> However 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 mo
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
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 Da
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 <- matr
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 exa
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 your
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] [
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
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 in which each subject received 3
>
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
moder
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
> Cc: r-help@r-project.org
> Subject: Re: [R] Model studies in one analysis using treatme
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 Mug
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 the five most common treat
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 compa
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 suggestion is great. There is no b
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 make
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,
>
> I have been trying really
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 serie
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 wit
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= (alpha_
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 + e_ij
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 validat
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 model
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
Thanks Thierry
What if I don't know the n in the offset term?
On Wednesday, July 22, 2015, Thierry Onkelinx
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 ~ offset(n), family = poiss
> On 22 Jul 2015, at 06:48 , Don McKenzie 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 so oth
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 &
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 wrote:
>
> what does CLT stand for?
>
>> On Tue, Jul 21, 2015 at 11
Or if there are enough averages of enough counts, the CLT provides another
option.
> On Jul 21, 2015, at 8:38 PM, David Winsemius 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
>> NB regressi
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
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 out
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
p@r-project.org
> Subject: [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,
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
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
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
On 2014-01-15 11:00, r-help-requ...@r-project.org wrote:
Date: Wed, 15 Jan 2014 16:39:17 +1000
From: Diana Virkki
To:r-help@r-project.org
Subject: [R] Model averaging using QAICc
Message-ID:
Content-Type: text/plain
Hi all,
I am having some trouble running GLMM's and using
Diana Virkki 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 o
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
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
nday, August 19, 2013 1:05 PM
> To: David Winsemius; r-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.
>
&g
On Aug 19, 2013, at 16:05, ivo welch 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' to be 'data' he
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]] %i
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 ti
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 spe
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)
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 fea
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) ~
>
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
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
[[ni]]) <- contrasts.arg[[nn]]
}
}
}
}
else {
isF <- FALSE
data <- data.frame(x = rep(0, nrow(data)))
}
ans <- .Internal(model.matrix(t, data))
cons <- if (any(isF))
lapply(data[isF], function(x) attr(x, "
Hello everyone,
I am trying to fit the following model
All X. variables are continuous, while the conditions are categoricals.
model <- lm(X2
+X3+X4+X5+X6+X7+X8+X9+X10+X11+X12+X13+X14+X15+X16+X17+X18+X19+X20+X21+X22+X23+X24+X25+X26+X27+X28+X29+X30+X31+X32+X33+X34+X35+X36+X37+X38+X39+X40+X41+X42+X
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
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 wrote:
> (Apologise for re-sending. I am re-sending in case subject name did not give
> enough informa
(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,
Â
I have data from the following experimental design and trying to fit a mixed
model with lme function according to following steps b
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
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.
__
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
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
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
>
> T
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]]
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 bay
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
bi
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 does
Hi Tony,
?nls
Cheers,
Tsjerk
On Feb 15, 2012 8:03 PM, "Anthony Fristachi" 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.
Thanks
Tony
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]]
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 1.30105
mwege 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 w
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 "ind
ELINX, 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
unde
glht(area_grass, linfct = 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 d
= 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] Mo
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
Ce
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.
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.
> View this message in context:
> 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
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
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:te
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
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 <-
> glm(cbin
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
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")
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:
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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