Last updated on 2025-12-06 00:48:36 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.1.3 | 4.09 | 42.88 | 46.97 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.1.3 | 2.86 | 26.63 | 29.49 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.1.3 | 10.00 | 65.23 | 75.23 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.1.3 | 23.00 | 50.15 | 73.15 | OK | |
| r-devel-windows-x86_64 | 1.1.3 | 5.00 | 62.00 | 67.00 | OK | |
| r-patched-linux-x86_64 | 1.1.3 | 4.60 | 37.75 | 42.35 | OK | |
| r-release-linux-x86_64 | 1.1.3 | 4.03 | 39.58 | 43.61 | OK | |
| r-release-macos-arm64 | 1.1.3 | OK | ||||
| r-release-macos-x86_64 | 1.1.3 | 4.00 | 57.00 | 61.00 | OK | |
| r-release-windows-x86_64 | 1.1.3 | 6.00 | 67.00 | 73.00 | OK | |
| r-oldrel-macos-arm64 | 1.1.3 | OK | ||||
| r-oldrel-macos-x86_64 | 1.1.3 | 4.00 | 47.00 | 51.00 | OK | |
| r-oldrel-windows-x86_64 | 1.1.3 | 8.00 | 78.00 | 86.00 | OK |
Version: 1.1.3
Check: HTML version of manual
Result: NOTE
Found the following HTML validation problems:
complexlm.html:2635:1 (zhatvalues.Rd:24): Warning: <script> anchor "MathJax-script" already defined
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.1.3
Check: tests
Result: ERROR
Running ‘complex_rlm.R’ [1s/1s]
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-devel-linux-x86_64-debian-gcc
Version: 1.1.3
Check: HTML version of manual
Result: NOTE
Skipping checking math rendering: package 'V8' unavailable
Flavor: r-devel-linux-x86_64-debian-gcc