pbmcp:

In musto101/wilcox_R: What the package does (short line)

Usage

pbmcp(x, alpha = 0.05, nboot = NA, grp = NA, est = onestep, con = 0, bhop = FALSE, SEED = TRUE, ...)

Arguments

x
alpha
nboot
grp
est
con
bhop
SEED
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, alpha = 0.05, nboot = NA, grp = NA, est = onestep, 
    con = 0, bhop = FALSE, SEED = TRUE, ...) 
{
    okay = FALSE
    if (identical(est, onestep)) 
        okay = TRUE
    if (identical(est, mom)) 
        okay = TRUE
    if (!okay) 
        stop("For estimators other than onestep and mom, use linconpb")
    con <- as.matrix(con)
    if (is.matrix(x)) 
        x <- listm(x)
    if (!is.list(x)) 
        stop("Data must be stored in list mode or in matrix mode.")
    if (!is.na(sum(grp))) {
        xx <- list()
        for (i in 1:length(grp)) xx[[i]] <- x[[grp[i]]]
        x <- xx
    }
    J <- length(x)
    tempn <- 0
    mvec <- NA
    for (j in 1:J) {
        temp <- x[[j]]
        temp <- temp[!is.na(temp)]
        tempn[j] <- length(temp)
        x[[j]] <- temp
        mvec[j] <- est(temp, ...)
    }
    nmax = max(tempn)
    Jm <- J - 1
    if (sum(con^2) == 0) {
        ncon <- (J^2 - J)/2
        con <- matrix(0, J, ncon)
        id <- 0
        for (j in 1:Jm) {
            jp <- j + 1
            for (k in jp:J) {
                id <- id + 1
                con[j, id] <- 1
                con[k, id] <- 0 - 1
            }
        }
    }
    ncon <- ncol(con)
    if (nrow(con) != J) {
        stop("Something is wrong with con; the number of rows does not match the number of groups.")
    }
    if (is.na(nboot)) {
        nboot <- 5000
        if (J <= 8) 
            nboot <- 4000
        if (J <= 3) 
            nboot <- 2000
    }
    if (!bhop) {
        if (!identical(est, onestep)) 
            print("When est is not equal to  onestep, suggest using bhop=TRUE")
        if (alpha == 0.05) {
            dvec <- c(0.025, 0.025, 0.0169, 0.0127, 0.0102, 0.00851, 
                0.0073, 0.00639, 0.00568, 0.00511)
            if (ncon > 10) {
                avec <- 0.05/c(11:ncon)
                dvec <- c(dvec, avec)
            }
        }
        if (alpha == 0.01) {
            dvec <- c(0.005, 0.005, 0.00334, 0.00251, 0.00201, 
                0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
            if (nmax >= 100) 
                dvec[1] = 0.01
            if (ncon > 10) {
                avec <- 0.01/c(11:ncon)
                dvec <- c(dvec, avec)
            }
        }
        if (alpha != 0.05 && alpha != 0.01) {
            dvec <- alpha/c(1:ncon)
            dvec[1] <- alpha/2
        }
        dvec <- 2 * dvec
    }
    if (nmax > 80) {
        if (alpha == 0.05) {
            dvec <- c(0.05, 0.025, 0.0169, 0.0127, 0.0102, 0.00851, 
                0.0073, 0.00639, 0.00568, 0.00511)
            if (ncon > 10) {
                avec <- 0.05/c(11:ncon)
                dvec <- c(dvec, avec)
            }
        }
        if (alpha == 0.01) {
            dvec <- c(0.01, 0.005, 0.00334, 0.00251, 0.00201, 
                0.00167, 0.00143, 0.00126, 0.00112, 0.00101)
            if (ncon > 10) {
                avec <- 0.01/c(11:ncon)
                dvec <- c(dvec, avec)
            }
        }
        if (alpha != 0.05 && alpha != 0.01) {
            dvec <- alpha/c(1:ncon)
        }
    }
    if (bhop) 
        dvec <- (ncon - c(1:ncon) + 1) * alpha/ncon
    bvec <- matrix(NA, nrow = J, ncol = nboot)
    if (SEED) 
        set.seed(2)
    for (j in 1:J) {
        data <- matrix(sample(x[[j]], size = length(x[[j]]) * 
            nboot, replace = TRUE), nrow = nboot)
        bvec[j, ] <- apply(data, 1, est, ...)
    }
    chkna = sum(is.na(bvec))
    if (chkna > 0) {
        print("Bootstrap estimates of location could not be computed")
        print("This can occur when using an M-estimator")
        print("Might try est=tmean")
    }
    test <- NA
    bcon <- t(con) %*% bvec
    tvec <- t(con) %*% mvec
    for (d in 1:ncon) {
        test[d] <- (sum(bcon[d, ] > 0) + 0.5 * sum(bcon[d, ] == 
            0))/nboot
        if (test[d] > 0.5) 
            test[d] <- 1 - test[d]
    }
    test <- 2 * test
    output <- matrix(0, ncon, 6)
    dimnames(output) <- list(NULL, c("con.num", "psihat", "p.value", 
        "p.crit", "ci.lower", "ci.upper"))
    temp2 <- order(0 - test)
    zvec <- dvec[1:ncon]
    sigvec <- (test[temp2] >= zvec)
    output[temp2, 4] <- zvec
    icl <- round(dvec[ncon] * nboot/2) + 1
    icu <- nboot - icl - 1
    for (ic in 1:ncol(con)) {
        output[ic, 2] <- tvec[ic, ]
        output[ic, 1] <- ic
        output[ic, 3] <- test[ic]
        temp <- sort(bcon[ic, ])
        output[ic, 5] <- temp[icl]
        output[ic, 6] <- temp[icu]
    }
    num.sig <- sum(output[, 3] <= output[, 4])
    list(output = output, con = con, num.sig = num.sig)
  }

musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.