R/profile.R

In bbmle: Tools for General Maximum Likelihood Estimation

## FIXME: abstract to general-purpose code?  (i.e. replace 'fitted' by
                                        #    objective function, parameter vector, optimizer, method, control settings,
##   min val, standard error/Hessian, ...
##
## allow starting values to be set by "mle" (always use mle), "prevfit"
##  (default?), and "extrap" (linear extrapolation from previous two fits)
## 

proffun <- function (fitted, which = 1:p, maxsteps = 100,
                     alpha = 0.01, zmax = sqrt(qchisq(1 - alpha/2, p)),
                     del = zmax/5, trace = FALSE, skiperrs=TRUE,
                     std.err,
                     tol.newmin = 0.001,
                     debug=FALSE,
                     prof.lower, prof.upper, skip.hessian=TRUE,
                     continuation = c("none","naive","linear"),
                     try_harder=FALSE, ...) {
    ## fitted: mle2 object
    ## which: which parameters to profile (numeric or char)
    ## maxsteps: steps to take looking for zmax
    ## alpha: max alpha level
    ## zmax: max log-likelihood difference to search to
    ## del: stepsize
    ## trace:
    ## skiperrs:
    continuation <- match.arg(continuation)
    if (fitted@optimizer=="optimx") {
        fitted@call$method <- fitted@details$method.used
    }
    if (fitted@optimizer=="constrOptim")
        stop("profiling not yet working for constrOptim -- sorry")
    Pnames <- names(fitted@coef)
    p <- length(Pnames)
    if (is.character(which)) which <- match(which,Pnames)
    if (any(is.na(which)))
        stop("parameters not found in model coefficients")
    ## global flag for better fit found inside profile fit
    newpars_found <- FALSE
    if (debug) cat("i","bi","B0[i]","sgn","step","del","std.err[i]","\n")
    pfit <- NULL ## save pfit to implement continuation methods
                 ## for subsequent calls to onestep
    onestep <- function(step,bi) {
        if (missing(bi)) {
            bi <- B0[i] + sgn * step * del * std.err[i]
            if (debug) cat(i,bi,B0[i],sgn,step,del,std.err[i],"\n")
        } else if (debug) cat(bi,"\n")
        fix <- list(bi)
        names(fix) <- p.i
        if (is.null(call$fixed)) call$fixed <- fix
        else call$fixed <- c(eval(call$fixed),fix)
        ## 
        if (continuation!="none") {
            if (continuation != "naive")
                stop("only 'naive' continuation implemented")
            if (!is.null(pfit)) {
                for (nm in setdiff(names(call$start),names(call$fixed))) {
                    call$start[[nm]] <- coef(pfit)[nm]
                }
            }
        }
        ## now try to fit ...
        if (skiperrs) {
            pfit0 <- try(eval(call, environment(fitted)), silent=TRUE)
        } else {
            pfit0 <- eval(call, environment(fitted))
        }
        ok <- !inherits(pfit0,"try-error")
        ## don't overwrite pfit in environment until we know it's OK ...
        if (ok) pfit <<- pfit0
        if (debug && ok) cat(coef(pfit),-logLik(pfit),"\n")
        if(skiperrs && !ok) {
            warning(paste("Error encountered in profile:",pfit0))
            return(NA)
        }
        else {
            ## pfit is current (profile) fit,
            ##   fitted is original fit
            ## pfit@min _should_ be > fitted@min
            ## thus zz below should be >0
            zz <- 2*(pfit@min - fitted@min)
            ri <- pv0
            ri[, names(pfit@coef)] <- pfit@coef
            ri[, p.i] <- bi
            ##cat(2*pfit@min,2*fitted@min,zz,
            ##   tol.newmin,zz<(-tol.newmin),"\n")
            if (!is.na(zz) && zz<0) {
                if (zz > (-tol.newmin)) {
                    z <- 0
                    ## HACK for non-monotonic profiles? z <- -sgn*sqrt(abs(zz))
                } else {
                    ## cat() instead of warning(); FIXME use message() instead???
                    ## FIXME:  why??? shouldn't this be a warning?
                    message("Profiling has found a better solution,",
                            "so original fit had not converged:\n")
                    message(sprintf("(new deviance=%1.4g, old deviance=%1.4g, diff=%1.4g)",
                                    2*pfit@min,2*fitted@min,2*(pfit@min-fitted@min)),"\n")
                    message("Returning better fit ...\n")
                    ## need to return parameters all the way up
                    ##   to top level
                    newpars_found <<- TRUE
                    ## return(pfit@fullcoef)
                    if (!try_harder) return(pfit) ## bail out, return full fit
                }
            } else {
                z <- sgn * sqrt(zz)
            }
            pvi <<- rbind(pvi, ri)
            zi <<- c(zi, z) ## nb GLOBAL set
        }
        if (trace) cat(bi, z, "\n")
        return(z)
    } ## end onestep
    ## Profile the likelihood around its maximum
    ## Based on profile.glm in MASS
    ## suppressWarnings (don't want to know e.g. about bad vcov)
    summ <- suppressWarnings(summary(fitted))
    if (missing(std.err)) {
        std.err <- summ@coef[, "Std. Error"]
    } else {
        n <- length(summ@coef)
        if (length(std.err)<n)
            std.err <- rep(std.err,length.out=length(summ@coef))
        if (any(is.na(std.err)))
            std.err[is.na(std.err)] <- summ@coef[is.na(std.err)]
    }
    if (any(is.na(std.err))) {
        std.err[is.na(std.err)] <- sqrt(1/diag(fitted@details$hessian))[is.na(std.err)]
        if (any(is.na(std.err))) {  ## still bad
            stop("Hessian is ill-behaved or missing, ",
                 "can't find an initial estimate of std. error ",
                 "(consider specifying std.err in profile call)")
        }
        ## warn anyway ...
        warning("Non-positive-definite Hessian, ",
                "attempting initial std err estimate from diagonals")
    }
    Pnames <- names(B0 <- fitted@coef)
    pv0 <- t(as.matrix(B0))
    p <- length(Pnames)
    prof <- vector("list", length = length(which))
    names(prof) <- Pnames[which]
    call <- fitted@call
    call$skip.hessian <- skip.hessian ## BMB: experimental
    call$minuslogl <- fitted@minuslogl
    ndeps <- eval.parent(call$control$ndeps)
    parscale <- eval.parent(call$control$parscale)
    nc <- length(fitted@coef)
    xf <- function(x) rep(x,length.out=nc) ## expand to length
    upper <- xf(unlist(eval.parent(call$upper)))
    lower <- xf(unlist(eval.parent(call$lower)))
    if (all(upper==Inf & lower==-Inf)) {
        lower <- upper <- NULL
        ## kluge: lower/upper may have been set to +/- Inf
        ##   in previous rounds, 
        ##  but we don't want them in that case
    }
    if (!missing(prof.lower)) prof.lower <- xf(prof.lower)
    if (!missing(prof.upper)) prof.upper <- xf(prof.upper)
    ## cat("upper\n")
    ## print(upper)
    stop_msg <- list()
    for (i in which) {
        zi <- 0
        pvi <- pv0
        p.i <- Pnames[i]
        wfun <- function(txt) paste(txt," (",p.i,")",sep="")
        ## omit values from control vectors:
        ##   is this necessary/correct?
        if (!is.null(ndeps)) call$control$ndeps <- ndeps[-i]
        if (!is.null(parscale)) call$control$parscale <- parscale[-i]
        if (!is.null(upper) && length(upper)>1) call$upper <- upper[-i]
        if (!is.null(lower) && length(lower)>1) call$lower <- lower[-i]
        stop_msg[[i]] <- list(down="",up="")
        for (sgn in c(-1, 1)) {
            pfit <- NULL ## reset for continuation method
            dir_ind <- (sgn+1)/2+1 ## (-1,1) -> (1,2)
            if (trace) {
                cat("\nParameter:", p.i, c("down", "up")[dir_ind], "\n")
                cat("par val","sqrt(dev diff)\n")
            }
            step <- 0
            z <- 0
            ## This logic was a bit frail in some cases with
            ## high parameter curvature. We should probably at least
            ## do something about cases where the mle2 call fails
            ## because the parameter gets stepped outside the domain.
            ## (We now have.)
            call$start <- as.list(B0)
            lastz <- 0
            valf <- function(b) {
                (!is.null(b) && length(b)>1) ||
                    (length(b)==1 && i==1 && is.finite(b))
            }
            lbound <- if (!missing(prof.lower)) {
                          prof.lower[i]
                      } else if (valf(lower))
                      { lower[i]
                      } else -Inf
            ubound <- if (!missing(prof.upper)) prof.upper[i] else if (valf(upper)) upper[i] else Inf
            stop_bound <- stop_na <- stop_cutoff <- stop_flat <- FALSE
            while ((step <- step + 1) < maxsteps &&
                   ## added is.na() test for try_harder case
                   ## FIXME: add unit test!
                   (is.na(z) || abs(z) < zmax)) {
                       curval <- B0[i] + sgn * step * del * std.err[i]
                       if ((sgn==-1 & curval<lbound) ||
                           (sgn==1 && curval>ubound)) {
                           stop_bound <- TRUE;
                           stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("hit bound"))
                           break
                       }
                       z <- onestep(step)
                       if (newpars_found && !try_harder) return(pfit)
                       ## stop on flat spot, unless try_harder
                       if (step>1 && (identical(oldcurval,curval) || identical(oldz,z))) {
                           stop_flat <- TRUE
                           stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("hit flat spot"),
                                                             sep=";")
                           if (!try_harder) break
                       }
                       oldcurval <- curval
                       oldz <- z
                       if(is.na(z)) {
                           stop_na <- TRUE
                           stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("hit NA"),sep=";")
                           if (!try_harder) break
                       }
                       lastz <- z
                   }
            stop_cutoff <- (!is.na(z) && abs(z)>=zmax)
            stop_maxstep <- (step==maxsteps)
            if (stop_maxstep) stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("max steps"),sep=";")
            if (debug) {
                if (stop_na) message(wfun("encountered NA"),"\n")
                if (stop_cutoff) message(wfun("above cutoff"),"\n")
            }
            if (stop_flat) {
                warning(wfun("stepsize effectively zero/flat profile"))
            } else {
                if (stop_maxstep) warning(wfun("hit maximum number of steps"))
                if(!stop_cutoff) {
                    if (debug) cat(wfun("haven't got to zmax yet, trying harder"),"\n")
                    stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("past cutoff"),sep=";")
                    ## now let's try a bit harder if we came up short
                    for(dstep in c(0.2, 0.4, 0.6, 0.8, 0.9)) {
                        curval <- B0[i] + sgn * (step-1+dstep) * del * std.err[i]
                        if ((sgn==-1 & curval<lbound) ||
                            (sgn==1 && curval>ubound)) break
                        z <- onestep(step - 1 + dstep)
                        if (newpars_found && !try_harder) return(pfit)
                        if(is.na(z) || abs(z) > zmax) break
                        lastz <- z
                        if (newpars_found && !try_harder) return(pfit)
                    }
                    if (!stop_cutoff && stop_bound) {
                        if (debug) cat(wfun("bounded and didn't make it, try at boundary"),"\n")
                        ## bounded and didn't make it, try at boundary
                        if (sgn==-1 && B0[i]>lbound) z <- onestep(bi=lbound)
                        if (newpars_found && !try_harder) return(pfit)
                        if (sgn==1  && B0[i]<ubound) z <- onestep(bi=ubound)
                        if (newpars_found && !try_harder) return(pfit)
                    }
                } else if (length(zi) < 5) { # try smaller steps
                    if (debug) cat(wfun("try smaller steps"),"\n")
                    stop_msg[[i]][[dir_ind]] <- paste(stop_msg[[i]][[dir_ind]],wfun("took more steps"),sep=";")
                    mxstep <- step - 1
                    step <- 0.5
                    while ((step <- step + 1) < mxstep) {
                        z <- onestep(step)
                        if (newpars_found && !try_harder) return(pfit)
                    }
                } ## smaller steps
            } ## !zero stepsize
        } ## step in both directions
        si <- order(pvi[, i])
        prof[[p.i]] <- data.frame(z = zi[si])
        prof[[p.i]]$par.vals <- pvi[si,, drop=FALSE]
    } ## for i in which
    if (newpars_found) return(pfit)
    return(list(prof=prof,summ=summ,stop_msg=stop_msg))
}

setMethod("profile", "mle2",
function(fitted,...) {
    ## cc <- match.call()
    ## cc[[1]] <- quote(proffun)
    ## prof <- eval.parent(cc)
    prof <- proffun(fitted,...)
    if (is(prof,"mle2")) return(prof)
    newprof <- new("profile.mle2", profile = prof$prof, summary = prof$summ)
    attr(newprof,"stop_msg") <- prof$stop_msg
    newprof
})


setMethod("plot", signature(x="profile.mle2", y="missing"),
          function (x, levels, which=1:p, conf = c(99, 95, 90, 80, 50)/100,
                    plot.confstr = TRUE, confstr = NULL, absVal = TRUE, add = FALSE,
                    col.minval="green", lty.minval=2,
                    col.conf="magenta", lty.conf=2,
                    col.prof="blue", lty.prof=1,
                    xlabs=nm, ylab="z",
                    onepage=TRUE,
                    ask=((prod(par("mfcol")) < length(which)) && dev.interactive() &&
                         !onepage),
                    show.points=FALSE,
                    main, xlim, ylim, ...)
{
    ## Plot profiled likelihood
    ## Based on profile.nls (package stats)
    obj <- x@profile
    nm <- names(obj)
    p <- length(nm)
    ## need to save these for i>1 below
    no.xlim <- missing(xlim)
    no.ylim <- missing(ylim)    
    if (is.character(which)) which <- match(which,nm)
    ask_orig <- par(ask=ask)
    op <- list(ask=ask_orig)
    if (onepage) {
        nplots <- length(which)
        ## Q: should we reset par(mfrow), or par(mfg), anyway?
        if (prod(par("mfcol")) < nplots) {
            rows <- ceiling(round(sqrt(nplots)))
            columns <- ceiling(nplots/rows)
            mfrow_orig <- par(mfrow=c(rows,columns))
            op <- c(op,mfrow_orig)
        }
    }
    on.exit(par(op))
    confstr <- NULL
    if (missing(levels)) {
        levels <- sqrt(qchisq(pmax(0, pmin(1, conf)), 1))
        confstr <- paste(format(100 * conf), "%", sep = "")
    }
    if (any(levels <= 0)) {
        levels <- levels[levels > 0]
        warning("levels truncated to positive values only")
    }
    if (is.null(confstr)) {
        confstr <- paste(format(100 * pchisq(levels^2, 1)), "%", sep = "")
    }
    mlev <- max(levels) * 1.05
    ##    opar <- par(mar = c(5, 4, 1, 1) + 0.1)
    if (!missing(xlabs) && length(which)<length(nm)) {
        xl2 = nm
        xl2[which] <- xlabs
        xlabs <- xl2
    }
    if (missing(main)) 
        main <- paste("Likelihood profile:",nm)
    main <- rep(main,length=length(nm))
    for (i in seq(along = nm)[which]) {
        ## <FIXME> This does not need to be monotonic
        ## cat("**",i,obj[[i]]$par.vals[,i],obj[[i]]$z,"\n")
        ## FIXME: reconcile this with confint!
        yvals <- obj[[i]]$par.vals[,nm[i],drop=FALSE]
        avals <- data.frame(x=unname(yvals), y=obj[[i]]$z)
        if (!all(diff(obj[[i]]$z)>0)) {
            warning("non-monotonic profile: reverting to linear interpolation.  Consider setting std.err manually")
            predback <- approxfun(obj[[i]]$z,yvals)
        } else {
            sp <- splines::interpSpline(yvals, obj[[i]]$z,
                                        na.action=na.omit)
            avals <- rbind(avals,as.data.frame(predict(sp)))
            avals <- avals[order(avals$x),]
            bsp <- try(splines::backSpline(sp),silent=TRUE)
            bsp.OK <- !inherits(class(bsp),"try-error")
            if (bsp.OK) {
                predback <- function(y) { predict(bsp,y)$y }
            } else { ## backspline failed
                warning("backspline failed: using uniroot(), confidence limits may be unreliable")
                ## what do we do?
                ## attempt to use uniroot
                predback <- function(y) {
                    pfun0 <- function(z1) {
                        t1 <- try(uniroot(function(z) {
                            predict(sp,z)$y-z1
                        }, range(obj[[i]]$par.vals[,nm[i]])),silent=TRUE)
                        if (inherits(t1,"try-error")) NA else t1$root
                    }
                    sapply(y,pfun0)
                }
            }
        }
        ## </FIXME>
        if (no.xlim) xlim <- sort(predback(c(-mlev, mlev)))
        xvals <- obj[[i]]$par.vals[,nm[i]]
        if (is.na(xlim[1]))
            xlim[1] <- min(xvals)
        if (is.na(xlim[2]))
            xlim[2] <- max(xvals)
        if (absVal) {
            if (!add) {
                if (no.ylim) ylim <- c(0,mlev)
                plot(abs(obj[[i]]$z) ~ xvals, 
                     xlab = xlabs[i],
                     ylab = if (missing(ylab)) expression(abs(z)) else ylab,
                     xlim = xlim, ylim = ylim,
                     type = "n", main=main[i], ...)
            }
            avals$y <- abs(avals$y)
            lines(avals, col = col.prof, lty=lty.prof)
            if (show.points) points(yvals,abs(obj[[i]]$z))
        } else { ## not absVal
            if (!add) {
                if (no.ylim) ylim <- c(-mlev,mlev)
                plot(obj[[i]]$z ~ xvals,  xlab = xlabs[i],
                     ylim = ylim, xlim = xlim,
                     ylab = if (missing(ylab)) expression(z) else ylab,
                     type = "n", main=main[i], ...)
            }
            lines(avals, col = col.prof, lty=lty.prof)
            if (show.points) points(yvals,obj[[i]]$z)
        }
        x0 <- predback(0)
        abline(v = x0, h=0, col = col.minval, lty = lty.minval)
        for (j in 1:length(levels)) {
            lev <- levels[j]
            confstr.lev <- confstr[j]
            ## Note: predict may return NA if we didn't profile
            ## far enough in either direction. That's OK for the
            ## "h" part of the plot, but the horizontal line made
            ## with "l" disappears.
            pred <- predback(c(-lev, lev))
            ## horizontal
            if (absVal) levs=rep(lev,2) else levs=c(-lev,lev)
            lines(pred, levs, type = "h", col = col.conf, lty = 2)
            ## vertical
            pred <- ifelse(is.na(pred), xlim, pred)
            if (absVal) {
                lines(pred, rep(lev, 2), type = "l", col = col.conf, lty = lty.conf)
            } else {
                lines(c(x0,pred[2]), rep(lev, 2), type = "l", col = col.conf, lty = lty.conf)
                lines(c(pred[1],x0), rep(-lev, 2), type = "l", col = col.conf, lty = lty.conf)
            }
            if (plot.confstr) {
                text(labels=confstr.lev,x=x0,y=lev,col=col.conf)
            }
        } ## loop over levels
    } ## loop over variables
    ## par(opar)
})