[R-sig-ME] question about convergence warning and some odd-looking odds ratios in a glmer model
Emma Mellor
emm@@me||or @end|ng |rom br|@to|@@c@uk
Thu Jun 23 17:21:23 CEST 2022
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
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