[R-sig-ME] question about convergence warning and some odd-looking odds ratios in a glmer model
Thierry Onkelinx
th|erry@onke||nx @end|ng |rom |nbo@be
Fri Jun 24 15:52:38 CEST 2022
Dear Emma,
Running m1 without specifying the optimiser made the model run. However it
returns extreme estimates for the fixed effects and the random effect
variance. The problem is in the fact that most owners have only one bird.
This makes the random effect nearly behave as an observation level random
effect.
See if you can cluster the owners into some sensible groups or use some
covariates describing the home situation of the bird.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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ensure that a reasonable answer can be extracted from a given body of data.
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<https://www.inbo.be>
Op do 23 jun. 2022 om 19:10 schreef Emma Mellor <emma.mellor using bristol.ac.uk>:
> Hi all,
>
> I'm running glmer models in lme4 on: R version 4.1.2,
> Platform: x86_64-w64-mingw32/x64 (64-bit),
> Running under: Windows 10 x64 (build 19044)
>
> My outcome (presence/absence of abnormal behaviour in pet birds) is
> binary, so I used family = binomial. Random effects are 'Owner_ID' - in
> reality, very few birds share a household, and the vast majority are single
> birds. I'm getting warning messages about convergence when 'Species_ID' is
> included the model as a predictor (I do not get error messages with other
> predictors - it's just this one). I've tabulated the data and I don't see
> any obvious reason for the error, such as missing/very few cases per levels
> within variables, so I'm at a bit of a loss as what to do about it. Copy of
> warning message next, and I'll provide a copy of my code below:
>
> Warning messages:
> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.0562293 (tol = 0.002,
> component 1)
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model is nearly unidentifiable: very large eigenvalue
> - Rescale variables?
>
> Species does have a significant effect on my outcome (which is expected),
> so I've spilt the dataset by species to do pairwise comparisons to try and
> work out what's what. When I do so, I don't get the warnings but I do get,
> in some cases, enormous odds ratios (and one set of normal-looking ones
> too).
>
> Perhaps I'm over-reacting? But given the previous warnings I got with the
> initial (all four species) model, it gave me reason to pause and want to
> check.
>
> Can anyone advise about what I should do about the warnings, and whether I
> can trust those odds ratios, please?
>
> Thanks for any help you're able to give!
>
> Best wishes,
>
> Emma
>
> Code to demonstrate here:
>
> library(lme4)
> library(dplyr)
>
> data<-structure(list(Owner_ID = c(62L, 222L, 147L, 187L, 407L, 208L, 205L,
> 348L, 29L, 244L,
> 146L, 414L, 278L, 528L, 433L, 1039L, 930L,
> 902L, 1466L, 1977L,
> 704L, 2020L, 1423L, 782L, 308L, 291L, 61L,
> 1512L, 1164L, 1164L,
> 28L, 1549L, 1882L, 1181L, 569L, 135L,
> 609L, 1059L, 663L, 1465L,
> 1207L, 713L, 1420L, 1318L, 1357L, 1727L,
> 1415L, 1572L, 948L,
> 948L, 948L, 1897L, 1649L, 1162L, 974L,
> 1153L, 1802L, 1412L, 872L,
> 1708L, 1360L, 1616L, 1960L, 1960L, 1965L,
> 1794L, 636L, 339L,
> 1783L, 1783L, 1594L, 1558L, 1695L, 1822L,
> 1700L, 1667L, 869L,
> 534L, 1667L, 524L, 1684L, 782L, 782L,
> 664L, 1700L, 67L, 1416L,
> 1418L, 1352L, 1321L, 1569L, 598L, 821L,
> 604L, 2013L, 1707L, 785L,
> 1534L, 1299L, 565L, 1284L, 1244L, 1385L,
> 853L, 1014L, 1542L,
> 1771L, 1876L, 1063L, 1212L, 1000L, 1784L,
> 1439L, 547L, 745L,
> 717L, 702L, 1829L, 1503L, 862L, 667L,
> 1135L, 1541L, 1507L, 1507L,
> 680L, 1597L, 741L, 801L, 856L, 1295L,
> 1295L, 955L, 1850L, 1769L,
> 1393L, 1393L, 1291L, 1111L, 1405L, 728L,
> 1993L, 901L, 566L, 1020L,
> 1293L, 1320L, 1087L, 1176L, 1898L, 1940L,
> 1177L, 1296L, 1462L,
> 863L, 1918L, 1854L, 1508L, 747L, 1647L,
> 1671L, 1646L, 1949L,
> 1094L, 1184L, 1455L, 899L, 1627L, 804L,
> 1490L, 1768L, 555L, 735L,
> 2022L, 1317L, 1620L, 1605L, 919L, 597L,
> 1849L, 1336L, 1898L,
> 29L, 1392L, 1438L, 1198L, 1943L, 1139L,
> 1716L, 1986L, 962L, 1372L,
> 1673L, 1640L, 1640L, 1805L, 1163L, 1172L,
> 1165L, 1915L, 1915L,
> 1576L, 1526L, 1436L, 1998L, 1590L, 906L,
> 770L, 1398L, 656L, 1207L,
> 1120L, 805L, 805L, 1437L, 1682L, 800L,
> 1543L, 1238L, 2008L, 1069L,
> 1243L, 1326L, 1170L, 802L, 873L, 943L,
> 1330L, 1586L, 1833L, 1717L,
> 1654L, 1748L, 881L, 1006L, 1637L, 1701L,
> 1823L, 1613L, 1599L,
> 875L, 1813L, 1725L, 1005L, 1322L, 1839L,
> 352L, 1674L, 1358L,
> 1688L, 1724L, 1367L, 1827L, 1286L, 1150L,
> 1780L, 842L, 1118L,
> 884L, 1440L, 173L, 1744L, 578L, 1635L,
> 1568L, 861L, 1091L, 650L,
> 1354L, 1368L, 815L, 721L, 1787L, 1787L,
> 1678L, 1864L, 1786L,
> 1698L, 1984L, 1612L, 1159L, 1611L, 822L,
> 1042L, 1042L, 858L,
> 858L, 1731L, 1655L, 993L, 160L, 478L,
> 445L, 239L, 531L, 303L,
> 18L, 171L, 531L, 492L, 159L, 74L, 456L,
> 488L, 305L, 423L, 477L,
> 467L, 9L, 464L, 102L, 232L, 131L, 118L,
> 143L, 34L, 196L, 30L,
> 153L, 412L, 371L, 104L, 16L, 63L, 248L,
> 212L, 482L, 480L, 266L,
> 259L, 283L, 283L, 148L, 156L, 523L, 368L,
> 513L, 241L, 246L, 441L,
> 229L, 320L, 245L, 287L, 468L, 468L, 383L,
> 367L, 54L, 380L, 25L,
> 37L, 73L, 496L, 546L, 382L, 369L, 236L,
> 113L, 365L, 499L, 227L,
> 465L, 425L, 1819L, 657L, 635L, 1910L,
> 924L, 924L, 1485L, 1289L,
> 627L, 1076L, 627L, 1219L, 874L, 1289L,
> 1292L, 1754L, 972L, 1084L,
> 1240L, 777L, 609L, 626L, 1500L, 788L,
> 1470L, 1136L, 582L, 1891L,
> 645L, 1557L, 339L, 1904L, 2029L, 1107L,
> 665L, 631L, 631L, 1214L,
> 1214L, 738L, 973L, 888L, 1919L, 1772L,
> 1772L, 351L, 314L, 342L,
> 1077L, 134L, 273L, 495L, 166L, 514L, 247L,
> 203L, 125L, 2026L,
> 154L, 1570L, 1947L, 1055L, 1231L, 1318L,
> 1437L, 626L, 206L, 164L,
> 708L, 1711L, 922L, 1900L, 93L, 385L, 84L,
> 1173L, 294L, 470L,
> 23L, 1113L, 870L, 870L, 1029L, 2009L,
> 963L, 1746L, 988L, 1108L,
> 1749L, 1749L, 1749L, 1749L, 1828L, 1103L,
> 1844L, 970L, 769L,
> 1188L, 1712L, 1188L, 1776L, 1901L, 860L,
> 1777L, 1325L, 1608L,
> 1828L, 1608L, 1542L, 328L, 1942L, 1843L,
> 967L, 1990L, 1852L,
> 1056L, 1847L, 204L, 1338L, 846L, 729L,
> 1393L, 864L, 1396L, 1393L,
> 1777L),
> Outcome = c(1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
> 0L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
> 0L, 1L, 0L, 0L, 0L,
> 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 1L, 0L, 0L, 0L,
> 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 1L, 0L,
> 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L,
> 0L, 0L, 0L, 0L, 1L,
> 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 1L,
> 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
> 0L, 0L, 1L, 0L, 1L,
> 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
> 0L, 0L, 0L, 1L, 0L,
> 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L,
> 0L, 0L, 0L, 0L, 1L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 1L, 0L,
> 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L,
> 0L, 1L, 0L, 0L, 1L,
> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 1L, 1L,
> 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 1L, 0L, 1L, 0L,
> 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L,
> 0L, 0L, 0L, 0L, 1L,
> 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L,
> 0L, 1L, 0L, 1L, 0L,
> 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
> 0L, 0L, 0L, 1L, 0L,
> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L,
> 1L, 1L, 0L, 0L, 1L,
> 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L,
> 1L, 0L, 0L, 0L, 0L,
> 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
> 0L, 1L, 0L, 1L, 0L,
> 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L,
> 0L, 0L, 1L, 0L, 1L,
> 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L,
> 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L,
> 1L, 1L, 0L, 1L, 1L,
> 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
> 0L, 0L, 1L, 0L, 0L,
> 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L,
> 0L, 0L, 0L, 0L, 1L,
> 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
> 1L),
> Species_ID = c("C", "C", "C", "C", "C", "C", "D", "C", "C",
> "C", "C",
> "C", "C", "C", "C", "D", "C", "A", "C", "C",
> "C", "C", "C", "C",
> "C", "C", "D", "C", "C", "C", "C", "C", "C",
> "D", "D", "C", "A",
> "A", "C", "C", "D", "C", "C", "D", "C", "C",
> "C", "C", "A", "A",
> "C", "C", "C", "C", "C", "C", "A", "C", "C",
> "C", "C", "C", "A",
> "D", "A", "C", "C", "A", "C", "C", "C", "C",
> "C", "C", "C", "C",
> "C", "C", "C", "C", "D", "C", "C", "A", "A",
> "C", "C", "C", "C",
> "C", "C", "C", "A", "A", "C", "C", "C", "C",
> "C", "C", "C", "D",
> "C", "C", "A", "C", "C", "C", "C", "C", "D",
> "C", "C", "C", "C",
> "D", "A", "C", "C", "C", "C", "C", "A", "C",
> "C", "C", "C", "C",
> "C", "C", "C", "C", "D", "C", "C", "A", "C",
> "A", "D", "C", "A",
> "C", "C", "D", "C", "C", "C", "D", "C", "C",
> "C", "C", "C", "C",
> "C", "C", "C", "C", "C", "C", "C", "C", "C",
> "C", "C", "C", "C",
> "C", "C", "C", "C", "C", "A", "C", "D", "C",
> "C", "C", "C", "C",
> "D", "C", "C", "C", "A", "C", "C", "C", "C",
> "D", "C", "C", "C",
> "A", "C", "A", "C", "C", "D", "A", "C", "A",
> "C", "A", "C", "A",
> "A", "D", "D", "D", "A", "D", "A", "A", "A",
> "A", "D", "D", "A",
> "C", "D", "D", "D", "C", "C", "C", "C", "C",
> "C", "A", "C", "C",
> "C", "C", "C", "C", "C", "C", "A", "C", "C",
> "C", "C", "C", "A",
> "C", "C", "C", "A", "C", "C", "C", "C", "D",
> "A", "C", "C", "C",
> "C", "C", "C", "C", "C", "C", "C", "C", "C",
> "C", "C", "D", "C",
> "C", "C", "C", "C", "A", "A", "A", "D", "A",
> "C", "C", "A", "A",
> "A", "A", "A", "C", "A", "A", "C", "C", "C",
> "C", "C", "D", "D",
> "D", "C", "C", "D", "C", "C", "D", "D", "D",
> "D", "D", "C", "D",
> "D", "C", "C", "C", "C", "D", "C", "C", "D",
> "A", "D", "D", "C",
> "C", "D", "A", "C", "A", "D", "D", "C", "C",
> "C", "D", "C", "C",
> "D", "C", "C", "D", "C", "C", "C", "C", "A",
> "A", "C", "C", "C",
> "C", "C", "D", "D", "C", "C", "C", "C", "C",
> "A", "D", "C", "D",
> "A", "A", "C", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B", "B", "B",
> "B", "B", "B", "B", "B", "B", "B", "B", "B",
> "B", "B")))
> data<-as.data.frame(data)
> attach(data)
>
> m1<-glmer(Outcome~ Species_ID+(1 |Owner_ID),
> data=data, family= (binomial(link="logit")),na.action = na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
> #get convergence error (tol >0.002)
>
> #Ben Bolker advised to first do this as a check because of the warning:
> m1_fit_all<-allFit(m1)
>
> ss<-summary(m1_fit_all)
> ss$which.OK
> #all seem OK
>
> m1b<-glmer(Outcome~ 1+(1 |Owner_ID),
> data=data, family= (binomial(link="logit")),na.action = na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
>
> anova(m1,m1b)
> #so, an effect of species (as expected - in line with our previous
> research)
> summary(m1)
> #z values look a bit suspect (to me)
>
> #splitting dataset by species (A v B first)
> # to suss what's going on. Will give you an example of what look like mad
> odds ratios
> a_b_data<-data %>% filter(Species_ID%in% c("A", "B"))
> detach(data)
> attach(a_b_data)
>
> #and then re-run
> m1c<-glmer(Outcome~ Species_ID+(1 |Owner_ID),
> data=a_b_data, family= (binomial(link="logit")),na.action =
> na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
> #no error this time
>
> m1e<-glmer(Outcome~ 1+(1 |Owner_ID),
> data=a_b_data, family= (binomial(link="logit")),na.action =
> na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
>
> anova(m1c,m1e)
> #species has an effect
> summary(m1c)
> #z value looks less crazy, but want to take a look at the odds ratios
> se <- sqrt(diag(vcov(m1c)))
> (tab <- cbind(Est = fixef(m1c), LL = fixef(m1c) - 1.96 * se, UL =
> fixef(m1c) + 1.96 *
> se))
>
> #this for odds ratios. Upper limit looks especially mad (24,231,520!!)
> exp(tab)
>
> #I'll give an example of a normal-looking one too
>
> #splitting dataset by another species pair (C v D)
>
> c_d_data<-data %>% filter(Species_ID%in% c("C", "D"))
> detach(a_b_data)
> attach(c_d_data)
>
> #and then re-run
> m1r<-glmer(Outcome~ Species_ID+(1 |Owner_ID),
> data=c_d_data, family= (binomial(link="logit")),na.action =
> na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
> #no error
> m1t<-glmer(Outcome~ 1+(1 |Owner_ID),
> data=c_d_data, family= (binomial(link="logit")),na.action =
> na.omit,
> glmerControl(optimizer="bobyqa", optCtrl = list(maxfun =
> 1000000)))
>
> anova(m1r,m1t)
> #effect of species here too
> #get the odds ratios
> se <- sqrt(diag(vcov(m1r)))
> (tab <- cbind(Est = fixef(m1r), LL = fixef(m1r) - 1.96 * se, UL =
> fixef(m1r) + 1.96 *
> se))
>
> #this for (less alarming) odds ratios.
> exp(tab)
>
> Dr Emma Mellor
> Research Associate
>
> University of Bristol
> Bristol Veterinary School
> Langford House
> Langford
> BS40 5DU
>
> My working days are generally Monday, Tuesday and Friday. My work schedule
> may not be the same as yours - please do not feel obliged to respond
> outside of your own working hours
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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