Wrong list! This is about R. Post on a statistics list like
stats.stackexchange.com for statistics questions.
Cheers,
Bert
Bert Gunter
"Data is not information. Information is not knowledge. And knowledge is
certainly not wisdom."
-- Clifford Stoll
On Mon, Jun 15, 2015 at 3:55 PM, bruno cid
Hi friends,
Im trying to make a model selection comparing models built with "lm" function
(package "stats") and "lme" function (package "nlme"). Do you know if there is
a problem to compare these models with the function "AICtab" (package "bbmle).
Thanks!!! Bruno Cid Crespo GuimarãesMestre em
K C gmail.com> writes:
>
[snip]
> I have a dataset composed of observations taken from 16 separate
> experimental panels, each nested into one of 4 conditions (Treatment A
> Level 1, Treatment A Level 2, Treatment B Level 1, Treatment
> B Level 2; see
> photo: http://imgur.com/ZbzFPNq). The
Stats beginner here.
I have a dataset composed of observations taken from 16 separate
experimental panels, each nested into one of 4 conditions (Treatment A
Level 1, Treatment A Level 2, Treatment B Level 1, Treatment B Level 2; see
photo: http://imgur.com/ZbzFPNq). There are 100 observations of t
Dear All,
I have fitted the exponential and gamma model to my univariate data and
obtained the MLE estimates using the R package "fitdistr", now I'm trying to do
model selection based on leave-one-out cross validation, are there any readily
avaliable R package to do this.
Thanks!
[[alt
Karen,
Look at the help for the drop1() function.
?drop1
There you will see, "The hierarchy is respected when considering terms to
be added or dropped: all main effects contained in a second-order
interaction must remain, and so on."
So, for fit2, the step() function will only consider dropp
I am using the step() function to select a model using backward
elimination, with AIC as the selection criterion. The full regression
model contains three predictors, plus all the second order terms and
two-way interactions. The full model is fit via lm() using two different
model formulae. One
This is off topic here-- it has nothing to do with R, per se.
Post on a statistics list such as stats.stackexchange.com instead.
-- Bert
On Sat, Apr 13, 2013 at 5:41 PM, Kaptue Tchuente, Armel
wrote:
> Dear all,
>
> I'm modeling growth curve of some ecosystems with respect to their
> rainfall
Dear all,
I'm modeling growth curve of some ecosystems with respect to their
rainfall-productivity relationship using a simple linear regression
(ANPP(t)=a+b*Rain(t)) and a modified version of the Brody Model
ANPP(t)=a*(1-exp(-b*rain(t)))
I would like to know why the "best model" is function o
To say that these strategies represent bad statistical practice is to put it
mildly.
Frank
mister_O wrote
> Dear R-Community,
>
> When writing my master thesis, I faced with difficult issue. Analyzing the
> capital structure determinants I have one dependent variable (Total debt
> ratio = TD) and
On 23.02.2013 19:33, Arun Kumar Saha wrote:
which method in statistics is completely free from model misspecification?
The data.
Uwe Ligges
Thanks and regards,
_
Arun Kumar Saha, FRM
QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
Vis
which method in statistics is completely free from model misspecification?
Thanks and regards,
_
Arun Kumar Saha, FRM
QUANTITATIVE RISK AND HEDGE CONSULTING SPECIALIST
Visit me at: http://in.linkedin.com/in/ArunFRM
__
Folks,
Is there any implementation available in R for the simultaneous selection of
lag order and rank of a nonstationary VAR as described in Chao & Phillips
(1999): Model selection in partially nonstationary vector autoregressive
processes with reduced rank structure, J. Econ. (91).
Or any othe
: Sunday, December 02, 2012 5:04 PM
To: Ravi Varadhan
Cc: Adam Zeilinger (zeil0...@umn.edu); r-help@r-project.org
Subject: Re: [R] model selection with spg and AIC (or, convert list to fitted
model object)
Dear Ravi,
Thank you so much for the help. I switched to using the optimx function but I
Adam Zeilinger umn.edu> writes:
>
> Dear R Help,
>
> I have two nested negative log-likelihood functions that I am optimizing
> with the spg function [BB package]. I would like to perform model
> selection on these two objective functions using AIC (and possibly
> anova() too). However, th
Dear Ravi,
Thank you so much for the help. I switched to using the optimx function
but I continue to use the spg method (for the most part) because I found
that only spg consistently converges give different datasets. I also
decided to use AIC rather that a likelihood ratio test.
I have a ne
Adam,
See the attached R code that solves your problem and beyond. One important
issue is that you are enforcing constraints only indirectly. You need to make
sure that P1, P2, and P3 (which are functions of original parameters and time)
are all between 0 and 1. It is not enough to impose co
Dear R Help,
I have two nested negative log-likelihood functions that I am optimizing
with the spg function [BB package]. I would like to perform model
selection on these two objective functions using AIC (and possibly
anova() too). However, the spg() function returns a list and I need a
fi
Stepwise variable selection without heavy penalization is invalid.
Frank
mael wrote:
>
> Hi everyone,
>
> I'm wondering how to select the "best" model when using logistf? AIC does
> not work neither does anova. I tried fitting a glm model but got the
> separation warning message so I tried using
Hi everyone,
I'm wondering how to select the "best" model when using logistf? AIC does
not work neither does anova. I tried fitting a glm model but got the
separation warning message so I tried using the logistf package but as I
stepwise simplify the model I don't know if the simplification is mot
rjswift gmail.com> writes:
>
> I'm trying to select a model under PCA using independent contrasts. Since
> PICs need to be forced through the origin I've been using lmorigin for the
> original regression, but it doesn't appear that stepAIC recognizes it. I
> keep receiving an error message - "Er
I'm trying to select a model under PCA using independent contrasts. Since
PICs need to be forced through the origin I've been using lmorigin for the
original regression, but it doesn't appear that stepAIC recognizes it. I
keep receiving an error message - "Error in na.fail.default(list(Phenology =
1. As this is not really appropriate for R, I suggest replies be private.
2. You might try posting on various statistical forums, e.g. on
http://stats.stackexchange.com/
-- Cheers, Bert
On Wed, Aug 24, 2011 at 12:15 PM, Arnaud Mosnier wrote:
> Hi,
>
> In order to find the best models I use AIC
Hi,
In order to find the best models I use AIC, more specifically I calculate
Akaike weights then Evidence Ratio (ER) and consider that models with a ER <
2 are equally likely.
But the same problem remain each time I do that. I selected the best models
from a set of them, but I don't know if those
Dear List,
I have some difficulties to work with the function lmer from lme4. My
responses are binary form and i want to use forward selection to my 12
covariates but i dont know how can I choose them based on deviance. Can
someone pls give me a example so i can apply. For example my covariates
ay Cichini [mailto:kay.cich...@uibk.ac.at]
Sent: Wednesday, April 28, 2010 3:56 AM
To: r-help@r-project.org
Subject: [R] model selection, lme
hello,
i'd like to evaluate the 2 factors (f1 with 4, f2 with 2 levels) and its
interactions in my model for writing up a summary table with
likelihood-
hello,
i'd like to evaluate the 2 factors (f1 with 4, f2 with 2 levels) and its
interactions in my model for writing up a summary table with
likelihood-ratio tests for publication purpose.
now i'm unsure about which terms of my model may be dropped from the full
model and which not.
mfull<-l
Hi Alina,
your approach sounds problematic - you can always get a smaller RSS if
you add terms to your model, so your approach will always go for larger
models, and you will end up overfitting. Consider information criteria,
e.g., AIC or BIC, which "penalize" larger models. References for AIC
I've created a number of models using lm and now want to pick one with the
smallest standard error or the smallest RSS,
I can get a list of RSS using anova function, but is the any way I can then
select one with the smallest RSS from the list?
[[alternative HTML version deleted]]
Dear R-helpers,
I'm considering two methods of selecting a poisson regression model within
R:
1. Using the step() function (stats package) to find the best model by a
stepwise algorithm and AIC
2. Using the bic.glm() function (BMA package) to find the best model by
Bayesian Model Averaging and BI
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