Last updated on 2026-10-24 01:51:39 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.2-2 | 4.78 | 45.55 | 50.33 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.2-2 | 2.88 | 29.01 | 31.89 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.2-2 | 38.19 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.2-2 | 39.25 | NOTE | |||
| r-devel-windows-x86_64 | 0.2-2 | 6.00 | 76.00 | 82.00 | OK | |
| r-patched-linux-x86_64 | 0.2-2 | 5.01 | 42.52 | 47.53 | OK | |
| r-release-linux-x86_64 | 0.2-2 | 3.72 | 42.26 | 45.98 | OK | |
| r-release-macos-arm64 | 0.2-2 | 1.00 | 18.00 | 19.00 | OK | |
| r-release-macos-x86_64 | 0.2-2 | 3.00 | 62.00 | 65.00 | OK | |
| r-release-windows-x86_64 | 0.2-2 | 7.00 | 65.00 | 72.00 | OK | |
| r-oldrel-macos-arm64 | 0.2-2 | OK | ||||
| r-oldrel-macos-x86_64 | 0.2-2 | 3.00 | 53.00 | 56.00 | OK | |
| r-oldrel-windows-x86_64 | 0.2-2 | 8.00 | 73.00 | 81.00 | OK |
Version: 0.2-2
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Walter K Kremers <kremers.walter@mayo.edu>’
The Description field contains
<arXiv:2106.14063> and is an extension of the works by Chen Y-H, Chen
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.2-2
Check: examples
Result: ERROR
Running examples in ‘meerva-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: meerva.fit
> ### Title: Analysis of Data with Measurement Error Using a Validation
> ### Subsample
> ### Aliases: meerva.fit
>
> ### ** Examples
>
> #======================================================
>
> # Simulate logistic regression data with measurement error
> simd = meerva.sim.brn(n=4000, m=400,
+ beta = c(-0.5, 0.5, 0.2, 1, 0.5) ,
+ alpha1 = c(0.95, 0.90, 0.90, 0.95) ,
+ alpha2 = c(0.98,0.94,0.95,0.95) ,
+ bx3s1 = c(0.05, 0, 0, NA, NA) ,
+ bx3s2 = c(NA,NA,NA) )
>
> # Read the simulated data to input data format
> x_val = simd$x_val
> y_val = simd$y_val
> xs_val = simd$xs_val
> ys_val = simd$ys_val
> xs_non = simd$xs_non
> ys_non = simd$ys_non
>
> # Analyze the data
> brn.me = meerva.fit(x_val, y_val, xs_val, ys_val, xs_non, ys_non)
> summary(brn.me)
meerva.fit(x_val = x_val, y_val = y_val, xs_val = xs_val, ys_val = ys_val,
xs_non = xs_non, ys_non = ys_non)
familyr familys compare comparec vmethod vmethodc vmethodr
[1,] "binomial" "binomial" "1" "ful" NA "ijk (alt)" "2"
vmethods n_val n_ful dim_beta dim_gamma
[1,] "2" "400" "4000" "5" "5"
Confidence intervals are for alpha = 0.05
Estimates for Beta using beta_aug (references augmented with surrogates)
estimate se lcl ucl z
(Intercept) -0.4776176 0.08631800 -0.6467977 -0.3084374 -5.5332327 3.14e-08
x1 0.6644174 0.18268888 0.3063538 1.0224810 3.6368792 2.76e-04
x2 0.1065074 0.23377642 -0.3516860 0.5647008 0.4555951 6.49e-01
x3 1.0010105 0.08553914 0.8333569 1.1686641 11.7023683 1.24e-31
x4 0.5822642 0.07758941 0.4301918 0.7343366 7.5044291 6.17e-14
Estimates for Beta using beta_val (references alone
estimate se lcl ucl z
(Intercept) -0.4177043 0.1443641 -0.7006527 -0.1347559 -2.8934085 3.81e-03
x1 0.7619142 0.2553111 0.2615138 1.2623147 2.9842587 2.84e-03
x2 -0.1404467 0.3467078 -0.8199816 0.5390882 -0.4050866 6.85e-01
x3 0.9286482 0.1230288 0.6875162 1.1697802 7.5482183 4.41e-14
x4 0.5461104 0.1175133 0.3157886 0.7764321 4.6472234 3.36e-06
Effective multiplicative increase in sample size by using augmented estimates
(Intercept) x_valx1 x_valx2 x_valx3 x_valx4
2.797149 1.953057 2.199510 2.068634 2.293870
Estimates for Gamma using gamma_non (surrogates alone)
not for direct comparisons
estimate se lcl ucl z
(Intercept) -0.4949856 0.04407893 -0.58137869 -0.4085924 -11.229527 2.92e-29
x1s 0.5436383 0.07736659 0.39200260 0.6952741 7.026784 2.11e-12
x2 0.2140913 0.09726672 0.02345208 0.4047306 2.201075 2.77e-02
x3s 0.7718524 0.03829480 0.69679597 0.8469088 20.155542 2.41e-90
x4 0.4052377 0.03552875 0.33560260 0.4748727 11.405908 3.91e-30
Correlations between beta_val and gamma_val
[,1] [,2] [,3] [,4] [,5]
[1,] 0.83681502 -0.41760586 -0.28813935 -0.11623075 -0.04809608
[2,] -0.41169363 0.73616502 0.03313435 0.08251206 -0.06588612
[3,] -0.27380770 0.01964565 0.76861324 0.16028492 0.14017659
[4,] -0.11179852 0.06620837 0.17255981 0.75038124 0.04701913
[5,] -0.03216435 -0.09194100 0.15029466 0.05611433 0.77737056
>
> #======================================================
>
> # Simulate linear regression data with measurement error
> simd = meerva.sim.nrm(n=4000, m=400,
+ beta=c(-0.5,0.5,0.2,1,0.5),
+ alpha1=c(-0.05,0.1,0.05,0.1),
+ alpha2=c(0.95,0.91,0.9,0.9),
+ bx3s1= c(0.05, 0, 0, NA, NA),
+ bx3s2=c(1.1,0.9,0.05),
+ sd=5)
>
> # Read the simulated data to input data format
> x_val = simd$x_val
> y_val = simd$y_val
> xs_val = simd$xs_val
> ys_val = simd$ys_val
> xs_non = simd$xs_non
> ys_non = simd$ys_non
>
> # Analyze the data
> nrm.me = meerva.fit(x_val, y_val, xs_val, ys_val, xs_non, ys_non)
> summary(nrm.me)
meerva.fit(x_val = x_val, y_val = y_val, xs_val = xs_val, ys_val = ys_val,
xs_non = xs_non, ys_non = ys_non)
familyr familys compare comparec vmethod vmethodc vmethodr
[1,] "gaussian" "gaussian" "1" "ful" NA "ijk (alt)" "1"
vmethods n_val n_ful dim_beta dim_gamma
[1,] "1" "400" "4000" "5" "6"
Confidence intervals are for alpha = 0.05
Estimates for Beta using beta_aug (references augmented with surrogates)
estimate se lcl ucl z
(Intercept) -0.5698285 0.13561745 -0.8356338 -0.3040232 -4.201735 2.65e-05
x1 0.6693488 0.48486771 -0.2809744 1.6196721 1.380477 1.67e-01
x2 0.3475805 0.23067420 -0.1045326 0.7996937 1.506803 1.32e-01
x3 1.0793804 0.08077933 0.9210558 1.2377050 13.362085 1.01e-40
x4 0.4169017 0.08153566 0.2570947 0.5767086 5.113121 3.17e-07
Estimates for Beta using beta_val (references alone
estimate se lcl ucl z
(Intercept) -0.37084145 0.3115148 -0.9813992 0.2397163 -1.1904457 2.34e-01
x1 0.59750181 0.6204448 -0.6185477 1.8135513 0.9630217 3.36e-01
x2 -0.03710372 0.8529296 -1.7088149 1.6346075 -0.0435015 9.65e-01
x3 1.57261531 0.2580589 1.0668292 2.0784014 6.0940171 1.10e-09
x4 0.46353412 0.2609601 -0.0479383 0.9750065 1.7762642 7.57e-02
Effective multiplicative increase in sample size by using augmented estimates
(Intercept) x_valx1 x_valx2 x_valx3 x_valx4
5.276261 1.637419 13.671882 10.205567 10.243610
Estimates for Gamma using gamma_non (surrogates alone)
not for direct comparisons
estimate se lcl ucl z
(Intercept) -0.4737105 0.10285621 -0.6753050 -0.2721160 -4.6055607 4.11e-06
x1s 0.2590751 0.18118822 -0.0960473 0.6141975 1.4298672 1.53e-01
x2 0.3411918 0.22659901 -0.1029341 0.7853177 1.5057075 1.32e-01
x3s1 0.5647190 1.06606442 -1.5247289 2.6541669 0.5297231 5.96e-01
x3s2 0.4769355 1.30518395 -2.0811780 3.0350491 0.3654163 7.15e-01
x4 0.4034420 0.08135178 0.2439954 0.5628885 4.9592275 7.08e-07
Correlations between beta_val and gamma_val
(Intercept) xs_valx1s xs_valx2 xs_valx3s1 xs_valx3s2
(Intercept) 0.92249061 -0.19936605 -0.25849846 0.03902979 -0.04393222
x_valx1 -0.35585114 0.62677375 -0.09562337 0.01836258 -0.01200348
x_valx2 -0.26645721 -0.03456141 0.99332183 0.13067080 -0.13685999
x_valx3 -0.03663188 -0.00799057 -0.08847558 0.03018284 0.03278126
x_valx4 -0.09320530 0.04513026 0.17275277 -0.01917341 0.01196612
xs_valx4
(Intercept) -0.03820293
x_valx1 -0.06619061
x_valx2 0.17816328
x_valx3 -0.12320506
x_valx4 0.99797375
>
> #======================================================
> # Simulate Cox regression data with measurement error
> simd = meerva.sim.cox(n=4000, m=400,
+ beta = c(-0.5, 0.5, 0.2, 1, 0.5) ,
+ alpha1 = c(0.95,0.90,0.90,0.95) ,
+ alpha2 = c(0.98,0.94,0.94,0.98) ,
+ bx3s1 = c(0.05,0,0,NA,NA) ,
+ bx3s2 = c(1.1, NA, NA) ,
+ sd=0.1)
>
> # Read the simulated data to input data format
> x_val = simd$x_val
> y_val = simd$y_val
> xs_val = simd$xs_val
> ys_val = simd$ys_val
> xs_non = simd$xs_non
> ys_non = simd$ys_non
> e_val = simd$e_val
> es_val = simd$es_val
> es_non = simd$es_non
>
> # Analyze the data
> cox.me = meerva.fit(x_val, y_val, xs_val, ys_val, xs_non, ys_non,
+ e_val, es_val, es_non)
Error in a$specials[[i]] : subscript out of bounds
Calls: meerva.fit ... model.matrix -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.2-2
Check: dependencies in R code
Result: NOTE
Namespace in Imports field not imported from:
‘dplyr’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc