CRAN Package Check Results for Package iNOTE

Last updated on 2020-02-17 00:49:14 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0 9.92 80.55 90.47 ERROR
r-devel-linux-x86_64-debian-gcc 1.0 8.11 65.06 73.17 ERROR
r-devel-linux-x86_64-fedora-clang 1.0 112.41 ERROR
r-devel-linux-x86_64-fedora-gcc 1.0 109.48 ERROR
r-devel-windows-ix86+x86_64 1.0 18.00 98.00 116.00 OK
r-devel-windows-ix86+x86_64-gcc8 1.0 26.00 113.00 139.00 OK
r-patched-linux-x86_64 1.0 8.77 77.06 85.83 OK
r-patched-solaris-x86 1.0 163.30 OK
r-release-linux-x86_64 1.0 8.62 77.10 85.72 OK
r-release-windows-ix86+x86_64 1.0 16.00 81.00 97.00 OK
r-release-osx-x86_64 1.0 OK
r-oldrel-windows-ix86+x86_64 1.0 7.00 96.00 103.00 OK
r-oldrel-osx-x86_64 1.0 OK

Check Details

Version: 1.0
Check: examples
Result: ERROR
    Running examples in 'iNOTE-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: itegs
    > ### Title: Integrative Total Effect of a Gene Set Test
    > ### Aliases: itegs
    > ### Keywords: multivariate
    >
    > ### ** Examples
    >
    > data(X); data(Y); data(CPG); data(GE)
    > itegs(iCPG=CPG, iGE=GE, iY=Y, iX=X, imodel='mgc', iapprox='pert', gsp.emp=FALSE);
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    iNOTE
     --- call from context ---
    my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
     --- call from argument ---
    if (class(X) == "logical") X <- rep(1, n)
     --- R stacktrace ---
    where 1: my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
    where 2: itegs(iCPG = CPG, iGE = GE, iY = Y, iX = X, imodel = "mgc", iapprox = "pert",
     gsp.emp = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (M, G, Y, X = NA, consider.gene = FALSE, consider.intx = FALSE,
     weight = "lambda", a = c(1, 1, 1), R.star = NA, fam = NA,
     method = "pert", n.pert = 1000, pert.app = TRUE, seed = NA)
    {
     n <- length(Y)
     p <- dim(M)[2]
     if (class(X) == "logical")
     X <- rep(1, n)
     C <- matrix(NA, nrow = n, ncol = p)
     if (consider.intx)
     for (i in 1:n) {
     C[i, ] <- G[i, ] * M[i, ]
     }
     G <- 1 * (G - mean(G))/sd(G)
     s.sd <- apply(M, 2, sd)
     s.m <- apply(M, 2, mean)
     M <- t((t(M) - s.m)/s.sd)
     c.sd <- apply(C, 2, sd)
     c.m <- apply(C, 2, mean)
     C <- t((t(C) - c.m)/c.sd)
     if (weight != "specify") {
     a <- weight1(M, G, C, Y, X, consider.gene, consider.intx)
     }
     fit0 <- glm(Y ~ X, family = binomial)
     eta0 <- predict(fit0)
     mu.0 <- exp(eta0)/(1 + exp(eta0))
     if (!consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M)
     if (consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G)
     if (consider.intx & consider.gene)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G) + a[3] *
     C %*% t(C)
     p <- dim(M)[2]
     offd <- 0
     ff <- fam
     mat <- matrix(0, nrow = length(ff), ncol = length(ff))
     mat2 <- matrix(0, nrow = length(ff), ncol = length(ff))
     for (i in 1:length(ff)) {
     for (j in 1:length(ff)) {
     if (ff[i] == ff[j]) {
     mat[i, j] <- offd
     mat2[i, j] <- offd
     }
     }
     }
     diag(mat) <- 1
     diag(mat2) <- 1
     R.star <- mat
     R <- mat2
     R.inv <- solve(R)
     W0.5 <- diag(exp(eta0 * 0.5)/(1 + exp(eta0)))
     if (method == "davies") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     DD <- eigen(W0.5 %*% R.inv.5 %*% A.cent %*% R.inv.5 %*%
     W0.5/m, symmetric = TRUE)$value
     pval <- list(davies.p = davies(Q.hat, lambda = DD[DD >
     1e-06])$Qq, Qhat = Q.hat, A.cent = A.cent)
     }
     if (method == "pert") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     if (!consider.gene & !consider.intx)
     V <- sqrt(a[1]) * M
     if (consider.gene & !consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G)
     if (consider.gene & consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G, sqrt(a[3]) *
     C)
     U <- cbind(X, V)
     CC <- 1/m * t(U) %*% W0.5 %*% R.inv %*% W0.5 %*% U
     if (!is.null(dim(X))) {
     q <- dim(X)[2]
     }
     else {
     q <- 1
     }
     Cvx <- CC[(q + 1):(dim(U)[2]), 1:q]
     Cxx <- CC[1:q, 1:q]
     Av <- cbind(-Cvx %*% solve(Cxx), diag(1, dim(V)[2]))
     ehalf <- (1/sqrt(m)) * t(U)
     QQ.0 <- rep(0, n.pert)
     if (!is.na(seed)) {
     set.seed(seed)
     }
     N.m <- rnorm(m * n.pert)
     N.m <- matrix(N.m, ncol = m)
     epsilon <- ehalf %*% ((Y - mu.0) * t(N.m))
     for (r in 1:dim(Av)[1]) QQ.0 <- QQ.0 + (Av[r, ] %*% epsilon)^2
     QQ.0 <- as.numeric(QQ.0)
     pval.qq0 <- (n.pert - rank(QQ.0) + 1)/n.pert
     pval.qq0[which.max(pval.qq0)] <- 1 - 0.5/n.pert
     if (!pert.app)
     pval <- mean(QQ.0 > Q.hat[1])
     if (pert.app) {
     EQ.p <- mean(QQ.0)
     VQ.p <- var(QQ.0)
     kappa.p <- VQ.p/(2 * EQ.p)
     nu.p <- 2 * (EQ.p)^2/VQ.p
     pval <- pchisq(Q.hat[1]/kappa.p, df = nu.p, lower.tail = FALSE)
     }
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     h <- R.inv.5 %*% W0.5 %*% X
     H <- h %*% solve(t(h) %*% h) %*% t(h)
     A <- (diag(1, n) - H) %*% R.inv %*% W0.5 %*% A.cent %*%
     W0.5 %*% R.inv %*% (diag(1, n) - H)
     SVQ <- 2 * sum(diag(A %*% R.star %*% A %*% R.star))
     pval <- list(pval, pval.qq0, Qhat = Q.hat, Q.pert.var = var(QQ.0),
     QQ.0 = QQ.0, lambdaWts = a)
     }
     return(pval)
    }
    <bytecode: 0xa32fc98>
    <environment: namespace:iNOTE>
     --- function search by body ---
    Function my.TEtest in namespace iNOTE has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(X) == "logical") X <- rep(1, n) :
     the condition has length > 1
    Calls: itegs -> my.TEtest
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘iNOTE-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: itegs
    > ### Title: Integrative Total Effect of a Gene Set Test
    > ### Aliases: itegs
    > ### Keywords: multivariate
    >
    > ### ** Examples
    >
    > data(X); data(Y); data(CPG); data(GE)
    > itegs(iCPG=CPG, iGE=GE, iY=Y, iX=X, imodel='mgc', iapprox='pert', gsp.emp=FALSE);
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    iNOTE
     --- call from context ---
    my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
     --- call from argument ---
    if (class(X) == "logical") X <- rep(1, n)
     --- R stacktrace ---
    where 1: my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
    where 2: itegs(iCPG = CPG, iGE = GE, iY = Y, iX = X, imodel = "mgc", iapprox = "pert",
     gsp.emp = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (M, G, Y, X = NA, consider.gene = FALSE, consider.intx = FALSE,
     weight = "lambda", a = c(1, 1, 1), R.star = NA, fam = NA,
     method = "pert", n.pert = 1000, pert.app = TRUE, seed = NA)
    {
     n <- length(Y)
     p <- dim(M)[2]
     if (class(X) == "logical")
     X <- rep(1, n)
     C <- matrix(NA, nrow = n, ncol = p)
     if (consider.intx)
     for (i in 1:n) {
     C[i, ] <- G[i, ] * M[i, ]
     }
     G <- 1 * (G - mean(G))/sd(G)
     s.sd <- apply(M, 2, sd)
     s.m <- apply(M, 2, mean)
     M <- t((t(M) - s.m)/s.sd)
     c.sd <- apply(C, 2, sd)
     c.m <- apply(C, 2, mean)
     C <- t((t(C) - c.m)/c.sd)
     if (weight != "specify") {
     a <- weight1(M, G, C, Y, X, consider.gene, consider.intx)
     }
     fit0 <- glm(Y ~ X, family = binomial)
     eta0 <- predict(fit0)
     mu.0 <- exp(eta0)/(1 + exp(eta0))
     if (!consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M)
     if (consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G)
     if (consider.intx & consider.gene)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G) + a[3] *
     C %*% t(C)
     p <- dim(M)[2]
     offd <- 0
     ff <- fam
     mat <- matrix(0, nrow = length(ff), ncol = length(ff))
     mat2 <- matrix(0, nrow = length(ff), ncol = length(ff))
     for (i in 1:length(ff)) {
     for (j in 1:length(ff)) {
     if (ff[i] == ff[j]) {
     mat[i, j] <- offd
     mat2[i, j] <- offd
     }
     }
     }
     diag(mat) <- 1
     diag(mat2) <- 1
     R.star <- mat
     R <- mat2
     R.inv <- solve(R)
     W0.5 <- diag(exp(eta0 * 0.5)/(1 + exp(eta0)))
     if (method == "davies") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     DD <- eigen(W0.5 %*% R.inv.5 %*% A.cent %*% R.inv.5 %*%
     W0.5/m, symmetric = TRUE)$value
     pval <- list(davies.p = davies(Q.hat, lambda = DD[DD >
     1e-06])$Qq, Qhat = Q.hat, A.cent = A.cent)
     }
     if (method == "pert") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     if (!consider.gene & !consider.intx)
     V <- sqrt(a[1]) * M
     if (consider.gene & !consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G)
     if (consider.gene & consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G, sqrt(a[3]) *
     C)
     U <- cbind(X, V)
     CC <- 1/m * t(U) %*% W0.5 %*% R.inv %*% W0.5 %*% U
     if (!is.null(dim(X))) {
     q <- dim(X)[2]
     }
     else {
     q <- 1
     }
     Cvx <- CC[(q + 1):(dim(U)[2]), 1:q]
     Cxx <- CC[1:q, 1:q]
     Av <- cbind(-Cvx %*% solve(Cxx), diag(1, dim(V)[2]))
     ehalf <- (1/sqrt(m)) * t(U)
     QQ.0 <- rep(0, n.pert)
     if (!is.na(seed)) {
     set.seed(seed)
     }
     N.m <- rnorm(m * n.pert)
     N.m <- matrix(N.m, ncol = m)
     epsilon <- ehalf %*% ((Y - mu.0) * t(N.m))
     for (r in 1:dim(Av)[1]) QQ.0 <- QQ.0 + (Av[r, ] %*% epsilon)^2
     QQ.0 <- as.numeric(QQ.0)
     pval.qq0 <- (n.pert - rank(QQ.0) + 1)/n.pert
     pval.qq0[which.max(pval.qq0)] <- 1 - 0.5/n.pert
     if (!pert.app)
     pval <- mean(QQ.0 > Q.hat[1])
     if (pert.app) {
     EQ.p <- mean(QQ.0)
     VQ.p <- var(QQ.0)
     kappa.p <- VQ.p/(2 * EQ.p)
     nu.p <- 2 * (EQ.p)^2/VQ.p
     pval <- pchisq(Q.hat[1]/kappa.p, df = nu.p, lower.tail = FALSE)
     }
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     h <- R.inv.5 %*% W0.5 %*% X
     H <- h %*% solve(t(h) %*% h) %*% t(h)
     A <- (diag(1, n) - H) %*% R.inv %*% W0.5 %*% A.cent %*%
     W0.5 %*% R.inv %*% (diag(1, n) - H)
     SVQ <- 2 * sum(diag(A %*% R.star %*% A %*% R.star))
     pval <- list(pval, pval.qq0, Qhat = Q.hat, Q.pert.var = var(QQ.0),
     QQ.0 = QQ.0, lambdaWts = a)
     }
     return(pval)
    }
    <bytecode: 0x55e3d9aa78e0>
    <environment: namespace:iNOTE>
     --- function search by body ---
    Function my.TEtest in namespace iNOTE has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(X) == "logical") X <- rep(1, n) :
     the condition has length > 1
    Calls: itegs -> my.TEtest
    Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘iNOTE-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: itegs
    > ### Title: Integrative Total Effect of a Gene Set Test
    > ### Aliases: itegs
    > ### Keywords: multivariate
    >
    > ### ** Examples
    >
    > data(X); data(Y); data(CPG); data(GE)
    > itegs(iCPG=CPG, iGE=GE, iY=Y, iX=X, imodel='mgc', iapprox='pert', gsp.emp=FALSE);
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    iNOTE
     --- call from context ---
    my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
     --- call from argument ---
    if (class(X) == "logical") X <- rep(1, n)
     --- R stacktrace ---
    where 1: my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
    where 2: itegs(iCPG = CPG, iGE = GE, iY = Y, iX = X, imodel = "mgc", iapprox = "pert",
     gsp.emp = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (M, G, Y, X = NA, consider.gene = FALSE, consider.intx = FALSE,
     weight = "lambda", a = c(1, 1, 1), R.star = NA, fam = NA,
     method = "pert", n.pert = 1000, pert.app = TRUE, seed = NA)
    {
     n <- length(Y)
     p <- dim(M)[2]
     if (class(X) == "logical")
     X <- rep(1, n)
     C <- matrix(NA, nrow = n, ncol = p)
     if (consider.intx)
     for (i in 1:n) {
     C[i, ] <- G[i, ] * M[i, ]
     }
     G <- 1 * (G - mean(G))/sd(G)
     s.sd <- apply(M, 2, sd)
     s.m <- apply(M, 2, mean)
     M <- t((t(M) - s.m)/s.sd)
     c.sd <- apply(C, 2, sd)
     c.m <- apply(C, 2, mean)
     C <- t((t(C) - c.m)/c.sd)
     if (weight != "specify") {
     a <- weight1(M, G, C, Y, X, consider.gene, consider.intx)
     }
     fit0 <- glm(Y ~ X, family = binomial)
     eta0 <- predict(fit0)
     mu.0 <- exp(eta0)/(1 + exp(eta0))
     if (!consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M)
     if (consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G)
     if (consider.intx & consider.gene)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G) + a[3] *
     C %*% t(C)
     p <- dim(M)[2]
     offd <- 0
     ff <- fam
     mat <- matrix(0, nrow = length(ff), ncol = length(ff))
     mat2 <- matrix(0, nrow = length(ff), ncol = length(ff))
     for (i in 1:length(ff)) {
     for (j in 1:length(ff)) {
     if (ff[i] == ff[j]) {
     mat[i, j] <- offd
     mat2[i, j] <- offd
     }
     }
     }
     diag(mat) <- 1
     diag(mat2) <- 1
     R.star <- mat
     R <- mat2
     R.inv <- solve(R)
     W0.5 <- diag(exp(eta0 * 0.5)/(1 + exp(eta0)))
     if (method == "davies") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     DD <- eigen(W0.5 %*% R.inv.5 %*% A.cent %*% R.inv.5 %*%
     W0.5/m, symmetric = TRUE)$value
     pval <- list(davies.p = davies(Q.hat, lambda = DD[DD >
     1e-06])$Qq, Qhat = Q.hat, A.cent = A.cent)
     }
     if (method == "pert") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     if (!consider.gene & !consider.intx)
     V <- sqrt(a[1]) * M
     if (consider.gene & !consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G)
     if (consider.gene & consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G, sqrt(a[3]) *
     C)
     U <- cbind(X, V)
     CC <- 1/m * t(U) %*% W0.5 %*% R.inv %*% W0.5 %*% U
     if (!is.null(dim(X))) {
     q <- dim(X)[2]
     }
     else {
     q <- 1
     }
     Cvx <- CC[(q + 1):(dim(U)[2]), 1:q]
     Cxx <- CC[1:q, 1:q]
     Av <- cbind(-Cvx %*% solve(Cxx), diag(1, dim(V)[2]))
     ehalf <- (1/sqrt(m)) * t(U)
     QQ.0 <- rep(0, n.pert)
     if (!is.na(seed)) {
     set.seed(seed)
     }
     N.m <- rnorm(m * n.pert)
     N.m <- matrix(N.m, ncol = m)
     epsilon <- ehalf %*% ((Y - mu.0) * t(N.m))
     for (r in 1:dim(Av)[1]) QQ.0 <- QQ.0 + (Av[r, ] %*% epsilon)^2
     QQ.0 <- as.numeric(QQ.0)
     pval.qq0 <- (n.pert - rank(QQ.0) + 1)/n.pert
     pval.qq0[which.max(pval.qq0)] <- 1 - 0.5/n.pert
     if (!pert.app)
     pval <- mean(QQ.0 > Q.hat[1])
     if (pert.app) {
     EQ.p <- mean(QQ.0)
     VQ.p <- var(QQ.0)
     kappa.p <- VQ.p/(2 * EQ.p)
     nu.p <- 2 * (EQ.p)^2/VQ.p
     pval <- pchisq(Q.hat[1]/kappa.p, df = nu.p, lower.tail = FALSE)
     }
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     h <- R.inv.5 %*% W0.5 %*% X
     H <- h %*% solve(t(h) %*% h) %*% t(h)
     A <- (diag(1, n) - H) %*% R.inv %*% W0.5 %*% A.cent %*%
     W0.5 %*% R.inv %*% (diag(1, n) - H)
     SVQ <- 2 * sum(diag(A %*% R.star %*% A %*% R.star))
     pval <- list(pval, pval.qq0, Qhat = Q.hat, Q.pert.var = var(QQ.0),
     QQ.0 = QQ.0, lambdaWts = a)
     }
     return(pval)
    }
    <bytecode: 0x6e214d0>
    <environment: namespace:iNOTE>
     --- function search by body ---
    Function my.TEtest in namespace iNOTE has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(X) == "logical") X <- rep(1, n) :
     the condition has length > 1
    Calls: itegs -> my.TEtest
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘iNOTE-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: itegs
    > ### Title: Integrative Total Effect of a Gene Set Test
    > ### Aliases: itegs
    > ### Keywords: multivariate
    >
    > ### ** Examples
    >
    > data(X); data(Y); data(CPG); data(GE)
    > itegs(iCPG=CPG, iGE=GE, iY=Y, iX=X, imodel='mgc', iapprox='pert', gsp.emp=FALSE);
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    iNOTE
     --- call from context ---
    my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
     --- call from argument ---
    if (class(X) == "logical") X <- rep(1, n)
     --- R stacktrace ---
    where 1: my.TEtest(M = as.matrix(iCPG[[g]]), G = as.matrix(iGE[, g]),
     Y = iY, fam = ifam, X = iX, method = iapprox, n.pert = no.pert,
     consider.gene = TRUE, consider.intx = TRUE)
    where 2: itegs(iCPG = CPG, iGE = GE, iY = Y, iX = X, imodel = "mgc", iapprox = "pert",
     gsp.emp = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (M, G, Y, X = NA, consider.gene = FALSE, consider.intx = FALSE,
     weight = "lambda", a = c(1, 1, 1), R.star = NA, fam = NA,
     method = "pert", n.pert = 1000, pert.app = TRUE, seed = NA)
    {
     n <- length(Y)
     p <- dim(M)[2]
     if (class(X) == "logical")
     X <- rep(1, n)
     C <- matrix(NA, nrow = n, ncol = p)
     if (consider.intx)
     for (i in 1:n) {
     C[i, ] <- G[i, ] * M[i, ]
     }
     G <- 1 * (G - mean(G))/sd(G)
     s.sd <- apply(M, 2, sd)
     s.m <- apply(M, 2, mean)
     M <- t((t(M) - s.m)/s.sd)
     c.sd <- apply(C, 2, sd)
     c.m <- apply(C, 2, mean)
     C <- t((t(C) - c.m)/c.sd)
     if (weight != "specify") {
     a <- weight1(M, G, C, Y, X, consider.gene, consider.intx)
     }
     fit0 <- glm(Y ~ X, family = binomial)
     eta0 <- predict(fit0)
     mu.0 <- exp(eta0)/(1 + exp(eta0))
     if (!consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M)
     if (consider.gene & !consider.intx)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G)
     if (consider.intx & consider.gene)
     A.cent <- a[1] * M %*% t(M) + a[2] * G %*% t(G) + a[3] *
     C %*% t(C)
     p <- dim(M)[2]
     offd <- 0
     ff <- fam
     mat <- matrix(0, nrow = length(ff), ncol = length(ff))
     mat2 <- matrix(0, nrow = length(ff), ncol = length(ff))
     for (i in 1:length(ff)) {
     for (j in 1:length(ff)) {
     if (ff[i] == ff[j]) {
     mat[i, j] <- offd
     mat2[i, j] <- offd
     }
     }
     }
     diag(mat) <- 1
     diag(mat2) <- 1
     R.star <- mat
     R <- mat2
     R.inv <- solve(R)
     W0.5 <- diag(exp(eta0 * 0.5)/(1 + exp(eta0)))
     if (method == "davies") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     DD <- eigen(W0.5 %*% R.inv.5 %*% A.cent %*% R.inv.5 %*%
     W0.5/m, symmetric = TRUE)$value
     pval <- list(davies.p = davies(Q.hat, lambda = DD[DD >
     1e-06])$Qq, Qhat = Q.hat, A.cent = A.cent)
     }
     if (method == "pert") {
     m <- length(unique(fam))
     Q.hat <- (1/m) * t(Y - mu.0) %*% R.inv %*% A.cent %*%
     R.inv %*% (Y - mu.0)
     if (!consider.gene & !consider.intx)
     V <- sqrt(a[1]) * M
     if (consider.gene & !consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G)
     if (consider.gene & consider.intx)
     V <- cbind(sqrt(a[1]) * M, sqrt(a[2]) * G, sqrt(a[3]) *
     C)
     U <- cbind(X, V)
     CC <- 1/m * t(U) %*% W0.5 %*% R.inv %*% W0.5 %*% U
     if (!is.null(dim(X))) {
     q <- dim(X)[2]
     }
     else {
     q <- 1
     }
     Cvx <- CC[(q + 1):(dim(U)[2]), 1:q]
     Cxx <- CC[1:q, 1:q]
     Av <- cbind(-Cvx %*% solve(Cxx), diag(1, dim(V)[2]))
     ehalf <- (1/sqrt(m)) * t(U)
     QQ.0 <- rep(0, n.pert)
     if (!is.na(seed)) {
     set.seed(seed)
     }
     N.m <- rnorm(m * n.pert)
     N.m <- matrix(N.m, ncol = m)
     epsilon <- ehalf %*% ((Y - mu.0) * t(N.m))
     for (r in 1:dim(Av)[1]) QQ.0 <- QQ.0 + (Av[r, ] %*% epsilon)^2
     QQ.0 <- as.numeric(QQ.0)
     pval.qq0 <- (n.pert - rank(QQ.0) + 1)/n.pert
     pval.qq0[which.max(pval.qq0)] <- 1 - 0.5/n.pert
     if (!pert.app)
     pval <- mean(QQ.0 > Q.hat[1])
     if (pert.app) {
     EQ.p <- mean(QQ.0)
     VQ.p <- var(QQ.0)
     kappa.p <- VQ.p/(2 * EQ.p)
     nu.p <- 2 * (EQ.p)^2/VQ.p
     pval <- pchisq(Q.hat[1]/kappa.p, df = nu.p, lower.tail = FALSE)
     }
     svdr <- svd(R.inv)
     R.inv.5 <- svdr$u %*% diag(sqrt(svdr$d))
     h <- R.inv.5 %*% W0.5 %*% X
     H <- h %*% solve(t(h) %*% h) %*% t(h)
     A <- (diag(1, n) - H) %*% R.inv %*% W0.5 %*% A.cent %*%
     W0.5 %*% R.inv %*% (diag(1, n) - H)
     SVQ <- 2 * sum(diag(A %*% R.star %*% A %*% R.star))
     pval <- list(pval, pval.qq0, Qhat = Q.hat, Q.pert.var = var(QQ.0),
     QQ.0 = QQ.0, lambdaWts = a)
     }
     return(pval)
    }
    <bytecode: 0x97a9a40>
    <environment: namespace:iNOTE>
     --- function search by body ---
    Function my.TEtest in namespace iNOTE has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(X) == "logical") X <- rep(1, n) :
     the condition has length > 1
    Calls: itegs -> my.TEtest
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc