Description Usage Arguments Details Value Author(s) Examples
print.gbp
enables users to see a compact group-level (unit-level) estimation result of gbp
function.
1 2 |
x |
a resultant object of |
sort |
|
... |
further arguments passed to other methods. |
As for the argument x
, if the result of gbp
is designated to
b
like
"b <- gbp(z, n, model = "binomial")
", the argument x
is supposed to be b
.
We do not need to type "print(b, sort = TRUE)
" but "b
" itself is enough to call
print(b, sort = TRUE)
. But if we want to see the result NOT sorted by the order of se
for Gaussian, or of n
for Binomial and Poisson data, print(b, sort = FALSE)
will show the result by the order of data input.
print(gbp.object)
will display:
obs.mean |
sample mean of each group |
se |
if Gaussian data, standard error of each group |
n |
if Binomial or Poisson data, total number of trials of each group |
X |
a covariate vector or matrix if designated. NA if not |
prior.mean |
numeric if entered, NA if not entered |
prior.mean.hat |
estimate of prior mean by a regression if prior mean is not assigned a priori. The variable name on the display will be "prior.mean" |
prior.mean.AR |
the posterior mean(s) of the expected random effects, if the acceptance-rejection method is used for the binomial model. The variable name on the display will be "prior.mean". |
shrinkage |
shrinkage estimate of each group (adjusted posterior mean) |
shrinkage.AR |
the posterior mean of the shrinkage factor, if the acceptance-rejection method is used for the binomial model. The variable name on the display will be "shrinkage". |
low.intv |
lower bound of 100*confidence.lvl% posterior interval |
post.mean |
posterior mean of each group |
upp.intv |
upper bound of 100*confidence.lvl% posterior interval |
post.sd |
posterior standard deviation of each group |
Hyungsuk Tak, Joseph Kelly, and Carl Morris
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | data(hospital)
z <- hospital$d
n <- hospital$n
y <- hospital$y
se <- hospital$se
###################################################################################
# We do not have any covariates and do not know a mean of the prior distribution. #
###################################################################################
###############################################################
# Gaussian Regression Interactive Multilevel Modeling (GRIMM) #
###############################################################
g <- gbp(y, se, model = "gaussian")
g
print(g, sort = FALSE)
###############################################################
# Binomial Regression Interactive Multilevel Modeling (BRIMM) #
###############################################################
b <- gbp(z, n, model = "binomial")
b
print(b, sort = FALSE)
##############################################################
# Poisson Regression Interactive Multilevel Modeling (PRIMM) #
##############################################################
p <- gbp(z, n, mean.PriorDist = 0.03, model = "poisson")
p
print(p, sort = FALSE)
|
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