R/plotConf.R

Defines functions plotConf

Documented in plotConf

##' Plot regression line (with interactions) and partial residuals.
##'
##' @title Plot regression lines
##' @param model Model object (e.g. \code{lm})
##' @param var1 predictor (Continuous or factor)
##' @param var2 Factor that interacts with \code{var1}
##' @param data data.frame to use for prediction (model.frame is used as default)
##' @param ci.lty Line type for confidence limits
##' @param ci Boolean indicating wether to draw pointwise 95\% confidence limits
##' @param level Level of confidence limits (default 95\%)
##' @param pch Point type for partial residuals
##' @param lty Line type for estimated regression lines
##' @param lwd Line width for regression lines
##' @param npoints Number of points used to plot curves
##' @param xlim Range of x axis
##' @param col Color (for each level in \code{var2})
##' @param colpt Color of partial residual points
##' @param alpha Alpha level
##' @param cex Point size
##' @param delta For categorical \code{var1}
##' @param centermark For categorical \code{var1}
##' @param jitter For categorical \code{var1}
##' @param cidiff For categorical \code{var1}
##' @param mean For categorical \code{var1}
##' @param legend Boolean (add legend)
##' @param trans Transform estimates (e.g. exponential)
##' @param partres Boolean indicating whether to plot partial residuals
##' @param partse .
##' @param labels Optional labels of \code{var2}
##' @param vcov Optional variance estimates
##' @param predictfun Optional predict-function used to calculate confidence limits and predictions
##' @param plot If FALSE return only predictions and confidence bands
##' @param new If FALSE add to current plot
##' @param \dots additional arguments to lower level functions
##' @return list with following members:
##' \item{x}{Variable on the x-axis (\code{var1})}
##' \item{y}{Variable on the y-axis (partial residuals)}
##' \item{predict}{Matrix with confidence limits and predicted values}
##' @author Klaus K. Holst
##' @seealso \code{termplot}
##' @aliases plotConf
##' @export
##' @examples
##' n <- 100
##' x0 <- rnorm(n)
##' x1 <- seq(-3,3, length.out=n)
##' x2 <- factor(rep(c(1,2),each=n/2), labels=c("A","B"))
##' y <- 5 + 2*x0 + 0.5*x1 + -1*(x2=="B")*x1 + 0.5*(x2=="B") + rnorm(n, sd=0.25)
##' dd <- data.frame(y=y, x1=x1, x2=x2)
##' lm0 <- lm(y ~ x0 + x1*x2, dd)
##' plotConf(lm0, var1="x1", var2="x2")
##' abline(a=5,b=0.5,col="red")
##' abline(a=5.5,b=-0.5,col="red")
##' ### points(5+0.5*x1 -1*(x2=="B")*x1 + 0.5*(x2=="B") ~ x1, cex=2)
##'
##' data(iris)
##' l <- lm(Sepal.Length ~ Sepal.Width*Species,iris)
##' plotConf(l,var2="Species")
##' plotConf(l,var1="Sepal.Width",var2="Species")
##' 
##' \dontrun{
##' ## lme4 model
##' dd$Id <- rbinom(n, size = 3, prob = 0.3)
##' lmer0 <- lme4::lmer(y ~ x0 + x1*x2 + (1|Id), dd)
##' plotConf(lmer0, var1="x1", var2="x2")
##' }
##' @keywords hplot regression
plotConf <- function(model,
                     var1=NULL,
                     var2=NULL,
                     data=NULL,
                     ci.lty=0,
                     ci=TRUE, level=0.95,
                     pch=16, lty=1, lwd=2,
                     npoints=100,
                     xlim,
                     col=NULL,
                     colpt,
                     alpha=0.5,
                     cex=1,
                     delta=0.07,
                     centermark=0.03,
                     jitter=0.2,
                     cidiff=FALSE,
                     mean=TRUE,
                     legend=ifelse(is.null(var1),FALSE,"topright"),
                     trans=function(x) {x},
                     partres=inherits(model,"lm"),
                     partse=FALSE,
                     labels,
                     vcov,
                     predictfun,
                     plot=TRUE,
                     new=TRUE,
                     ...) {


    if (inherits(model,"formula")) model <- lm(model,data=data,...)
    if (inherits(model,"lmerMod")) {
        intercept <- lme4::fixef(model)["(Intercept)"]
    } else {
        intercept <- coef(model)["(Intercept)"]
    }
    if (is.na(intercept)) intercept <- 0

    if (is.null(data)) {
        curdata <- get_all_vars(model,data=model.frame(model))
    } else {
        curdata <- get_all_vars(formula(model), data=data)
    }
    
    if (inherits(model,"lmerMod")) {
      curdata0 <- model.frame(model, data = data, fixed.only = FALSE)
    } else {
      curdata0 <- model.frame(model,data) ## Checking for factors
    }
    
    
    if (is.null(var1) && is.null(var2)) {
        var1 <- colnames(curdata)[2]
        var10 <- colnames(curdata0)[2]
    } else var10 <- var1
        
    responseorig <- colnames(curdata)[1]
    if (inherits(curdata0[,var10],c("character","factor"))) {
        curdata <- curdata0
        var2 <- var10; var1 <- NULL
    }
    dots <- list(...)


    response <- all.vars(formula(model))[1]
    cname <- colnames(curdata)[-1]
    if (!is.factor(curdata[,var2]) & !is.null(var2)) {
        curdata[,var2] <- as.factor(curdata[,var2])
        colnames(curdata)[1] <- response
        model <- update(model,as.formula(paste(response,"~.")),data=curdata)
    }
    thelevels <- levels(curdata[,var2])
    if (missing(labels)) labels <- thelevels

    k <- ifelse(is.null(var2),1,length(thelevels))
    if (is.null(col)) {
        col <- c("black","darkblue","darkred","goldenrod","mediumpurple",
                 "seagreen","aquamarine3","violetred1","salmon1",
                 "lightgoldenrod1","darkorange2","firebrick1","violetred1", "gold")
    }

    if (missing(xlim)) {
        if (!is.null(var1))
            xlim <- range(curdata[,var1])
        else
            xlim <- c(0,length(thelevels))+0.5
    }
    dots$xlim <- xlim

    if (is.null(var1) & !is.null(var2)) {
        ##npoints <- 1
        x <- unique(curdata[,var2])
        npoints <- 1#length(x)
    } else {
        x <- seq(xlim[1], xlim[2], length.out=npoints)
    }
    xx <- c()

    newdata <- data.frame(id=seq(npoints))
    partdata <- curdata
    var1.idx <- 0
    ii <- 1
    for (nn in cname) {
        ii <- ii+1
        v <- curdata[,nn]
        if (!is.null(var1) && nn==var1) {
            var1.idx <- ii
            newdata <- cbind(newdata, rep(x, k))
            partdata[,nn] <- 0
        } else {
            if (is.factor(v)) {
                if (nn%in%var2) {
                    newdata <- cbind(newdata, factor(rep(levels(v), each=npoints),levels=thelevels))
                    partdata[,nn] <- factor(rep(levels(v)[1], nrow(partdata)),levels=levels(v))
                } else {
                    newdata <- cbind(newdata, factor(rep(levels(v)[1], k*npoints),
                                                     levels=levels(v)))
                }
            } else {
                if (is.logical(v))
                    newdata <- cbind(newdata,FALSE)
                else
                    newdata <- cbind(newdata,0)
            }
        }
    };

    colnames(newdata) <- c("_id", cname)

    partdata[,response] <- newdata[,response] <- 0
    atr <- c("terms")
    attributes(newdata)[atr] <- attributes(curdata)[atr]
    attributes(partdata)[atr] <- attributes(partdata)[atr]
    Y <- model.frame(model)[,1]
    if(inherits(Y,"Surv")) Y <- Y[,1]
    XX <- model.matrix(formula(terms(model)),data=newdata)
    if (inherits(model,"lmerMod")) {
        bb <- lme4::fixef(model)
    } else {
        bb <- coef(model)
    }
    if (!missing(vcov)) SS <- vcov else {
        if (inherits(model,"geeglm")) {
            SS <- (summary(model)$cov.unscaled)
        } else {
            SS <- as.matrix(stats::vcov(model))
        }
    }
    bidx <- which(apply(XX,2,function(x) !all(x==0)))
    notbidx <- setdiff(seq(length(bb)),bidx)
    bb0 <- bb; bb0[notbidx] <- 0
    myse <- apply(XX[,bidx,drop=FALSE],1,function(x) rbind(x)%*%SS[bidx,bidx,drop=FALSE]%*%cbind(x))^.5
    ci.all <- list(fit=XX%*%bb0,se.fit=myse)
    z <- qnorm(1-(1-level)/2)
    ci.all$fit <- cbind(ci.all$fit,ci.all$fit-z*ci.all$se.fit,ci.all$fit+z*ci.all$se.fit)

    if (!missing(predictfun)) {
        R <- Y-predict(model, newdata=partdata)
        ci.all <- predict(model, newdata=newdata, se.fit=TRUE, interval = "confidence", level=level,...)
    } else {
        XX0 <- model.matrix(formula(terms(model)),data=partdata)
        R <- Y-XX0%*%bb
    }
    if (inherits(model,"lmerMod")) {
        uz <- as.matrix(unlist(lme4::ranef(model))%*%do.call(rbind,lme4::getME(model,"Ztlist")))[1,]
        R <- R-uz
    }

    pr <- trans(intercept + R)
    if (is.na(intercept)) {
        intercept <- 0
        if (!is.null(var2)) {
            intercept <- coef(model)[paste0(var2,thelevels)][as.numeric(curdata[,var2])]
        }
    }

    if (is.null(dots$ylim)) {
        if (partres) {
            if (cidiff)
                dots$ylim <- range(pr)
            else
                dots$ylim <- range(trans(c(ci.all$fit)),pr)
        }
        else
            dots$ylim <- trans(range(ci.all$fit))
    }
    if (is.null(dots$ylab))
        dots$ylab <- responseorig

    if (is.null(var1)) {
        dots$axes=FALSE
        if (is.null(dots$xlab))
            dots$xlab <- ""
    } else  {
        if (is.null(dots$xlab))
            dots$xlab <- var1
    }

    if (!plot) return(list(x=x, y=pr, predict=ci.all, predict.newdata=newdata))

    plot.list <- c(x=0,y=0,type="n",dots)
    if (new) {
        do.call(graphics::plot, plot.list)
        if (is.null(var1)) {
            box()
            axis(2)
            axis(1,at=seq(length(thelevels)),labels)
        }
    }

    col.trans <- Col(col,alpha)

    Wrap <- function(k,n) { (seq_len(k)-1)%%n +1 }
    col.i <- Wrap(k,length(col));  col.k <- col[col.i];
    lty.k <- lty[Wrap(k,length(lty))]
    pch.k <- pch[Wrap(k,length(pch))]

    if (!is.null(var1)) {
        for (i in seq_len(k)) {
            ci0 <- trans(ci.all$fit[(npoints*(i-1)+1):(i*npoints),])
            y <- ci0[,1]; yu <- ci0[,3]; yl <- ci0[,2]
            lines(y ~ x, col=col.k[i], lwd=lwd, lty=lty.k[i])

            if (ci) {
                lines(yl ~ x, lwd=1, col=col.k[i], lty=ci.lty)
                lines(yu ~ x, lwd=1, col=col.k[i], lty=ci.lty)
                xx <- c(x, rev(x))
                yy <- c(yl, rev(yu))
                polygon(xx,yy, col=col.trans[col.i[i]], lty=0)
            }

        }
    }
    ii <- as.numeric(curdata[,var2])

    if (is.null(var1)) {
        xx <- curdata[,var2]
        x <- jitter(as.numeric(xx),jitter)
        if (missing(colpt)) colpt <- Col(col[1],alpha)
        if (partres>0)
            points(pr ~ x,pch=pch[1], col=colpt[1], cex=cex, ...)
        mycoef <- bb[paste0(var2,thelevels)][-1]
        if (inherits(model,c("lm","glm")))
            myconf <- confint(model)[paste0(var2,thelevels)[-1],,drop=FALSE]
        else {
            myconf <- matrix(mycoef,ncol=2,nrow=length(mycoef))
            myconf <- myconf + qnorm(0.975)*cbind((diag(as.matrix(SS))[-1])^0.5)%x%cbind(-1,1)
        }
        for (pos in seq(k)) {
            if (cidiff) {
                if (pos>1) {
                    ci0 <- trans(intercept+myconf[pos-1,])
                    yl <- ci0[1]; yu <- ci0[2]; y <- trans(intercept+mycoef[pos-1])
                } else {
                    yu <- yl <- NULL; y <- trans(intercept)
                }
            } else if (partse) {
                y0 <- pr[xx==levels(xx)[pos]]
                ci0 <- confint(lm(y0~1))
                yl <- ci0[1]; yu <- ci0[2]; y <- trans(mean(y0))
            } else {
                ci0 <- rbind(trans(ci.all$fit[(npoints*(pos-1)+1):(pos*npoints),]))
                y <- ci0[,1]; yu <- ci0[,3]; yl <- ci0[,2]
            }
            if (!mean) y <- NULL
            confband(pos,yl,yu,delta=delta,center=y,centermark=centermark,col=col[1],lwd=lwd[1],lty=lty[1],cex=cex)
        }
    } else {
        if (partres) {
            xx <- curdata[,var1]
            if (!missing(colpt)) {
                points(pr ~ xx, col=colpt, cex=cex, pch=pch[1],...)
            } else {
                if (!is.null(var2))
                    points(pr ~ xx, col=col.k[ii], pch=pch.k[ii], cex=cex, ...)
                else
                    points(pr ~ xx, col=col[1], pch=pch[1], cex=cex,...)
            }
        }
    }

    if (k>1 && legend!=FALSE) {
        if (length(lty)>1)
            legend(legend, legend=thelevels, col=col.k, pch=pch.k, bg="white", lty=lty.k,cex=cex)
        else
            legend(legend, legend=thelevels, col=col.k, pch=pch.k, bg="white",cex=cex)
    }

    invisible(list(x=xx, y=pr, predict=ci.all, predict.newdata=newdata))
}
kkholst/lava documentation built on Sept. 6, 2021, 11:36 p.m.