> To: lig...@statistik.tu-dortmund.de
> Subject: Re: [R] Stepwise rQTL-unknown warning message and odd QTL curve
>
> Sorry, I'll try to provide more detail about what I have done so far with
> code and any relevant output results.
>
> >library(qtl)
> >sawfly.cross &
ne
> Kingston ON Canada
>
>
> > -Original Message-
> > From: lig...@statistik.tu-dortmund.de
> > Sent: Sat, 23 May 2015 09:36:15 +0200
> > To: claire.oq...@uky.edu, r-help@r-project.org
> > Subject: Re: [R] Stepwise rQTL-unknown warning message and odd QTL curve
>
Sorry, I'll try to provide more detail about what I have done so far with
code and any relevant output results.
>library(qtl)
>sawfly.cross <- read.cross(format="csv",
file="~/Desktop/Sawfly_data/QTL/Sawfly_QTL.csv", na.strings="NA",
genotypes=c("A", "B"), alleles=c("A", "B"), estimate.map=F)
--Re
o: claire.oq...@uky.edu, r-help@r-project.org
> Subject: Re: [R] Stepwise rQTL-unknown warning message and odd QTL curve
>
>
>
> On 23.05.2015 01:07, Claire O'Quin wrote:
>> Hi There,
>>
>> I am running a stepwise QTL for a backcross and got the following
&g
On 23.05.2015 01:07, Claire O'Quin wrote:
Hi There,
I am running a stepwise QTL for a backcross and got the following warning
message:
Warning message:
In lastout[[i]] - (max(lastout[[i]]) - dropresult[rn == qn[i], 3]) :
longer object length is not a multiple of shorter object length
So
Here is a solution I applied using qAIC and package bbmle so I share it
for next ones. It is not really automatized as I need to read every
results of the drop() test an enter manually the less significant
variable but I guess a function can be created in this goal.
nullQ <- update (null, fami
Unless you have detailed simulations to back up the performance of this
method I would avoid it. It violates several statistical principles.
Frank
Hari wrote
> Hello R geeks,
>
> Waiting for an reply.
>
> Thanks,
> Hari
-
Frank Harrell
Department of Biostatistics, Vanderbilt Universit
On 26.04.2013 13:58, Jonathan Jansson wrote:
Hi! I am trying to make a stepwise regression in the multivariate case, using
Wilks' Lambda test.
I've tried this:
greedy.wilks(cbind(Y1,Y2) ~ . , data=my.data )
But it only returns:
Error in model.frame.default(formula = X[, j] ~ grouping, drop
Since stepwise methods do not work as advertised in the univariate case I'm
wondering why they should work in the multivariate case.
Frank
Jonathan Jansson wrote
> Hi! I am trying to make a stepwise regression in the multivariate case,
> using Wilks' Lambda test.
> I've tried this:
>> greedy.wil
On 19.01.2013 01:57, Julien Mehl Vettori wrote:
Dear Herry,
This is the R-help mailing list with thousands of readers. Your message
is without any context. Do you really expect an answer?
Best,
Uwe Ligges
I would like to know if you found an answer elsewhere to your question.
I'm trying
Thanks.
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https://stat.ethz.ch/m
eter alpha in a step function.
Regards
Petr
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Einat
> Sent: Wednesday, November 21, 2012 1:41 PM
> To: r-help@r-project.org
> Subject: Re: [R] Stepwise a
On Nov 21, 2012, at 6:41 AM, Einat wrote:
> These are my questions:
>
> 1. For example, if this is my code:
>
>> RegModel =
> lm(glucose~sex+BMI+height+weight+education+ses,weight=w_without_non_response)
>> summary(RegModel)
>> step(RegModel, direction ="backward",scope=list(lower=?,upper=?)
These are my questions:
1. For example, if this is my code:
>RegModel =
lm(glucose~sex+BMI+height+weight+education+ses,weight=w_without_non_response)
>summary(RegModel)
>step(RegModel, direction ="backward",scope=list(lower=?,upper=?))
and I want the sex and height variables to be fixed, but
These are my questions:
1. For example, if this is my code:
>RegModel =
lm(glucose~sex+BMI+height+weight+education+ses,weight=w_without_non_response)
>summary(RegModel)
>step(RegModel, direction ="backward",scope=list(lower=?,upper=?))
and I want the sex and height variables to be fixed, b
Hi.
What questions? I do not see any.
Petr
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Einat
> Sent: Wednesday, November 21, 2012 9:32 AM
> To: r-help@r-project.org
> Subject: Re: [R] Stepwise a
I am sorry, but I think my questions were forgotten. Can someone please
answer them?
Thank you :)
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__
David, thanks for the feedback!
Steve, thanks for the direction! I have heard and read some about Dr. Harrell's
work but somehow had missed the term "penalized logistic regression." That was
helpful for finding more specific sources to follow Dr. Harrell's (and other's)
suggestions. I may have
Thank you for the quick reply.
Two more questions:
1. For example, if this is my code:
>RegModel =
lm(glucose~sex+BMI+height+weight+education+ses,weight=w_without_non_response)
>summary(RegModel)
>step(RegModel, direction ="backward",scope=list(lower=?,upper=?))
and I want the sex and height var
On 19.11.2012 08:49, Einat wrote:
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
Read ?step and about its argument "scope" that can be a list with a
"lower" component where
Hi Mark,
To put some context to David's response below, you can search the list
archives for times when people ask about stepwise regression. You can
get started here:
http://search.gmane.org/search.php?group=gmane.comp.lang.r.general&query=stepwise+penalized
The long and short of it is that you
On Nov 16, 2012, at 12:16 PM, Mark Ebbert wrote:
> I haven't heard anything on this question. Is there something fundamentally
> wrong with my question? Any feedback is appreciated.
>
Perhaps failure to read this sig at the bottom of every posted message to rhelp?
"PLEASE do read the posting
I haven't heard anything on this question. Is there something fundamentally
wrong with my question? Any feedback is appreciated.
Mark
On Nov 15, 2012, at 8:13 AM, Mark T. W. Ebbert wrote:
> Dear Gurus,
>
> Thank you in advance for your assistance. I'm trying to understand scope
> better when p
Hi,
May be this helps:
set.seed(232)
mat1<-matrix(sample(1:100,80,replace=TRUE),ncol=8) #with 8 columns
dat1<-data.frame(mat1)
names(dat1)[1]<-"Y"
form1<-as.formula(paste("Y~",paste(names(dat1)[2:8],collapse="+"))) #should
change "8" to "400"
fit.final<-lm(form1,data=dat1)
fit.final
#Call:
#lm
On Tue, Jul 24, 2012 at 2:48 PM, Diana Marcela Martinez Ruiz
wrote:
> Hello,
>
> I want to know how to perform stepwise elimination of variables to svyglm
>
If that's actually what you want to do (which as Frank points out, it
probably isn't), you'll have to do it by hand.
The function step() us
None, except when the bootstrap is used correctly to fully document how well
or poorly the modeling strategy worked and one is not interested in doing
better or hasn't the time to do so.
Cheers,
Frank
Bert Gunter wrote
>
> ... which begs the question: In what context is it valid? ;-)
>
> -- Be
... which begs the question: In what context is it valid? ;-)
-- Bert
On Tue, Jul 24, 2012 at 3:00 PM, Frank Harrell wrote:
> Stepwise variable selection is invalid in this context.
> Frank
>
> Diana Marcela Martinez Ruiz wrote
>>
>> Hello,
>>
>> I want to know how to perform stepwise eliminat
Stepwise variable selection is invalid in this context.
Frank
Diana Marcela Martinez Ruiz wrote
>
> Hello,
>
> I want to know how to perform stepwise elimination of variables to svyglm
>
> thanks
> [[alternative HTML version deleted]]
>
> __
Please so not double post.
After personal communication it turned out that
cor(data_indiciN2$I022N2, data_indiciN2$I025N2)
was exactly 1. Hence you cannot use both of them in your model and have
to skip one of them.
Therefore,
gw_obj <- greedy.wilks(gruppo ~ . - I025N2, data = data_indiciN2
On 2012-05-20 16:22, ejulia17 wrote:
Dear Brian,
I found this in the archives and was going to follow your advice, but can't
get the source code of the function extractAIC you suggest modifying.
Getting the full source code of stepAIC straight from the R session (by
typing
the function name) was
Dear Brian,
I found this in the archives and was going to follow your advice, but can't
get the source code of the function extractAIC you suggest modifying.
Getting the full source code of stepAIC straight from the R session (by
typing
the function name) was immediate. Do I need to go down anot
On 2012-04-09 00:44, Hien Nguyen wrote:
Dear R-helpers,
I am trying to do a stepwise procedure in which I want to force some
variables in the model. I have searched around and it seems that only
leaps package allows to force the variable in the stepwise procedure. I
use the leaps package and use
Stepwise variable selection is an invalid statistical method. Who or which
book recommended it?
Frank
Subha P. T. wrote
>
> Hi,
>
> Is there any function available to do stepwise selection of variables in
> Conditional(matched) logistic regression( clogit)? step, stepwise etc are
> failing in
On Feb 22, 2012, at 12:03 AM, Subha P. T. wrote:
Hi David
My data set has about 20 significant variables and step function
with logistic regression is working fine( in R-commander). I tried
to get conditional logistic by introducing the stratum variable and
clogit. The clogit is not conv
Hi David
My data set has about 20 significant variables and step function with logistic
regression is working fine( in R-commander). I tried to get conditional
logistic by introducing the stratum variable and clogit. The clogit is not
converging but is giving the summary of the model. When step
Thanks Steve.
From: Steve Lianoglou
To: David Winsemius
ject.org>
Sent: Friday, February 17, 2012 9:27 PM
Subject: Re: [R] stepwise selection for conditional logistic regression
Also, when you're doing reading through David's suggestions:
Thanks Weidong for your help.I had earlier tried Step AIC also but no use.
Trying other options as suggested by the R-group.
Subha
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
h
Also, when you're doing reading through David's suggestions:
On Fri, Feb 17, 2012 at 10:41 AM, David Winsemius
wrote:
[snip]
> Stepwise procedures are supported somewhat grudgingly on r-help. You ought
> to read some of the critical comments about stepwise procedures in the
> Archives:
>
> http:/
On Feb 17, 2012, at 2:10 AM, Subha P. T. wrote:
Hi,
Is there any function available to do stepwise selection of
variables in Conditional(matched) logistic regression( clogit)?
step, stepwise etc are failing in case of conditional logistic
regression.
"Failing" is open to a variety
stepAIC works for an object of clogit.
Weidong Gu
On Fri, Feb 17, 2012 at 2:10 AM, Subha P. T. wrote:
>
>
> Hi,
>
> Is there any function available to do stepwise selection of variables in
> Conditional(matched) logistic regression( clogit)? step, stepwise etc are
> failing in case of condit
"Anova.mlm" would be one way to do model selection.
On Fri, Feb 10, 2012 at 4:29 PM, Fugate, Michael L wrote:
> Good Day,
>
> I fit a multivariate linear regression model with 3 dependent variables and
> several predictors using the lm function. I would like to use stepwise
> variable select
Stepwise regression without proper penalization is invalid.
Frank
Pablo wrote
>
> I'm manually doing a form of stepwise regression in a mixed model but with
> many variables, it is time consuming. I thought I'd try to use an
> automated approach. stepAIC gave me false convergence when I used it
Pablo gmail.com> writes:
>
> I'm manually doing a form of stepwise regression in a mixed model but with
> many variables, it is time consuming. I thought I'd try to use an automated
> approach. stepAIC gave me false convergence when I used it with my model,
> so I thought it can't be hard to s
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of behave
> Sent: Wednesday, October 05, 2011 7:07 AM
> To: r-help@r-project.org
> Subject: [R] "stepwise" sum
>
> dear R-Community
>
> is there a function which sums data "stepwis
> cumsum(c(2,1,4,5))
[1] 2 3 7 12
On Wed, Oct 5, 2011 at 10:06 AM, behave wrote:
> dear R-Community
>
> is there a function which sums data "stepwise"
>
> exp:
>
> 2
> 1
> 4
> 5
>
> Desired result
>
> 2 = 2
> 2+1 = 3
> 2+1+4 = 7
> 2+1+4+5 = 12
>
> Is there a built in function for this?
>
> T
Hi Louis,
It seems to me that the easiest way to accomplish what you want is to
just define an extractAIC method for objects of class "mlogit". I am
not familiar with multinomial logistic models, so this may not be
correct (I am especially uncertain about the degrees of freedom I
used), but it sh
Sorry, my question was: Are these two functions (Stata and fastbw
(rule="p") R function) should give the same results to the same data? Maybe
I need to run these two functions on more than one datasets to answer
myself.
Many thanks,
Linda
2011/5/25 David Winsemius
>
> On May 25, 2011, at 12:11
Many thanks for your reply. I have run a stepwise selection in Stata and R
using the function fastbw (rule="p") from Design package. Both functions
give the same results. Is this because both functions do the same job or can
it be that for different data one will have different results?
Many thank
On May 25, 2011, at 12:11 PM, linda Porz wrote:
Many thanks for your reply. I have run a stepwise selection in Stata
and R using the function fastbw (rule="p") from Design package. Both
functions give the same results. Is this because both functions do
the same job or can it be that for di
See the Vignette in the glmnet package for one alternative approach to
variable selection. Of course, you need to gain some background to
know what you're doing here.
-- Bert
On Wed, May 25, 2011 at 8:38 AM, Marc Schwartz wrote:
> Hi,
>
> You are unlikely to find one, as fundamentally, stepwise
Hi,
You are unlikely to find one, as fundamentally, stepwise procedures are a bad
way to engage in covariate selection. Search the list archives at rseek.org
using 'stepwise' as the keyword to see a plethora of discussion on this point.
This is not a new issue BTW, as I happened to stumble upon
On May 25, 2011, at 5:28 AM, linda Porz wrote:
Sorry, I have wrote a wrong subject in the first email!
Regards,
Linda
-- Forwarded message --
From: linda Porz
Date: 2011/5/25
Subject: combined odds ratio
To: r-help@r-project.org
Cc: r-help-requ...@stat.math.ethz.ch
Dear all
011 10:28:10 AM
Subject: Re: [R] Stepwise Regression with no Origin
On 12.04.2011 21:52, Zd Gibbs wrote:
> Sorry, the first version was incomplete. I'm trying again.
>
> I am running a regression equation and i want to enter in 12 IV then stepwise
> enter 8 variables and not
On 12.04.2011 21:52, Zd Gibbs wrote:
Sorry, the first version was incomplete. I'm trying again.
I am running a regression equation and i want to enter in 12 IV then stepwise
enter 8 variables and not have an origin.
DV is "shfl".
I want to enter in the following 12 independent dummy variable
I have tried this with my actual data, and everything works smoothly,
including the monotonically decreasing error rates. e.g.:
formula Error
1 Cr 0.4167
2 Cr + Mg 0.1667
If you want to do a stepwise selection there is a function in the klaR package
to do it. This is not what you are asking for, though. You want a way of
finding the successive error rates as additional variables are added in the
forward selection process. As far as I can see you have to do thi
On 06/01/11 23:10:59, Noah Silverman wrote:
> I have a data set with about 30,000 training cases and 103 variable.
> I've trained an SVM (using the e1071 package) for a binary classifier
> {0,1}. The accuracy isn't great. I used a grid search over the C and G
> parameters with an RBF kernel to fi
I'll give it a try,
Thanks!
-N
On 1/6/11 11:34 PM, Steve Lianoglou wrote:
Hi,
On Fri, Jan 7, 2011 at 2:10 AM, Noah Silverman wrote:
I have a data set with about 30,000 training cases and 103 variable.
I've trained an SVM (using the e1071 package) for a binary classifier {0,1}.
The accur
Hi,
On Fri, Jan 7, 2011 at 2:10 AM, Noah Silverman wrote:
> I have a data set with about 30,000 training cases and 103 variable.
>
> I've trained an SVM (using the e1071 package) for a binary classifier {0,1}.
> The accuracy isn't great.
>
> I used a grid search over the C and G parameters with
Shubha Vishwanath Karanth wrote:
> Hi R,
>
>
>
> Does the "step" function used to perform stepwise regression has the
> option to specify the entry/exit significance levels for the independent
> variables? (This is similar to the 'slentry' and 'slstay' option in
> 'Proc reg' of SAS.). Or do we
The values of slentry and slstay that will avoid ruining the
statistical properties of the result are slentry=1.0 and slstay=1.0.
Frank
Frank E Harrell Jr Professor and ChairmanSchool of Medicine
Department of Biostatistics Vanderbilt University
On Sat, 14 Aug
Hi,
On Tue, Apr 13, 2010 at 12:51 PM, aspa wrote:
>
> Dear All,
>
> I am new to R and I would like to do the following:
>
> I want to fit a logistic model with 3 predictors and then perform a stepwise
> regression to select the best possible model using either the AIC/BIC
> criterion.
>
> I have
Lucas Sevilla García wrote:
Hi Community R
I need to make a stepwise using F statistics as a criteria to choose variables.I have 3 independant variables and one dependant variable, and I need to choose the best model fitting to my data using F statistics. The problem is I haven't found any packa
It's a bit of a puzzle to me that this has remained unanswered for 7
hours. Perhaps the readers who know the answers are reluctant to offer
them because they have such low opinions of stepwise strategies but do
not want to express such negativity. Or perhaps they thought that an
RSiteSearch
David Freedman wrote:
Didn't a 2008 paper by Austin in J Clin Epidemiol show that bootstrapping was
just as bad as backward stepwise regression for finding the true predictors?
Yes
Any variable selection without shrinkage is problematic.
Frank
http://xrl.in/26em
Dimitris Rizopoulos-4 wro
Didn't a 2008 paper by Austin in J Clin Epidemiol show that bootstrapping was
just as bad as backward stepwise regression for finding the true predictors?
http://xrl.in/26em
Dimitris Rizopoulos-4 wrote:
>
> Greg Snow wrote:
>> There is not a meaningful alternative way since the way you propos
Greg Snow wrote:
There is not a meaningful alternative way since the way you propose is not meaningful. The Wald tests have some know problems even in the well defined cases. Both types of tests are designed to test a predefined hypothesis, not a conditional hypothesis on the stepwise procedure.
There is not a meaningful alternative way since the way you propose is not
meaningful. The Wald tests have some know problems even in the well defined
cases. Both types of tests are designed to test a predefined hypothesis, not a
conditional hypothesis on the stepwise procedure. It is best to
On Dec 10, 2008, at 8:03 AM, Frank E Harrell Jr wrote:
Maithili Shiva wrote:
Hi,
I have the response variable 'Y' and four predictors say X1, X2, X3
and X4. Assuming all the assmptions like Y follows normal
distribution etc. hold and I want to run linear multiple
regression. How do I run
Maithili Shiva wrote:
Hi,
I have the response variable 'Y' and four predictors say X1, X2, X3 and X4.
Assuming all the assmptions like Y follows normal distribution etc. hold and I
want to run linear multiple regression. How do I run the stepwise regression
(forward as well as the backward re
You should note that the author of the lrm function (at least the one in the
Design package, I don't know of others) is also one of the most vocal opponents
of stepwise regression methods. Using stepwise with lrm() is kind of like
borrowing someone's "down with violence" sign to hit them over t
Also consider the redun function in the Hmisc package, which does not
use the response variable but uses flexible nonlinear additive models to
predict each predictor variable from all the others, using a stepwise
procedure in a formal redundancy analysis.
Frank
Ben Bolker wrote:
Peter Flom
Peter Flom brainscope.com> writes:
>
> Robin Williams wrote
>
> Is there any facility in R to perform a stepwise process on a model,
> which will remove any highly-correlated explanatory variables? I am told
> there is in SPSS. I have a large number of variables (some correlated),
> which I
Robin Williams wrote
Is there any facility in R to perform a stepwise process on a model,
which will remove any highly-correlated explanatory variables? I am told
there is in SPSS. I have a large number of variables (some correlated),
which I would like to just chuck in to a model and perform
On Fri, 9 May 2008, Berthold wrote:
I am using stepAIC for stepwise regression modeling.
Is there a way to change the entry and exit alpha levels for the
stepwise regression using stepAIC ?
No, because it does not use 'entry and exit alpha levels', rather AIC.
I do not believe you have consu
Berthold,
stepAIC does model selected based on AIC (or a similar criterion like
BIC). So it does not uses the alpha levels. Hence it's pointless to
specify them in stepAIC.
HTH,
Thierry
ir. Thierry Onkelinx
Instituu
On Sun, 20 Apr 2008, Marko Milicic wrote:
> Dear R helpers,
>
> I'm trying to build logistic regression model large dataset 360 factors and
> 850 observations. All 360 factors are known to be good predictors of outcome
> variable but I have to find best model with maximum 10 factors. I tried to
>
For model selection using BIC you can have a look at stepAIC() from
package MASS and boot.stepAIC() from package bootStepAIC. For
instance,
library(bootStepAIC)
boot.stepAIC(glmFit1, data, B = 50, k = log(nrow(n)))
where `glmFit1' is the object represinting the fitted model, `data'
the data.f
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