Displaying 20 results from an estimated 9000 matches similar to: "Glmnet Logistic Variable Questions"
2011 Feb 03
1
glmnet with binary predictors
Hi Everybody!
I must start with a declaration that I am a sparse user of R. I am
creating a credit scorecard using a dataset which has a variable
depicting actual credit history (good/bad) and 41 other variables of
yes/no type. The procedure I am asked to follow is to use a penalized
logistic procedure for variable selection. I have located the package
"glmnet" which gives the complete
2009 Oct 14
1
different L2 regularization behavior between lrm, glmnet, and penalized?
The following R code using different packages gives the same results for a
simple logistic regression without regularization, but different results
with regularization. This may just be a matter of different scaling of the
regularization parameters, but if anyone familiar with these packages has
insight into why the results differ, I'd appreciate hearing about it. I'm
new to
2013 Jul 17
1
glmnet on Autopilot
Dear List,
I'm running simulations using the glmnet package. I need to use an
'automated' method for model selection at each iteration of the simulation.
The cv.glmnet function in the same package is handy for that purpose.
However, in my simulation I have p >> N, and in some cases the selected
model from cv.glmet is essentially shrinking all coefficients to zero. In
this case,
2011 Jul 22
4
glmnet with binary logistic regression
Hi all,
I am using the glmnet R package to run LASSO with binary logistic
regression. I have over 290 samples with outcome data (0 for alive, 1 for
dead) and over 230 predictor variables. I currently using LASSO to reduce
the number of predictor variables.
I am using the cv.glmnet function to do 10-fold cross validation on a
sequence of lambda values which I let glmnet determine. I then take
2011 May 01
1
Different results of coefficients by packages penalized and glmnet
Dear R users:
Recently, I learn to use penalized logistic regression. Two packages
(penalized and glmnet) have the function of lasso.
So I write these code. However, I got different results of coef. Can someone
kindly explain.
# lasso using penalized
library(penalized)
pena.fit2<-penalized(HRLNM,penalized=~CN+NoSus,lambda1=1,model="logistic",standardize=TRUE)
pena.fit2
2012 Jul 12
1
using glmnet for the dataset with numerical and categorical
Dear R users,
if all my numerical variables in my datasets having the same units, may I
leave them unnormalized, just do cv.glmnet
directly(cv.glmnet(data,standardize=FALSE))?
i know normally if there is a mixture of numerical and categorical , one has
to standardize the numerical part before applying cv.glmnet with
standardize=fase, but that's due to the different units in the numerical
2011 Mar 25
2
A question on glmnet analysis
Hi,
I am trying to do logistic regression for data of 104 patients, which
have one outcome (yes or no) and 15 variables (9 categorical factors
[yes or no] and 6 continuous variables). Number of yes outcome is 25.
Twenty-five events and 15 variables mean events per variable is much
less than 10. Therefore, I tried to analyze the data with penalized
regression method. I would like please some of the
2010 Jun 02
2
glmnet strange error message
Hello fellow R users,
I have been getting a strange error message when using the cv.glmnet
function in the glmnet package. I am attempting to fit a multinomial
regression using the lasso. covars is a matrix with 80 rows and roughly 4000
columns, all the covariates are binary. resp is an eight level factor. I can
fit the model with no errors but when I try to cross-validate after about 30
seconds
2008 Jun 02
0
New glmnet package on CRAN
glmnet is a package that fits the regularization path for linear, two-
and multi-class logistic regression
models with "elastic net" regularization (tunable mixture of L1 and L2
penalties).
glmnet uses pathwise coordinate descent, and is very fast.
Some of the features of glmnet:
* by default it computes the path at 100 uniformly spaced (on the log
scale) values of the
2008 Jun 02
0
New glmnet package on CRAN
glmnet is a package that fits the regularization path for linear, two-
and multi-class logistic regression
models with "elastic net" regularization (tunable mixture of L1 and L2
penalties).
glmnet uses pathwise coordinate descent, and is very fast.
Some of the features of glmnet:
* by default it computes the path at 100 uniformly spaced (on the log
scale) values of the
2010 Apr 04
0
Major glmnet upgrade on CRAN
glmnet_1.2 has been uploaded to CRAN.
This is a major upgrade, with the following additional features:
* poisson family, with dense or sparse x
* Cox proportional hazards family, for dense x
* wide range of cross-validation features. All models have several criteria for cross-validation.
These include deviance, mean absolute error, misclassification error and "auc" for logistic or
2010 Apr 04
0
Major glmnet upgrade on CRAN
glmnet_1.2 has been uploaded to CRAN.
This is a major upgrade, with the following additional features:
* poisson family, with dense or sparse x
* Cox proportional hazards family, for dense x
* wide range of cross-validation features. All models have several criteria for cross-validation.
These include deviance, mean absolute error, misclassification error and "auc" for logistic or
2013 Mar 02
0
glmnet 1.9-3 uploaded to CRAN (with intercept option)
This update adds an intercept option (by popular request) - now one can fit a model without an intercept
Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net"
regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast.
The current list of models covered are:
2013 Mar 02
0
glmnet 1.9-3 uploaded to CRAN (with intercept option)
This update adds an intercept option (by popular request) - now one can fit a model without an intercept
Glmnet is a package that fits the regularization path for a number of generalized linear models, with with "elastic net"
regularization (tunable mixture of L1 and L2 penalties). Glmnet uses pathwise coordinate descent, and is very fast.
The current list of models covered are:
2011 Aug 04
1
Can glmnet handle models with numeric and categorical data?
Dear All,
Can the x matrix in the glmnet() function of glmnet package be a
data.frame with numeric columns and factor columns? I am asking this
because I have a model with both numeric and categorical predictors,
which I would like to study with glmnet. I have already tried to use a
data.frame, but with no success -- as far as I know, the matrix object
can only have data of a single type. Is
2012 Mar 21
2
glmnet: obtain predictions using predict and also by extracting coefficients
All,
For my understanding, I wanted to see if I can get glmnet predictions
using both the predict function and also by multiplying coefficients
by the variable matrix. This is not worked out. Could anyone suggest
where I am going wrong?
I understand that I may not have the mean/intercept correct, but the
scaling is also off, which suggests a bigger mistake.
Thanks for your help.
Juliet Hannah
2013 Jul 06
1
problem with BootCV for coxph in pec after feature selection with glmnet (lasso)
Hi,
I am attempting to evaluate the prediction error of a coxph model that was
built after feature selection with glmnet.
In the preprocessing stage I used na.omit (dataset) to remove NAs.
I reconstructed all my factor variables into binary variables with dummies
(using model.matrix)
I then used glmnet lasso to fit a cox model and select the best performing
features.
Then I fit a coxph model
2013 Dec 07
1
combine glmnet and coxph (and survfit) with strata()
Dear All,
I want to generate survival curve with cox model but I want to estimate the
coefficients using glmnet. However, I also want to include a strata() term
in the model. Could anyone please tell me how to have this strata() effect
in the model in glmnet? I tried converting a formula with strata() to a
design matrix and feeding to glmnet, but glmnet just treats the strata()
term with one
2011 Feb 17
1
cv.glmnet errors
Hi,
I am trying to do multinomial regression using the glmnet package, but the
following gives me an error (for no reason apparent to me):
library(glmnet)
cv.glmnet(x=matrix(c(1,2,3,4,5,6,1,2,3,4,5,6),
nrow=6),y=as.factor(c(1,2,1,2,3,3)),family='multinomial',alpha=0.5,
nfolds=2)
The error i get is:
Error in if (outlist$msg != "Unknown error") return(outlist) :
argument is of
2009 Apr 24
1
Can't install package "glmnet"
Hi, I was trying to install package glmnet in R, but failed and it show such messages:
* Installing *source* package glmnet ...
This package has only been tested with gfortran.
So some checks are needed.
R_HOME is /home/username/R/R-2.9.0
Attempting to determine R_ARCH...
R_ARCH is
Attempting to detect how R was configured for Fortran 90....
Unsupported Fortran 90 compiler or Fortran 90