CRAN Package Check Results for Package laGP

Last updated on 2021-10-20 16:50:39 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.5-5 17.04 80.17 97.21 OK --no-vignettes
r-devel-linux-x86_64-debian-gcc 1.5-5 12.35 59.54 71.89 OK --no-vignettes
r-devel-linux-x86_64-fedora-clang 1.5-5 130.14 OK --no-vignettes
r-devel-linux-x86_64-fedora-gcc 1.5-5 115.55 OK --no-vignettes
r-devel-windows-x86_64 1.5-5 25.00 96.00 121.00 OK --no-vignettes
r-devel-windows-x86_64-gcc10-UCRT 1.5-5 ERROR
r-patched-linux-x86_64 1.5-5 14.77 78.72 93.49 OK --no-vignettes
r-patched-solaris-x86 1.5-5 180.40 OK --no-vignettes
r-release-linux-x86_64 1.5-5 13.51 78.15 91.66 OK --no-vignettes
r-release-macos-arm64 1.5-5 OK --no-vignettes
r-release-macos-x86_64 1.5-5 OK --no-vignettes
r-release-windows-ix86+x86_64 1.5-5 33.00 165.00 198.00 OK --no-vignettes
r-oldrel-macos-x86_64 1.5-5 OK --no-vignettes
r-oldrel-windows-ix86+x86_64 1.5-5 43.00 172.00 215.00 OK --no-vignettes

Additional issues

ATLAS

Check Details

Version: 1.5-5
Check: running R code from vignettes
Result: ERROR
    Errors in running code in vignettes:
    when running code in 'laGP.Rnw'
    
    > library("laGP")
    
    > library("MASS")
    
    > library("lhs")
    
    > library("akima")
    
    > library("tgp")
    
    > options(prompt = "R> ", width = 65)
    
    > set.seed(1)
    
    > X <- matrix(seq(0, 2 * pi, length = 6), ncol = 1)
    
    > Z <- sin(X)
    
    > gp <- newGP(X, Z, 2, 1e-06, dK = TRUE)
    
    > mleGP(gp, tmax = 20)
    $d
    [1] 4.386202
    
    $its
    [1] 6
    
    
    > XX <- matrix(seq(-1, 2 * pi + 1, length = 499), ncol = ncol(X))
    
    > p <- predGP(gp, XX)
    
    > deleteGP(gp)
    
    > library("mvtnorm")
    
    > N <- 100
    
    > ZZ <- rmvt(N, p$Sigma, p$df)
    
    > ZZ <- ZZ + t(matrix(rep(p$mean, N), ncol = N))
    
    > matplot(XX, t(ZZ), col = "gray", lwd = 0.5, lty = 1,
    + type = "l", bty = "n", main = "simple sinusoidal example",
    + xlab = "x", ylab = "Y( ..." ... [TRUNCATED]
    
    > points(X, Z, pch = 19)
    
    > x <- seq(-2, 2, by = 0.02)
    
    > X <- as.matrix(expand.grid(x, x))
    
    > N <- nrow(X)
    
    > f2d <- function(x) {
    + g <- function(z) return(exp(-(z - 1)^2) + exp(-0.8 * (z +
    + 1)^2) - 0.05 * sin(8 * (z + 0.1)))
    + -g(x[, 1]) .... [TRUNCATED]
    
    > Y <- f2d(X)
    
    > Xref <- matrix(c(-1.725, 1.725), nrow = 1)
    
    > p.mspe <- laGP(Xref, 6, 50, X, Y, d = 0.1, method = "mspe")
    
    > p.alc <- laGP(Xref, 6, 50, X, Y, d = 0.1, method = "alc")
    
    > Xi <- rbind(X[p.mspe$Xi, ], X[p.alc$Xi, ])
    
    > plot(X[p.mspe$Xi, ], xlab = "x1", ylab = "x2", type = "n",
    + main = "comparing local designs", xlim = range(Xi[, 1]),
    + ylim = range(Xi[, .... [TRUNCATED]
    
    > text(X[p.mspe$Xi, ], labels = 1:length(p.mspe$Xi),
    + cex = 0.7)
    
    > text(X[p.alc$Xi, ], labels = 1:length(p.alc$Xi), cex = 0.7,
    + col = 2)
    
    > points(Xref[1], Xref[2], pch = 19, col = 3)
    
    > legend("topright", c("mspe", "alc"), text.col = c(1,
    + 2), bty = "n")
    
    > p <- rbind(c(p.mspe$mean, p.mspe$s2, p.mspe$df), c(p.alc$mean,
    + p.alc$s2, p.alc$df))
    
    > colnames(p) <- c("mean", "s2", "df")
    
    > rownames(p) <- c("mspe", "alc")
    
    > p
     mean s2 df
    mspe -0.3725312 2.518566e-06 50
    alc -0.3724820 2.445077e-06 50
    
    > p.mspe$mle
     d dits
    1 0.3588186 7
    
    > p.alc$mle
     d dits
    1 0.3378369 7
    
    > c(p.mspe$time, p.alc$time)
    elapsed elapsed
     0.64 0.25
    
    > xx <- seq(-1.97, 1.95, by = 0.04)
    
    > XX <- as.matrix(expand.grid(xx, xx))
    
    > YY <- f2d(XX)
    
    > nth <- as.numeric(Sys.getenv("OMP_NUM_THREADS"))
    
    > if (is.na(nth)) nth <- 2
    
    > print(nth)
    [1] 2
    
    > P.alc <- aGP(X, Y, XX, omp.threads = nth, verb = 0)
    
    > persp(xx, xx, -matrix(P.alc$mean, ncol = length(xx)),
    + phi = 45, theta = 45, main = "", xlab = "x1", ylab = "x2",
    + zlab = "yhat(x)")
    
    > med <- 0.51
    
    > zs <- XX[, 2] == med
    
    > sv <- sqrt(P.alc$var[zs])
    
    > r <- range(c(-P.alc$mean[zs] + 2 * sv, -P.alc$mean[zs] -
    + 2 * sv))
    
    > plot(XX[zs, 1], -P.alc$mean[zs], type = "l", lwd = 2,
    + ylim = r, xlab = "x1", ylab = "predicted & true response",
    + bty = "n", main = "sl ..." ... [TRUNCATED]
    
    > lines(XX[zs, 1], -P.alc$mean[zs] + 2 * sv, col = 2,
    + lty = 2, lwd = 2)
    
    > lines(XX[zs, 1], -P.alc$mean[zs] - 2 * sv, col = 2,
    + lty = 2, lwd = 2)
    
    > lines(XX[zs, 1], YY[zs], col = 3, lwd = 2, lty = 3)
    
    > diff <- P.alc$mean - YY
    
    > plot(XX[zs, 1], diff[zs], type = "l", lwd = 2, main = "systematic bias in prediction",
    + xlab = "x1", ylab = "y(x) - yhat(x)", bty = "n")
    
    > plot(XX[zs, 1], P.alc$mle$d[zs], type = "l", lwd = 2,
    + main = "spatially varying lengthscale", xlab = "x1", ylab = "thetahat(x)",
    + bty = .... [TRUNCATED]
    
    > df <- data.frame(y = log(P.alc$mle$d), XX)
    
    > lo <- loess(y ~ ., data = df, span = 0.01)
    
    > lines(XX[zs, 1], exp(lo$fitted)[zs], col = 2, lty = 2,
    + lwd = 2)
    
    > legend("topright", "loess smoothed", col = 2, lty = 2,
    + lwd = 2, bty = "n")
    
    > P.alc2 <- aGP(X, Y, XX, d = exp(lo$fitted), omp.threads = nth,
    + verb = 0)
    
    > rmse <- data.frame(alc = sqrt(mean((P.alc$mean - YY)^2)),
    + alc2 = sqrt(mean((P.alc2$mean - YY)^2)))
    
    > rmse
     alc alc2
    1 0.0006227472 0.0003024842
    
    > p.alcray <- laGP(Xref, 6, 50, X, Y, d = 0.1, method = "alcray")
    
    > plot(X[p.alc$Xi, ], xlab = "x1", ylab = "x2", type = "n",
    + main = "comparing local designs", xlim = range(Xi[, 1]),
    + ylim = range(Xi[, 2 .... [TRUNCATED]
    
    > text(X[p.alc$Xi, ], labels = 1:length(p.alc$Xi), cex = 0.7,
    + col = 2)
    
    > text(X[p.alcray$Xi, ], labels = 1:length(p.mspe$Xi),
    + cex = 0.7, col = 3)
    
    > points(Xref[1], Xref[2], pch = 19, col = 3)
    
    > legend("topright", c("alc", "alcray"), text.col = c(2,
    + 3), bty = "n")
    
    > p.alcray$time
    elapsed
     0.03
    
    > p <- rbind(p, c(p.alcray$mean, p.alcray$s2, p.alcray$df))
    
    > rownames(p)[3] <- c("alcray")
    
    > p
     mean s2 df
    mspe -0.3725312 2.518566e-06 50
    alc -0.3724820 2.445077e-06 50
    alcray -0.3723245 1.840875e-06 50
    
    > P.alcray <- aGP(X, Y, XX, method = "alcray", omp.threads = nth,
    + verb = 0)
    
    > dfray <- data.frame(y = log(P.alcray$mle$d), XX)
    
    > loray <- loess(y ~ ., data = dfray, span = 0.01)
    
    > P.alcray2 <- aGP(X, Y, XX, method = "alcray", d = exp(loray$fitted),
    + omp.threads = nth, verb = 0)
    
    > c(P.alcray$time, P.alcray2$time)
    elapsed elapsed
     156.94 125.99
    
    > rmse <- cbind(rmse, data.frame(alcray = sqrt(mean((P.alcray$mean -
    + YY)^2)), alcray2 = sqrt(mean((P.alcray2$mean - YY)^2))))
    
    > rmse
     alc alc2 alcray alcray2
    1 0.0006227472 0.0003024842 0.0004415616 0.0002102268
    
    > borehole <- function(x) {
    + rw <- x[1] * (0.15 - 0.05) + 0.05
    + r <- x[2] * (50000 - 100) + 100
    + Tu <- x[3] * (115600 - 63070) + 63070
     .... [TRUNCATED]
    
    > N <- 1e+05
    
    > Npred <- 1000
    
    > dim <- 8
    
    > library("lhs")
    
    > T <- 10
    
    > nas <- rep(NA, T)
    
    > times <- rmse <- data.frame(mspe = nas, mspe2 = nas,
    + alc.nomle = nas, alc = nas, alc2 = nas, nn.nomle = nas, nn = nas,
    + big.nn.nomle = .... [TRUNCATED]
    
    > for (t in 1:T) {
    + x <- randomLHS(N + Npred, dim)
    + y <- apply(x, 1, borehole)
    + ypred.0 <- y[-(1:N)]
    + y <- y[1:N]
    + xpred <- x .... [TRUNCATED]
    
    ... incomplete output. Crash?
    
     'laGP.Rnw'... failed to complete the test
Flavor: r-devel-windows-x86_64-gcc10-UCRT

Version: 1.5-5
Check: re-building of vignette outputs
Result: NOTE
    Error(s) in re-building vignettes:
    --- re-building 'laGP.Rnw' using Sweave
Flavor: r-devel-windows-x86_64-gcc10-UCRT