boot.comp=function (y, x = NULL, N = NULL, max.comp = 2, B = 100, sig = 0.05, 
    arbmean = TRUE, arbvar = TRUE, mix.type = c("logisregmix", 
        "multmix", "mvnormalmix", "normalmix", "poisregmix", 
        "regmix", "regmix.mixed", "repnormmix"), hist = TRUE, 
    ...) 
{
    mix.type <- match.arg(mix.type)
    k = max.comp
    p = 0
    sigtest = 1
    Q.star = list()
    i = 0
    if (mix.type == "regmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  H0.fit = lm(y ~ x)
                  beta = coef(H0.fit)
                  Q0[i] = as.numeric(logLik(H0.fit))
                  H1.fit = try(regmixEM(y = y, x = x, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) 
                      w = 1
                    else w = 2
                    beta = coef(H0.fit)
                    xbeta = cbind(1, x) %*% beta
                    xy.sd = sqrt(sum(H0.fit$res^2)/(length(y) - 
                      2))
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rnorm(length(y), mean = xbeta, sd = xy.sd)
                  xy.simout = lm(y.sim ~ x)
                  em.out = try(regmixEM(y = y.sim, x = x, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - as.numeric(logLik(xy.simout)))
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(regmixEM(y = y, x = x, k = i, 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  H1.fit = try(regmixEM(y = y, x = x, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    if (arbmean == FALSE) {
                      scale = H0.fit$scale
                      beta = matrix(rep(H0.fit$beta, i), ncol = i)
                    }
                    else {
                      scale = 1
                    }
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) 
                      w = 1
                    else w = 2
                  }
                  beta.new = H0.fit$beta
                  xbeta.new = cbind(1, x) %*% beta.new
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(length(y), size = 1, prob = H0.fit$lambda)
                  if (arbmean == FALSE) {
                    y.sim = sapply(1:length(y), function(i) rnorm(1, 
                      mean = xbeta.new, sd = ((scale * H0.fit$sigma)[wt[, 
                        i] == 1])))
                  }
                  else {
                    if (arbvar == FALSE) {
                      y.sim = sapply(1:length(y), function(i) rnorm(1, 
                        mean = xbeta.new[i, (wt[, i] == 1)], 
                        sd = H0.fit$sigma))
                    }
                    else {
                      y.sim = sapply(1:length(y), function(i) rnorm(1, 
                        mean = xbeta.new[i, (wt[, i] == 1)], 
                        sd = H0.fit$sigma[wt[, i] == 1]))
                    }
                  }
                  em.out.0 = try(regmixEM(y = y.sim, x = x, k = i, 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  em.out.1 = try(regmixEM(y = y.sim, x = x, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "repnormmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  dens = dnorm(y, mean = mean(y), sd = sd(y))
                  Q0[i] = sum(log(dens[dens > 0]))
                  H1.fit = try(repnormmixEM(x = y, k = (i + 1), 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rmvnormmix(nrow(y), mu = rep(mean(y), 
                    ncol(y)), sigma = rep(sd(y), ncol(y)))
                  dens.sim = dnorm(y.sim, mean = mean(y), sd = sd(y))
                  x.simout = sum(log(dens.sim[dens.sim > 0]))
                  em.out = try(repnormmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - x.simout)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(repnormmixEM(x = y, k = i, arbmean = arbmean, 
                    arbvar = arbvar, ...), silent = TRUE)
                  H1.fit = try(repnormmixEM(x = y, k = (i + 1), 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silen = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    if (arbmean == FALSE) 
                      scale = H0.fit$scale
                    else scale = 1
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                  }
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(length(y), size = 1, prob = H0.fit$lambda)
                  if (arbmean == FALSE) {
                    y.sim = sapply(1:ncol(y), function(i) rnorm(nrow(y), 
                      mean = H0.fit$mu, sd = ((scale * H0.fit$sigma)[wt[, 
                        i] == 1])))
                  }
                  else {
                    if (arbvar == FALSE) {
                      y.sim = sapply(1:ncol(y), function(i) rnorm(nrow(y), 
                        mean = H0.fit$mu[wt[, i] == 1], sd = H0.fit$sigma))
                    }
                    else {
                      y.sim = sapply(1:ncol(y), function(i) rnorm(nrow(y), 
                        mean = H0.fit$mu[wt[, i] == 1], sd = H0.fit$sigma[wt[, 
                          i] == 1]))
                    }
                  }
                  em.out.0 = try(repnormmixEM(x = y.sim, k = i, 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  em.out.1 = try(repnormmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "regmix.mixed") {
        if (is.list(y)) {
            if (length(y) != length(x)) 
                stop("Number of elements in lists for x and y must match!")
        }
        tt = sapply(1:length(x), function(i) x[[i]][, 1])
        beta = t(sapply(1:length(y), function(i) lm(y[[i]] ~ 
            x[[i]])$coef))
        y = beta
        mix.type = "mvnormalmix"
    }
    if (mix.type == "mvnormalmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  y.mean = apply(y, 2, mean)
                  y.cov = cov(y)
                  dens = dmvnorm(y, mu = y.mean, sigma = y.cov)
                  Q0[i] = sum(log(dens[dens > 0]))
                  H1.fit = try(mvnormalmixEM(x = y, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) 
                      w = 1
                    else w = 2
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rmvnorm(nrow(y), mu = apply(y, 2, mean), 
                    sigma = y.cov)
                  dens.sim = dmvnorm(y.sim, mu = y.mean, sigma = y.cov)
                  y.simout = sum(log(dens.sim[dens.sim > 0]))
                  em.out = try(mvnormalmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - y.simout)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(mvnormalmixEM(x = y, k = i, ...), 
                    silent = TRUE)
                  H1.fit = try(mvnormalmixEM(x = y, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    if (arbmean == FALSE) {
                      H0.fit$mu = lapply(1:i, function(l) H0.fit$mu)
                    }
                    if (arbvar == FALSE) {
                      H0.fit$sigma = lapply(1:i, function(l) H0.fit$sigma)
                    }
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                  }
                  j <- 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(nrow(y), size = 1, prob = H0.fit$lambda)
                  if (arbmean == FALSE) {
                    y.sim = t(sapply(1:nrow(y), function(i) rmvnorm(1, 
                      mu = H0.fit$mu, sigma = H0.fit$sigma[wt[, 
                        i] == 1][[1]])))
                  }
                  else {
                    if (arbvar == FALSE) {
                      y.sim = t(sapply(1:nrow(y), function(i) rmvnorm(1, 
                        mu = H0.fit$mu[wt[, i] == 1][[1]], sigma = H0.fit$sigma)))
                    }
                    else {
                      y.sim = t(sapply(1:nrow(y), function(i) rmvnorm(1, 
                        mu = H0.fit$mu[wt[, i] == 1][[1]], sigma = H0.fit$sigma[wt[, 
                          i] == 1][[1]])))
                    }
                  }
                  em.out.0 = try(mvnormalmixEM(x = y.sim, k = i, 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  em.out.1 = try(mvnormalmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "normalmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  dens = dnorm(y, mean = mean(y), sd = sd(y))
                  Q0[i] = sum(log(dens[dens > 0]))
                  H1.fit = try(normalmixEM(x = y, k = (i + 1), 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rnorm(length(y), mean = mean(y), sd = sd(y))
                  dens.sim = dnorm(y.sim, mean = mean(y), sd = sd(y))
                  x.simout = sum(log(dens.sim[dens.sim > 0]))
                  em.out = try(normalmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - x.simout)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(normalmixEM(x = y, k = i, arbmean = arbmean, 
                    arbvar = arbvar, ...), silent = TRUE)
                  H1.fit = try(normalmixEM(x = y, k = (i + 1), 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    if (arbmean == FALSE) 
                      scale = H0.fit$scale
                    else scale = 1
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                  }
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(length(y), size = 1, prob = H0.fit$lambda)
                  if (arbmean == FALSE) {
                    y.sim = sapply(1:length(y), function(i) rnorm(1, 
                      mean = H0.fit$mu, sd = ((scale * H0.fit$sigma)[wt[, 
                        i] == 1])))
                  }
                  else {
                    if (arbvar == FALSE) {
                      y.sim = sapply(1:length(y), function(i) rnorm(1, 
                        mean = H0.fit$mu[(wt[, i] == 1)], 
                        sd = H0.fit$sigma))
                    }
                    else {
                      y.sim = sapply(1:length(y), function(i) rnorm(1, 
                        mean = H0.fit$mu[(wt[, i] == 1)], 
                        sd = H0.fit$sigma[wt[, i] == 1]))
                    }
                  }
                  em.out.0 = try(normalmixEM(x = y.sim, k = i, 
                    arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  em.out.1 = try(normalmixEM(x = y.sim, k = (i + 
                    1), arbmean = arbmean, arbvar = arbvar, ...), 
                    silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "multmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  m = apply(y, 1, sum)
                  n.i = apply(y, 2, sum)
                  theta = n.i/sum(n.i)
                  Q0[i] = sum(log(exp(apply(y, 1, ldmult, theta = theta))))
                  H1.fit = try(multmixEM(y = y, k = (i + 1), 
                    ...), silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = matrix(0, ncol = ncol(y), nrow = nrow(y))
                  for (l in 1:length(m)) {
                    y.sim[l, ] <- rmultinom(1, size = m[l], prob = theta)
                  }
                  theta.sim = apply(y.sim, 2, sum)/sum(apply(y.sim, 
                    2, sum))
                  y.simout = sum(log(exp(apply(y.sim, 1, ldmult, 
                    theta = theta))))
                  em.out = try(multmixEM(y = y.sim, k = (i + 
                    1), ...), silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - y.simout)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(multmixEM(y = y, k = i, ...), 
                    silent = TRUE)
                  H1.fit = try(multmixEM(y = y, k = (i + 1), 
                    ...), silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    theta = H0.fit$theta
                    Q0[i] = H0.fit$loglik
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                  }
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(nrow(y), size = 1, prob = H0.fit$lambda)
                  y.sim = t(sapply(1:nrow(y), function(i) rmultinom(1, 
                    size = n.i[i], prob = H0.fit$theta[(wt[, 
                      i] == 1), ])))
                  new.y.sim = 0
                  em.out.0 = try(multmixEM(y = new.y.sim, k = i, 
                    ...), silent = TRUE)
                  em.out.1 = try(multmixEM(y = new.y.sim, k = (i + 
                    1), ...), silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    Q.star[[i]][j]
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "logisregmix") {
        if (is.null(N)) 
            stop("Number of trials must be specified!")
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        logit <- function(x) 1/(1 + exp(-x))
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  H0.fit = glm(cbind(y, N - y) ~ x, family = binomial())
                  Q0[i] = logLik(H0.fit)
                  H1.fit = try(logisregmixEM(y = y, x = x, N = N, 
                    k = (i + 1), ...), silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    beta = coef(H0.fit)
                    xbeta = cbind(1, x) %*% beta
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rbinom(length(y), size = N, prob = logit(xbeta))
                  xy.simout = glm(cbind(y.sim, N - y.sim) ~ x, 
                    family = binomial())
                  em.out = try(logisregmixEM(y = y.sim, x = x, 
                    N = N, k = (i + 1), ...), silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - logLik(xy.simout))
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(logisregmixEM(y = y, x = x, N = N, 
                    k = i, ...), silent = TRUE)
                  H1.fit = try(logisregmixEM(y = y, x = x, N = N, 
                    k = (i + 1), ...), silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    beta = H0.fit$beta
                    xbeta = cbind(1, x) %*% beta
                  }
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(length(y), size = 1, prob = H0.fit$lambda)
                  y.sim = sapply(1:length(y), function(i) rbinom(1, 
                    size = N[i], prob = logit(xbeta)[, (wt[, 
                      i] == 1)]))
                  em.out.0 = try(logisregmixEM(y = y.sim, x = x, 
                    N = N, k = i, ...), silent = TRUE)
                  em.out.1 = try(logisregmixEM(y = y.sim, x = x, 
                    N = N, k = (i + 1), ...), silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (mix.type == "poisregmix") {
        Q0 = 0
        Q1 = 0
        obs.Q = 0
        i = 1
        while (sigtest == 1 && i <= k) {
            Q.star[[i]] = 0
            if (i == 1) {
                w = 1
                while (w == 1) {
                  H0.fit = glm(y ~ x, family = poisson())
                  Q0[i] = logLik(H0.fit)
                  H1.fit = try(poisregmixEM(y = y, x = x, k = (i + 
                    1), ...), silent = TRUE)
                  if (class(H1.fit) == "try-error") {
                    w = 1
                  }
                  else {
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    beta = coef(H0.fit)
                    xbeta = cbind(1, x) %*% beta
                    j = 0
                  }
                }
                while (j < B) {
                  j = j + 1
                  y.sim = rpois(length(y), lambda = exp(xbeta))
                  xy.simout = glm(y.sim ~ x, family = poisson())
                  em.out = try(poisregmixEM(y = y.sim, x = x, 
                    k = (i + 1), ...), silent = TRUE)
                  if (class(em.out) == "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out$loglik - logLik(xy.simout))
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            else {
                w = 1
                while (w == 1) {
                  H0.fit = try(poisregmixEM(y = y, x = x, k = i, 
                    ...), silent = TRUE)
                  H1.fit = try(poisregmixEM(y = y, x = x, k = (i + 
                    1), ...), silent = TRUE)
                  if (class(H0.fit) == "try-error" || class(H1.fit) == 
                    "try-error") {
                    w = 1
                  }
                  else {
                    Q0[i] = H0.fit$loglik
                    Q1[i] = H1.fit$loglik
                    obs.Q[i] = 2 * (Q1[i] - Q0[i])
                    if (obs.Q[i] < 0) {
                      w = 1
                    }
                    else {
                      w = 2
                    }
                    beta = H0.fit$beta
                    xbeta = cbind(1, x) %*% beta
                  }
                  j = 0
                }
                while (j < B) {
                  j = j + 1
                  wt = rmultinom(length(y), size = 1, prob = H0.fit$lambda)
                  y.sim = sapply(1:length(y), function(i) rpois(1, 
                    lambda = exp(xbeta)[, (wt[, i] == 1)]))
                  em.out.0 = try(poisregmixEM(y = y.sim, x = x, 
                    k = i, ...), silent = TRUE)
                  em.out.1 = try(poisregmixEM(y = y.sim, x = x, 
                    k = (i + 1), ...), silent = TRUE)
                  if (class(em.out.0) == "try-error" || class(em.out.1) == 
                    "try-error") {
                    j = j - 1
                  }
                  else {
                    Q.star[[i]][j] = 2 * (em.out.1$loglik - em.out.0$loglik)
                    if (Q.star[[i]][j] < 0) {
                      j = j - 1
                    }
                  }
                }
            }
            p[i] = mean(Q.star[[i]] >= obs.Q[i])
            sigtest = (p[i] < sig)
            i = i + 1
        }
    }
    if (hist) {
        if (length(p) == 2) {
            par(mfrow = c(1, 2))
            for (i in 1:length(p)) {
                hist(Q.star[[i]], xlab = c("Bootstrap Likelihood", 
                  "Ratio Statistic"), main = paste(i, "versus", 
                  i + 1, "Components"))
                segments(obs.Q[i], 0, obs.Q[i], B, col = 2, lwd = 2)
            }
        }
        else {
            g = ceiling(sqrt(length(p)))
            par(mfrow = c(g, g))
            for (i in 1:length(p)) {
                hist(Q.star[[i]], xlab = c("Bootstrap Likelihood", 
                  "Ratio Statistic"), main = paste(i, "versus", 
                  i + 1, "Components"))
                segments(obs.Q[i], 0, obs.Q[i], B, col = 2, lwd = 2)
            }
        }
    }
    if (p[length(p)] < sig) {
        cat("Decision: Select", length(p) + 1, "Component(s)", 
            "\n")
    }
    else {
        cat("Decision: Select", length(p), "Component(s)", "\n")
    }
    list(p.values = p, log.lik = Q.star, obs.log.lik = obs.Q)
}