The R package BFI
(Bayesian
Federated Inference) provides several
functions to carry out the Bayesian Federated Inference method for two
kinds of models (GLM
and Survival
) with
multicenteral data without combining/sharing them. In this tutorial we
focus on GLM
only, so that this version of the package is
available for two commonly used families: "binomial"
and
"gaussian"
. The mostly using functions include
bfi()
, MAP.estimation()
, and
inv.prior.cov()
. In the following, we will see how the
BFI
package can be applied to real datasets included in the
package.
Before we go on, we first install and load the BFI
package using the devtools
package:
# First install and load the package 'devtools'
#if(!require(devtools)) {install.packages("devtools")}
library(devtools)
# Now install BFI from GitHub
#devtools::install_github("hassanpazira/BFI", force = TRUE)
# load BFI
library(BFI)
By the following code we can see there two available datasets in the
package: trauma
and Nurses
.
The trauma
data can be utilized for the
"binomial"
family and Nurses
data can be used
for "gaussian"
. To avoid repetition, we only use the
trauma
data set. Load and inspect the trauma
data as follows:
## [1] 371 6
## sex age hospital ISS GCS mortality
## 1 1 20 3 24 15 0
## 2 0 38 3 34 13 0
## 3 0 37 3 50 15 0
## 4 0 17 3 43 4 1
## 5 0 49 3 29 15 0
## 6 0 30 3 22 15 0
## 7 1 84 2 66 3 1
This data set consists of data of 371 trauma patients from three hospitals (peripheral hospital without a neuro-surgical unit, status=1, peripheral hospital with a neuro-surgical unit, status=2, and academic medical center, status=3).
As we can see it has 6 columns:
## [1] "sex" "age" "hospital" "ISS" "GCS" "mortality"
The covariates sex
(dichotomous), age
(continuous), ISS
(Injury Severity Score, continuous), and
GCS
(Glasgow Coma Scale, continuous) are the predictors,
and mortality
is the response variable.
hospital
is a categorical variable which indicates the
hospitals involved in the study. For more information about this dataset
use