Ben Ben - 1 year ago 89
R Question

R-Squared of lmer model fit

I have a mixed effects model and I would like to see the R²- and p-value. I thought this is acessible by summary() but it's not. Or at least I don't realize it.

> summary(fit1.lme <- lmer(log(log(Amplification)) ~ poly(Voltage, 3) + (1 | Serial_number), data = bdf))
Linear mixed model fit by REML ['lmerMod']
Formula: log(log(Amplification)) ~ poly(Voltage, 3) + (1 | Serial_number)
Data: bdf

REML criterion at convergence: -253237.6

Scaled residuals:
Min 1Q Median 3Q Max
-14.8183 -0.4863 -0.0681 0.2941 9.3292

Random effects:
Groups Name Variance Std.Dev.
Serial_number (Intercept) 0.008435 0.09184
Residual 0.001985 0.04456
Number of obs: 76914, groups: Serial_number, 1270

Fixed effects:
Estimate Std. Error t value
(Intercept) 0.826745 0.002582 320
poly(Voltage, 3)1 286.978430 0.045248 6342
poly(Voltage, 3)2 -74.061993 0.045846 -1615
poly(Voltage, 3)3 39.605454 0.045505 870

Correlation of Fixed Effects:
(Intr) p(V,3)1 p(V,3)2
ply(Vlt,3)1 0.001
ply(Vlt,3)2 0.002 0.021
ply(Vlt,3)3 0.001 0.032 0.028

Answer Source

For the R², you can use r.squaredGLMM(fit1.lme) from ‘MuMIn package. It will returns the marginal and the conditional R².

For the p-value, you can find them by using summary with the lmerTest package.

For more information on p-values with mixed models: http://mindingthebrain.blogspot.ch/2014/02/three-ways-to-get-parameter-specific-p.html

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