问题
I have a set of linear mixed models, and have created an average model. I'd like to plot the model fits for two levels of a factor, included in the average model. A simple example:
library(lme4)
library(MuMIn)
mtcars2 <- mtcars
mtcars2$vs <- factor(mtcars2$vs)
gl <- lmer(mpg ~ am + disp + hp + qsec + (1 | cyl), mtcars2,
REML = FALSE, na.action = 'na.fail')
d <- dredge(gl)
av <- model.avg(d, subset = cumsum(weight) <= 0.95)
summary(av)
Call: model.avg(object = d, subset = cumsum(weight) <= 0.95) Component model call: lme4::lmer(formula = mpg ~ <7 unique rhs>, data = mtcars2, REML = FALSE, na.action = na.fail) Component models: df logLik AICc delta weight 13 5 -77.81 167.92 0.00 0.37 123 6 -76.34 168.05 0.13 0.35 134 6 -77.54 170.43 2.51 0.11 1234 7 -76.25 171.16 3.24 0.07 23 5 -79.85 172.00 4.08 0.05 2 4 -81.63 172.75 4.83 0.03 124 6 -78.99 173.34 5.42 0.02 Term codes: am disp hp qsec 1 2 3 4 Model-averaged coefficients: (full average) Estimate Std. Error Adjusted SE z value Pr(>|z|) (Intercept) 25.457505 6.467643 6.648016 3.829 0.000129 *** am 4.103425 1.861593 1.898182 2.162 0.030636 * hp -0.043829 0.017926 0.018265 2.400 0.016415 * disp -0.009419 0.011834 0.011983 0.786 0.431821 qsec 0.081973 0.284147 0.292015 0.281 0.778929 (conditional average) Estimate Std. Error Adjusted SE z value Pr(>|z|) (Intercept) 25.45751 6.46764 6.64802 3.829 0.000129 *** am 4.46519 1.46823 1.51835 2.941 0.003273 ** hp -0.04651 0.01471 0.01515 3.070 0.002140 ** disp -0.01793 0.01068 0.01099 1.632 0.102634 qsec 0.40421 0.51757 0.53873 0.750 0.453075 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Relative variable importance: hp am disp qsec Importance: 0.94 0.92 0.53 0.20 N containing models: 5 5 5 3
I want to plot the effect of am
as estimated by the full averaged model.
Normally I would use lsmeans::lsmeans(gl, ~am)
or lmerTest::lsmeansLT(gl, 'am')
and plot the least squares means for the two groups and their confidence intervals.
How can I do the same for the average model?
回答1:
(This is a revised answer, after some discussion and further findings. Note that I'm the emmeans
package author.)
Here is something that appears to work.
First, define methods needed by the emmeans package:
library(emmeans)
terms.averaging = function(x, ...)
terms(x$formula)
recover_data.averaging = emmeans:::recover_data.lm
### NOTE: still have to provide 'data' argument
emm_basis.averaging = function(object, trms, xlev, grid, ...) {
bhat = coef(object, full = TRUE)
m = model.frame(trms, grid, na.action = na.pass, xlev = xlev)
X = model.matrix(trms, m, contrasts.arg = object$contrasts)
V = vcov(object, full = TRUE)
dffun = function(k, dfargs) NA
dfargs = list()
list(X=X, bhat=bhat, nbasis=estimability::all.estble, V=V,
dffun=dffun, dfargs=dfargs, misc=list())
}
The terms
method is needed because there isn't one. The other two are adapted from the existing methods for lm
objects. Now there is one catch: the vcov()
call requires the object to have a non-NULL
"modelList"
attribute. And your av
object fails. But the examples at the bottom of the help page for model.avg
shows what to do:
cs95 = get.models(d, cumsum(weight) <= .95)
AV = model.avg(cs95)
Now, AV
has the required attribute. Now we get:
em = emmeans(AV, ~ am, at = list(am = c("0", "1")), data = mtcars)
em
## am emmean SE df asymp.LCL asymp.UCL
## 0 15.42665 2.985460 NA 9.575257 21.27805
## 1 19.53008 1.986149 NA 15.637297 23.42286
pairs(em)
## contrast estimate SE df z.ratio p.value
## 0 - 1 -4.103425 1.861593 NA -2.204 0.0275
Note that the contrast result matches the estimate and unadjusted SE for av
in the model summary table.
Note: Using coef(..., full = FALSE)
and vcov(... full = FALSE)
yielded a non-positive-definite covariance matrix, resulting in negative variance estimates for the EMMs.
And I caution that while this seems to work computationally, that does not imply that the answers are right!
来源:https://stackoverflow.com/questions/47472205/plot-model-fit-for-discrete-variable-from-average-model