Re: [R] Model Comparision for case control studies in R

ver the problem is solved, I would start by trying to determine if >> any >> > one model was appropriate. Are the model assumptions satisfied? If the >> > answer is no, then try another model until you find one that does >> > satisfy >> > the model assump

Re: [R] Model Comparision for case control studies in R

ertension correlated in any way or are > they independent (correlation=0)? > Are the correlations large enough to adversely influence your model? > Tim > > -Original Message- > From: R-help On Behalf Of anteneh asmare > Sent: Wednesday, June 15, 2022 7:29 AM > To: r

Re: [R] Model To Simulate Dice Roll

Sorry, The last three lines should read: all <- apply(results, 1, function(x) length(intersect(x, seq(sides)))==sides) sum(all)/reps results <- as.data.frame(results) To generalize them for values of sides other than 6. On Fri, Apr 22, 2022 at 11:05 PM Paul Bernal wrote: > Thank you so much

Re: [R] Model To Simulate Dice Roll

Thank you so much David! El El vie, 22 de abr. de 2022 a la(s) 11:04 p. m., David Carlson < dcarl...@tamu.edu> escribió: > Since the rolls are independent, it is not necessary to separate the rolls > into two stages: > > sides <- 6 > months <- 12 > reps <- 100 > > set.seed(2022) > results <-

Re: [R] Model To Simulate Dice Roll

Dear Bert, Thank you for your kind reply. That is fine, I appreciate your feedback anyway. Have a great day/night. Best, Paul El mié, 20 abr 2022 a las 23:31, Bert Gunter () escribió: > I believe I gave you sufficient information (the vector of dice roll > results would replace 1:36 in my

Re: [R] Model To Simulate Dice Roll

I believe I gave you sufficient information (the vector of dice roll results would replace 1:36 in my example). Furthermore, this sounds like homework, which we try not to do here. But even if it is not, I expect you to fill in the details based on what I have provided. If I have misunderstood

Re: [R] Model To Simulate Dice Roll

If I understand you correctly, it's simple. Matrices in R are vectors with a dimension attribute. By default, they are populated column by column. Use 'byrow = TRUE to populate by row instead. For example: > matrix (1:36, ncol = 12, byrow = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]

[R] Model frame of lme objects

From a linear model like `mod <- lm(y ~ x)`, I can obtain a data frame with all the variables involved in the model (in that case, `y` and `x`). How could I get a similar data frame from an lme object, e.g. fitted as `mod <- lme(y ~ x, random=~1|g)` ? I know that `getData` might work if the

Re: [R] Model within subjects treatment variable and multiple measurements per treatment: is this the correct model?

Post on r-sig-mixed-models, not here. In PLAIN TEXT NOT HTML. -- Bert On Oct 13, 2017 10:55 AM, "Nynke l" wrote: > Hello all, > > > I have a question regarding my analysis and how to correctly model this in > r syntax. > > I have a dataset from an experiment

Re: [R] Model studies in one analysis using treatment as a five level moderator in a meta-regression

Dear Jay I am not that familiar with the meta package but it looks as though it does not allow you to do a meta-regression within metaprop. However there is a function metareg which takes the object you created with metaprop and allows you to add a moderator so i would try that next. By

Re: [R] Model studies in one analysis using treatment as a five level moderator in a meta-regression

Petr > -Original Message- > From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Jay Zola > Sent: Monday, June 26, 2017 11:44 PM > To: Vito Michele Rosario Muggeo <vito.mug...@unipa.it> > Cc: r-help@r-project.org > Subject: Re: [R] Model studies in one

Re: [R] Model studies in one analysis using treatment as a five level moderator in a meta-regression

Dear Vito, Thank you for your reply. I tried to contact the statistics departement numerous times, but did not receive any reply. That is why I started to look on the internet for help. Yours sincerely, Jay Verstuurd vanaf mijn iPhone > Op 26 jun. 2017 om 22:05 heeft Vito Michele Rosario

Re: [R] Model studies in one analysis using treatment as a five level moderator in a meta-regression

hi Jay, Consult a local statistician. Statistics is not you think is (namely simple computations, R and probably plotting..). regards, vito Jay Zola ha scritto: Hello, I am medical student, writing a meta-analysis on complication and reoperation rates after

[R] Model studies in one analysis using treatment as a five level moderator in a meta-regression

Hello, I am medical student, writing a meta-analysis on complication and reoperation rates after the five most common treatments of distal radius fractures. I have been busy with the statistics for months by my self, but find it quite hard since our classes were very basic. Now I want to

Re: [R] Forecast Modeling using R model node in SPSS Modeler

Hi Paul, Not everything people ask for makes sense. If you insist on trying to get R integration working in SPSS you should reach out to the SPSS company and/or community for support. Best, Ista On Dec 6, 2016 8:05 AM, "Paul Bernal" wrote: > Hi Ista, > > Your

Re: [R] Forecast Modeling using R model node in SPSS Modeler

Hi Ista, Your suggestion is great. There is no better way to work with R than working with R directly, however, I have been asked to generate forecasts in SPSS Modeler using the R integration that comes with it, so unfortunately, I need to find a way around. If I find the way to do it, I will

Re: [R] Forecast Modeling using R model node in SPSS Modeler

Hi Paul, I suggest forgetting about SPSS and using R directly. Getting started with R is easier than ever thanks to the growing number of tutorials, workshops, mailing lists and forums. Best, Ista On Mon, Dec 5, 2016 at 4:09 PM, Paul Bernal wrote: > Hello everyone, > >

[R] Forecast Modeling using R model node in SPSS Modeler

Hello everyone, I have been trying really hard to use the SPSS Modeler´s R modeling node to generate forecasts without success. I personally think the R integration in SPSS Modeler is kind of poor, since you are only allowed to work with R version 2.15.2. Is there anyone who has worked time

Re: [R] model specification using lme

Dear Hanna, None of the models are correct is you want the same random intercept for the different methods but different random slope per method. You can random = ~ 1 + time:method | individual The easiest way to get alpha_0 and tau_i is to apply post-hoc contrasts. That is fairly easy to do

Re: [R] model specification using lme

li li gmail.com> writes: > > Hi all, > For the following data, I consider the following random intercept and > random slope model. Denote as y_ijk the response value from *j*th > individual within *i*th method at time point *k*. Assume the following > model for y_ijk: > > y_ijk=

[R] model specification using lme

Hi all, For the following data, I consider the following random intercept and random slope model. Denote as y_ijk the response value from *j*th individual within *i*th method at time point *k*. Assume the following model for y_ijk: y_ijk= (alpha_0+ tau_i +a_j(i))+(beta_i+b_j(i)) T_k +

Re: [R] R model developing & validating - Open to Discussion

This is way OT for this list, and really has nothing to do with R. Post on a statistical list like stats.stackexchange.com if you want to repeat a discussion that has gone on for decades and has no resolution. You really should be spending time with the literature, though. Have you? "Cross

[R] R model developing & validating - Open to Discussion

Throughout my R journey I have noticed the way we can use given data to develop and validate a model. Assume that you have given data for a problem 1. train.csv 2. test.csv *Method A* *Combine train+test data* and develop a model using the combined data. Then use test.data to validate the

[R] Model after random forest

Hi there, I´m using random Forest package to create a random Forest: model<-randomForest(A~.,data=mydata) , and I use the varImpPlot(model) to see which are the most important variables, so I obtain that C, D and F are the most important ones, but... How can I see the model, in which levels I

Re: [R] model non-integer count outcomes

Sorry. Central limit theorem. Enough averaging and you get a normal distribution (simply stated, perhaps too simply). If so others will correct me before long. :-( Sent from my iPad On Jul 21, 2015, at 8:52 PM, Wensui Liu liuwen...@gmail.com wrote: what does CLT stand for? On Tue, Jul

Re: [R] model non-integer count outcomes

Or if there are enough averages of enough counts, the CLT provides another option. On Jul 21, 2015, at 8:38 PM, David Winsemius dwinsem...@comcast.net wrote: On Jul 21, 2015, at 8:21 PM, Wensui Liu wrote: Dear Lister When the count outcomes are integers, we could use either Poisson or

Re: [R] model non-integer count outcomes

If you know the number of counts (n) used to calculate the average then you can still use a poisson distribution. Total = average * n glm(total ~ offset(n), family = poisson) ​ ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie

Re: [R] model non-integer count outcomes

Thanks Thierry What if I don't know the n in the offset term? On Wednesday, July 22, 2015, Thierry Onkelinx thierry.onkel...@inbo.be wrote: If you know the number of counts (n) used to calculate the average then you can still use a poisson distribution. Total = average * n glm(total ~

Re: [R] model non-integer count outcomes

On 22 Jul 2015, at 06:48 , Don McKenzie d...@uw.edu wrote: Sorry. Central limit theorem. Or some sort of vegetarian sandwich. Celery, Lettuce, Tomato sounds almost edible with sufficient mayo. ;-) Enough averaging and you get a normal distribution (simply stated, perhaps too simply). If

Re: [R] model non-integer count outcomes

On Jul 21, 2015, at 8:21 PM, Wensui Liu wrote: Dear Lister When the count outcomes are integers, we could use either Poisson or NB regression to model them. However, there are cases that the count outcomes are non-integers, e.g. average counts. I am wondering if it still makes sense to use

[R] model non-integer count outcomes

Dear Lister When the count outcomes are integers, we could use either Poisson or NB regression to model them. However, there are cases that the count outcomes are non-integers, e.g. average counts. I am wondering if it still makes sense to use Poisson or NB regression to model these non-integer

Re: [R] model selection

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

[R] model selection

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

Re: [R] Model for lm keeps producing an error

: [R] Model for lm keeps producing an error Hi, I still seem to be getting errors from trying to run my altered R script, any advice? Thanks Jess Model1A = function(meth_matrix,exposure, X1, X2, X3, batch) { + + mod = lm(methcol ~ exposure+X1+X2+X3+batch, data = meth_matrix

[R] Model for lm keeps producing an error

Hi, I still seem to be getting errors from trying to run my altered R script, any advice? Thanks Jess Model1A = function(meth_matrix,exposure, X1, X2, X3, batch) { + + mod = lm(methcol ~ exposure+X1+X2+X3+batch, data = meth_matrix) + + + res=coef(summary(mod))[2,] + + + } ##Run

[R] model selection for nested factorial design

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

Re: [R] model selection for nested factorial design

K C interlocutorbrl2 at 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:

[R] model specification issue

Hi all, I'm reviewing a paper and ran into a mystery (hopefully not to you) trying to reproduce the stats. The authors uploaded their data to a repository. I've created a similar (though random) data set for confidentiality reasons. So the authors include one four-level treatment variable, but

Re: [R] Model averaging using QAICc

On 2014-01-15 11:00, r-help-requ...@r-project.org wrote: Date: Wed, 15 Jan 2014 16:39:17 +1000 From: Diana Virkkid.vir...@griffith.edu.au To:r-help@r-project.org Subject: [R] Model averaging using QAICc Message-ID: CAL6nRQcAyN-3SVeZSMXoJq=vsxotpg3e0prwjw7iu7g20b+...@mail.gmail.com Content

Re: [R] Model averaging using QAICc

Diana Virkki d.virkki at griffith.edu.au writes: Hi all, I am having some trouble running GLMM's and using model averaging with QAICc. Let me know if you need more detail here: I am trying to run GLMM's on count data in the package glmmADMB with a negative binomial distribution due to

[R] Model averaging using QAICc

Hi all, I am having some trouble running GLMM's and using model averaging with QAICc. Let me know if you need more detail here: I am trying to run GLMM's on count data in the package glmmADMB with a negative binomial distribution due to overdispersion. The dispersion parameter has now reduced to

[R] Model selection exponential and gamma distribution using cross validation

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!

[R] model selection with step()

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

Re: [R] model selection with step()

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

[R] model syntax processed --- probably common

dear R experts---I was programming a fama-macbeth panel regression (a fama-macbeth regression is essentially T cross-sectional regressions, with statistics then obtained from the time-series of coefficients), partly because I wanted faster speed than plm, partly because I wanted some additional

Re: [R] model syntax processed --- probably common

Ivo: I may not get your question, but you seem to be confusing the name of an object, which is essentially a pointer into memory and a language construct -- (correction requested if I have misstated! -- and the names attribute of (some) objects. You can, of course, attach a lab or (whatever)

Re: [R] model syntax processed --- probably common

On Aug 19, 2013, at 9:45 AM, ivo welch wrote: dear R experts---I was programming a fama-macbeth panel regression (a fama-macbeth regression is essentially T cross-sectional regressions, with statistics then obtained from the time-series of coefficients), partly because I wanted faster speed

Re: [R] model syntax processed --- probably common

On Aug 19, 2013, at 12:48 PM, David Winsemius wrote: On Aug 19, 2013, at 9:45 AM, ivo welch wrote: dear R experts---I was programming a fama-macbeth panel regression (a fama-macbeth regression is essentially T cross-sectional regressions, with statistics then obtained from the

Re: [R] model syntax processed --- probably common

thank you. but uggh...sorry for my html post. and sorry again for having been obscure in my attempt to be brief. here is a working program. fama.macbeth - function( formula, din ) { fnames - terms( formula ) dnames - names( din ) stopifnot( all(dimnames(attr(fnames, factors))[[1]] %in%

Re: [R] model syntax processed --- probably common

On Aug 19, 2013, at 16:05, ivo welch ivo.we...@gmail.com wrote: thank you. but uggh...sorry for my html post. and sorry again for having been obscure in my attempt to be brief. here is a working program. fama.macbeth - function( formula, din ) { I think most users would expect 'din'

Re: [R] model syntax processed --- probably common

-help Subject: Re: [R] model syntax processed --- probably common thank you. but uggh...sorry for my html post. and sorry again for having been obscure in my attempt to be brief. here is a working program. fama.macbeth - function( formula, din ) { fnames - terms( formula ) dnames

[R] Model ranking (AICc, BIC, QIC) with coxme regression

Hi, I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below). #Model1 spring.cox - coxme (Surv(start, stop, Real_rand) ~

Re: [R] model frame and formula mismatch in model.matrix()

Hi Eva, you're right, it works with 50 variables. Then, how could I change this variable limit in the lm function? Thank you very much for your help. Julien. -- View this message in context: http://r.789695.n4.nabble.com/model-frame-and-formula-mismatch-in-model-matrix-tp4664093p4664226.html

Re: [R] model frame and formula mismatch in model.matrix()

))     lapply(data[isF], function(x) attr(x, contrasts))     else NULL     attr(ans, contrasts) - cons     ans } Regards, Eva --- El vie, 12/4/13, jul 2-pom j.pom...@ibmc-cnrs.unistra.fr escribió: De: jul 2-pom j.pom...@ibmc-cnrs.unistra.fr Asunto: [R] model frame and formula mismatch

[R] Model selection: On the use of the coefficient determination(R2) versus the frequenstist (AIC) and Bayesian (AIC) approaches

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 of

Re: [R] Model selection: On the use of the coefficient determination(R2) versus the frequenstist (AIC) and Bayesian (AIC) approaches

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 armel.kap...@sdstate.edu wrote: Dear all, I'm modeling growth curve of some ecosystems with

[R] model frame and formula mismatch in model.matrix()

Hello everyone, I am trying to fit the following model All X. variables are continuous, while the conditions are categoricals. model - lm(X2

Re: [R] Model Selection based on individual t-values with the largest possible number of variables in regression

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 15

[R] model fitting with lme

(Apologise for re-sending. I am re-sending in case subject name did not give enough information. Any shared experience with lme is deeply appreciated) Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps

Re: [R] model fitting with lme

You did not get any replies because this is largely off topic. Please stop posting here and post to the r-sig-mixed-models list instead. -- Bert On Fri, Mar 1, 2013 at 9:33 AM, KAYIS Seyit Ali s_a_ka...@yahoo.com wrote: (Apologise for re-sending. I am re-sending in case subject name did not

Re: [R] Model selection in nonstationary VAR

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

Re: [R] Model selection in nonstationary VAR

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

[R] Model selection in nonstationary VAR

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

Re: [R] model selection with spg and AIC (or, convert list to fitted model object)

: 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

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 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

Re: [R] model selection with spg and AIC (or, convert list to fitted model object)

Adam Zeilinger zeil0006 at 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).

Re: [R] model frame and formula mismatch with latent class analysis poLCA

I've had the same problem, but although seems ridiculous I have solved by reducing the length of the name of the variables (yes the character length of each variable, e.g: if you have many variables named big_name_variable, rename it with bnv) I hope this solves your problem.

[R] model average for GLM comparison

I have several parameters that I measured to model with the presence of frogs. (example. mod30-glm(FROG~ maximum.depth, minimum.depth, Tree, Shrub, data = regional_biotic, family=binomial) )I've calculated my AIC table but can't for the life of me come up with code to model average for models

[R] model selection with spg and AIC (or, convert list to fitted model object)

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

[R] model selection with spg and AIC (or, convert list to fitted model object)

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

[R] Model Description

Hello dear, I am new to R, Have developed the model for prediction. I dont know exactly about the followed terms residual standard error degrees of freedom, multiple R-squared, adjusted R-squared F-statistics p-values Thanks in advance. [[alternative HTML version deleted]]

Re: [R] Model Description

On Sep 5, 2012, at 1:58 PM, mahout user wrote: Hello dear, I am new to R, Have developed the model for prediction. I dont know exactly about the followed terms residual standard error degrees of freedom, multiple R-squared, adjusted R-squared F-statistics p-values Thanks in

[R] Model comparison with bayesglm

Dear list I have a data set involving binary responses (successes failures) for which some explanatory variables result in a quasi complete separation problem. To deal with the separation problem I tried to run a glm with bayesglm in the arm package. However when I try to compare different

[R] model frame and formula mismatch with latent class analysis poLCA

Dear R-users, I keep getting an ERROR saying Error in model.matrix.default(formula, mframe) : model frame and formula mismatch in model.matrix() when i fit poLCA with more than 63 variables. Below are the details. I am trying to do a Latent Class Analysis using poLCA. My data set contains

[R] Model Averaging Help

Dear All, I'm using AIC-weighted model averaging to calculate model averaged parameter estimates and partial r-squares of each variable in a 10-variable linear regression. I've been using the MuMIn package to calculate parameter estimates, but am struggling with partial r-squares. There

[R] model fitting

Greetings, Any suggestions for approaching the fitting of the function y = b/exp(a*x) + c*x + y0 where a, b, c, and y0 are unknown constants and y and x are variables in a give dataset. Thanks Tony [[alternative HTML version deleted]]

Re: [R] model fitting

Hi Tony, ?nls Cheers, Tsjerk On Feb 15, 2012 8:03 PM, Anthony Fristachi antak...@gmail.com wrote: Greetings, Any suggestions for approaching the fitting of the function y = b/exp(a*x) + c*x + y0 where a, b, c, and y0 are unknown constants and y and x are variables in a give dataset.

[R] model non-nested random effects in nlme library

Hello all, In lme4 if you want to model two non-nested random effects you code it like this: mod1 - lmer(y~x + (1|randomvar1) + (1|randomvar2)) How would you go about to model something similar in nlme? In my database I have two variables for which I have repeated measures, lets call them

Re: [R] model non-nested random effects in nlme library

mwege RProgStuff at gmail.com writes: Hello all, In lme4 if you want to model two non-nested random effects you code it like this: mod1 - lmer(y~x + (1|randomvar1) + (1|randomvar2)) How would you go about to model something similar in nlme? In my database I have two variables for

Re: [R] model non-nested random effects in nlme library

Season Individual FT FTLengthCurvIndex dir.lin 2009W GW522 1 20 0.538931977 1.8884631 2009W GW522 2 28 0.498651384 0.8379838 2010W A1841 17 0.492549537 1.23907 2010W A1842 23 0.630582873

[R] Model design

Dear List, I am realtively inexperienced so i apologise in advance and ask for understanding in the simplicity of my question: I have data on the amount of grass per km in a cell ( of which i have lots) grass and for each cell i have x/y coordinates - required due to spatial autocorrelation

Re: [R] Model design

= mcp(AREA = Tukey)) Best regards, Thierry Van: r-help-boun...@r-project.org [r-help-boun...@r-project.org] namens alfreda morinez [alfredamori...@gmail.com] Verzonden: vrijdag 16 december 2011 14:07 Aan: r-help@r-project.org Onderwerp: [R] Model design Dear

Re: [R] Model design

= mcp(AREA = Tukey)) Best regards, Thierry Van: r-help-boun...@r-project.org [r-help-boun...@r-project.org] namens alfreda morinez [alfredamori...@gmail.com] Verzonden: vrijdag 16 december 2011 14:07 Aan: r-help@r-project.org Onderwerp: [R] Model

Re: [R] Model design

, Thierry CC: r-help@r-project.org Onderwerp: Re: [R] Model design Hi Thierry I looked at running an ANOVA but I have spatial autocorrelation in the data set as indicated by Variograms and significant moran's I i.e the cells closer together are more likely to be similar than expected under a normal

[R] model frame problem

Dear R community: I am working on a model frame problem which is important for my data analysis. What I am trying to get is to get data set d3 through the model formula ff=y~a+b+a*b and data set d1 to generate a new data set d3 rather than d2. I have tried several ways but did not get that done.

[R] model frame problem

Dear R community: I am working on a model frame problem which is important for my data analysis. What I am trying to get is to get data set d3 through the model formula ff=y~a+b+a*b and data set d1 to generate a new data set d3 which includes a interaction column between a and b rather than d2. I

Re: [R] Import/convert PMML to R model

: http://r.789695.n4.nabble.com/Import-convert-PMML-to-R-model-tp3332772p3565260.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read

[R] model selection using logistf package

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

Re: [R] model selection using logistf package

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 the

[R] Model Selection with Phylogenetic Independent Contrasts

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 =

Re: [R] Model Selection with Phylogenetic Independent Contrasts

rjswift rosejswift at 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

[R] Model selection and model efficiency - Search for opinions

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

Re: [R] Model selection and model efficiency - Search for opinions

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 a.mosn...@gmail.com wrote: Hi, In order to find the best

[R] model formula

Hello useRs, Pls help with removing a single interaction term from a formula: summary( glm.turn.2 - glm(cbind(turn.cnt,tot.cnt-turn.cnt)~sn+poly(relAge,2,raw=T)+termfac+rate:termfac,data=fix, family=quasibinomial) ) Gives Coefficients:

Re: [R] model formula

On 11.08.2011 17:27, Bond, Stephen wrote: Hello useRs, Pls help with removing a single interaction term from a formula: summary( glm.turn.2- glm(cbind(turn.cnt,tot.cnt-turn.cnt)~sn+poly(relAge,2,raw=T)+termfac+rate:termfac,data=fix, family=quasibinomial)

Re: [R] model formula

in the way a glm model can be used. Thanks everybody. Stephen -Original Message- From: Uwe Ligges [mailto:lig...@statistik.tu-dortmund.de] Sent: Thursday, August 11, 2011 11:40 AM To: Bond, Stephen Cc: r-help@r-project.org Subject: Re: [R] model formula On 11.08.2011 17:27, Bond, Stephen

Re: [R] model formula

Hi Stephen, have a look at ?update.formula glm.turn.3-update(glm.turn.2,.~.-termfac1:rate) should do the trick. hth. Am 11.08.2011 17:27, schrieb Bond, Stephen: Hello useRs, Pls help with removing a single interaction term from a formula: summary( glm.turn.2 -

Re: [R] model formula

On 11.08.2011 18:32, Eik Vettorazzi wrote: Hi Stephen, have a look at ?update.formula glm.turn.3-update(glm.turn.2,.~.-termfac1:rate) No, 1 is a level of the variable termfac here. Uwe Ligges should do the trick. hth. Am 11.08.2011 17:27, schrieb Bond, Stephen: Hello useRs, Pls help

Re: [R] model formula

Am 11.08.2011 17:39, schrieb Uwe Ligges: On 11.08.2011 17:27, Bond, Stephen wrote: Hello useRs, Pls help with removing a single interaction term from a formula: summary( glm.turn.2- glm(cbind(turn.cnt,tot.cnt-turn.cnt)~sn+poly(relAge,2,raw=T)+termfac+rate:termfac,data=fix,

[R] Model selection

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

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