CRAN Package Check Results for Package complexlm

Last updated on 2024-11-03 11:49:17 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1.2 4.14 36.93 41.07 NOTE
r-devel-linux-x86_64-debian-gcc 1.1.2 0.34 1.59 1.93 ERROR
r-devel-linux-x86_64-fedora-clang 1.1.2 66.98 OK
r-devel-linux-x86_64-fedora-gcc 1.1.2 62.24 OK
r-devel-windows-x86_64 1.1.2 5.00 61.00 66.00 NOTE
r-patched-linux-x86_64 1.1.2 4.85 35.07 39.92 OK
r-release-linux-x86_64 1.1.2 3.73 32.24 35.97 ERROR
r-release-macos-arm64 1.1.2 21.00 OK
r-release-macos-x86_64 1.1.2 37.00 OK
r-release-windows-x86_64 1.1.2 6.00 63.00 69.00 OK
r-oldrel-macos-arm64 1.1.2 27.00 OK
r-oldrel-macos-x86_64 1.1.2 62.00 OK
r-oldrel-windows-x86_64 1.1.2 4.00 67.00 71.00 OK

Check Details

Version: 1.1.2
Check: Rd cross-references
Result: NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: rlm.Rd: lqs Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. Flavors: r-devel-linux-x86_64-debian-clang, r-devel-windows-x86_64

Version: 1.1.2
Check: whether package can be installed
Result: ERROR Installation failed. Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.1.2
Check: package dependencies
Result: NOTE Packages suggested but not available for checking: 'ggforce', 'reshape2' Flavor: r-release-linux-x86_64

Version: 1.1.2
Check: tests
Result: ERROR Running ‘complex_rlm.R’ [1s/2s] Running the tests in ‘tests/complex_rlm.R’ failed. Complete output: > #### > #### William Ryan > #### 12 September, 2021 > #### Test script for complex robust linear fit (rlm). > #### Modified from rlm in MASS. > #### Test script based on Wuber's answer on stackexchange https://stats.stackexchange.com/questions/66088/analysis-with-complex-data-anything-different# > #### > > > library(complexlm) Loading required package: MASS Attaching package: 'complexlm' The following objects are masked from 'package:MASS': psi.bisquare, psi.hampel, psi.huber, rlm The following objects are masked from 'package:stats': cor, cov, lm, lm.fit, lm.wfit, mad, mahalanobis, var The following object is masked from 'package:base': range > library(MASS, include.only = "mvrnorm") > library(ggplot2) > # Synthesize data. > # (1) the independent variable `w`. > # > w.max <- 6 # Max extent of the independent values > w <- expand.grid(seq(-w.max,w.max, 3), seq(-w.max,w.max, 3)) > w <- complex(real=w[[1]], imaginary=w[[2]]) > w <- w[Mod(w) <= w.max] ### This drops any element of w that has a modulus greater than w.max > n <- length(w) > # > # (2) the dependent variable `z`. > # > beta <- c(-20+5i, complex(argument=2*pi/3, modulus=3/2)) ### The fist is the intercept, the 2nd the slope. > print(beta) [1] -20.00+5.000000i -0.75+1.299038i > sigma <- 1.5; rho <- 0.7 # Parameters of the error distribution > set.seed(17) > e <- mvrnorm(n, c(0,0), matrix(c(1,rho,rho,1)*sigma^2, 2)) ### A bunch of random numbers to add errors to the example data. > e <- complex(real=e[,1], imaginary=e[,2]) ### Make those random numbers complex. > z <- as.vector((desX <- cbind(rep(1,n), w)) %*% beta + e) ### The design matrix desX is defined in this line. > z.pure <- as.vector((desX <- cbind(rep(1,n), w)) %*% beta) # z data without the noise. > # > # Add m outliers to z. Draw the outliers from a > # > z.clean <- z # Preserve outlier free z. > m <- 3 > outlier.pos <- sample(1:n, m) # Positions of outliers in z. > outliers <- mvrnorm(m, c(5.8, -7.32), matrix(c(1,rho,rho,1)*sigma^2, 2)) # The real and imaginary values of the outliers. > outliers <- complex(real=outliers[,1], imaginary=outliers[,2]) # Make the outliers comlex numbers. > z <- replace(z, outlier.pos, outliers) > # > # Collect everything into a dataframe. > # > fitframe <- data.frame(w, z.pure, z.clean, z) > # > # Whuber's ordinary complex linear fit. > # > print(beta.hat <- solve(Conj(t(desX)) %*% desX, Conj(t(desX)) %*% z), digits=4) [,1] [1,] -13.6046+2.0829i [2,] -0.1656+0.7124i > z.whuber <- beta.hat[2] * w + beta.hat[1] > fitframe$z.whuber <- z.whuber > fitframe$res.whuber <- as.vector(z - desX %*% beta.hat) > # > # Robust complex linear fit. > # > print(mytestfit <- rlm(x = w, y = z, interc = TRUE)) # Uses default psi=psi.huber, the Huber objective function. Call: rlm(x = w, y = z, interc = TRUE) Converged in 18 iterations Coefficients: (intercept) w -18.9125204+5.294413i -0.5504508+1.098552i Degrees of freedom: 13 total; 11 residual Scale estimate: 2.26 > rbeta.hat <- mytestfit$coefficients > fitframe$z.robust <- mytestfit$coefficients[2] * w + mytestfit$coefficients[1] > fitframe$res.robust <- mytestfit$residuals > # Robust complex linear fit, with Hampel objective function. > # > print(mytestfitHam <- rlm(x = w, y = z, psi = psi.hampel, interc = TRUE)) # Uses psi=psi.hampel, the Hampel objective function. Call: rlm(x = w, y = z, psi = psi.hampel, interc = TRUE) Converged in 9 iterations Coefficients: (intercept) w -13.6739082+2.1406753i -0.1870532+0.7302799i Degrees of freedom: 13 total; 11 residual Scale estimate: 13.1 > rHambeta.hat <- mytestfitHam$coefficients > fitframe$z.robustHam <- mytestfitHam$coefficients[2] * w + mytestfitHam$coefficients[1] > fitframe$res.robustHam <- mytestfitHam$residuals > # Robust complex linear fit, with Tukey's bisquare objective function. > # > print(mytestfitBi <- rlm(x = w, y = z, psi = psi.bisquare, interc = TRUE)) # Uses psi=psi.bisquare, Tukey's bisquare objective function. Call: rlm(x = w, y = z, psi = psi.bisquare, interc = TRUE) Converged in 6 iterations Coefficients: (intercept) w -19.7175660+5.751964i -0.5898994+1.139501i Degrees of freedom: 13 total; 11 residual Scale estimate: 2.58 > rBibeta.hat <- mytestfitBi$coefficients > fitframe$z.robustBi <- mytestfitBi$coefficients[2] * w + mytestfitBi$coefficients[1] > fitframe$res.robustBi <- mytestfitBi$residuals > > library(reshape2) Error in library(reshape2) : there is no package called 'reshape2' Execution halted Flavor: r-release-linux-x86_64

Version: 1.1.2
Check: HTML version of manual
Result: NOTE Skipping checking math rendering: package 'V8' unavailable Flavor: r-release-linux-x86_64