Saving umxReduceACE output

Hi again all, very sorry for double posting, but I figured since it's two different questions they should be in two separate topics.

Using umxReduceACE on an ACE-model will print out a wonderful comparison between different models, but is there any way to save it?

> # Import data
> data(twinData)
> twinData[, c("ht1", "ht2")] = twinData[, c("ht1", "ht2")] * 10
> mzData = twinData[twinData$zygosity %in% "MZFF", ]
> dzData = twinData[twinData$zygosity %in% "DZFF", ]
>
> # Do the umxACE-stuff
> ACE = umxACE(selDVs = "ht", selCovs = "age", sep = "", dzData = dzData, mzData = mzData)
1 row(s) dropped from 'data' due to missing definition variable(s)
Running ACE with 5 parameters
ACE -2 × log(Likelihood) = 5944.831

Table: Standardized parameter estimates from a 1-factor Cholesky ACE model. A: additive genetic; C: common environment; E: unique environment.

| | a1| c1| e1|
|:--|-----:|-----:|----:|
|ht | 0.929| 0.082| 0.36|

Table: Means and (raw) betas from model$top$intercept and model$top$meansBetas

| | ht1| ht2|
|:---------|------:|------:|
|intercept | 16.446| 16.446|
|age | -0.005| -0.005|

?umxPlotACE options: std=T/F, means=T/F, digits=n, strip_zero=T/F, file=, min=, max =
> # Try to store results of umxReduceACE comparisons...
> reduce_output <- umxReduceACE(ACE, silent = T)
You gave me an ACE model

Solution found! Final fit=5944.8455 (started at 5944.8658) (1 attempt(s): 1 valid, 0 errors)

Solution found! Final fit=6464.8584 (started at 23701.837) (1 attempt(s): 1 valid, 0 errors)

Solution found! Final fit=5944.8455 (started at 5944.8968) (1 attempt(s): 1 valid, 0 errors)

Solution found! Final fit=7776.1643 (started at 25927.514) (1 attempt(s): 1 valid, 0 errors)

|Model |a |c | e|d | EP|Δ Fit |Δ df |p | AIC|Δ AIC |Compare with Model |Fit units |
|:-----|:----|:----|----:|:--|--:|:-------|:----|:-------|-------:|:-------|:------------------|:---------|
|ACE |0.93 |0.08 | 0.36| | 5| | | | 5954.83|0 | |-2lnL |
|ADE |0.93 | | 0.36|0 | 5|0.01 |0 | | 5954.85|0.01 |ACE |-2lnL |
|CE | |0.84 | 0.54| | 4|520.03 |1 |< 0.001 | 6472.86|518.03 |ACE |-2lnL |
|AE |0.93 | | 0.36| | 4|0.01 |1 |0.904 | 5952.85|-1.99 |ACE |-2lnL |
|E | | | 1.00| | 3|1831.33 |2 |< 0.001 | 7782.16|1827.33 |ACE |-2lnL |
Among ACE, ADE, CE, and AE models 'AE' fit best according to AIC.
Conditional AIC probability {Wagenmakers, 2004, 192-196} indicates relative model support as'ACE', 'ADE', 'CE', and 'AE' respectively are: '0.21', '0.21', '0', and '0.58' Using MuMIn::Weights(AIC()).
ACE (0.21%)ADE (0.21%)CE (0%)AE (0.58%)
Running AE with 4 parameters
> # ... but it doesn't work and only saves a model
> reduce_output
MxModel 'AE'
type : default
$matrices : NULL
$algebras : NULL
$penalties : NULL
$constraints : NULL
$intervals : 'top.a_std', 'top.c_std', and 'top.e_std'
$latentVars : none
$manifestVars : none
$data : NULL
$submodels : 'top', 'MZ', and 'DZ'
$expectation : NULL
$fitfunction : MxFitFunctionMultigroup
$compute : MxComputeSequence
$independent : FALSE
$options :
$output : TRUE