Displaying 20 results from an estimated 4000 matches similar to: "Stepwise procedure with force.in command"
2013 Jun 04
1
How to write a loop in R to select multiple regression model and validate it ?
I would like to run a loop in R. I have never done this before, so I would be
very grateful for your help !
1. I have a sample set: 25 objects. I would like to draw 1 object from it
and use it as a test set for my future external validation. The remaining 24
objects I would like to use as a training set (to select a model). I would
like to repeat this process until all 25 objects are used as a
2004 Feb 01
5
Stepwise regression and PLS
Dear all,
I am a newcomer to R. I intend to using R to do stepwise regression and
PLS with a data set (a 55x20 matrix, with one dependent and 19
independent variable). Based on the same data set, I have done the same
work using SPSS and SAS. However, there is much difference between the
results obtained by R and SPSS or SAS.
In the case of stepwise, SPSS gave out a model with 4 independent
2007 Sep 27
1
SAS proc reg stepwise procedure in R
I try to reproduce the SAS proc reg stepwise model selection procedure in R, but the only function I found was "step" which select new variables based on AIC. The SAS procedure I use add a new variable to the model based on F statistics and a pre defined significant level. Then before any new variables are added variables in the model that not meet F statistics at the significant level
2012 Jun 01
4
regsubsets (Leaps)
Hi
i need to create a model from 250 + variables with high collinearity, and
only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC,
and/or BIC to narrow down the number of variables, and then use VIF to
choose a model without collinearity (if possible). I realize that having a
huge p and small n is going to give me extreme linear dependency problems,
but I *think* these model
2009 Mar 11
1
regsubsets() [leaps package] - please share some good examples of use
Hello dear R-help members,
I recently became interested in using biglm with leaps, and found myself
somewhat confused as to how to use the two together, in different settings.
I couldn't find any example codes for the leaps() package (except for in the
help file, and the examples there are not as rich as they could be). That
is why I turn to you in case you could share some good tips and
2005 Mar 02
1
Leaps & regsubsets
Hello
I am trying to use all subsets regression on a test dataset consisting
of 11 trails and 46 potential predictor variables.
I would like to use Mallow's Cp as a selection criterion.
The leaps function would provide the required output but does not work
with this many variables (see below).
The alternative function regsubsets should be used, but I am not able to
define the function in
Error with regsubset in leaps package - vcov and all.best option (plus calculating VIFs for subsets)
2009 May 20
1
Error with regsubset in leaps package - vcov and all.best option (plus calculating VIFs for subsets)
Hi all
I am hoping this is just a minor problem, I am trying to implement a best subsets regression procedure on some ecological datasets using the regsubsets function in the leaps package. The dataset contains 43 predictor variables plus the response (logcount) all in a dataframe called environment. I am implementing it as follows:
library(leaps)
2012 Nov 19
9
Stepwise analysis with fixed variables
Hello,
How can I run a backward stepwise regression with part of the variables
fixed, while the others participate in the backward stepwise analysis?
Thank you, Einat
--
View this message in context: http://r.789695.n4.nabble.com/Stepwise-analysis-with-fixed-variables-tp4650015.html
Sent from the R help mailing list archive at Nabble.com.
2010 Dec 26
1
Calculation of BIC done by leaps-package
Hi Folks,
I've got a question concerning the calculation of the Schwarz-Criterion
(BIC) done by summary.regsubsets() of the leaps-package:
Using regsubsets() to perform subset-selection I receive an regsubsets
object that can be summarized by summary.regsubsets(). After this
operation the resulting summary contains a vector of BIC-values
representing models of size i=1,...,K.
My problem
2007 May 11
2
PRESS criterion in leaps
I'm interested in writing some model selection functions (for linear
regression models, as a start), which incorporate the PRESS criterion since
it, to my knowledge, is not currently implemented in any available model
selection procedure.
I thought it would be simplest to build on already existing functions like
regsubsets in package leaps. It's easy enough to calculate the PRESS
2005 Sep 27
4
regsubsets selection criterion
Hello,
I am using the 'regsubsets' function
(from leaps package)
to get the best linear models
to explain 1 variable
from 1 to 5 explanatory variables
(exhaustive search).
Is there anyone who can tell me
on which criterion is based
the 'regsubsets' function ?
Thank you.
samuel
Samuel BERTRAND
Doctorant
Laboratoire de Biomecanique
LBM - ENSAM - CNRS UMR 8005
2008 May 07
1
help with regsubsets
Hi,
I'm new to R and this mailing list, so I will attempt to state my question as appropriately as possible.
I am running R version 2.7 with Windows XP and have recently been exploring the use of the function regsubsets in the leaps package in order to perform all-subsets regression.
So, I'm calling the function as:
2005 Dec 08
1
mle.stepwise versus step/stepAIC
Hello,
I have a question pertaining to the stepwise regression which I am trying to
perform. I have a data set in which I have 14 predictor variables
accompanying my response variable. I am not sure what the difference is
between the function "mle.stepwise" found in the wle package and the
functions "step" or "stepAIC"? When would one use
2010 Aug 17
2
how to selection model by BIC
Hi All:
the package "MuMIn" can be used to select the model based on AIC or AICc.
The code is as follows:
data(Cement)
lm1 <- lm(y ~ ., data = Cement)
dd <- dredge(lm1,rank="AIC")
print(dd)
If I want to select the model by BIC, what code do I need to use? And when
to select the best model based on AIC, what the differences between the
function "dredge" in
2005 May 11
2
Regsubsets()
Dear List members
I am using the regsubsets function to select a few predictor variables
using Mallow's Cp:
> sel.proc.regsub.full <- regsubsets(CO2 ~ v + log(v) + v.max + sd.v +
tad + no.stops.km + av.stop.T + a + sd.a + a.max + d + sd.d + d.max +
RPA + P + perc.stop.T + perc.a.T + perc.d.T + RPS + RPSS + sd.P.acc +
P.dec + da.acc.1 + RMSACC + RDI + RPSI + P.acc + cov.v + cov.a +
2007 Aug 08
1
Regsubsets statistics
Dear R-help,
I have used the regsubsets function from the leaps package to do subset
selection of a logistic regression model with 6 independent variables and
all possible ^2 interactions. As I want to get information about the
statistics behind the selection output, I?ve intensively searched the
mailing list to find answers to following questions:
1. What should I do to get the statistics
2011 Feb 22
1
regsubsets {leaps}
Hi,
I'd like to run regsubsets for model selection by exhaustive search. I have
a list with 20 potential explanatory variables, which represent the real and
the imaginary parts of 10 "kinds" of complex numbers:
x <- list(r1=r1, r2=r2, r3=r3, ..., r10=r10, i1=i1, i2=i2, i3=i3, ...,
i10=i10)
Is there an easy way to constrain the model search so that "r"s and
2006 Apr 28
4
stepwise regression
Dear all,
I have encountered a problem when perform stepwise regression.
The dataset have more 9 independent variables, but 7 observation.
In R, before performing stepwise, a lm object should be given.
fm <- lm(y ~ X1 + X2 + X3 + X11 + X22 + X33 + X12 + X13 + X23)
However, summary(fm) will give:
Residual standard error: NaN on 0 degrees of freedom
Multiple R-Squared: 1, Adjusted
2001 Nov 30
1
Stepwise regression
I need to do a classic stepwise regression based not on AIC but on F in and
F out (or on R2, or R2 adjusted). I have many variables and it will very
useful for me to have a fast stepwise algorithm. Does anyone know if this
exists for R and where I can find that ?
Thank you very much.
Pascal Grandeau
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r-help mailing
2011 Oct 25
2
Logistic Regression - Variable Selection Methods With Prediction
Hello,
I am pretty new to R, I have always used SAS and SAS products. My
target variable is binary ('Y' and 'N') and i have about 14 predictor
variables. My goal is to compare different variable selection methods
like Forward, Backward, All possible subsests. I am using
misclassification rate to pick the winner method.
This is what i have as of now,
Reg <- glm (Graduation ~.,