    time <- mf[,2]
    n <- length(state)
    if (missing(subject)) subject <- rep(1, n)
    else if (!is.null(data))
        subject <- eval(substitute(subject), as.list(data), parent.frame())
    if (is.null(subject)) subject <- rep(1, n)
    notna <- !is.na(subject) & !is.na(time) & !is.na(state)
    subject <- subject[notna]; time <- time[notna]; state <- state[notna]
    msm.check.qmatrix(qmatrix)
    msm.check.state(nrow(qmatrix), state, cens$censor, list(hidden=FALSE))
    msm.check.times(time, subject, state)
    nocens <- (! (state %in% cens$censor) )
    state <- state[nocens]; subject <- subject[nocens]; time <- time[nocens]
    n <- length(state)
    lastsubj <- !duplicated(subject, fromLast=TRUE)
    timecontrib <- ifelse(lastsubj, NA, c(time[2:n], 0) - time)
    tottime <- tapply(timecontrib[!lastsubj], state[!lastsubj], sum) # total time spent in each state
    ntrans <- statetable.msm(state, subject, data=NULL) # table of transitions
    nst <- nrow(qmatrix)
    estmat <- matrix(0, nst, nst)
    rownames(estmat) <- colnames(estmat) <- paste(1:nst)
    tab <- sweep(ntrans, 1, tottime, "/")
    for (i in 1:nst) # Include zero rows for states for which there were no transitions
        for (j in 1:nst)
            if ((paste(i) %in% rownames(tab)) && (paste(j) %in% colnames(tab)))
                estmat[paste(i), paste(j)] <- tab[paste(i),paste(j)]
    estmat[qmatrix == 0] <- 0 #
    estmat <- msm.fixdiag.qmatrix(estmat)
    rownames(estmat) <- rownames(qmatrix)
    colnames(estmat) <- colnames(qmatrix)
    estmat
}

### Construct a model with time-dependent transition intensities.
### Form a new dataset with censored states and extra covariate, and
### form a new censor model, given change times in tcut

msm.pci <- function(tcut, mf, qmodel, cmodel, covariates)
{
    if (!is.numeric(tcut)) stop("Expected \"tcut\" to be a numeric vector of change points")
    ## Make new dataset with censored observations at time cut points
    ntcut <- length(tcut)
    npts <- length(unique(model.extract(mf, "subject")))
    nextra <- ntcut*npts
    basenames <- c("(state)","(time)","(subject)","(obstype)","(obstrue)","(obs)","(pci.imp)")
    covnames <- setdiff(colnames(mf), basenames)
    extra <- mf[rep(1,nextra),]
    extra$"(state)" = rep(NA, nextra)
    extra$"(time)" = rep(tcut, npts)
    extra$"(subject)" <- rep(unique(model.extract(mf, "subject")), each=ntcut)
    extra$"(obstype)" = rep(1, nextra)
    extra$"(obstrue)" = rep(TRUE, nextra)
    extra$"(obs)" <- NA
    extra$"(pci.imp)" = 1
    extra[, covnames] <- NA
    mf$"(pci.imp)" <- 0

    ## Merge new and old observations
    new <- rbind(mf, extra)
    new <- new[order(new$"(subject)", new$"(time)"),]
    label <- if (cmodel$ncens > 0) max(cmodel$censor)*2 else qmodel$nstates + 1
    new$"(state)"[is.na(new$"(state)")] <- label
    ## Only keep cutpoints within range of each patient's followup
    mintime <- tapply(mf$"(time)", mf$"(subject)", min)[as.character(unique(mf$"(subject)"))]
    maxtime <- tapply(mf$"(time)", mf$"(subject)", max)[as.character(unique(mf$"(subject)"))]
    nobspt <- as.numeric(table(new$"(subject)")[as.character(unique(new$"(subject)"))])
    new <- new[new$"(time)" >= rep(mintime, nobspt) & new$"(time)" <= rep(maxtime, nobspt), ]

    ## Drop imputed observations at times when there was already an observation
    ## assumes there wasn't already duplicated obs times
    prevsubj <- c(NA,new$"(subject)"[1:(nrow(new)-1)]); nextsubj <- c(new$"(subject)"[2:nrow(new)], NA)
    prevtime <- c(NA,new$"(time)"[1:(nrow(new)-1)]); nexttime <- c(new$"(time)"[2:nrow(new)], NA)
    prevstate <- c(NA,new$"(state)"[1:(nrow(new)-1)]); nextstate <- c(new$"(state)"[2:nrow(new)], NA)
    new <- new[!((new$"(subject)"==prevsubj & new$"(time)"==prevtime & new$"(state)"==label & prevstate!=label) |
                 (new$"(subject)"==nextsubj & new$"(time)"==nexttime & new$"(state)"==label & nextstate!=label))
               ,]

    ## Carry last value forward for other covariates
    if (length(covnames) > 0) {
        eind <- which(is.na(new[,covnames[1]]) & new$"(pci.imp)"==1)
        while(length(eind) > 0){
            new[eind,covnames] <- new[eind - 1, covnames]
            eind <- which(is.na(new[,covnames[1]]))
        }
    }

    ## Check range of cut points
    if (any(tcut <= min(mf$"(time)")))
      warning("Time cut point", if (sum(tcut <= min(mf$"(time)")) > 1) "s " else " ",
                paste(tcut[tcut<=min(mf$"(time)")],collapse=","),
                " less than or equal to minimum observed time of ",min(mf$"(time)"))
    if (any(tcut >= max(mf$"(time)")))
        warning("Time cut point", if (sum(tcut >= max(mf$"(time)")) > 1) "s " else " ",
                paste(tcut[tcut>=max(mf$"(time)")],collapse=","),
                " greater than or equal to maximum observed time of ",max(mf$"(time)"))
    tcut <- tcut[tcut > min(mf$"(time)") & tcut < max(mf$"(time)")]
    ntcut <- length(tcut)
    if (ntcut==0)
        res <- NULL # no cut points in range of data, continue with no time-dependent model
    else {
        ## Insert new covariate in data representing time period
        tcovlabel <- "timeperiod"
        if (any(covnames=="timeperiod")) stop("Cannot have a covariate called \"timeperiod\" if \"pci\" is supplied")
        tcov <- factor(cut(new$"(time)", c(-Inf,tcut,Inf), right=FALSE))
        levs <- levels(tcov)
        levels(tcov) <- gsub(" ","", levs) # get rid of spaces in e.g. [10, Inf) levels
        assign(tcovlabel, tcov)
        new[,tcovlabel] <- tcov

        ## Add "+ timeperiod" to the Q covariates formula.
        current.covs <- if(attr(mf,"ncovs")>0) attr(terms(covariates), "term.labels") else NULL
        covariates <- reformulate(c(current.covs, tcovlabel))
        attr(new, "covnames") <- c(attr(mf, "covnames"), "timeperiod")
        attr(new, "covnames.q") <- c(attr(mf, "covnames.q"), "timeperiod")
        attr(new, "ncovs") <- attr(mf, "ncovs") + 1
        ## New censoring model
        cmodel$ncens <- cmodel$ncens + 1
        cmodel$censor <- c(cmodel$censor, label)
        cmodel$states <- c(cmodel$states, 1:qmodel$nstates)
        cmodel$index <- if (is.null(cmodel$index)) 1 else cmodel$index
        cmodel$index <- c(cmodel$index, length(cmodel$states) + 1)
        res <- list(mf=new, covariates=covariates, cmodel=cmodel, tcut=tcut)
    }
    res
}

msm.check.covlist <- function(covlist, qemodel) {
    check.numnum <- function(str)
        length(grep("^[0-9]+-[0-9]+$", str)) == length(str)
    num <- sapply(names(covlist), check.numnum)
    if (!all(num)) {
        badnums <- which(!num)
        plural1 <- if (length(badnums)>1) "s" else "";
        plural2 <- if (length(badnums)>1) "e" else "";
        badnames <- paste(paste("\"",names(covlist)[badnums],"\"",sep=""), collapse=",")
        badnums <- paste(badnums, collapse=",")
        stop("Name", plural1, " ", badnames, " of \"covariates\" formula", plural2, " ", badnums, " not in format \"number-number\"")
    }
    for (i in seq(along=covlist))
        if (!inherits(covlist[[i]], "formula"))
            stop("\"covariates\" should be a formula or list of formulae")
    trans <- sapply(strsplit(names(covlist), "-"), as.numeric)
    tm <- if(inherits(qemodel,"msmqmodel")) "transition" else "misclassification"
    qe <- if(inherits(qemodel,"msmqmodel")) "qmatrix" else "ematrix"
    imat <- qemodel$imatrix
    for (i in seq(length=ncol(trans))){
        if (imat[trans[1,i],trans[2,i]] != 1)
            stop("covariates on ", names(covlist)[i], " ", tm, " requested, but this is not permitted by the ", qe, ".")
    }
}

## Form indicator matrix for effects that will be fixed to zero when
## "covariates" specified as a list of transition-specific formulae

msm.form.cri <- function(covlist, qmodel, mf, mm) {
    imat <- t(qmodel$imatrix) # order named transitions / misclassifications by row
    tnames <- paste(col(imat)[imat==1],row(imat)[imat==1],sep="-")
    covlabs <- colnames(mm)[-1]
    npars <- qmodel$npars
    cri <- matrix(0, nrow=npars, ncol=length(covlabs), dimnames = list(tnames, covlabs))
    sorti <- function(x) {
        ## converts, e.g. c("b:a:c","d:f","f:e") to c("a:b:c", "d:f", "e:f")
        sapply(lapply(strsplit(x, ":"), sort), paste, collapse=":")
    }
    for (i in 1:npars) {
        if (tnames[i] %in% names(covlist)) {
            covlabsi <- colnames(model.matrix(covlist[[tnames[i]]], data=mf))[-1]
            cri[i, match(sorti(covlabsi), sorti(covlabs))] <- 1
        }
    }
    cri
}

## adapted from stats:::na.omit.data.frame.  ignore handling of
## non-atomic, matrix within df

na.omit.msmdata <- function(object, ...) {
    omit <- na.find.msmdata(object)
    xx <- object[!omit, , drop = FALSE]
    if (any(omit > 0L)) {
        temp <- setNames(seq(omit)[omit], attr(object, "row.names")[omit])
        attr(temp, "class") <- "omit"
        attr(xx, "na.action") <- temp
    }
    xx
}

na.fail.msmdata <- function(object, ...) {
    omit <- na.find.msmdata(object)
    if (any(omit))
        stop("Missing values or subjects with only one observation in data")
    else object
}

na.find.msmdata <- function(object, ...) {
    subj <- as.character(object[,"(subject)"])
    firstobs <- !duplicated(subj)
    lastobs <- !duplicated(subj, fromLast=TRUE)
    nm <- names(object)
    omit <- FALSE
    for (j in seq(along=object)) {
        ## Drop all NAs in time, subject as usual
        if (nm[j] %in% c("(time)", "(subject)"))
            omit <- omit | is.na(object[[j]])
        if (nm[j] == "(state)") {
            ## For matrix HMM outcomes ("states"), only drop a row if all columns are NA
            nas <- if (is.matrix(object[[j]])) apply(object[[j]], 1, function(x)all(is.na(x)))
                   else is.na(object[[j]])
            omit <- omit | nas
        }
        ## Don't drop NAs in obstype at first observation for a subject
        else if (nm[j]=="(obstype)")
            omit <- omit | (is.na(object[[j]]) & !firstobs)
        ## covariates on initial state probs - only drop if NA at initial observation
        else if (j %in% attr(object, "icovi"))
            omit <- omit | (is.na(object[[j]]) & firstobs)
        ## Don't drop NAs in covariates at last observation for a subject
        ## Note NAs in obstrue should have previously been replaced by zeros in msm.form.obstrue, so could assert for this here. 
        else if (nm[j]!="(obstrue)")
            omit <- omit | (is.na(object[[j]]) & !lastobs)
    }
    ## Drop obs with only one subject remaining after NAs have been omitted
    nobspt <- table(subj[!omit])[subj]
    omit <- omit | (nobspt==1)
    omit
}

msm.form.mf.agg <- function(x){
    mf <- x$data$mf; qmodel <- x$qmodel; hmodel <- x$hmodel; cmodel <- x$cmodel
    if (!hmodel$hidden && cmodel$ncens==0){
        mf.trans <- msm.obs.to.fromto(mf)
        msm.check.model(mf.trans$"(fromstate)", mf.trans$"(tostate)", mf.trans$"(obs)", mf.trans$"(subject)", mf.trans$"(obstype)", qmodel$qmatrix, cmodel)
        ## Aggregate over unique from/to/timelag/cov/obstype
        mf.agg <- msm.aggregate.data(mf.trans)
    } else mf.agg <- NULL
    mf.agg
}

msm.obs.to.fromto <- function(mf)
{
    n <- nrow(mf)
    subj <- model.extract(mf, "subject")
    time <- model.extract(mf, "time")
    state <- model.extract(mf, "state")
    firstsubj <- !duplicated(subj)
    lastsubj <- !duplicated(subj, fromLast=TRUE)
    mf.trans <- mf[!lastsubj,,drop=FALSE]   ## retains all covs corresp to the start of the transition
    names(mf.trans)[names(mf.trans)=="(state)"] <- "(fromstate)"
    mf.trans$"(tostate)" <- if(is.matrix(state)) state[!firstsubj,,drop=FALSE] else state[!firstsubj]
    mf.trans$"(obstype)" <- model.extract(mf, "obstype")[!firstsubj] # obstype matched with end of transition
    mf.trans$"(obs)" <- model.extract(mf, "obs")[!firstsubj]
    mf.trans$"(timelag)" <- diff(time)[!firstsubj[-1]]
    ## NOTE not kept constants npts, ncovs, covlabels, covdata, hcovdata, covmeans
    ## NOTE: orig returned dat$time, dat$obstype.obs of orig length, assume not needed
    ## NOTE: firstsubj in old only used in msm.aggregate.hmmdata, which is not used
    mf.trans
}

### Aggregate the data by distinct values of time lag, covariate values, from state, to state, observation type
### Result is passed to the C likelihood function (for non-hidden multi-state models)

msm.aggregate.data <- function(mf.trans)
{
    n <- nrow(mf.trans)
    apaste <- do.call("paste", mf.trans[,c("(fromstate)","(tostate)","(timelag)","(obstype)", attr(mf.trans, "covnames"))])
    mf.agg <- mf.trans[!duplicated(apaste),]
    mf.agg <- mf.agg[order(unique(apaste)),]
    mf.agg$"(nocc)" <- as.numeric(table(apaste))
    apaste2 <- mf.agg[,"(timelag)"]
    if (attr(mf.agg, "ncovs") > 0)
        apaste2 <- paste(apaste2,  do.call("paste", mf.agg[,attr(mf.agg, "covnames"),drop=FALSE]))
    ## which unique timelag/cov combination each row of aggregated data corresponds to
    ## lik.c needs this to know when to recalculate the P matrix.
    mf.agg$"(whicha)" <- match(apaste2, sort(unique(apaste2)))
    mf.agg <- mf.agg[order(apaste2,mf.agg$"(fromstate)",mf.agg$"(tostate)"),]
    ## for Fisher information: number of obs over timelag/covs starting in
    ## fromstate, replicated for all tostates.
    apaste2 <- paste(mf.agg$"(fromstate)", apaste2)
    mf.agg$"(noccsum)" <- unname(tapply(mf.agg$"(nocc)", apaste2, sum)[apaste2])
    ## NOTE not kept covdata, hcovdata, npts, covlabels, covmeans, nobs, ntrans
    mf.agg
}

### Form indicator for which unique timelag/obstype/cov each observation belongs to
### Used in HMMs/censoring to calculate P matrices efficiently in C

msm.form.hmm.agg <- function(mf){
    mf.trans <- msm.obs.to.fromto(mf)
    mf.trans$"(apaste)" <- do.call("paste", mf.trans[,c("(timelag)","(obstype)",attr(mf.trans,"covnames"))])
    mf.trans$"(pcomb)" <- match(mf.trans$"(apaste)", unique(mf.trans$"(apaste)"))
    mf$"(pcomb)" <- NA
    mf$"(pcomb)"[duplicated(model.extract(mf,"subject"))] <- mf.trans$"(pcomb)"
    mf$"(pcomb)"
}

### FORM DESIGN MATRICES FOR COVARIATE MODELS.
msm.form.mm.cov <- function(x){
    mm.cov <- model.matrix.wrap(x$covariates, x$data$mf)
    msm.center.covs(mm.cov, attr(x$data$mf,"covmeans"), x$center)
}

msm.form.mm.cov.agg <- function(x){
    mm.cov.agg <- if (x$hmodel$hidden || (x$cmodel$ncens > 0)) NULL else model.matrix(x$covariates, x$data$mf.agg)
    msm.center.covs(mm.cov.agg, attr(x$data$mf.agg,"covmeans"), x$center)
}

msm.form.mm.mcov <- function(x){
    mm.mcov <- if (x$emodel$misc) model.matrix.wrap(x$misccovariates, x$data$mf) else NULL
    msm.center.covs(mm.mcov, attr(x$data$mf,"covmeans"), x$center)
}

msm.form.mm.hcov <- function(x){
    hcov <- x$hcovariates
    nst <- x$qmodel$nstates
    if (x$hmodel$hidden) {
        mm.hcov <- vector(mode="list", length=nst)
        if (is.null(hcov))
            hcov <- rep(list(~1), nst)
        for (i in seq_len(nst)){
            if (is.null(hcov[[i]])) hcov[[i]] <- ~1
            mm.hcov[[i]] <- model.matrix.wrap(hcov[[i]], x$data$mf)
            mm.hcov[[i]] <- msm.center.covs(mm.hcov[[i]], attr(x$data$mf,"covmeans"), x$center)
        }
    } else mm.hcov <- NULL
    mm.hcov
}

msm.form.mm.icov <- function(x){
    if (x$hmodel$hidden) {
        if (is.null(x$initcovariates)) x$initcovariates <- ~1
        mm.icov <- model.matrix.wrap(x$initcovariates, x$data$mf[!duplicated(x$data$mf$"(subject)"),])
    } else mm.icov <- NULL
    msm.center.covs(mm.icov, attr(x$data$mf,"covmeans"), x$center)
}

model.matrix.wrap <- function(formula, data){
    mm <- model.matrix(formula, data)
    polys <- unlist(attr(mm, "contrasts") == "contr.poly")
    covlist <- paste(names(polys),collapse=",")
    if (any(polys))
        warning(sprintf("Polynomial factor contrasts (found for covariates \"%s\") not supported in msm output functions.  Use treatment contrasts for ordered factors", covlist))   
    mm
}

msm.center.covs <- function(covmat, cm, center=TRUE){
    if (is.null(covmat)||is.null(cm)) return(covmat)
    means <- cm[colnames(covmat)]
    attr(covmat, "means") <- means[-1]
    if (center)
        covmat <- sweep(covmat, 2, means)
    covmat
}

expand.data <- function(x){
    x$data$mf.agg <- msm.form.mf.agg(x)
    x$data$mm.cov <- msm.form.mm.cov(x)
    x$data$mm.cov.agg <- msm.form.mm.cov.agg(x)
    x$data$mm.mcov <- msm.form.mm.mcov(x)
    x$data$mm.hcov <- msm.form.mm.hcov(x)
    x$data$mm.icov <- msm.form.mm.icov(x)
    x$data
}

model.frame.msm <- function(formula, agg=FALSE, ...){
    x <- formula
    if (agg) x$data$mf.agg else x$data$mf
}

model.matrix.msm <- function(object, model="intens", state=1, ...){
    switch(model,
           intens=msm.form.mm.cov(object),
           misc=msm.form.mm.mcov(object),
           hmm=msm.form.mm.hcov(object)[[state]],
           init=msm.form.mm.icov(object))
}

.onLoad <- function(libname, pkgname) {
    assign("msm.globals", new.env(), envir=parent.env(environment()))
    assign("nliks", 0, envir=msm.globals)
}

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msm documentation built on May 31, 2017, 5:10 a.m.