Displaying 20 results from an estimated 5000 matches similar to: "Penalized Gamma GLM"
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
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
2010 Jul 31
1
Feature selection via glmnet package (LASSO)
Hello,
I'm trying to select features of cetain numbers(like 100 out of 1000) via
LASSO, based on multinomial model, however, it seems the glmnet package
provides a very sparse estimation of coefficients(most of coefficients are
0), which selects very few number of variables, like only 10, based on my
easy dataset.
I try to connect the choice of lambda to the selecting
2013 Apr 17
1
Regularized Regressions
Hi all,
I would greatly appreciate if someone was so kind and share with us a
package or method that uses a regularized regression approach that balances
a regression model performance and model complexity.
That said I would be most grateful is there is an R-package that combines
Ridge (sum of squares coefficients), Lasso: Sum of absolute coefficients
and Best Subsets: Number of coefficients as
2009 Sep 25
1
Penalized Logistic Regression - Query
Dear R users,
Is there any package that I could use to perform Penalized Logistic
Regression (i.e. Ridge/Lasso regularization) including also an offset term
in the model (i.e. a variable with a known coefficient of 1 rather than an
estimated coefficient)? I couldn't find any package that would allow using
offset terms.
Any guidance will help.
Many thanks!
Axel.
[[alternative HTML version
2011 May 24
1
seeking help on using LARS package
Hi,
I am writing to seek some guidance regarding using Lasso regression with the
R package LARS. I have introductory statistics background but I am trying to
learn more. Right now I am trying to duplicate the results in a paper for
shRNA prediction "An accurate and interpretable model for siRNA efficacy
prediction, Jean-Philippe Vert et. al, Bioinformatics" for a Bioinformatics
project
2009 Aug 25
1
Elastic net in R (enet package)
Dear R users,
I am using "enet" package in R for applying "elastic
net" method. In elastic net, two penalities are applied one is lambda1 for
LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the
analysis, I realised tht, I optimised only one lambda. ( even when I
looked at the example in R, they used only one penality) So, I am
2011 May 20
1
Contrasts in Penalized Package
Hi,
The "penalized" documentation says that "Unordered factors are turned
into as many dummy variables as the factor has levels". This is done
by a function in the package called contr.none. I'm trying to figure
out how exactly is a model matrix created with this contrast option
when the user calls the function with a formula. I typed
"library(penalized) ;
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
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 Nov 04
0
glmnet_1.5 uploaded to CRAN
This is a new version of glmnet, that incorporates some bug fixes and
speedups.
* a new convergence criterion which which offers 10x or more speedups for
saturated fits (mainly effects logistic, Poisson and Cox)
* one can now predict directly from a cv.object - see the help files for cv.glmnet
and predict.cv.glmnet
* other new methods are deviance() for "glmnet" and coef() for
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
2009 Sep 26
2
Design Package - Penalized Logistic Reg. - Query
Dear R experts,
The lrm function in the Design package can perform penalized (Ridge)
logistic regression. It is my understanding that the ridge solutions are not
equivalent under scaling of the inputs, so one normally standardizes the
inputs. Do you know if input standardization is done internally in lrm or I
would have to do it prior to applying this function.
Also, as I'm new in R (coming
2012 Dec 27
1
Ridge Regression variable selection
Unlike L1 (lasso) regression or elastic net (mixture of L1 and L2), L2 norm
regression (ridge regression) does not select variables. Selection of
variables would not work properly, and it's unclear why you would want to
omit "apparently" weak variables anyway.
Frank
maths123 wrote
> I have a .txt file containing a dataset with 500 samples. There are 10
> variables.
>
>
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:
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,
2012 May 13
1
R package dependency issues when namespace is not attached
I have always assumed that having a package in the 'Depends' field
would automatically also?import?the namespace. However, it seems that
in R 2.15, dependencies do not become available until the package is
actually?attached?to the searchpath. Is this intended behavior?
The problem appears as follows: Suppose there is a package 'Child'
which?Depends, but does not explicitly