Question regarding large data.frame in LMER?
Not sure if these are solutions, but I'd try: a) centering/scaling Age and/or b) using poly(Age, 2), rather than I(age^2) (i.e., an orthogonal polynomial) Maybe related to "badly scaled parameters"? https://github.com/lme4/lme4/issues/173 Jo?o
On Thu, 2020-12-10 at 12:11 +0000, Jad Moawad wrote:
I am working with a large data.frame that contains around 1.4 million
observations. Initially when i was running my models, i was working
on a sub-sample (10% of my full-sample). This is because running one
model can take a lot of time using the original data. Once i was sure
that all variables are well harmonized and all regressions were
running fine, i ran my models using the full sample. However, the
regression did not converge and i received the following two errors
from two different models:
Error in fun(xaa, ...) : Downdated VtV is not positive definite
Error in fun(xss, ...) : Downdated VtV is not positive definite
I use the lmer function to fit my model and i include a random slopes
at the country and country_year level. Below you find the code that i
use.
Model1 <- lmer(health~ class + age + I(age^2) +
class*macro_unemployment +
(class + age + I(age^2)|country) +
(class+ age + I(age^2) |country_year) +
(1|id), data=df)
Model2 <- lmer(health~ education + age + I(age^2) +
education*macro_unemployment+
(education + age + I(age^2)|country) +
(education + age + I(age^2) |country_year) +
(1|id), data=df)
Could someone help me please with solving this issue?
Below you find a glimpse (str) of my data and my sessionInfo():
tibble [1,370,264 8] (S3: grouped_df/tbl_df/tbl/data.frame)
$ health : num [1:1370264] 100 100 50 100 0 75 75 100 100
50 ...
$ class : Factor w/ 3 levels "Upper-middle class",..: 3
3 NA 3 3 3 3 1 1 3 ...
$ education : Factor w/ 3 levels "low","mid","high": 1 1 1 1
1 1 2 3 3 1 ...
$ age : num [1:1370264] 24 25 24 25 42 43 34 34 35 58
...
$ macro_unemployment: num [1:1370264] 5.24 4.86 5.24 4.86 5.24 ...
$ id : int [1:1370264] 2 2 3 3 4 4 6 7 7 8 ...
$ country_year : int [1:1370264] 1 2 1 2 1 2 1 1 2 1 ...
$ country : Factor w/ 30 levels "Austria","Belgium",..: 1
1 1 1 1 1 1 1 1 1 ...
- attr(*, "groups")= tibble [27 2] (S3: tbl_df/tbl/data.frame)
..$ country: Factor w/ 30 levels "Austria","Belgium",..: 1 2 3 6 7
8 9 10 11 12 ...
..$ .rows : list<int> [1:27]
.. ..$ : int [1:47204] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ : int [1:41361] 47205 47206 47207 47208 47209 47210 47211
47212 47213 47214 ...
.. ..$ : int [1:42407] 88566 88567 88568 88569 88570 88571 88572
88573 88574 88575 ...
.. ..$ : int [1:48253] 130973 130974 130975 130976 130977 130978
130979 130980 130981 130982 ...
.. ..$ : int [1:31917] 179226 179227 179228 179229 179230 179231
179232 179233 179234 179235 ...
.. ..$ : int [1:44047] 211143 211144 211145 211146 211147 211148
211149 211150 211151 211152 ...
.. ..$ : int [1:62087] 255190 255191 255192 255193 255194 255195
255196 255197 255198 255199 ...
.. ..$ : int [1:94309] 317277 317278 317279 317280 317281 317282
317283 317284 317285 317286 ...
.. ..$ : int [1:37246] 411586 411587 411588 411589 411590 411591
411592 411593 411594 411595 ...
.. ..$ : int [1:77253] 448832 448833 448834 448835 448836 448837
448838 448839 448840 448841 ...
.. ..$ : int [1:16823] 526085 526086 526087 526088 526089 526090
526091 526092 526093 526094 ...
.. ..$ : int [1:24687] 542908 542909 542910 542911 542912 542913
542914 542915 542916 542917 ...
.. ..$ : int [1:116263] 567595 567596 567597 567598 567599 567600
567601 567602 567603 567604 ...
.. ..$ : int [1:43218] 683858 683859 683860 683861 683862 683863
683864 683865 683866 683867 ...
.. ..$ : int [1:28709] 727076 727077 727078 727079 727080 727081
727082 727083 727084 727085 ...
.. ..$ : int [1:27583] 755785 755786 755787 755788 755789 755790
755791 755792 755793 755794 ...
.. ..$ : int [1:77960] 783368 783369 783370 783371 783372 783373
783374 783375 783376 783377 ...
.. ..$ : int [1:36922] 861328 861329 861330 861331 861332 861333
861334 861335 861336 861337 ...
.. ..$ : int [1:93194] 898250 898251 898252 898253 898254 898255
898256 898257 898258 898259 ...
.. ..$ : int [1:9004] 991444 991445 991446 991447 991448 991449
991450 991451 991452 991453 ...
.. ..$ : int [1:40074] 1000448 1000449 1000450 1000451 1000452
1000453 1000454 1000455 1000456 1000457 ...
.. ..$ : int [1:29342] 1040522 1040523 1040524 1040525 1040526
1040527 1040528 1040529 1040530 1040531 ...
.. ..$ : int [1:85124] 1069864 1069865 1069866 1069867 1069868
1069869 1069870 1069871 1069872 1069873 ...
.. ..$ : int [1:92350] 1154988 1154989 1154990 1154991 1154992
1154993 1154994 1154995 1154996 1154997 ...
.. ..$ : int [1:50188] 1247338 1247339 1247340 1247341 1247342
1247343 1247344 1247345 1247346 1247347 ...
.. ..$ : int [1:7598] 1297526 1297527 1297528 1297529 1297530
1297531 1297532 1297533 1297534 1297535 ...
.. ..$ : int [1:65141] 1305124 1305125 1305126 1305127 1305128
1305129 1305130 1305131 1305132 1305133 ...
.. ..@ ptype: int(0)
..- attr(*, ".drop")= logi TRUE
Session Info: R version 4.0.2 (2020-06-22) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Catalina 10.15.6 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Fr ameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack .dylib locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] stats graphics grDevices [4] utils datasets methods [7] base other attached packages: [1] sessioninfo_1.1.1 [2] sjlabelled_1.1.5 [3] varhandle_2.0.5 [4] labelled_2.7.0 [5] dplyr_1.0.0 [6] ggplot2_3.3.2 [7] forcats_0.5.0 [8] reprex_0.3.0 [9] lmerTest_3.1-3 [10] lme4_1.1-25 [11] Matrix_1.2-18 loaded via a namespace (and not attached): [1] Rcpp_1.0.4.6 [2] compiler_4.0.2 [3] pillar_1.4.4 [4] nloptr_1.2.2.1 [5] tools_4.0.2 [6] digest_0.6.25 [7] boot_1.3-25 [8] statmod_1.4.34 [9] lifecycle_0.2.0 [10] tibble_3.0.1 [11] nlme_3.1-148 [12] gtable_0.3.0 [13] lattice_0.20-41 [14] pkgconfig_2.0.3 [15] rlang_0.4.7 [16] cli_2.0.2 [17] rstudioapi_0.11 [18] haven_2.3.1 [19] withr_2.2.0 [20] hms_0.5.3 [21] generics_0.0.2 [22] vctrs_0.3.1 [23] fs_1.4.1 [24] grid_4.0.2 [25] tidyselect_1.1.0 [26] glue_1.4.1 [27] R6_2.4.1 [28] fansi_0.4.1 [29] minqa_1.2.4 [30] farver_2.0.3 [31] purrr_0.3.4 [32] magrittr_1.5 [33] scales_1.1.1 [34] ellipsis_0.3.1 [35] MASS_7.3-51.6 [36] splines_4.0.2 [37] insight_0.11.0 [38] assertthat_0.2.1 [39] colorspace_1.4-1 [40] numDeriv_2016.8-1.1 [41] labeling_0.3 [42] utf8_1.1.4 [43] munsell_0.5.0 [44] crayon_1.3.4 Sincerely, Jad Moawad PhD candidate and teaching assistant University of Lausanne - NCCR Lives Institut des Sciences Sociales Btiment Geopolis - 5621 1015 Lausanne Switzerland [[alternative HTML version deleted]]
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models