print.summary.gbp {Rgbp} | R Documentation |
Displaying 'summary.gbp' Class
Description
summary(gbp.object)
enables users to see a compact summary of estimation result.
Usage
## S3 method for class 'summary.gbp'
print(x, ...)
Arguments
x |
a resultant object of |
... |
further arguments passed to other methods. |
Details
The summary has three parts depending on the model fitted by gbp
; Main Summary
, Second-level Variance Component Estimation Summary
, and Regression Summary
(if fitted).
A display of Main Summary
changes depending on whether all the groups (units) has the same standard error for Gaussian data (or the same total number of trials for Binomial and Poisson data). If they are not the same,
Main Summary
lists groups (units) with minimum, median, and maximum values of the standard error for Gaussian data (or of the total number of trials for Binomial and Poisson data). And the last row of Main Summary
is about the overall average for all the groups (units) within each column. Note that this last row is not an average over displayed groups (units) above.
If groups (units) have the same standard error for Gaussian (or the same total number of trials for Binomial and Poisson), Main Summary
lists groups (units) with minimum, median, and maximum values of the sample mean.
For reference, if there are several units with the same median value, they will show up with numbering.
The second part is about the Second-level Variance Component Estimation Summary
. For reference, the second level variance component can be interpreted as variation among the first-level parameters (\theta_{j}
) or variance in ensemble information. It is A for Gaussian, \frac{\mu_{0j}}{r}
for Poisson, and \frac{\mu_{0j}(1 - \mu_{0j})}{r}
for Binomial data. To be specific, this part shows estimate of \alpha
(a posterior mode) defined as log(A) for Gaussian or log(\frac{1}{r}
) for Binomial and Poisson data, and its standard error.
The last part depends on whether gbp
fitted a regression or not. For reference, gbp
fits a regression if the second-level mean (mean.PriorDist
) was not designated. In this case, summary(gbp.object)
will display the result of regression fit.
Value
summary(gbp.object)
shows a compact summary of estimation result such as:
Main summary |
|
Second-level Variance Component Estimation Summary |
|
Regression Summary (if fitted) |
|
Author(s)
Hyungsuk Tak, Joseph Kelly, and Carl Morris
Examples
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")
summary(g)
###############################################################
# Binomial Regression Interactive Multilevel Modeling (BRIMM) #
###############################################################
b <- gbp(z, n, model = "binomial")
summary(b)
##############################################################
# Poisson Regression Interactive Multilevel Modeling (PRIMM) #
##############################################################
p <- gbp(z, n, mean.PriorDist = 0.03, model = "poisson")
summary(p)