Displaying 20 results from an estimated 10000 matches similar to: "Age of an object?"
2004 Jun 24
3
problem with model.matrix
This works:
> model.matrix(~I(pos>3),data=data.frame(pos=c(1:5)))
(Intercept) I(pos > 3)TRUE
1 1 0
2 1 0
3 1 0
4 1 1
5 1 1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$"I(pos > 3)"
[1] "contr.treatment"
2005 Aug 09
8
Digest reading is tedious
Like many, I am sure, I get R-Help in digest form. Its easy enough to
browse the
subject lines, but then if an entry interests you, you have to embark
on this tedious search or scroll to find it.
It would be great to have a "clickable" digest, where the topics list
is a set of pointers, and clicking on a topic
takes you to that entry. I can think of at least one way to do this via
2004 Aug 06
2
gam --- a new contributed package
I have contributed a "gam" library to CRAN,
which implements "Generalized Additive Models".
This implementation follows closely the description in
the GAM chapter 7 of the "white" book "Statistical Models in S"
(Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy
in "Generalized Additive Models" (Hastie & Tibshirani 1990,
2004 Aug 06
2
gam --- a new contributed package
I have contributed a "gam" library to CRAN,
which implements "Generalized Additive Models".
This implementation follows closely the description in
the GAM chapter 7 of the "white" book "Statistical Models in S"
(Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy
in "Generalized Additive Models" (Hastie & Tibshirani 1990,
2003 Sep 14
3
Re: Logistic Regression
Christoph Lehman had problems with seperated data in two-class logistic regression.
One useful little trick is to penalize the logistic regression using a quadratic penalty on the coefficients.
I am sure there are functions in the R contributed libraries to do this; otherwise it is easy to achieve via IRLS
using ridge regressions. Then even though the data are separated, the penalized
2010 Apr 30
1
help needed with help
I installed
R version 2.11.0 (2010-04-22)
on may macbook (snow leopard)
and run R from within emacs
Now when I try to get help, I get
> ?lm
(in the new "help" window)
Error in help("lm", htmlhelp = FALSE) :
unused argument(s) (htmlhelp = FALSE)
Help!
p.s. I am running:
This is GNU Emacs 22.2.50.1 (i386-apple-darwin9.4.0, Carbon Version 1.6.0)
of 2008-07-17 on
2007 Jan 09
1
contingency table analysis; generalized linear model
Dear List,
I would appreciate help on the following matter:
I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model:
log-linear:
loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T))
GLM:
glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,
2004 Apr 06
4
missing values for mda package
Dear helpers,
I am trying to use the mda package downloaded from the R website, but
the data set has missing values so I got an error message. Should I
manually handle these missing values? I was trying to read the documents
to specify any option related to missing values, but I did not find it.
Please forgive me if I ignore something obvious.
Thanks,
Zhu Wang
Statistical Science Department
2004 Jan 07
0
Statistical Learning and Datamining course based on R/Splus tools
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel
Palo Alto, CA
Feb 26-27, 2004
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we rely increasingly more on data
2004 Jul 12
0
Statistical Learning and Data Mining Course
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Georgetown University Conference Center
Washington DC
September 20-21, 2004
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we
2005 Jan 04
0
Statistical Learning and Data Mining Course
Short course: Statistical Learning and Data Mining
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California
February 24 & 25, 2005
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics and other high-tech
industries, we rely
2006 Mar 07
0
Statistical Learning and Datamining Course
Short course: Statistical Learning and Data Mining II:
tools for tall and wide data
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California,
April 3-4, 2006.
This two-day course gives a detailed overview of statistical models for
data mining, inference and prediction. With the rapid developments
in internet technology, genomics, financial
2006 Jan 14
0
Data Mining Course
Short course: Statistical Learning and Data Mining II:
tools for tall and wide data
Trevor Hastie and Robert Tibshirani, Stanford University
Sheraton Hotel,
Palo Alto, California,
April 3-4, 2006.
This two-day course gives a detailed overview of statistical models for
data mining, inference and prediction. With the rapid developments
in internet technology, genomics, financial
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
2003 Apr 30
0
Least Angle Regression packages for R
Least Angle Regression software: LARS
"Least Angle Regression" ("LAR") is a new model selection
algorithm; a useful and less greedy version of traditional
forward selection methods. LAR is described in detail in a paper
by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani,
soon to appear in the Annals of Statistics.
The paper, as well as R and Splus packages, are
2003 Apr 30
0
Least Angle Regression packages for R
Least Angle Regression software: LARS
"Least Angle Regression" ("LAR") is a new model selection
algorithm; a useful and less greedy version of traditional
forward selection methods. LAR is described in detail in a paper
by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani,
soon to appear in the Annals of Statistics.
The paper, as well as R and Splus packages, are
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