R/lin.test.R

In DoseResponse/drc: Analysis of Dose-Response Data

"lin.test" <- function(object, noksSim = 20, seed = 20070325, plotit = TRUE, log = "", bp = 1e-2, 
xlab, ylab, ylim, ...)
{
    ## Setting seed
    if (!is.null(seed)) {set.seed(seed)}

    ## Retrieving relevant quantities from model fit
    parVec <- coef(object)
    lenpv <- length(parVec)    
    resVec <- residuals(object)
    resVar <- summary(object)$resVar
    xVec <- object$"data"[, 1]
    
    ## Sorting x values
    orderX <- order(xVec)
    xVec <- xVec[orderX]
    resVec <- resVec[orderX]    
    
    lenxv <- length(xVec)
    noObs <- lenxv 
    derVec <- object$"fct"$"deriv1"(xVec, matrix(parVec, lenxv, lenpv, byrow = TRUE))

    ## Calculating W tilde
    eta <- matrix(0, lenxv, lenpv)
    for (i in 1:lenpv)
    {
        eta[, i] <- cumsum(derVec[, i])  # /lenxv
    }
#    term2 <- eta %*% vcov(object) %*% t(matrix(resVec/resVar, 1, lenxv) %*% derVec)

    ## Adjusting in case of replicates
    lenuxv <- length(unique(xVec))
    if (lenuxv < lenxv)
    {
        tempeta <- matrix(0, lenuxv, lenpv)
        for (i in 1:lenpv)
        {
            tempeta[, i] <- as.vector(unlist(tapply(eta[, i], xVec, tail, 1)))
        }
        eta <- tempeta
        lenxv <- lenuxv
        repAdjust <- TRUE
        uxVec <- unique(xVec)
    }  else {
        repAdjust <- FALSE
    }

    ## Generating realisations from the limit distribution
    maxVec <- rep(0, noksSim)
    tempMat <- eta %*% vcov(object)         
    wtMat <- matrix(0, lenxv, noksSim)
    for (i in 1:noksSim)
    {
        rnVec <- rnorm(noObs)
#        wtMat[, i] <- (cumsum(resVec * rnVec) - term2 * rnVec)/sqrt(noObs)
        if (repAdjust)
        {
            term1 <- as.vector(unlist(tapply(cumsum(resVec * rnVec), xVec, tail, 1)))
        } else {
            term1 <- cumsum(resVec * rnVec)
        }
#        print(dim(tempMat))
#        print(dim(matrix(resVec*rnVec/resVar, 1, noObs)))
#        print(dim(derVec))
        term2 <- tempMat %*% t(matrix(resVec*rnVec/resVar, 1, noObs) %*% derVec)
        wti <- (term1 - term2)/sqrt(noObs) 
        wtMat[, i] <- wti
        maxVec[i] <- max(abs(wti))
    }

    ## Calculating the observed process
    if (repAdjust)
    {
        cumRes <- as.vector(unlist(tapply(cumsum(resVec), xVec, tail, 1)))/sqrt(noObs)
        xVec <- unique(xVec)
    } else {
        cumRes <- cumsum(resVec)/sqrt(noObs)
    }

    ## Creating graphical display
    if (plotit)
    {
        ## Finding ranges for x and y in plot
        maxY <- max(c(max(cumRes), max(apply(wtMat, 1, max))))
        minY <- min(c(min(cumRes), min(apply(wtMat, 1, min))))
        
        ## Setting range right in case of log scale
        if (identical(log, "x"))
        {
            lowLim <- bp
            llInd <- xVec > bp
            yStart <- sum(!llInd)
        } else {
            lowLim <- 0
            llInd <- rep(TRUE, length(xVec))
            yStart <- 1
        }
        
        ## Plotting observed and simulated processes
        if (missing(ylim))
        {
            yLim <- c(minY, maxY) 
        } else {
            yLim <- ylim
        }
        
        plot(c(lowLim, xVec[llInd]), c(cumRes[yStart], cumRes[llInd]), type = "s", 
        ylim = yLim, lwd = 2,
        xlab = ifelse(missing(xlab), names(object$"data")[1], xlab), 
        ylab = ifelse(missing(ylab), "Cumulative residuals", ylab), log = log, ...)
        for (i in 1:noksSim)
        {
            lines(c(lowLim, xVec[llInd]), c(wtMat[yStart, i], wtMat[llInd, i]), lty = 2, type = "s")
        }
    }
    
    ## Returning p value for KS test
    sum(max(abs(cumRes)) < maxVec)/noksSim
}