Doing this in an efficient way (ie minimal extra copies, clean code workflow) while allowing for matrices not to be group-wise orthonormalized for non-group penalties may be very tricky

Would it be a lot of trouble to define methods to calculate AIC and BIC values for a fitted oem model, similar to the way the ncvreg package does this? (i.e. returning a vector of AIC or BIC values for each lambda value that was used) This would help selecting the best lambda based on the AIC or BIC criterion, which is faster than using cross validation.

Hi, in the vignette from 4/2018 (https://jaredhuling.org/oem/articles/oem_vignette.html#available-model-families) it is mentioned that there are plans to support Cox ph. Is that expected to be supported soon?

Thank you!

Hi Jared,

We have constructed a model for Adaptive Group Lasso, and we are going to run an out-of-sample prediction.
We are struggling to make an out-of-sample prediction, where we want to measure the corresponding MSE.

Please find the codes below, hope this is something you can help us with!

second lasso step

m2 <- oem(x=Z[,-1], y=y, intercept=T, penalty="grp.lasso", groups=group.ind.lasso, group.weights=lasso.weights, ncores=8)
B <- m2$beta[[1]]
n.nonzeros <- colSums(abs(B)>0)
lssr <- log(colSums(((Z %*% B)-YY)**2))
lssr[!is.finite(lssr)] <- 1e10

information criterion

b_np <- solve(crossprod(Z), crossprod(Z,y))
group.norm.ols <- sapply(1:p2, function(idx) sqrt(sum((b_np[-1][group.ind.lasso==idx])**2)))
group.norm.ols <- matrix(rep(group.norm.ols,times=dim(B)[2]),ncol=dim(B)[2],nrow=p2)
group.norm.lasso <- sapply(1:dim(B)[2],function(i) sapply(1:p2, function(idx) sqrt(sum((B[-1,][group.ind.lasso==idx,i])**2))))
df <- (Kn+1) * colSums((group.norm.lasso / group.norm.ols)) + n.nonzeros / (Kn+2)
bic <- lssr + df * log(n)/n
min.ic <- which(bic==min(bic))

if(n.nonzeros[min.ic]>1) {
selected.chars.final <- unique(sapply(strsplit(names(B[abs(B[,min.ic])>0,min.ic])[-1],"[.]"),function(x) x[1]))
}
else { selected.chars.final <- NA }

Hi all,

Thanks for the excellent package! Is there any way to use the Area Under the Precision-Recall Curve (AUPRC) as the measure to evaluate cross-validation? This would be useful in cases of an imbalanced dataset.

Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PloS one, 10(3), e0118432.

Cheers,
Pedro

Hi. Package looks awesome, want to try it. During installation got following error:

In file included from oem_dense.cpp:3:
./DataStd.h:10:10: fatal error: 'Linalg/AVX.h' file not found
#include "Linalg/AVX.h"
^
1 error generated.
make: *** [oem_dense.o] Error 1
ERROR: compilation failed for package ‘oem’
removing ‘/Users/dymitriyselivanov/Library/R/3.3/library/oem’
Error: Command failed (1)

This issue is for an implementation of the fast cross validation algorithm as implemented in xval.oem() for big.matrix objects. This may take some serious work.

this will require major changes in code structure

self-explanatory

currently, the calculation of the maximum lambda ignores the penalty.factor argument. need to fix this

Currently, the user is forced to use the same sequence of tuning parameters (lambda) for each penalty, yet different penalties (like elastic net vs lasso vs grp lasso) require their tuning parameters to be on different scales. A fix would be to change all code structures to allow for separate sequence for each penalty. If user specifies lambda=NULL, then a sequence will be computed for each penalty. Users can specify a vector for lambda which would apply the same sequence for all penalties, or the user could supply a list of vectors.

This issue will take a decent amount of work to resolve

this will require major changes in code structure

I noticed that observation weights are not yet supported - are these not simple to incorporate by noticing that weighted least squares minimizes sum( w * (Y - X %% beta)^2 )
= sum_i( (sqrt(w[i]) * X[i, ] %% beta - sqrt(w[i]) * Y[i])^2 ), meaning that one could simulate observation weights simply by multiplying both the design matrix X and the dependent variance Y by sqrt(weights) ?

Would you see any possibility to support box constraints on the fitted coefficients, similar to what can be achieved with lower.limits and upper.limits in glmnet?

Were there any plans to add Mnet or Snet penalties? I might give it a try, if you don't mind.

self-explanatory, however for some reason the last time I tried this some strange errors came up

Hello,
I am trying to use OEM for grouped LASSO on a tall dataset which contains many categorical variables (and as a result, lots of binary variables in the model matrix). While running cv.oem, I keep getting the following error:

Error in oemfit.binomial(is.sparse, x, y, family, penalty, weights, groups, :
TridiagEigen: failed to compute all the eigenvalues

This does not just extend to my use case, but also to smaller datasets like the birthwt data from the MASS package. I am pasting a reproducible example below:

`
library(MASS)
library(splines)
library(oem)

#Load and create Model Matrix
data("birthwt")
view(birthwt)

birthwt$race = as.factor(birthwt$race)
birthwt$smoke = as.factor(birthwt$smoke)
birthwt$low = as.factor(birthwt$low)

X = model.matrix(low~ns(age,3)+ns(lwt,3)+race+smoke+ptl, birthwt)[,-1]
Y = birthwt$low

#Define Groups
grouping = c(1,1,1,2,2,2,3,3,4,5,5,5)

#Run cv.oem for Logistic Regression with Group LASSO penalty:
cvoem = cv.oem(X, Y, family = "binomial", penalty = "grp.lasso", groups = grouping, nfolds = 10)
`

Any help regarding: 1) an explanation of the issue and 2) a workaround would be much appreciated. cv.gglasso is just too slow!

Thanks.

this will require only minor changes (ie prox operator of nested penalties has simple composite form)

Dear OEM,

I have been using cv.oem. I was trying to keep n > p. The problem is that once n is slightly closer to p, R session either crashes or does not finish, both on Windows and Linux (both in RStudio or the console). The issue gets worse with grouping and cross validation. Please find my example.

Thank you.

nobs  <- 150
nvars <- 60

X <- matrix(rnorm(nobs * nvars), ncol = nvars)
group.indicators <- rep(1:(60/10), each = 6)
y <- rbinom(nobs, 1, 
  prob = 1 / (1 + exp(-drop(X %*% c(0.15, 0.15, -0.15, -0.15, 0.25, rep(0, nvars - 5)))))
)

input <- X

train_rows <- sample(1:nobs, 0.66 * nobs)
x.train <- as.matrix(input[train_rows, ])
x.test <- as.matrix(input[- (train_rows), ])

y.train <- y[train_rows]
y.test <- y[-train_rows]

cvfit <- oem::cv.oem(
  x = x.train, y = y.train,
  penalty = "grp.lasso",
  groups = group.indicators,
  type.measure = "auc",
  nlambda = 100,
  grouped = TRUE,
  family = "binomial"
)

> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.4 LTS

Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
[1] compiler_3.4.3 tools_3.4.3    yaml_2.1.19 

Hi, I am trying to install the package OEM through devtools and install.package, but it there seems to be something wrong with the package. Does anyone know another way to download OEM? :)

devtools::install_github("jaredhuling/oem")

Error:` Failed to install 'oem' from GitHub:
  (converted from warning) installation of package ‘/var/folders/yq/wkf_lslx1p92415pjkl238_c0000gn/T//Rtmp9eG72C/file5538fb525f0/oem_2.0.10.tar.gz’ had non-zero exit status

I tryed using the oem package for variable selection for a quite large dataset, about 1 million observations and several variabales, around 60, both numerical and categorical. I used the sparse.model.matrix to transform the categorical to numerical, so I get some 1500 colons to the dataset. But then the X^TX is no longer invertible and so the oem function does not work. Can you recommend me what can I do when I want to do variable selection in a dataset with huge amount of lines and some categorical explanatory variables and also a few numerical ?

I'm getting the following error when installing oem on windows, using devtools::install_github("jaredhuling/oem")

*** arch - i386
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c lanczos.cpp -o lanczos.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem.cpp -o oem.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_big.cpp -o oem_big.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_dense.cpp -o oem_dense.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_fb_big.cpp -o oem_fb_big.o
g++ -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -O3 -Wall -std=gnu99 -mtune=generic -c oem_init.c -o oem_init.o
cc1plus.exe: warning: command line option '-std=gnu99' is valid for C/ObjC but not for C++
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_logistic_dense.cpp -o oem_logistic_dense.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_logistic_sparse.cpp -o oem_logistic_sparse.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_sparse.cpp -o oem_sparse.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_xtx.cpp -o oem_xtx.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c oem_xval_dense.cpp -o oem_xval_dense.o
C:/Rtools/mingw_32/bin/g++ -std=gnu++11 -I"C:/PROGRA1/R/R-361.2/include" -DNDEBUG -I. -DNDEBUG -I"C:/Users/Bryor/Documents/R/win-library/3.6/Rcpp/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppEigen/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/BH/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/bigmemory/include" -I"C:/Users/Bryor/Documents/R/win-library/3.6/RcppArmadillo/include" -fopenmp -O3 -c utils.cpp -o utils.o
C:\Rtools\mingw_32\bin\nm.exe: oem_init.o: File format not recognized
C:/Rtools/mingw_32/bin/g++ -shared -s -static-libgcc -o oem.dll tmp.def lanczos.o oem.o oem_big.o oem_dense.o oem_fb_big.o oem_init.o oem_logistic_dense.o oem_logistic_sparse.o oem_sparse.o oem_xtx.o oem_xval_dense.o utils.o -fopenmp -LC:/PROGRA1/R/R-361.2/bin/i386 -lRlapack -LC:/PROGRA1/R/R-361.2/bin/i386 -lRblas -lgfortran -lm -lquadmath -LC:/PROGRA1/R/R-361.2/bin/i386 -lR
oem_init.o: file not recognized: File format not recognized
collect2.exe: error: ld returned 1 exit status
no DLL was created
ERROR: compilation failed for package 'oem'

• removing 'C:/Users/Bryor/Documents/R/win-library/3.6/oem'
  Error: Failed to install 'oem' from GitHub:
  (converted from warning) installation of package ‘C:/Users/Bryor/AppData/Local/Temp/Rtmp4auAQT/file53b832487104/oem_2.0.10.tar.gz’ had non-zero exit status