I have a stan glm model in R defined as
library("bayesplot")
library("rstanarm")
library("ggplot2")
fit <- stan_glm(mpg ~ ., data = mtcars, verbose=F, refresh=0)
if I write fit on the R console I get the following
stan_glm
family: gaussian [identity]
formula: mpg ~ .
observations: 32
predictors: 11
------
Median MAD_SD
(Intercept) 12.8 18.9
cyl -0.1 1.0
disp 0.0 0.0
hp 0.0 0.0
drat 0.8 1.7
wt -3.6 1.9
qsec 0.8 0.7
vs 0.4 2.2
am 2.5 2.1
gear 0.7 1.5
carb -0.2 0.8
Auxiliary parameter(s):
Median MAD_SD
sigma 2.7 0.4
I want to access the value of sigma 2.7, but I have searched the whole fit object and I cannot find said value. Can someone please tell me how to get it?
Best regards.
As with most models in R:
> sigma(fit)
[1] 2.73695
Or you could look at the summary from the model fit:
> data.frame(summary(fit))["sigma", "X50."]
[1] 2.73695
Here's the whole thing as a data frame:
> data.frame(summary(fit))
mean mcse sd X10. X50. X90. n_eff Rhat
(Intercept) 12.54279879 0.3688500381 20.09362601 -13.28687387 12.67403334 37.657467989 2968 0.9998009
cyl -0.14129417 0.0222245575 1.14496327 -1.57271970 -0.13706998 1.304047966 2654 1.0010753
disp 0.01254241 0.0004192311 0.01894256 -0.01120337 0.01249882 0.036613060 2042 1.0022231
hp -0.02016698 0.0004809532 0.02358536 -0.05058122 -0.01989331 0.009224238 2405 1.0009357
drat 0.82805959 0.0304632349 1.77274405 -1.41607775 0.84471838 3.027727259 3386 0.9999689
wt -3.63321804 0.0431121638 1.98966613 -6.07796020 -3.69661264 -1.056483472 2130 1.0014011
qsec 0.80342804 0.0153319656 0.76715953 -0.13873111 0.79090338 1.764204167 2504 0.9995748
vs 0.25512529 0.0419790836 2.24989830 -2.60945572 0.24651107 3.088975901 2872 1.0004325
am 2.44688868 0.0373708904 2.15216938 -0.33564211 2.45524074 5.149956580 3317 1.0002911
gear 0.66267292 0.0301722194 1.58913529 -1.33284819 0.70235564 2.674174920 2774 1.0003578
carb -0.24624226 0.0206652408 0.88774657 -1.37569859 -0.22005705 0.835823826 1845 1.0015972
sigma 2.79090631 0.0107311424 0.46091723 2.25714743 2.73695031 3.393562388 1845 1.0011221
mean_PPD 20.11202008 0.0109117118 0.71485562 19.22123467 20.09763027 21.022826385 4292 1.0007802
log-posterior -91.69944650 0.1091773334 3.17634767 -95.99935951 -91.28320154 -88.015240185 846 1.0034657
EDIT:
The values seem to be stored here:
> fit$stan_summary
mean se_mean sd 2.5% 10% 25% 50% 75%
(Intercept) 12.54279879 0.3688500381 20.09362601 -26.48634773 -13.28687387 -8.276114e-01 12.67403334 25.93675158
cyl -0.14129417 0.0222245575 1.14496327 -2.44158279 -1.57271970 -8.798672e-01 -0.13706998 0.61281351
disp 0.01254241 0.0004192311 0.01894256 -0.02494649 -0.01120337 2.693235e-04 0.01249882 0.02490041
hp -0.02016698 0.0004809532 0.02358536 -0.06852215 -0.05058122 -3.558996e-02 -0.01989331 -0.00427638
drat 0.82805959 0.0304632349 1.77274405 -2.70741993 -1.41607775 -3.076770e-01 0.84471838 1.96589168
wt -3.63321804 0.0431121638 1.98966613 -7.48103961 -6.07796020 -4.918608e+00 -3.69661264 -2.34235650
qsec 0.80342804 0.0153319656 0.76715953 -0.70986523 -0.13873111 2.888309e-01 0.79090338 1.31428152
vs 0.25512529 0.0419790836 2.24989830 -3.98977789 -2.60945572 -1.236378e+00 0.24651107 1.74000097
am 2.44688868 0.0373708904 2.15216938 -1.87673358 -0.33564211 1.040208e+00 2.45524074 3.90693690
gear 0.66267292 0.0301722194 1.58913529 -2.49950225 -1.33284819 -4.218142e-01 0.70235564 1.67673182
carb -0.24624226 0.0206652408 0.88774657 -1.98389149 -1.37569859 -8.289038e-01 -0.22005705 0.32451771
sigma 2.79090631 0.0107311424 0.46091723 2.07779393 2.25714743 2.463056e+00 2.73695031 3.03865908
mean_PPD 20.11202008 0.0109117118 0.71485562 18.69243403 19.22123467 1.963573e+01 20.09763027 20.58426856
log-posterior -91.69944650 0.1091773334 3.17634767 -99.30705329 -95.99935951 -9.352336e+01 -91.28320154 -89.39973660
90% 97.5% n_eff Rhat
(Intercept) 37.657467989 51.97966438 2967.6793 0.9998009
cyl 1.304047966 2.10645215 2654.0975 1.0010753
disp 0.036613060 0.05017971 2041.5976 1.0022231
hp 0.009224238 0.02580724 2404.8022 1.0009357
drat 3.027727259 4.30044085 3386.4139 0.9999689
wt -1.056483472 0.26494242 2129.9076 1.0014011
qsec 1.764204167 2.32883378 2503.6620 0.9995748
vs 3.088975901 4.75387634 2872.4989 1.0004325
am 5.149956580 6.59500933 3316.5458 1.0002911
gear 2.674174920 3.81082898 2774.0050 1.0003578
carb 0.835823826 1.45681246 1845.4278 1.0015972
sigma 3.393562388 3.85731224 1844.8199 1.0011221
mean_PPD 21.022826385 21.51807143 4291.9129 1.0007802
log-posterior -88.015240185 -86.82211851 846.4301 1.0034657