thepira / cv.glmmlasso Goto Github PK

This goes in the DESCRIPTION file under imports. See devtools::use_package for help.

Make the result of cv.glmmLasso have a class of cv.glmmLasso

Need to add @md to roxygen block, then code them as [package::function()]

I keep getting this error from the master branch since the latest commit (git-lfs smudge 'data/soccer.rda': git-lfs: command not found). I was getting it when I tried to rebase my fork so I deleted my version completely and tried to start from scratch, but I still get it just from cloning the new master branch.

git clone https://github.com/thepira/cv.glmmLasso
Cloning into 'cv.glmmLasso'...
remote: Enumerating objects: 284, done.
remote: Counting objects: 100% (6/6), done.
remote: Compressing objects: 100% (5/5), done.
remote: Total 284 (delta 0), reused 3 (delta 0), pack-reused 278
Receiving objects: 100% (284/284), 3.22 MiB | 2.11 MiB/s, done.
Resolving deltas: 100% (138/138), done.
git-lfs smudge 'data/soccer.rda': git-lfs: command not found
error: external filter 'git-lfs smudge %f' failed 127
error: external filter 'git-lfs smudge %f' failed
fatal: data/soccer.rda: smudge filter lfs failed
warning: Clone succeeded, but checkout failed.
You can inspect what was checked out with 'git status'
and retry with 'git restore --source=HEAD :/'

Hi, I tried to install this package, but always fail. could you please give some guide on that? infos are down below. Thank you!
`remotes::install_github("thepira/cv.glmmLasso")
Downloading GitHub repo thepira/cv.glmmLasso@HEAD
These packages have more recent versions available.
It is recommended to update all of them.
Which would you like to update?

1: All
2: CRAN packages only
3: None
4: dismo (1.1-4 -> 1.3-2) [CRAN]

Enter one or more numbers, or an empty line to skip updates:3
✓ checking for file ‘/private/var/folders/cj/v2lhdtmx6b1_q3lh6vsxnppm0000gn/T/RtmppfjYhc/remotes66b730f07a65/thepira-cv.glmmLasso-dbe1c57/DESCRIPTION’ ...
─ preparing ‘cv.glmmLasso’:
✓ checking DESCRIPTION meta-information ...
─ checking for LF line-endings in source and make files and shell scripts
─ checking for empty or unneeded directories
─ looking to see if a ‘data/datalist’ file should be added
─ building ‘cv.glmmLasso_0.0.0.9000.tar.gz’

• installing source package ‘cv.glmmLasso’ ...
  ** using staged installation
  ** R
  ** data
  *** moving datasets to lazyload DB
  Warning: file ‘soccer.rda’ has magic number 'versi'
  Use of save versions prior to 2 is deprecated
  Error in load(zfile, envir = tmp_env) :
  復原文件幻数出错(文件可能有损坏)-- 没有载入任何数据
  ERROR: lazydata failed for package ‘cv.glmmLasso’
• removing ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library/cv.glmmLasso’
  错误: Failed to install 'cv.glmmLasso' from GitHub:
  (由警告转换成)installation of package ‘/var/folders/cj/v2lhdtmx6b1_q3lh6vsxnppm0000gn/T//RtmppfjYhc/file66b77bf395fc/cv.glmmLasso_0.0.0.9000.tar.gz’ had non-zero exit status
  My R version as follows. I also tried the new version of R(4.0.3), but still not working.> R.version
  _
  platform x86_64-apple-darwin15.6.0
  arch x86_64
  os darwin15.6.0
  system x86_64, darwin15.6.0
  status
  major 3
  minor 6.3
  year 2020
  month 02
  day 29
  svn rev 77875
  language R
  version.string R version 3.6.3 (2020-02-29)
  nickname Holding the Windsock `

Change

cvsd <- apply(cvLossMatrix, 1, sd, na.rm = TRUE)

to

cvsd <- apply(cvLossMatrix, 2, sd, na.rm = TRUE)

at line 137 of cv.glmmLasso.R

In cv.glmmLasso

CRAN won't allow non-standard folders

This would require saving each control list, which might not be worth it

http://r-pkgs.had.co.nz/data.html

Hello, the error message I kept getting from running this is "x-matrix not supported: "'nrow' or 'ncol' cannot be specified when 'x' is a matrix""

cv_lasso_fit_glmm <- cv.glmmLasso(fix = log(DWELL_TIME) ~
                                 Unigram1_Count,
                         rnd = list(Participant_ID=~1, Word1=~1, Word2=~1, Word3=~1, Text_ID1=~1, Word_Number1=~1, Trigram_Freq_Group_ID=~1),
                         family = gaussian(link = "identity"),
                         data = datasc[complete.cases(datasc[ , 'DWELL_TIME']),],
                         lambdas = NULL,
                         nlambdas = 100,
                         kfold = 5,
                         lambda.min.ratio = 1e-04,
                         loss = cv.glmmLasso::calc_mse(),
                         lambda.final = c("lambda.1se", "lambda.min")
)