R: how to extract information from summary model fit

Question

library(nlme)
fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
            data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3))
> summary(fm1)
Nonlinear mixed-effects model fit by maximum likelihood
  Model: height ~ SSasymp(age, Asym, R0, lrc) 
 Data: Loblolly 
       AIC      BIC    logLik
  239.4856 251.6397 -114.7428

Random effects:
 Formula: Asym ~ 1 | Seed
            Asym  Residual
StdDev: 3.650642 0.7188625

Fixed effects: Asym + R0 + lrc ~ 1 
         Value Std.Error DF   t-value p-value
Asym 101.44960 2.4616951 68  41.21128       0
R0    -8.62733 0.3179505 68 -27.13420       0
lrc   -3.23375 0.0342702 68 -94.36052       0
 Correlation: 
    Asym   R0    
R0   0.704       
lrc -0.908 -0.827

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.23601930 -0.62380854  0.05917466  0.65727206  1.95794425 

Number of Observations: 84
Number of Groups: 14 

I'm interested in extracting information from the summary output of an NLME fit.

I would like to extract the

1. StdDev of the random effects (i.e. StdDev of Asym, which = 3.65) For this one I've tried fm1$apVar but no luck.
2. Parameter estimates of the fixed effects (i.e. Asym = 101.44960, R0 = -8.62733, etc), which can be extracted via fixef(fm1)
3. Std.Error of the fixed effects (i.e. 2.46, 0.317, 0.034). For this one I've tried sqrt(diag(fm1$varFix)) but those values don't exactly match the ones under Std.Error column under fixed effects?
4. logLikelihood (i.e. -114.7428, which can be extracted using fm1$logLik)
5. Residuals (i.e. 0.7188625, which can be extracted using fm1$Residuals)

My ultimate goal is to fit multiple models and store their respective summary estimates into an organized data.frame.

fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
            data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -8.5, lrc = -3.3))

fm2 <- nlme(height ~ SSasymp(age, Asym, R0, lrc),
            data = Loblolly,
            fixed = Asym + R0 + lrc ~ 1,
            random = Asym ~ 1,
            start = c(Asym = 103, R0 = -5.4, lrc = -3.3))

summary(fm1)
summary(fm2)

mylist = list(NULL, summary(fm1), NULL, summary(fm2), NULL, NULL)

Suppose my list object looks like mylist. Now I would like to create a data.frame that looks like:

model    FixedAsym    FixedAsymStdError   FixedR0      ...     Residual
 1       101.44960        2.4616951       -8.62733            0.7188625
 2       101.44934        2.4616788       -8.62736     ...    0.7188625

And to create this data.frame (the number of rows corresponds to how many model summaries I have in mylist) I would need to systematically extract those values (numbered 1-5) from the model summary output.

Answer 1

Here are a few more pieces...

as.numeric(VarCorr(fm1)[,2])
# [1] 3.6506418 0.7188625

summary(fm1)$tTable[,2]
#       Asym         R0        lrc 
# 2.46169512 0.31795045 0.03427017 

# looks like you don't need this one anymore, but here's a way of getting it
summary(fm1)$corFixed
#            Asym         R0        lrc
# Asym  1.0000000  0.7039498 -0.9077793
# R0    0.7039498  1.0000000 -0.8271022
# lrc  -0.9077793 -0.8271022  1.0000000

Apologies that this isn't a complete answer -- It may prove difficult to create a summary table like you're describing, since the structure of each potential row will be different, and will depend on which variables are included as fixed and random effects.