r - Back transform mixed-effects model's regression coefficients for fixed-effects from log to original scale -
i running mixed-effects model lme4
package. model specifications are: log(dv) ~ 1 + a*b*c + (1+a*b|random1) + (1+a|random2)
, a
, b
within-group conditions , c
between-group condition.
the first problem coefficients fixed effects on log scale , intercept makes sense when exp(coef)
(see below).
the second problem if exponentiation transform, how should account random-effects structure? understand it, random-effects structure affects fixed-effects coefficients (i might wrong here).
this sample output of fixed-effects coefficients:
estimate (intercept) 6.533079 a1 0.062171 a2 0.077409 b1 -0.184366 b2 -0.154115 c 0.152238 a1:b1 -0.015494 a2:b1 -0.017655 a1:b2 0.001674 a2:b2 -0.003641 a1:c 0.013021 a2:c 0.038995 b1:c 0.010087 b2:c 0.013721 a1:b1:c 0.016025 a2:b1:c 0.016453 a1:b2:c 0.012746 a2:b2:c 0.003113
now, exp(6.533079)
gives 687.5118, makes sense in original scale, rest of numbers not make sense once transformed.
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