search for: mermod

I'm sorry I haven't boiled this down to a more-minimal example yet, but ... I'm working on an S3 method (tidy.merMod, in the 'broom' package). It normally handles 'merMod' objects from the lme4 package, but I'm trying to make it handle 'merModLmerTest' objects from the lmerTest package too. The merModLmerTest class inherits (as an S4) class from the merMod class. The important diff...

Hi, The R CMD check is successful in R 3.0.1 but fails to install package lmerTest under R-devel r63910, Here is what I get: ** preparing package for lazy loading Error in reconcilePropertiesAndPrototype(name, slots, prototype, superClasses, : no definition was found for superclass "merMod" in the specification of class "merModLmerTest" In DESCRIPTION file I have: Depends: Matrix, stats, methods, lme4 Imports: numDeriv, MASS, Hmisc, gplots, pbkrtest I have classes.R file where I specify that "merModLmerTest" class should inherit "merMod": merMod...

>>>>> Ben Bolker <bbolker at gmail.com> >>>>> on Fri, 15 Jul 2016 16:45:50 -0400 writes: > I'm sorry I haven't boiled this down to a more-minimal example yet, > but ... > I'm working on an S3 method (tidy.merMod, in the 'broom' package). It > normally handles 'merMod' objects from the lme4 package, but I'm trying > to make it handle 'merModLmerTest' objects from the lmerTest package too. > The merModLmerTest class inherits (as an S4) class from the merMod...

...oint) , data = myCountDB) > > > > I now would like to add a curve to the histogram representing the negative binomial density function fitted to my data using > > >> curve(dnbinom(x=, size=, prob=, mu=), add=TRUE) Why not use the predict function in that package? See ?merMod -- David. > > > But I am struggling defining the arguments to dnbinom. > > > Using the str() function on the nbFit object I see there are many fields returned. And I get lost reading the ?glmer.nb help, greatly because of my lack of knowledge. Which ones should I use? >...

...function fails. It even fails when I > try to calculate the confint() inside the same function as the > glmer() call (see the fit_model_ci function). > > Best regards, > > Thierry Ugh. I can get this to work if I also try searching up the call stack, as follows (within update.merMod). This feels like "code smell" to me though -- i.e., if I have to hack this hard I must be doing something wrong/misunderstanding how the problem *should* be done. if (evaluate) { ff <- environment(formula(object)) pf <- parent.frame() ## save parent frame in...

Dear list, I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning. I have ~20% of 0's. I want to know if the cleaning process was useful at reducing the number of particles. I first fit a

...9;m also including the structure of an example model that did converge (but I can I tell from the output?). List of 18 ?$ methTitle?? : chr "Linear mixed model fit by maximum likelihood? \nt-tests use? Satterthwaite approximations to degrees of freedom" ?$ objClass??? : atomic [1:1] lmerMod ? ..- attr(*, "package")= chr "lme4" ?$ devcomp???? :List of 2 ? ..$ cmp : Named num [1:10] 176.85 59.09 95.43 3.84 99.27 ... ? .. ..- attr(*, "names")= chr [1:10] "ldL2" "ldRX2" "wrss" "ussq" ... ? ..$ dims: Named int [1:12...

...ing_point) , data = myCountDB) > > > > I now would like to add a curve to the histogram representing the negative binomial density function fitted to my data using > > >> curve(dnbinom(x=, size=, prob=, mu=), add=TRUE) Why not use the predict function in that package? See ?merMod -- David. > > > But I am struggling defining the arguments to dnbinom. > > > Using the str() function on the nbFit object I see there are many fields returned. And I get lost reading the ?glmer.nb help, greatly because of my lack of knowledge. Which ones should I use? > > &...

...s when I try to calculate the confint() inside the same >> function as the glmer() call (see the fit_model_ci function). > >> Best regards, > >> Thierry > > > Ugh. I can get this to work if I also try searching up the call > stack, as follows (within update.merMod). This feels like "code > smell" to me though -- i.e., if I have to hack this hard I must be > doing something wrong/misunderstanding how the problem *should* be > done. > > > if (evaluate) { ff <- environment(formula(object)) pf <- > parent.frame() ## sav...

That's more or less right. We wrote a terms.merMod method, which accesses the terms component of the @frame slot, which we have modified upstream ... Do you mean term.labels rather than term.names? BTW ?terms.object says (under the "term.labels" element): Non-syntactic names will be quoted by backticks: this makes it easie...

...ss-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental "merMod", "lme", and "glmmTMB" methods for mixed-effects models. cv() supports parallel computations. - mse() (mean-squared error) and BayesRule() are cross-validation criteria ("cost functions"), suitable for use with cv(). - cvSelect() cross-validates a selecti...

...ss-validation of regression models. Some of the functions supplied by the package: - cv() is a generic function with a default method and computationally efficient "lm" and "glm" methods, along with a method for a list of competing models. There are also experimental "merMod", "lme", and "glmmTMB" methods for mixed-effects models. cv() supports parallel computations. - mse() (mean-squared error) and BayesRule() are cross-validation criteria ("cost functions"), suitable for use with cv(). - cvSelect() cross-validates a selecti...

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model > comparison statistics. For objects of class ?lmerMod? the > default behavior is to refit the models with ML if fitted &...

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 WARNING: this is long. Sorry I couldn't find a way to compress it. Is there a reasonable way to design an update method so that it's robust to a variety of reasonable use cases of generating calls or data inside or outside a function? Is it even possible? Should I just tell users "don't do that"? * `update.default()` uses

...On Tuesday, November 28, 2017 6:51 AM, Phillip Alday > <phillip.alday at mpi.nl> wrote: > > > Be careful when mixing lme4 and lmerTest together -- lmerTest extends > and changes the behavior of various lme4 functions. > > From the help page for lme4-anova (?lme4::anova.merMod) > >> ?anova?: returns the sequential decomposition of the contributions >> of fixed-effects terms or, for multiple arguments, model >> comparison statistics. For objects of class ?lmerMod? the >> default behavior is to refit the models w...

...or si alguno está interesado en utilizar el paquete LMERconvenientsfucntions Dear Javier, The package has been updated and should work for you fine now. Note that function mcp.fnc does not return the fourth plot (dffits) anymore. We still have to figure out a way to compute the dffits for the new merMod objects. Also note that mcposthoc.fnc doesn't work with pvals.fnc / MCMC anymore. Please see Note in help page. Finally, the package gained function plotLMER.fnc from archived package languageR. It's the same, except for the bits related to mcmc. Please let me know if you encounter other...

...d <- lmer( RT ~ Condicion1 * Condicion2 + (1|Subject), data=dat) summary(mod) pamer.fnc(mod) #Error en qr(model@X) : # error in evaluating the argument 'x' in selecting a method for #function 'qr': Error: no hay un slot de nombre "X" para ese objeto #de clase "lmerMod" #---------------------------------------------------------------------------------------------------------------------------------- También os dejo un ejemplo de la ayuda del paquete. ¿Alguna idea de como solucionar esto? ¿volver a una versión anterior? ¿Contactar con el autor? Si alguien...

Ben, Looking at your notes, it appears that your solution is to write your own terms() function for lme.? It is easy to verify that the "varnames.fixed" attribute is not returned by the ususal terms function. Then I also need to write my own terms function for the survival and coxme pacakges? Because of the need to treat strata() terms in a special way I manipulate the formula/terms in

...) : + not meaningful for > factors > > > mod <- lmer( RT ~ Condicion1 * Condicion2 + (1|Subject), data=dat) > > Error en FUN(X[[1L]], ...) : > > Invalid grouping factor specification, Subject > > > summary(mod) > > Linear mixed model fit by REML ['lmerMod'] > > Formula: RT ~ Condicion1 * Condicion2 + (1 | Subject) > > Data: dat > > > > REML criterion at convergence: 145.1507 > > > > Random effects: > > Groups Name Variance Std.Dev. > > Subject (Intercept) 0.0 0.00 > > Re...