> 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
Dear Tim, Thanks. the first vector
y<-c(0,1,1,0,0,1,0,0,1,1,1,0,1,1,1,0,0,0,0,1) is the disease status y=
(1=Case,0=Control). The covariate age, smoking status and hypertension
are independent(uncorrelated). The logistic regression (unconditional)
will used. But I need to compare other models with
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] [
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
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_
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
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
No reproducible example so lust general comments.
1. Your function does not return any value
2. You do not get errors but only warnings
3. In your function you get results only for X1. Is it really your intention?
4. I do not know mclapply but from quick look into help page parameters you use
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
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 model averagin
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
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
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)
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
Hi Julien,
>From my point of view this error may be produced by the fact you are working
>with a lot of variables, so the number of characters if you take them into
>account as character is bigger than the system can support.
Have you tried to test an example with 50 variables, for example?.
S
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
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
__
: 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.
__
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
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
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
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
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 distibution - is it possible to make this approach take
it into c
Dear Alfreda,
anova(area_grass) will tell you IF the average grass area is different among
areas.
If you want to know WHICH areas are different from each other, then you have to
do some multiple comparisons. You can use the multcomp package: e.g.
library(multcomp)
glht(area_grass, linfct = mc
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
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
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
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")
Look at package smatr. It fits RMA (called here standardized major axis)
regression, plots the line, and provides confidence limits.
--
David L Carlson
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77843-4352
-Orig
On Mon, Jun 20, 2011 at 1:31 PM, albeam wrote:
> Hi everyone,
>
> Thank you for the help, I apologize for not "providing commented, minimal,
> self-contained, reproducible code." I was looking for some pointers about
> how to do this in general, but it would have been helpful for me to post a
> sp
Hi everyone,
Thank you for the help, I apologize for not "providing commented, minimal,
self-contained, reproducible code." I was looking for some pointers about
how to do this in general, but it would have been helpful for me to post a
specific example. Anyway, after the feedback this is the solu
On Mon, Jun 20, 2011 at 9:46 AM, Gabor Grothendieck
wrote:
> On Mon, Jun 20, 2011 at 9:08 AM, albeam wrote:
>> Please allow me to clarify my original question. What I really need to be
>> able to do it is to take arbitrary functions and evaluate them for arbitrary
>> parameter values. I'm doing t
On Jun 20, 2011, at 15:08 , albeam wrote:
> Please allow me to clarify my original question. What I really need to be
> able to do it is to take arbitrary functions and evaluate them for arbitrary
> parameter values. I'm doing the optimization myself, so I need to be able to
> take a user's funct
On Mon, Jun 20, 2011 at 9:08 AM, albeam wrote:
> Please allow me to clarify my original question. What I really need to be
> able to do it is to take arbitrary functions and evaluate them for arbitrary
> parameter values. I'm doing the optimization myself, so I need to be able to
> take a user's f
Please allow me to clarify my original question. What I really need to be
able to do it is to take arbitrary functions and evaluate them for arbitrary
parameter values. I'm doing the optimization myself, so I need to be able to
take a user's function and evaluate them at the current parameter value
Hi Mark,
You are quite correct. My apologies. That should have been
rcs(x1, 3) + rcs(x2, 3) + x1 %ia% x2
or
pol(x1, 2) + pol(x2, 2) + x1 %ia% x2
Frank
Mark Seeto wrote:
>
> Thank you for your reply, Frank. %ia% drops the product x1'*x2', but
> keeps x1*x2' and x1'*x2. Is there a way to drop
Thank you for your reply, Frank. %ia% drops the product x1'*x2', but
keeps x1*x2' and x1'*x2. Is there a way to drop all three of these and
include only x1*x2? Or is this not a sensible thing to want to do?
Thanks,
Mark
Frank E Harrell Jr wrote:
For the first example you want the restricted int
For the first example you want the restricted interaction operator: y ~
rcs(x1, 3) + rcs(x2, 3) + rcs(x1, 3) %ia% rcs(x2, 3).
For the second example use pol(x,2) or something like pol(x1,2) + pol(x2,2)
+ pol(x1, 2) %ia% pol(x2, 2)
If you have to create new variables for R formulas you're usually
The curve is caused by the zeroes in your data.
raw.residual = response - fitted
so if response=0 then
raw.residual = -fitted.
You get a curve, rather than a straight line, on the fitted vs residual
plot because the residuals are standardised in a way that also depends
on the fitted value. (
; Date: Wed, 16 Mar 2011 08:17:49 +0100
>> From: niederlein-rs...@yahoo.de
>> To: r-help@r-project.org
>> Subject: Re: [R] model fine, predict gives an error
>>
>> Anybody who can help me with this issue?
>
> did you post your data? I was curious so I thought I ma
Anybody who can help me with this issue?
On 15 March 2011 14:15, Antje Niederlein wrote:
> Hi there,
>
> I try to model some dose response curves (drc-package). In most cases
> it is fine but now I got some data which produces me the following
> error:
>
> load("drmData.RData")
> library(drc)
Diogo B. Provete wrote:
I have a data set and I want to procedure to model fitting (e.g., Poisson,
Gausian, binomial, quasipoisson etc.). I'd like to know if there is an
easier way to do this in R.
Easier than what ?
There is no shortage of R functions and packages to fit almost any type
of
Leslie Young gmail.com> writes:
>
> I’ve used logistic regression to create models to assess the effect of
> 3 variables on the presence or absence of a species, including the
> interaction terms between variables and model averaging using MuMI:
> model.avg
>
> The top models (delta<4) include
AIC(sum.coef7)
Erreur dans UseMethod("logLik") : pas de méthode applicable pour "logLik"
I tried but no usemethod("loglik")-> svyglm doesn't use Likelihood.
2010/8/3 Brad Fulton
> Hi Rwanuza
>
>
>
> Here is the code I used to calculate the BIC score from the AIC score
>
>
>
> Model.1=svyglm(a
On Tue, 29 Jun 2010, Mark Seeto wrote:
I’ve been using Frank Harrell’s rms package to do bootstrap model
validation. Is it the case that the optimum penalization may still
give a model which is substantially overfitted?
I calculated corrected R^2, optimism in R^2, and corrected slope for
variou
Dear Kay,
It is not an issue of whether it is "allowed". The issue is understanding
what comparisons are taking place between the models. Your advisor should
help with that.
Perhaps you had a more focused question related to R?
Sincerely,
KeithC.
-Original Message-
From: Kay Cichini [ma
om] On Behalf Of
> bluesky...@gmail.com
> Sent: February-09-10 11:44 PM
> To: John Fox
> Cc: r-h...@stat.math.ethz.ch
> Subject: Re: [R] Model matrix using dummy regressors or deviation
regressors
>
> On Tue, Feb 9, 2010 at 6:09 PM, John Fox wrote:
> > Dear bluesky315,
> &
On Tue, Feb 9, 2010 at 6:09 PM, John Fox wrote:
> Dear bluesky315,
>
> There are several ways in R to determine regressors associated with factors.
> One way is to set the global contrasts option. To get "deviation"
> regressors, use options(contrasts=c("contr.sum", "contr.poly")), and see
> ?opti
Dear bluesky315,
There are several ways in R to determine regressors associated with factors.
One way is to set the global contrasts option. To get "deviation"
regressors, use options(contrasts=c("contr.sum", "contr.poly")), and see
?options and ?contrasts for details. Also see Section 11.1.1 of t
On Feb 9, 2010, at 6:33 PM, bluesky...@gmail.com wrote:
The model matrix for the code at the end the email is shown below.
Since the model matrix doesn't have -1, I think that it is made of
dummy regressors rather than deviation regressors. I'm wondering how
to make a model matrix using deviati
Jim,
Did you read the posting guide?
Did you do a google search, for example, with terms like "[R] generalized
linear models", "[R] count models", "[R] poisson regression"?
I think you should do.
Walmes.
-
..oooO
..
Hello,
I have a barchart. The y-axis represents counts and thex-axis is divided
into 10 equal intervals ranging fronm 0 to 0.1, 0.1 to 0.2, ..0.9 to
1.0.
Is there a way to model the counts in R?
thanks,
Jim
[[alternative HTML version deleted]]
_
So here is some information that I hope gets criticized by the
higher-intelligences that posted on this topic. Beware that I'm not a
statistician and I'm just saying about what I think is correct.
First, before fitting any model, check the distribution of your data, in
some cases a simple anova i
On Dec 19, 2009, at 2:22 AM, David Hugh-Jones wrote:
Hi all
I want to get the design matrix for a model, evaluated at a single
value.
For example, if I pass in a data frame with a=2, b=2, y=3, and my
model is y ~ a+b+a:b, then I would like to get
the values 3, 2, 2, 4 out. I can do this wit
In your model driver truncatedmodel() the fit function looks like:
z...@fit <- function(x, y, w) {
para <- list(mean = mean(x), sd = sd(x), lower = lower, upper= upper)
para$df <- 4
with(para, eval(z...@definecomponent))
}
w are the a-posteriori probabilities and denote the weights with which
o
On Sep 7, 2009, at 1:55 PM, Rafael Moral wrote:
Dear useRs,
Is there a package or a function able to simulate models with sets
of differential equations?
Where we could input our model and give R some value to start with
and it would generate the graphs?
Your request seems a bit on the
Rafael Moral yahoo.com.br> writes:
>
> Dear useRs,
>
> Is there a package or a function able to simulate models with sets of
differential equations?
> Where we could input our model and give R some value to start with and it
would generate the graphs?
>
> Regards,
> Rafael.
>
install.packag
On Tue, 2009-06-23 at 14:18 -0400, Paul Simonin wrote:
> Hello R Users,
> I have a question regarding fitting a model with GAM{mgcv}. I have data
> from several predictor (X) variables I wish to use to develop a model to
> predict one Y variable. I am working with ecological data, so have data
On 06/05/2009 8:22 AM, xavier.char...@free.fr wrote:
Hi all,
I'm doing nonlinear regressions on data with several factors. I want to fit say
a logistic curve with different parameter values for each factor level. So I'm
doing something like:
tmp <- by( myData, list(myFactor1, myFactor2), func
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
Dear Luciano,
The "1" in (1|NestID) indicates only a random intercept. Note that in
most models in R, a "1" on the righthandside of the formula indicates
the intercept, "-1" or "0" indicates no intercept. ~X, which is
equivalent to ~X + 1, indicates a slope along X and an intercept. Hence
a random
PM
> To: r-help@r-project.org
> Cc: Mark Difford
> Subject: Re: [R] model II regression - how do I do it?
>
> On Friday 29 August 2008, Mark Difford wrote:
>> Hi Danilo,
>>
>> >> I need to do a model II linear regression, but I could not find out
>> >>
-project.org
Cc: Mark Difford
Subject: Re: [R] model II regression - how do I do it?
On Friday 29 August 2008, Mark Difford wrote:
> Hi Danilo,
>
> >> I need to do a model II linear regression, but I could not find out
> >> how!!
>
> The smatr package does so-called mo
Hi Dylan,
>> While this topic is fresh, are there any compelling reasons to use Model
>> II
>> regression?
The fact that it is the type of regression used in principal component
analysis makes it a compelling method. Compelling reason? It is used to take
account of measurement errors in both y
On Friday 29 August 2008, Mark Difford wrote:
> Hi Danilo,
>
> >> I need to do a model II linear regression, but I could not find out
> >> how!!
>
> The smatr package does so-called model II (major axis) regression.
>
> Regards, Mark.
While this topic is fresh, are there any compelling reasons to
Hi Danilo,
>> I need to do a model II linear regression, but I could not find out how!!
The smatr package does so-called model II (major axis) regression.
Regards, Mark.
Danilo Muniz wrote:
>
> I need to do a model II linear regression, but I could not find out how!!
>
> I tryed to use the
Hi Danilo,
>> I need to do a model II linear regression, but I could not find out how!!
The smatr package does so-called model II (major axis) regression.
Regards, Mark.
Danilo Muniz wrote:
>
> I need to do a model II linear regression, but I could not find out how!!
>
> I tryed to use the
Dear Danilo:
Here is one approach with the formal reference being:
Computational Statistics & Data Analysis 23 ( 1997 ) 355-372
COMPUTATIONAL
STATISTICS
& DATA ANALYSIS
Generalization of the geometric mean
functional relationship
Norman R. Draper, Yonghong (Fred) Yang
Department of Statistics, 12
On Fri, 2008-08-29 at 15:37 -0200, Danilo Muniz wrote:
> I need to do a model II linear regression, but I could not find out how!!
>
> I tryed to use the lm function, but I did not discovered how to specify the
> model (type I or type II) to the function... could you help me?
Jari Oksanen and Pie
Hello:
I have not seen a reply to this email, so I will offer a couple of
suggestions. The error "false convergence" often means that the model
is overparameterized. Sometimes, specifying lme(..., control =
list(returnObject=TRUE)) will convert this type of error into a
warning. If th
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