Linear mixed effect model
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On 11-03-19 11:27 AM, Manuel Sp?nola wrote:
Thank you very much Ben, I decided to keep "otter" as a random factor: modA = lmer(Swiftness.1 ~ Lure + Sex + Facility.Size + (1|Subject), REML = F, data = otter) summary(modA) modB = lmer(Swiftness.1 ~ (1|Subject), REML = F, data = otter) summary(modB) modC = lmer(Swiftness.1 ~ Lure + (1|Subject), REML = F, data = otter) summary(modC)
AICctab(modA, modB, modC, weights = T, delta = TRUE, base = T, sort =
TRUE, nobs = 17)
AICc df dAICc weight
modB 1313.1 3 0.0 1
modC 1336.3 8 23.2 <0.001
modA 1374.5 11 61.4 <0.001
Output for best model:
summary(modB)
Linear mixed model fit by maximum likelihood
Formula: Swiftness.1 ~ (1 | Subject)
Data: otter
AIC BIC logLik deviance REMLdev
1311 1319 -652.6 1305 1298
Random effects:
Groups Name Variance Std.Dev.
Subject (Intercept) 0 0.00
Residual 21133 145.37
Number of obs: 102, groups: Subject, 17
Fixed effects:
Estimate Std. Error t value
(Intercept) 99.76 14.39 6.931
Is it fair to say that there is no effect of any of the factors?
Did you say that the variance 0 in the random effect output is low power?
Yes, although technically I would say that the factors are not useful for prediction; if you want to test for the presence of a significant effect, then fit the full model and report the p-values and confidence intervals from it. Yes, I would say that the zero variance represents noise/ low power: if you were to do the equivalent aov()-analysis it would probably report a negative variance (i.e., among-group mean square < within-group mean square). Ben -----BEGIN PGP SIGNATURE----- Version: GnuPG v1.4.10 (GNU/Linux) Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org/ iEYEARECAAYFAk2E0bYACgkQc5UpGjwzenNYEgCfVyZWOsGQKku23cl9P2QYJE2P xf4AmQESmtzRM01AIdBJ38clLc9c2JXA =OGDG -----END PGP SIGNATURE-----