#  File src/library/stats/R/mantelhaen.test.R
#  Part of the R package, https://www.R-project.org
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mantelhaen.test <-
function(x, y = NULL, z = NULL,
         alternative = c("two.sided", "less", "greater"),
         correct = TRUE, exact = FALSE, conf.level = 0.95)
{
    DNAME <- deparse1(substitute(x))
    if(is.array(x)) {
        if(length(dim(x)) == 3L) {
            if(anyNA(x)) stop("NAs are not allowed")
            if(any(dim(x) < 2L)) stop("each dimension in table must be >= 2")
        }
        else
            stop("'x' must be a 3-dimensional array")
    }
    else {
        if(is.null(y)) stop("if 'x' is not an array, 'y' must be given")
        if(is.null(z)) stop("if 'x' is not an array, 'z' must be given")
        if(any(diff(c(length(x), length(y), length(z))) != 0L ))
            stop("'x', 'y', and 'z' must have the same length")
        DNAME <- paste(DNAME, "and", deparse1(substitute(y)), "and",
                       deparse1(substitute(z)))
        OK <- complete.cases(x, y, z)
        x <- factor(x[OK])
        y <- factor(y[OK])
        if((nlevels(x) < 2L) || (nlevels(y) < 2L))
            stop("'x' and 'y' must have at least 2 levels")
        else
            x <- table(x, y, z[OK])
    }

    if(any(apply(x, 3L, sum) < 2))
        stop("sample size in each stratum must be > 1")

    I <- dim(x)[1L]
    J <- dim(x)[2L]
    K <- dim(x)[3L]

    if((I == 2) && (J == 2)) {
        ## 2 x 2 x K case
        alternative <- match.arg(alternative)
        if(!missing(conf.level) &&
           (length(conf.level) != 1 || !is.finite(conf.level) ||
            conf.level < 0 || conf.level > 1))
            stop("'conf.level' must be a single number between 0 and 1")

        NVAL <- c("common odds ratio" = 1)

        if(!exact) {
            ## Classical Mantel-Haenszel 2 x 2 x K test
            s.x <- apply(x, c(1L, 3L), sum)
            s.y <- apply(x, c(2L, 3L), sum)
            n <- as.double(apply(x, 3L, sum)) # avoid overflows below
            DELTA <- sum(x[1, 1, ] - s.x[1, ] * s.y[1, ] / n)
	    YATES <- if(correct && (abs(DELTA) >= .5)) .5 else 0
            STATISTIC <- ((abs(DELTA) - YATES)^2 /
                          sum(apply(rbind(s.x, s.y), 2L, prod)
                              / (n^2 * (n - 1))))
            PARAMETER <- 1
            if (alternative == "two.sided")
                PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
            else {
                z <- sign(DELTA) * sqrt(STATISTIC)
                PVAL <- pnorm(z, lower.tail = (alternative == "less"))
            }

            names(STATISTIC) <- "Mantel-Haenszel X-squared"
            names(PARAMETER) <- "df"
            METHOD <- paste("Mantel-Haenszel chi-squared test",
                            if(YATES) "with" else "without",
                            "continuity correction")
            s.diag <- sum(x[1L, 1L, ] * x[2L, 2L, ] / n)
            s.offd <- sum(x[1L, 2L, ] * x[2L, 1L, ] / n)
            ## Mantel-Haenszel (1959) estimate of the common odds ratio.
            ESTIMATE <- s.diag / s.offd
            ## Robins et al. (1986) estimate of the standard deviation
            ## of the log of the Mantel-Haenszel estimator.
            sd <-
                sqrt(  sum((x[1L,1L,] + x[2L,2L,]) * x[1L,1L,] * x[2L,2L,]
                           / n^2)
                     / (2 * s.diag^2)
                     + sum((  (x[1L,1L,] + x[2L,2L,]) * x[1L,2L,] * x[2L,1L,]
                            + (x[1L,2L,] + x[2L,1L,]) * x[1L,1L,] * x[2L,2L,])
                           / n^2)
                     / (2 * s.diag * s.offd)
                     + sum((x[1L,2L,] + x[2L,1L,]) * x[1L,2L,] * x[2L,1L,]
                           / n^2)
                     / (2 * s.offd^2))
            CINT <-
                switch(alternative,
                       less = c(0, ESTIMATE * exp(qnorm(conf.level) * sd)),
                       greater = c(ESTIMATE * exp(qnorm(conf.level,
                                   lower.tail = FALSE) * sd), Inf),
                       two.sided = {
                           ESTIMATE * exp(c(1, -1) *
                                          qnorm((1 - conf.level) / 2) * sd)
                       })
            RVAL <- list(statistic = STATISTIC,
                         parameter = PARAMETER,
                         p.value = PVAL)
        }
        else {
            ## Exact inference for the 2 x 2 x k case can be carried out
            ## conditional on the strata margins, similar to the case
            ## for Fisher's exact test (k = 1).  Again, the distribution
            ## of S (in our case, sum(x[2, 1, ]) to be consistent with
            ## the notation in Mehta et al. (1985), is of the form
            ##    P(S = s) \propto d(s) * or^s,   lo <= s <= hi
            ## where or is the common odds ratio in the k tables (and
            ## d(.) is a product hypergeometric distribution).

            METHOD <- paste("Exact conditional test of independence",
                            "in 2 x 2 x k tables")
            mn <- apply(x, c(2L, 3L), sum)
            m <- mn[1L, ]
            n <- mn[2L, ]
            t <- apply(x, c(1L, 3L), sum)[1L, ]
            s <- sum(x[1L, 1L, ])
            lo <- sum(pmax(0, t - n))
            hi <- sum(pmin(m, t))

            support <- lo : hi
            ## Density of the *central* product hypergeometric
            ## distribution on its support: store for once as this is
            ## needed quite a bit.

            dc <- .Call(C_d2x2xk, K, m, n, t, hi - lo + 1L)
            logdc <- log(dc)

            dn2x2xk <- function(ncp) {
                ## Does not work for boundary values for ncp (0, Inf)
                ## but it does not need to.
                if(ncp == 1) return(dc)
                d <- logdc + log(ncp) * support
                d <- exp(d - max(d))    # beware of overflow
                d / sum(d)
            }
            mn2x2xk <- function(ncp) {
                if(ncp == 0)
                    return(lo)
                if(ncp == Inf)
                    return(hi)
                sum(support * dn2x2xk(ncp))
            }
            pn2x2xk <- function(q, ncp = 1, upper.tail = FALSE) {
                if(ncp == 0) {
                    as.numeric(if(upper.tail) q <= lo else q >= lo)
                }
                else if(ncp == Inf)
                    as.numeric(if(upper.tail) q <= hi else q >= hi)
                else {
                    d <- dn2x2xk(ncp)
                    sum(d[if(upper.tail) support >= q else support <= q])
                }
            }

            ## Determine the p-value.
            PVAL <-
                switch(alternative,
                       less = pn2x2xk(s, 1),
                       greater = pn2x2xk(s, 1, upper.tail = TRUE),
                       two.sided = {
                           ## Note that we need a little fuzz.
                           relErr <- 1 + 10 ^ (-7)
                           d <- dc      # same as dn2x2xk(1)
                           sum(d[d <= d[s - lo + 1] * relErr])
                       })

            ## Determine the MLE for ncp by solving E(S) = s, where the
            ## expectation is with respect to the above distribution.
            mle <- function(x) {
                if(x == lo)
                    return(0)
                if(x == hi)
                    return(Inf)
                mu <- mn2x2xk(1)
                if(mu > x)
                    uniroot(function(t) mn2x2xk(t) - x,
                            c(0, 1))$root
                else if(mu < x)
                    1 / uniroot(function(t) mn2x2xk(1/t) - x,
                                c(.Machine$double.eps, 1))$root
                else
                    1
            }
            ESTIMATE <- mle(s)

            ## Determine confidence intervals for the odds ratio.
            ncp.U <- function(x, alpha) {
                if(x == hi)
                    return(Inf)
                p <- pn2x2xk(x, 1)
                if(p < alpha)
                    uniroot(function(t) pn2x2xk(x, t) - alpha,
                            c(0, 1))$root
                else if(p > alpha)
                    1 / uniroot(function(t) pn2x2xk(x, 1/t) - alpha,
                                c(.Machine$double.eps, 1))$root
                else
                    1
            }
            ncp.L <- function(x, alpha) {
                if(x == lo)
                    return(0)
                p <- pn2x2xk(x, 1, upper.tail = TRUE)
                if(p > alpha)
                    uniroot(function(t)
                            pn2x2xk(x, t, upper.tail = TRUE) - alpha,
                            c(0, 1))$root
            else if (p < alpha)
                1 / uniroot(function(t)
                            pn2x2xk(x, 1/t, upper.tail = TRUE) - alpha,
                            c(.Machine$double.eps, 1))$root
            else
                1
            }
            CINT <- switch(alternative,
                           less = c(0, ncp.U(s, 1 - conf.level)),
                           greater = c(ncp.L(s, 1 - conf.level), Inf),
                           two.sided = {
                               alpha <- (1 - conf.level) / 2
                               c(ncp.L(s, alpha), ncp.U(s, alpha))
                           })

            STATISTIC <- c(S = s)
            RVAL <- list(statistic = STATISTIC,
                         p.value = PVAL)
        }

        names(ESTIMATE) <- names(NVAL)
        attr(CINT, "conf.level") <- conf.level
        RVAL <- c(RVAL,
                  list(conf.int = CINT,
                       estimate = ESTIMATE,
                       null.value = NVAL,
                       alternative = alternative))
    }
    else {
        ## Generalized Cochran-Mantel-Haenszel I x J x K test
        ## Agresti (1990), pages 234--235.
        ## Agresti (2002), pages 295ff.
        ## Note that n in the reference is in column-major order.
        ## (Thanks to Torsten Hothorn for spotting this.)
        df <- (I - 1) * (J - 1)
        n <- m <- double(length = df)
        V <- matrix(0, nrow = df, ncol = df)
        for (k in 1 : K) {
            f <- x[ , , k]              # frequencies in stratum k
            ntot <- as.double(sum(f))   # n_{..k}   (and avoid overflow)
            rowsums <- rowSums(f)[-I]   # n_{i.k}, i = 1 to I-1
            colsums <- colSums(f)[-J]   # n_{.jk}, j = 1 to J-1
            n <- n + c(f[-I, -J])
            m <- m + c(outer(rowsums, colsums)) / ntot
            V <- V + (kronecker(diag(ntot * colsums, nrow = J - 1L)
                                - outer(colsums, colsums),
                                diag(ntot * rowsums, nrow = I - 1L)
                                - outer(rowsums, rowsums))
                      / (ntot^2 * (ntot - 1)))
        }
        n <- n - m
        STATISTIC <- c(crossprod(n, qr.solve(V, n)))
        PARAMETER <- df
        PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
        names(STATISTIC) <- "Cochran-Mantel-Haenszel M^2"
        names(PARAMETER) <- "df"
        METHOD <- "Cochran-Mantel-Haenszel test"
        RVAL <- list(statistic = STATISTIC,
                     parameter = PARAMETER,
                     p.value = PVAL)
    }
    structure(c(RVAL,
                list(method = METHOD,
                     data.name = DNAME)),
              class = "htest")
}
