CRAN Package Check Results for Package GeDS

Last updated on 2025-03-12 01:51:42 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.2.6 37.98 170.53 208.51 OK
r-devel-linux-x86_64-debian-gcc 0.2.6 26.61 120.24 146.85 OK
r-devel-linux-x86_64-fedora-clang 0.2.7 342.45 OK
r-devel-linux-x86_64-fedora-gcc 0.2.7 425.16 OK
r-devel-macos-arm64 0.2.7 104.00 OK
r-devel-macos-x86_64 0.2.7 156.00 OK
r-devel-windows-x86_64 0.2.6 35.00 177.00 212.00 OK
r-patched-linux-x86_64 0.2.6 35.80 160.17 195.97 OK
r-release-linux-x86_64 0.2.6 33.97 163.55 197.52 OK
r-release-macos-arm64 0.2.7 117.00 OK
r-release-macos-x86_64 0.2.7 172.00 OK
r-release-windows-x86_64 0.2.6 35.00 169.00 204.00 OK
r-oldrel-macos-arm64 0.2.7 74.00 ERROR
r-oldrel-macos-x86_64 0.2.7 134.00 ERROR
r-oldrel-windows-x86_64 0.2.6 44.00 215.00 259.00 OK

Additional issues

clang-UBSAN

Check Details

Version: 0.2.7
Check: R code for possible problems
Result: NOTE plot,GeDS-ANY: no visible global function definition for ‘%||%’ Undefined global functions or variables: %||% Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 0.2.7
Check: examples
Result: ERROR Running examples in ‘GeDS-Ex.R’ failed The error most likely occurred in: > ### Name: GGeDS > ### Title: Generalized Geometrically Designed Spline regression estimation > ### Aliases: GGeDS > > ### ** Examples > > ###################################################################### > # Generate a data sample for the response variable Y and the covariate X > # assuming Poisson distributed error and log link function > # See section 4.1 in Dimitrova et al. (2023) > set.seed(123) > N <- 500 > f_1 <- function(x) (10*x/(1+100*x^2))*4+4 > X <- sort(runif(N, min = -2, max = 2)) > # Specify a model for the mean of Y to include only a component > # non-linear in X, defined by the function f_1 > means <- exp(f_1(X)) > > ############# > ## POISSON ## > ############# > # Generate Poisson distributed Y according to the mean model > Y <- rpois(N, means) > > # Fit a Poisson GeDS regression using GGeDS > (Gmod <- GGeDS(Y ~ f(X), beta = 0.2, phi = 0.99, q = 2, family = poisson(), + Xextr = c(-2,2))) Call: GGeDS(formula = Y ~ f(X), family = poisson(), beta = 0.2, phi = 0.99, q = 2, Xextr = c(-2, 2)) Number of internal knots of the second order spline: 13 Deviances: Order 2 Order 3 Order 4 490.6 483.1 511.7 > # Plot the quadratic and cubic GeDS fits > plot(X, log(Y), xlab = "x", ylab = expression(f[1](x))) > lines(X, sapply(X, f_1), lwd = 2) > lines(Gmod, n = 3, col = "red") > lines(Gmod, n = 4, col = "blue", lty = 2) > legend("topleft", + legend = expression(f[1](x), "Quadratic", "Cubic"), + col = c("black", "red", "blue"), + lty = c(1, 1, 2), + lwd = c(2, 1, 1), + bty = "n") > > # Generate GeDS prediction at X=0, first on the response scale and then on > # the predictor scale > predict(Gmod, n = 3, newdata = data.frame(X = 0)) [,1] [1,] 434.4222 > predict(Gmod, n = 3, newdata = data.frame(X = 0), type = "link") [,1] [1,] 6.074017 > > # Apply some of the other available methods, e.g. > # knots, coefficients and deviance extractions for the > # quadratic GeDS fit > knots(Gmod) [1] -2.000000000 -2.000000000 -2.000000000 -1.524015922 -1.085910542 [6] -0.699338583 -0.278677095 -0.077787009 -0.007524582 0.040184661 [11] 0.078467796 0.132250944 0.189800300 0.356034439 0.605776373 [16] 2.000000000 2.000000000 2.000000000 > coef(Gmod) N1 N2 N3 N4 N5 N6 N7 N8 3.831712 3.738951 3.682653 3.541883 3.433803 2.273420 2.043467 4.805209 N9 N10 N11 N12 N13 N14 N15 5.849636 6.074017 5.728570 5.284927 4.700625 4.185276 4.261842 > deviance(Gmod) [1] 483.0868 > > # the same but for the cubic GeDS fit > knots(Gmod, n = 4) [1] -2.00000000 -2.00000000 -2.00000000 -2.00000000 -1.33555637 -0.87062032 [7] -0.50533049 -0.19853797 -0.04412116 0.01403654 0.06004872 0.10722022 [13] 0.15979247 0.29226465 0.47547609 2.00000000 2.00000000 2.00000000 [19] 2.00000000 > coef(Gmod, n = 4) N1 N2 N3 N4 N5 N6 N7 N8 3.818281 3.767209 3.674072 3.588658 3.384239 2.817902 1.356245 4.366175 N9 N10 N11 N12 N13 N14 N15 6.028346 6.029461 5.634932 5.071845 4.089328 4.391411 4.214580 > deviance(Gmod, n = 4) [1] 511.7263 > > ########### > ## GAMMA ## > ########### > # Generate Gamma distributed Y according to the mean model > Y <- rgamma(N, shape = means, rate = 0.1) > # Fit a Gamma GeDS regression using GGeDS > Gmod <- GGeDS(Y ~ f(X), beta = 0.1, phi = 0.99, q = 2, family = Gamma(log), + Xextr = c(-2,2)) > plot(Gmod, f = function(x) exp(f_1(x))/0.1) > > ############## > ## BINOMIAL ## > ############## > # Generate Binomial distributed Y according to the mean model > eta <- f_1(X) - 4 > means <- exp(eta)/(1+exp(eta)) > Y <- rbinom(N, size = 50, prob = means) / 50 > # Fit a Binomial GeDS regression using GGeDS > Gmod <- GGeDS(Y ~ f(X), beta = 0.1, phi = 0.99, family = "quasibinomial", + Xextr = c(-2,2)) > plot(Gmod, f = function(x) exp(f_1(x) - 4)/(1 + exp(f_1(x) - 4))) > > > ########################################## > # A real data example > # See Dimitrova et al. (2023), Section 4.2 > > data("coalMining") > (Gmod2 <- GGeDS(formula = accidents ~ f(years), beta = 0.1, phi = 0.98, + family = poisson(), data = coalMining)) Call: GGeDS(formula = accidents ~ f(years), family = poisson(), data = coalMining, beta = 0.1, phi = 0.98) Number of internal knots of the second order spline: 5 Deviances: Order 2 Order 3 Order 4 117.2 118.0 119.7 > (Gmod3 <- GGeDS(formula = accidents ~ f(years), beta = 0.1, phi = 0.985, + family = poisson(), data = coalMining)) Call: GGeDS(formula = accidents ~ f(years), family = poisson(), data = coalMining, beta = 0.1, phi = 0.985) Number of internal knots of the second order spline: 14 Deviances: Order 2 Order 3 Order 4 99.4 102.8 105.8 > plot(coalMining$years, coalMining$accidents, type = "h", xlab = "Years", + ylab = "Accidents") > lines(Gmod2, tr = exp, n = 4, col = "red") > lines(Gmod3, tr = exp, n = 4, col = "blue", lty = 2) > legend("topright", c("phi = 0.98","phi = 0.985"), col = c("red", "blue"), + lty=c(1, 2)) > > > ## Not run: > ##D ########################################## > ##D # The same regression in the example of GeDS > ##D # but assuming Gamma and Poisson responses > ##D # See Dimitrova et al. (2023), Section 4.2 > ##D > ##D data('BaFe2As2') > ##D (Gmod4 <- GGeDS(intensity ~ f(angle), data = BaFe2As2, beta = 0.6, phi = 0.995, q = 3, > ##D family = Gamma(log), stoptype = "RD")) > ##D plot(Gmod4) > ##D > ##D (Gmod5 <- GGeDS(intensity ~ f(angle), data = BaFe2As2, beta = 0.1, phi = 0.995, q = 3, > ##D family = poisson(), stoptype = "SR")) > ##D plot(Gmod5) > ## End(Not run) > > ########################################## > # Life tables > # See Dimitrova et al. (2023), Section 4.2 > > data(EWmortality) > attach(EWmortality) > (M1 <- GGeDS(formula = Deaths ~ f(Age) + offset(log(Exposure)), + family = quasipoisson(), phi = 0.99, beta = 0.1, q = 3, + stoptype = "LR")) Call: GGeDS(formula = Deaths ~ f(Age) + offset(log(Exposure)), family = quasipoisson(), beta = 0.1, phi = 0.99, q = 3, stoptype = "LR") Number of internal knots of the second order spline: 24 Deviances: Order 2 Order 3 Order 4 644.1 805.7 802.3 > > Exposure_init <- Exposure + 0.5 * Deaths > Rate <- Deaths / Exposure_init > (M2 <- GGeDS(formula = Rate ~ f(Age), weights = Exposure_init, + family = quasibinomial(), phi = 0.99, beta = 0.1, + q = 3, stoptype = "LR")) Call: GGeDS(formula = Rate ~ f(Age), family = quasibinomial(), weights = Exposure_init, beta = 0.1, phi = 0.99, q = 3, stoptype = "LR") Number of internal knots of the second order spline: 20 Deviances: Order 2 Order 3 Order 4 739.8 937.7 724.3 > > > op <- par(mfrow=c(2,2)) > plot(Age, Deaths/Exposure, ylab = expression(mu[x]), xlab = "Age") > lines(M1, n = 3, tr = exp, lwd = 1, col = "red") > plot(Age, Rate, ylab = expression(q[x]), xlab = "Age") > lines(M2, n = 3, tr = quasibinomial()$linkinv, lwd = 1, col = "red") > plot(Age, log(Deaths/Exposure), ylab = expression(log(mu[x])), xlab = "Age") > lines(M1, n = 3, lwd = 1, col = "red") > plot(Age, quasibinomial()$linkfun(Rate), ylab = expression(logit(q[x])), xlab = "Age") > lines(M2, n = 3, lwd = 1, col = "red") > par(op) > > ######################################### > # bivariate example > set.seed(123) > doublesin <- function(x) { + # Adjusting the output to ensure it's positive + exp(sin(2*x[,1]) + sin(2*x[,2])) + } > X <- round(runif(400, min = 0, max = 3), 2) > Y <- round(runif(400, min = 0, max = 3), 2) > # Calculate lambda for Poisson distribution > lambda <- doublesin(cbind(X,Y)) > # Generate Z from Poisson distribution > Z <- rpois(400, lambda) > data <- data.frame(X, Y, Z) > > # Fit a Poisson GeDS regression using GGeDS > BivGeDS <- GGeDS(Z ~ f(X,Y), beta = 0.2, phi = 0.99, family = "poisson") > > # Poisson mean deviance w.r.t data > deviance(BivGeDS, n = 2) # or sum(poisson()$dev.resids(Z, BivGeDS$Linear.Fit$Predicted, wt = 1)) [1] 395.044 > deviance(BivGeDS, n = 3) [1] 374.905 > deviance(BivGeDS, n = 4) [1] 373.0677 > > # Poisson mean deviance w.r.t true function#' > f_XY <- apply(cbind(X, Y), 1, function(row) doublesin(matrix(row, ncol = 2))) > mean(poisson()$dev.resids(f_XY, BivGeDS$Linear.Fit$Predicted, wt = 1)) [1] 0.1113454 > mean(poisson()$dev.resids(f_XY, BivGeDS$Quadratic.Fit$Predicted, wt = 1)) [1] 0.05282198 > mean(poisson()$dev.resids(f_XY, BivGeDS$Cubic.Fit$Predicted, wt = 1)) [1] 0.05211246 > > # Surface plot of the generating function (doublesin) > plot(BivGeDS, f = doublesin) Error in others$xlab %||% "X" : could not find function "%||%" Calls: plot -> plot -> .local Execution halted Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64