I am trying to use lmerTest to have p values for my fixed effects. I have 4 different random intercepts, 3 crossed and one nested:
test.reml <- lmerTest::lmer(y ~ s1 + min + cot + min:cot + ge + vis + dur + mo + nps + dist + st1 + st2 + di1 + s1:cot + s1:min + s1:cot:min + s1:ge + s1:vis + s1:dur + s1:mo + s1:nps + s1:dist + s1:st1 + s1:st2 + s1:di1 + (1|Unique_key) + (s1-1|object) + (ns1-1|object) + (1|region), bdr, REML=1)
Objects are observed twice, and the correlation between the two measures is introduced by a random effect on Unique_key, the unique identifier of object i in area j. Each object can be observed in any region. S1 is a binary variable that takes the value 1 if the observation is observed in the first period of time and 0. There is one random interception for the first period and one random interception for the second period for each object. ns1 is actually a binary variable that complements s1 and s1 + ns1 = 1 for each observation.
I can put the model and get estimates and p values using summary ():
summary( test.reml) Linear mixed model fit by REML ['merModLmerTest'] Formula: y ~ s1 + min + cot + min:cot + ge + vis + dur + mo + nps + dist + st1 + st2 + di1 + s1:cot + s1:min + s1:cot:min + s1:ge + s1:vis + s1:dur + s1:mo + s1:nps + s1:dist + s1:st1 + s1:st2 + s1:di1 + (1|Unique_key) + (s1-1|object) + (ns1-1|object) + (1|region), bdr, REML=1) Data: bdr REML criterion at convergence: 204569.1 Random effects: Groups Name Variance Std.Dev. Unique_key (Intercept) 0.2023 0.4497 object s1 0.3528 0.5940 object.1 ns1 0.5954 0.7716 Region (Intercept) 0.7563 0.8697 Residual 0.1795 0.4237 Number of obs: 113396, groups: Unique_key , 58541; object, 1065; Region, 87 Fixed effects: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.7341569 0.2382673 28.263 < 2e-16 *** s1 0.7391924 0.2004413 3.688 0.000233 *** min -0.0067606 0.0171385 -0.394 0.694205 cot 0.1235093 0.0353693 3.492 0.000499 *** ge2 -0.1535452 0.0800998 -1.917 0.055525 . ge3 -0.2131246 0.0986559 -2.160 0.030982 * ge4 -0.1032694 0.1115603 -0.926 0.354830 ge5 -0.1769347 0.1296558 -1.365 0.172663 ge6 0.0117401 0.1115897 0.105 0.916231 ge7 -0.2692483 0.1022565 -2.633 0.008589 ** vis2 -0.0928661 0.0607950 -1.528 0.126938 vis3 -0.3026112 0.1246595 -2.428 0.015375 * dur2 0.1479195 0.0786369 1.881 0.060249 . dur3 0.1406340 0.0809379 1.738 0.082590 . dur4 0.2742243 0.0884301 3.101 0.001981 ** dur5 0.1946761 0.1065815 1.827 0.068059 . mo2 -0.1168591 0.1256017 -0.930 0.352386 mo3 -0.0611162 0.1267657 -0.482 0.629824 mo4 -0.2725720 0.1263740 -2.157 0.031248 * mo5 -0.6107000 0.1379264 -4.428 1.05e-05 *** mo6 -0.3635142 0.1299799 -2.797 0.005260 ** mo7 -0.0899233 0.1275164 -0.705 0.480846 mo8 -0.2349548 0.1253422 -1.875 0.061140 . mo9 -0.2624888 0.1263051 -2.078 0.037934 * mo10 -0.2882749 0.1244404 -2.317 0.020724 * mo11 -0.1702823 0.1356031 -1.256 0.209497 mo12 0.1989155 0.1322339 1.504 0.132819 nps 0.0278418 0.0010393 26.790 < 2e-16 *** dist2 0.4065093 0.1118916 3.633 0.000294 *** dist3 0.0155691 0.0906664 0.172 0.863693 dist4 -0.2910960 0.1595805 -1.824 0.068424 . dist5 -0.1316553 0.0913394 -1.441 0.149782 dist6 0.0477956 0.0995679 0.480 0.631308 dist7 0.1383000 0.0981247 1.409 0.159011 dist8 -0.3985620 0.0886316 -4.497 7.69e-06 *** dist9 -0.2036683 0.0799584 -2.547 0.011005 * st11 -0.0258775 0.0591631 -0.437 0.661919 st21 0.0089230 0.0573352 0.156 0.876356 di11 -0.0910207 0.0838321 -1.086 0.277846 min:cot 0.0066210 0.0006195 10.688 < 2e-16 *** s1:cot -0.1505670 0.0443186 -3.397 0.000694 *** s1:min 0.0079478 0.0015051 5.280 1.29e-07 *** s1:ge2 0.0329272 0.1007943 0.327 0.743948 s1:ge3 0.2150927 0.1241590 1.732 0.083367 . s1:ge4 0.1786057 0.1404119 1.272 0.203526 s1:ge5 -0.0422380 0.1631757 -0.259 0.795780 s1:ge6 0.1372051 0.1404415 0.977 0.328717 s1:ge7 0.1343314 0.1287059 1.044 0.296755 s1:vis2 0.1354091 0.0765084 1.770 0.076913 . s1:vis3 0.2449180 0.1568745 1.561 0.118637 s1:dur2 -0.0888179 0.0989573 -0.898 0.369547 s1:dur3 -0.0532473 0.1018481 -0.523 0.601167 s1:dur4 -0.1239068 0.1112907 -1.113 0.265696 s1:dur5 -0.1191069 0.1341435 -0.888 0.374705 s1:mo2 -0.1357615 0.1574365 -0.862 0.388618 s1:mo3 0.0130976 0.1588743 0.082 0.934306 s1:mo4 0.0343900 0.1579532 0.218 0.827669 s1:mo5 0.2257241 0.1732449 1.303 0.192761 s1:mo6 0.0500347 0.1628755 0.307 0.758728 s1:mo7 -0.0451271 0.1596277 -0.283 0.777435 s1:mo8 -0.0200467 0.1572383 -0.127 0.898564 s1:mo9 0.0394005 0.1584268 0.249 0.803620 s1:mo10 0.0641038 0.1562518 0.410 0.681662 s1:mo11 -0.3136235 0.1703456 -1.841 0.065764 . s1:mo12 -0.7003775 0.1660455 -4.218 2.58e-05 *** s1:nps -0.0095428 0.0013077 -7.297 4.31e-13 *** s1:dist2 -0.3867962 0.1407463 -2.748 0.006050 ** s1:dist3 -0.0516400 0.1140519 -0.453 0.650762 s1:dist4 -0.0567491 0.2008542 -0.283 0.777562 s1:dist5 0.0025780 0.1147143 0.022 0.982073 s1:dist6 -0.1456445 0.1252219 -1.163 0.244940 s1:dist7 -0.0452712 0.1234110 -0.367 0.713785 s1:dist8 0.0546400 0.1114865 0.490 0.624117 s1:dist9 0.0540697 0.1000415 0.540 0.588934 s1:st11 0.0784027 0.0744677 1.053 0.292549 s1:st21 -0.0394419 0.0721720 -0.546 0.584788 s1:di11 0.0463040 0.1055326 0.439 0.660882 s1:min:cot -0.0012850 0.0006004 -2.140 0.032344 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
but with anova () I get:
type3.bonmodele <- lmerTest::anova(test.reml, ddf="Satterthwaite") Analysis of Variance Table Df Sum Sq Mean Sq F value s1 1 7.385 7.385 41.1448 min 1 0.081 0.081 0.4536 cot 1 29.384 29.384 163.7026 ge 6 25.198 4.200 23.3968 vis 2 0.464 0.232 1.2929 dur 4 22.763 5.691 31.7042 mo 11 15.581 1.416 7.8914 nps 1 234.535 234.535 1306.6487 dist 8 18.547 2.318 12.9162 st1 1 0.034 0.034 0.1879 st2 1 0.058 0.058 0.3220 di1 1 0.261 0.261 1.4549 min:cot 1 22.537 22.537 125.5611 s1:cot 1 9.146 9.146 50.9555 s1:min 1 18.383 18.383 102.4171 s1:ge 6 5.152 0.859 4.7843 s1:vis 2 1.698 0.849 4.7311 s1:dur 4 2.829 0.707 3.9404 s1:mo 11 8.157 0.742 4.1312 s1:nps 1 10.102 10.102 56.2803 s1:dist 8 2.233 0.279 1.5550 s1:st1 1 0.188 0.188 1.0481 s1:st2 1 0.046 0.046 0.2560 s1:di1 1 0.035 0.035 0.1927 s1:min:cot 1 0.822 0.822 4.5804
When I try to remove the triple interaction, the anova () function returns p values ... I also tried to split my data frame and fit the model into half the data, and anova () works well.
When I use functions, there is no warning, and I also tried changing the ddf parameter and method, but nothing works.
Here is my session information:
R version 3.0.0 (2013-04-03) Platform: x86_64-w64-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=French_Canada.1252 LC_CTYPE=French_Canada.1252 LC_MONETARY=French_Canada.1252 LC_NUMERIC=C [5] LC_TIME=French_Canada.1252 attached base packages: [1] parallel splines stats graphics grDevices utils datasets methods base other attached packages: [1] ggplot2_0.9.3.1 snow_0.3-13 Snowball_0.0-10 xtable_1.7-1 lmerTest_2.0-0 pbkrtest_0.3-7 MASS_7.3-29 [8] papeR_0.3 gmodels_2.15.4 survival_2.37-4 nlme_3.1-111 car_2.0-19 lme4_1.1-1 Matrix_1.1-0 [15] lattice_0.20-15 loaded via a namespace (and not attached): [1] bitops_1.0-6 caTools_1.16 cluster_1.14.4 colorspace_1.2-4 dichromat_2.0-0 digest_0.6.3 [7] gdata_2.13.2 gplots_2.12.1 grid_3.0.0 gtable_0.1.2 gtools_3.0.0 Hmisc_3.12-2 [13] KernSmooth_2.23-10 labeling_0.2 minqa_1.2.1 munsell_0.4.2 nnet_7.3-7 numDeriv_2012.9-1 [19] plyr_1.8 proto_0.3-10 RColorBrewer_1.0-5 RCurl_1.95-4.1 reshape2_1.2.2 rJava_0.9-4 [25] ROAuth_0.9.3 rpart_4.1-3 scales_0.2.3 stringr_0.6.2 tools_3.0.0 twitteR_1.1.7
I can’t share the data, but if necessary I can add more information! I wanted to use the Satterthwaite approximation for degrees of freedom, but if you have any other suggestions for getting p-values, please share! Thank you very much!