Last updated on 2026-10-24 12:51:47 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2.5 | 37.03 | 109.65 | 146.68 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 1.2.5 | 27.14 | 69.92 | 97.06 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.2.5 | 31.00 | 92.16 | 123.16 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.2.5 | 30.00 | 67.74 | 97.74 | OK | |
| r-devel-windows-x86_64 | 1.2.5 | 49.00 | 199.00 | 248.00 | OK | |
| r-patched-linux-x86_64 | 1.2.5 | 38.72 | 100.86 | 139.58 | OK | |
| r-release-linux-x86_64 | 1.2.5 | 36.19 | 98.00 | 134.19 | OK | |
| r-release-macos-arm64 | 1.2.5 | 8.00 | 21.00 | 29.00 | OK | |
| r-release-macos-x86_64 | 1.2.5 | 24.00 | 138.00 | 162.00 | OK | |
| r-release-windows-x86_64 | 1.2.5 | 45.00 | 135.00 | 180.00 | OK | |
| r-oldrel-macos-arm64 | 1.2.5 | OK | ||||
| r-oldrel-macos-x86_64 | 1.2.5 | 21.00 | 110.00 | 131.00 | OK | |
| r-oldrel-windows-x86_64 | 1.2.5 | 55.00 | 172.00 | 227.00 | OK |
Version: 1.2.5
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Rob Tibshirani <tibs@stanford.edu>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Ryan",
family = "Tibshirani",
role = "aut"),
person(given = "Rob",
family = "Tibshirani",
role = c("aut", "cre"),
email = "tibs@stanford.edu"),
person(given = "Jonathan",
family = "Taylor",
role = "aut"),
person(given = "Joshua",
family = "Loftus",
role = "aut"),
person(given = "Stephen",
family = "Reid",
role = "aut"),
person(given = "Jelena",
family = "Markovic",
role = "aut"))
as necessary.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 1.2.5
Check: examples
Result: ERROR
Running examples in ‘selectiveInference-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: fixedLassoInf
> ### Title: Inference for the lasso, with a fixed lambda
> ### Aliases: fixedLassoInf
>
> ### ** Examples
>
> set.seed(43)
> n = 50
> p = 10
> sigma = 1
>
> x = matrix(rnorm(n*p),n,p)
> x = scale(x,TRUE,TRUE)
>
> beta = c(3,2,rep(0,p-2))
> y = x%*%beta + sigma*rnorm(n)
>
> # first run glmnet
> gfit = glmnet(x,y,standardize=FALSE)
>
> # extract coef for a given lambda; note the 1/n factor!
> # (and we don't save the intercept term)
> lambda = .8
> beta = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)[-1]
>
> # compute fixed lambda p-values and selection intervals
> out = fixedLassoInf(x,y,beta,lambda,sigma=sigma)
> out
Call:
fixedLassoInf(x = x, y = y, beta = beta, lambda = lambda, sigma = sigma)
Standard deviation of noise (specified or estimated) sigma = 1.000
Testing results at lambda = 0.800, with alpha = 0.100
Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
1 2.912 18.897 0.000 2.655 3.196 0.048 0.049
2 2.123 13.803 0.000 1.856 2.377 0.049 0.049
3 0.141 0.817 0.495 -0.728 0.404 0.049 0.049
4 0.109 0.699 0.578 -0.854 0.337 0.050 0.049
5 -0.206 -1.240 0.257 -0.486 0.310 0.048 0.049
6 -0.145 -0.968 0.358 -0.395 0.392 0.048 0.049
7 0.141 0.909 0.374 -0.432 0.477 0.050 0.049
8 0.045 0.304 0.865 -2.872 0.165 0.050 0.050
9 -0.113 -0.722 0.510 -0.355 0.658 0.050 0.049
Note: coefficients shown are partial regression coefficients
>
>
> ## as above, but use lar function instead to get initial
> ## lasso fit (should get same results)
> lfit = lar(x,y,normalize=FALSE)
> beta = coef(lfit, s=lambda, mode="lambda")
> out2 = fixedLassoInf(x, y, beta, lambda, sigma=sigma)
> out2
Call:
fixedLassoInf(x = x, y = y, beta = beta, lambda = lambda, sigma = sigma)
Standard deviation of noise (specified or estimated) sigma = 1.000
Testing results at lambda = 0.800, with alpha = 0.100
Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
1 2.912 18.897 0.000 2.655 3.196 0.048 0.049
2 2.123 13.803 0.000 1.856 2.377 0.049 0.049
3 0.141 0.817 0.495 -0.728 0.404 0.049 0.049
4 0.109 0.699 0.578 -0.854 0.337 0.050 0.049
5 -0.206 -1.240 0.257 -0.486 0.310 0.048 0.049
6 -0.145 -0.968 0.358 -0.395 0.392 0.048 0.049
7 0.141 0.909 0.374 -0.432 0.477 0.050 0.049
8 0.045 0.304 0.865 -2.872 0.165 0.050 0.050
9 -0.113 -0.722 0.510 -0.355 0.658 0.050 0.049
Note: coefficients shown are partial regression coefficients
>
> ## mimic different penalty factors by first scaling x
> set.seed(43)
> n = 50
> p = 10
> sigma = 1
>
> x = matrix(rnorm(n*p),n,p)
> x=scale(x,TRUE,TRUE)
>
> beta = c(3,2,rep(0,p-2))
> y = x%*%beta + sigma*rnorm(n)
> pf=c(rep(1,7),rep(.1,3)) #define penalty factors
> pf=p*pf/sum(pf) # penalty factors should be rescaled so they sum to p
> xs=scale(x,FALSE,pf) #scale cols of x by penalty factors
> # first run glmnet
> gfit = glmnet(xs, y, standardize=FALSE)
>
> # extract coef for a given lambda; note the 1/n factor!
> # (and we don't save the intercept term)
> lambda = .8
> beta_hat = coef(gfit, x=xs, y=y, s=lambda/n, exact=TRUE)[-1]
>
> # compute fixed lambda p-values and selection intervals
> out = fixedLassoInf(xs,y,beta_hat,lambda,sigma=sigma)
>
> #rescale conf points to undo the penalty factor
> out$ci=t(scale(t(out$ci),FALSE,pf[out$vars]))
> out
Call:
fixedLassoInf(x = xs, y = y, beta = beta_hat, lambda = lambda,
sigma = sigma)
Standard deviation of noise (specified or estimated) sigma = 1.000
Testing results at lambda = 0.800, with alpha = 0.100
Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
1 3.987 18.880 0.000 2.657 3.229 0.049 0.050
2 2.911 13.765 0.000 1.454 2.364 0.050 0.049
3 0.187 0.776 0.303 -0.747 1.671 0.050 0.050
4 0.149 0.695 0.625 -1.040 0.353 0.050 0.049
5 -0.294 -1.221 0.743 -0.379 2.681 0.050 0.050
6 -0.206 -0.978 0.685 -0.349 1.568 0.049 0.050
7 0.195 0.914 0.407 -0.487 0.401 0.049 0.049
8 0.006 0.295 0.758 -1.711 0.363 0.050 0.049
9 -0.015 -0.723 0.458 -0.368 0.531 0.050 0.050
10 -0.003 -0.157 0.948 -0.011 7.828 0.050 0.050
Note: coefficients shown are partial regression coefficients
>
> #logistic model
> set.seed(43)
>
> n = 50
> p = 10
> sigma = 1
>
> x = matrix(rnorm(n*p),n,p)
> x=scale(x,TRUE,TRUE)
>
> beta = c(3,2,rep(0,p-2))
> y = x%*%beta + sigma*rnorm(n)
> y=1*(y>mean(y))
> # first run glmnet
> gfit = glmnet(x,y,standardize=FALSE,family="binomial")
>
> # extract coef for a given lambda; note the 1/n factor!
> # (and here we DO include the intercept term)
> lambda = .8
> beta_hat = coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE)
>
> # compute fixed lambda p-values and selection intervals
> out = fixedLassoInf(x,y,beta_hat,lambda,family="binomial")
> out
Call:
fixedLassoInf(x = x, y = y, beta = beta_hat, lambda = lambda,
family = "binomial")
Testing results at lambda = 0.800, with alpha = 0.100
Var Coef Z-score P-value LowConfPt UpConfPt LowTailArea UpTailArea
1 5.023 4.645 0.000 3.211 6.808 0.050 0.049
2 3.418 3.814 0.003 1.600 4.892 0.049 0.050
4 0.964 1.404 0.290 -1.689 2.053 0.049 0.049
7 0.794 1.639 0.157 -0.558 1.586 0.049 0.049
8 0.793 1.225 0.427 -2.636 1.777 0.050 0.050
10 0.301 0.656 0.747 -5.216 0.820 0.050 0.050
Note: coefficients shown are full regression coefficients
>
>
> # Cox model
>
> set.seed(43)
> n = 50
> p = 10
> sigma = 1
>
> x = matrix(rnorm(n*p), n, p)
> x = scale(x, TRUE, TRUE)
>
> beta = c(3,2,rep(0,p-2))
> tim = as.vector(x%*%beta + sigma*rnorm(n))
> tim= tim-min(tim)+1
> status=sample(c(0,1),size=n,replace=TRUE)
> # first run glmnet
>
>
> y = Surv(tim,status)
> gfit = glmnet(x, y, standardize=FALSE, family="cox")
Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
>
> # extract coef for a given lambda; note the 1/n factor!
>
> lambda = 1.5
> beta_hat = as.numeric(coef(gfit, x=x, y=y, s=lambda/n, exact=TRUE))
Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
>
> # compute fixed lambda p-values and selection intervals
> out = fixedLassoInf(x, tim, beta_hat, lambda, status=status, family="cox")
Error in a$specials[[i]] : subscript out of bounds
Calls: fixedLassoInf ... model.matrix -> model.matrix.coxph -> delete.response -> off
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc