Dear Ben, Fitting the model and calculating the confidence intervals within the same function works ( https://github.com/ThierryO/testlme4/blob/master/tests/testthat/test_fit_model_ci.R passes). Fitting the model inside a function and calculating the confidence intervals on the output still fails ( https://github.com/ThierryO/testlme4/blob/master/tests/testthat/test_fit_model.R fails).
Directly calculating the confidence intervals in the same function is an acceptable solution for my work. Thank you for this solution. Best regards, Thierry ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2015-03-27 22:36 GMT+01:00 Ben Bolker <bbol...@gmail.com>: > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA1 > > [Sorry to those who don't like it for top-posting] > > Thierry, I'm curious whether this addresses your problem (although > we don't have a hard timetable for the next release [it has to avoid > conflicts with the 3.2.0 release in 2.5 weeks at the very least], so > this might be problematic if your package needs to depend on it). > > I'm still curious whether there are any ideas/opinions from other > readers. Has anyone else struggled with this? Is there a canonical > solution? > > Ben Bolker > > > On 15-03-24 07:55 PM, Ben Bolker wrote: > > On 15-03-23 12:55 PM, Thierry Onkelinx wrote: > >> Dear Ben, > > > >> Last week I was struggling with incorporating lme4 into a > >> package. I traced the problem and made a reproducible example ( > >> https://github.com/ThierryO/testlme4). It looks very simular to > >> the problem you describe. > > > >> The 'tests' directory contains the reproducible examples. > >> confint() of a model as returned by a 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 case we need it sf <- > > sys.frames()[[1]] tryCatch(eval(call, env=ff), error=function(e) { > > tryCatch(eval(call, env=sf), error=function(e) { eval(call, pf) }) > > }) } else call > > > > Here is an adapted even-more-minimal version of your code, which > > seems to work with the version of update.merMod I just pushed to > > github, but fails for glm(): > > > > > > ## > > https://github.com/ThierryO/testlme4/blob/master/R/fit_model_ci.R > > fit_model_ci <- function(formula, dataset, mfun=glmer){ model <- > > mfun( formula = formula, data = dataset, family = "poisson" ) ci <- > > confint(model) return(list(model = model, confint = ci)) } > > > > library("lme4") set.seed(101) dd <- > > data.frame(f=factor(rep(1:10,each=100)), y=rpois(2,1000)) > > fit_model_ci(y~(1|f),dataset=dd) > > fit_model_ci(y~(1|f),dataset=dd,mfun=glm) > > > > > > > > > > > >> ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / > >> Research Institute for Nature and Forest team Biometrie & > >> Kwaliteitszorg / team Biometrics & Quality Assurance > >> Kliniekstraat 25 1070 Anderlecht Belgium > > > >> To call in the statistician after the experiment is done may be > >> no more than asking him to perform a post-mortem examination: he > >> may be able to say what the experiment died of. ~ Sir Ronald > >> Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner > >> The combination of some data and an aching desire for an answer > >> does not ensure that a reasonable answer can be extracted from a > >> given body of data. ~ John Tukey > > > >> 2015-03-22 17:45 GMT+01:00 Ben Bolker <bbol...@gmail.com>: > > > >> 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 `eval(call, parent.frame())`; this > >> fails when the call depends on objects that were defined in a > >> different environment (e.g., when the data are generated and the > >> model initially fitted within a function scope) > > > >> * an alternative is to store the original environment in which > >> the fitting is done in the environment of the formula and use > >> `eval(call, env=environment(formula(object)))`; this fails if the > >> user tries to update the model originally fitted outside a > >> function with data modified within a function ... > > > >> * I think I've got a hack that works below, which first tries in > >> the environment of the formula and falls back to the parent > >> frame if that fails, but I wonder if I'm missing something much > >> simpler .. > > > >> Thoughts? My understanding of environments and frames is still, > >> after all these years, not what it should be ... > > > >> I've thought of some other workarounds, none entirely > >> satisfactory: > > > >> * force evaluation of all elements in the original call * > >> printing components of the call can get ugly (can save the > >> original call before evaluating) * large objects in the call get > >> duplicated * don't use `eval(call)` for updates; instead try to > >> store everything internally * this works OK but has the same > >> drawback of potentially storing large extra copies * we could try > >> to use the model frame (which is stored already), but there are > >> issues with this (the basis of a whole separate rant) because the > >> model frame stores something in between predictor variables and > >> input variables. For example > > > >> d <- data.frame(y=1:10,x=runif(10)) > >> names(model.frame(lm(y~log(x),data=d))) ## "y" "log(x)" > > > >> So if we wanted to do something like update to "y ~ sqrt(x)", it > >> wouldn't work ... > > > >> ================== update.envformula <- function(object,...) { > >> extras <- match.call(expand.dots = FALSE)$... call <- > >> getCall(object) for (i in names(extras)) { existing <- > >> !is.na(match(names(extras), names(call))) for (a in > >> names(extras)[existing]) call[[a]] <- extras[[a]] if > >> (any(!existing)) { call <- c(as.list(call), extras[!existing]) > >> call <- as.call(call) } } eval(call, > >> env=environment(formula(object))) ## enclos=parent.frame() > >> doesn't help } > > > >> update.both <- function(object,...) { extras <- > >> match.call(expand.dots = FALSE)$... call <- getCall(object) for > >> (i in names(extras)) { existing <- !is.na(match(names(extras), > >> names(call))) for (a in names(extras)[existing]) call[[a]] <- > >> extras[[a]] if (any(!existing)) { call <- c(as.list(call), > >> extras[!existing]) call <- as.call(call) } } pf <- > >> parent.frame() ## save parent frame in case we need it > >> tryCatch(eval(call, env=environment(formula(object))), > >> error=function(e) { eval(call, pf) }) } > > > >> ### TEST CASES > > > >> set.seed(101) d <- data.frame(x=1:10,y=rnorm(10)) m1 <- > >> lm(y~x,data=d) > > > >> ##' define data within function, return fitted model f1 <- > >> function() { d2 <- d lm(y~x,data=d2) return(lm(y~x,data=d2)) } > >> ##' define (and modify) data within function, try to update ##' > >> model fitted elsewhere f2 <- function(m) { d2 <- d; d2[1] <- > >> d2[1]+0 ## force copy update.default(m,data=d2) } ##' define (and > >> modify) data within function, try to update ##' model fitted > >> elsewhere (use envformula) f3 <- function(m) { d2 <- d; d2[1] <- > >> d2[1]+0 ## force copy update.envformula(m,data=d2) } > > > >> ##' hack: first try the formula, then the parent frame ##' if > >> that doesn't work for any reason f4 <- function(m) { d2 <- d; > >> d2[1] <- d2[1]+0 ## force copy update.both(m,data=d2) } > > > >> ## Case 1: fit within function m2 <- f1() > >> try(update.default(m2)) ## default: object 'd2' not found m3A <- > >> update.envformula(m2) ## envformula: works m3B <- > >> update.both(m2) ## works > > > >> ## Case 2: update within function m4A <- f2(m1) ## default: > >> works try(f3(m1)) ## envformula: object 'd2' not found m4B <- > >> f4(m1) ## works > > > >>> > >>> ______________________________________________ > >>> R-devel@r-project.org mailing list > >>> https://stat.ethz.ch/mailman/listinfo/r-devel > >>> > > > > > > > > -----BEGIN PGP SIGNATURE----- > Version: GnuPG v1.4.11 (GNU/Linux) > > iQEcBAEBAgAGBQJVFc1CAAoJEOCV5YRblxUHF+MH/3Y9uFZFolhx5b5jWSyXwQgp > i9oawx4K6il0qiAiDiO5D7NSSdc0u9jlgj8NjH0G2O9u3ctpvcYNVwa7cP9288Xz > xRyInnnh2FIpT6W0XyzJDivw5EX3IkyYuv6eDNqVyGcYXkvzJMA+vwMMWdGWEZbL > jKtDc0trG+9yJnwIi6DW6IQWPovrDaNxEinS+V7+DmYACQvJ4P2kg2u/ZsxAx89q > mcA1pS5usJjkOiQwBVUvV7l2UKNhHPFNwbBK1QdHgpP7PTdB52EQr+IyERhpf56s > 8tYyNbSSPWoG9vt6/1pKyUK4iNRBtGgxtuozAv5OUjF8VGWGwUXBLo5G2yrBbs4= > =o1PJ > -----END PGP SIGNATURE----- > [[alternative HTML version deleted]] ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
