On 26/12/2019 11:14 p.m., Spencer Graves wrote:
Hello, All:
The default "simulate" method for lm and glm seems to ignore the
sampling variance of the parameter estimates; see the trivial lm and
glm examples below. Both these examples estimate a mean with formula =
x~1. In both cases, the variance of the estimated mean is 1.
That's how it's documented to operate. Nothing in the help page
suggests it would try to simulate parameter values. Indeed, it doesn't
have enough information on the distribution to sample from: the
appropriate distribution to simulate from if you want to include
uncertainty in the parameter estimates is the posterior distribution,
but lm and glm take a classical point of view, not a Bayesian point of
view, so they have no concept of a posterior.
* In the lm example with x0 = c(-1, 1), var(x0) = 2, and
var(unlist(simulate(lm(x0~1), 10000, 1))) is 2.0064. Shouldn't it be 3
= var(mean(x0)) + var(x0) = (2/2) + 2?
That calculation ignores the uncertainty in the estimation of sigma.
Duncan Murdoch
* In the glm example with x1=1,
var(unlist(simulate(glm(x1~1, poisson), 10000, 1))) = 1.006. Shouldn't
it be 2 = var(glm estimate of the mean) + var(simulated Poisson
distribution) = 1 + 1?
I'm asking, because I've recently written "simulate" methods for
objects of class stats::glm and BMA::bic.glm, where my primary interest
was simulating the predicted mean with "newdata". I'm doing this, so I
can get Monte Carlo prediction intervals. My current code for
"simulate.glm" and "simulate.bic.glm" are available in the development
version of the "Ecfun" package on GitHub:
https://github.com/sbgraves237/Ecfun
Comparing my new code with "stats:::simulate.lm" raises the
following questions in my mind regarding "simulate" of a fit object:
1. Shouldn't "simulate" start by simulating the random
variability in the estimated parameters? I need that for my current
application. If a generic "simulate" function should NOT include this,
what should we call something that does include this? And how does the
current stats:::simulate.lm behavior fit with this?
2. Shouldn't "simulate" of a model fit include an option
for "newdata"? I need that for my application.
3. By comparing with "predict.glm", I felt I needed an
argument 'type = c("link", "response")'. "predict.glm" has an argument
'type = c("link", "response", "terms")'. I didn't need "terms", so I
didn't take the time to code that. However, a general "simulate"
function should probably include that.
4. My application involves assumed Poisson counts. I need
to simulate those as well. If I combined those with "simulate.glm",
what would I call them? I can't use the word "response", because that's
already used with a different meaning. Might "observations" be the
appropriate term?
What do you think?
Thanks,
Spencer Graves
> x0 <- c(-1, 1)
> var(x0)
[1] 2
> fit0 <- lm(x0~1)
> vcov(fit0)
(Intercept)
(Intercept) 1
> sim0 <- simulate(fit0, 10000, 1)
> var(unlist(sim0))
[1] 2.006408
> x1 <- 1
> fit1 <- glm(x1~1, poisson)
> coef(fit1)
(Intercept)
4.676016e-11
> exp(coef(fit1))
(Intercept)
1
> vcov(fit1)
(Intercept)
(Intercept) 0.9999903
> sim1 <- simulate(fit1, 10000, 1)
> var(unlist(sim1))
[1] 1.00617
> sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
Matrix products: default
BLAS:
/System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK:
/Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.6.2 tools_3.6.2
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