When I run the following model I get the below error.
> w7 <- glmmTMB(weight_t ~
+ tagged + ns(age.x, knots = c(73, 134, 220, 292)) + (age.x || scale_id) + (1 | clutch_id),
+ data = long, family = gaussian)
Error in names(object) <- nm :
'names' attribute [3] must be the same length as the vector [1]
>
When I google this error message, it get lots of results and it often seems to be the result of trying to assign names to a vector that is shorter than the vector of names, for example see here. However, in my example I cannot work out why I am getting this error. Any help would be much appreciated?
The length of all of my variables in my model are equal
> length(long$weight_t)
[1] 1200
> length(long$tagged)
[1] 1200
> length(long$age.x)
[1] 1200
> length(long$scale_id)
[1] 1200
> length(long$clutch_id)
[1] 1200
>
I'm not sure that it is relevant but below are also the number of levels of each factor variable
> levels(long$clutch_id)
[1] "1" "2" "3" "4" "5" "6" "7" "8"
> levels(long$scale_id)
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "21"
[22] "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42"
[43] "43" "44" "45" "46" "47" "48" "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" "61" "62" "63"
[64] "64" "65" "66" "67" "68" "69" "70" "71" "72" "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
[85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96" "97" "98" "99" "100" "101" "102" "103" "104" "105"
[106] "106" "107" "108" "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121" "122" "123" "124" "125" "126"
[127] "127" "128" "129" "130" "131" "132" "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144" "145" "146" "147"
[148] "148" "149" "150" "151" "152" "153" "154" "155" "156" "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168"
[169] "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180" "181" "182" "183" "184" "185" "186" "187" "188" "189"
[190] "190" "191" "192" "193" "194" "195" "196" "197" "198" "199" "200"
> levels(long$tagged)
[1] "0" "1"
>
and summaries for each variable
> summary(long$weight_t)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
70 325 1628 2649 4200 16000 107
> summary(long$tagged)
0 1
600 600
> summary(long$age.x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
9.0 73.0 177.0 185.5 292.0 385.0
> summary(long$scale_id)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
97 98 99 (Other)
6 6 6 606
> summary(long$clutch_id)
1 2 3 4 5 6 7 8
162 180 126 162 156 168 120 126
>
Edit 1: I am unable to post original data, but have also now included the results of traceback if this helps.
> traceback()
8: setNames(ans, names(reTrms$Ztlist))
7: getReStruc(reTrms, ss)
6: eval(substitute(expr), data, enclos = parent.frame())
5: eval(substitute(expr), data, enclos = parent.frame())
4: with.default(condList, getReStruc(reTrms, ss))
3: with(condList, getReStruc(reTrms, ss))
2: mkTMBStruc(formula, ziformula, dispformula, combForm, mf, fr,
yobs = y, respCol, weights, contrasts = contrasts, family = family,
se = se, call = call, verbose = verbose, REML = REML, start = start,
map = map, sparseX = sparseX)
1: glmmTMB(weight_t ~ tagged + ns(age.x, knots = c(73, 134, 220,
292)) + (age.x || scale_id) + (1 | clutch_id), data = long,
family = gaussian)
Edit 2:
This posts suggests that this error can occur if a particular column in the data has fewer unique values than rows in the column, with many consecutively repeating values. This may be my problem here? long$clutch_id has only 8 unique values that repeat throughout the column of data, in fact long$scale_id is actually nested within long$clutch_id. Maybe this is also partly why I get this error, maybe R is recognising this nesting/clustering? If I specify the random effects in my model slightly differently, I no longer get this error, but instead just get an error message that suggests my model is overparametised.
> w7 <- glmmTMB(weight_t ~
+ tagged + ns(age.x, knots = c(73, 134, 220, 292)) + (age.x || clutch_id/scale_id),
+ data = long, family = gaussian)
Warning message:
In fitTMB(TMBStruc) :
Model convergence problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
>
