Calling BFI from Python

On MacOS

Open a terminal window (cmd + space and search ‘Terminal’) and install the package manager ‘Homebrew’ by executing the following command:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

Install the latest version of Python (Python 3) with the following command:

brew install python@3

Verify the installation by checking the Python version:

python3 --version

It’s assumed that R is also installed and configured on your system. If not, the following can do it:

# Install XCode command-line tools
xcode-select --install

# Install R
brew install --cask r

On Ubuntu

Update package repositories and get latest package information by running the following command in a terminal window:

sudo apt-get update -y

Verify if Python is indeed installed on your system by the following command:

python3 --version

If you already have Python (Python 3) installed on your system, you only need to upgrade it to the latest version as follows:

sudo apt-get upgrade python3

In case you did not have Python installed in the first place, the latest version of Python can be installed using the following command:

sudo apt-get install python3

Verify the installation by checking the Python version:

python3 --version

If you don’t have R installed and configured, just run the following:

# Install R
sudo apt-get install r-base

On Windows

By default, Python is usually not installed on Windows. However, you can check if it exists on the system by running one line of command on the command prompt. Go to Start and enter cmd in the search bar, and then click Command Prompt. Enter the command python --version or python3 --version in the command prompt.

To download Python, go to the official Python download website for Windows: https://www.python.org. Find a stable Python 3 release, and download the executable file for your system from the appropriate link.

After the installer is downloaded, open/run the Python installer to install Python. Select the Add Python 3.x to PATH checkbox, which enables users to launch Python from the command line, and then select Install Now.

Once the installation is complete, you can verify whether the Python installation is successful through the command line. Enter the command python --version or python3 --version in the command prompt.

It’s assumed that R is also installed on your system. If you don’t have R installed, just visit CRAN downloads (https://cran.r-project.org) and get the last version.

On MacOS

Install/Upgrade the latest version of PIP by running

# Installing PIP
python3 -m pip install --user --upgrade pip

Install required modules by running the following commands

# Installing the 'pandas' module
pip3 install pandas

# Installing the 'numpy' module
pip3 install numpy

# Installing the 'rpy2' module
pip3 install rpy2

On Ubuntu

To install PIP use the code

# Installing PIP
sudo apt-get install python3-pip

Verify you have successfully installed PIP by running

pip3 -V

Now, install required modules by typing the following commands

# Installing the 'pandas' module
sudo pip3 install pandas

# Installing the 'numpy' module
sudo pip3 install numpy

# Installing the 'rpy2' module
sudo pip3 install rpy2

If the lines above for installation of the modules did not install them, use the following:

# Installing the 'pandas' module
sudo apt-get install python3-pandas

# Installing the 'numpy' module
sudo apt-get install python3-numpy

# Installing the 'rpy2' module
sudo apt-get install python3-rpy2

Finally, use the show command to verify whether the module is now part of your Python packages:

pip3 show pandas
pip3 show numpy
pip3 show rpy2

On Windows

Usually, PIP is automatically installed if you are using Python downloaded from https://www.python.org. If you have followed the previous steps provided in this vignette, then you have PIP installed on your system. To check if PIP is already installed on Windows, you should open the command line again, type pip -V, and press Enter.

If PIP is not installed: First download get-pip.py to a folder on your computer. Then, open a command prompt and navigate to the folder containing the get-pip.py installer. Finally, run the following command:

python get-pip.py

PIP should now be installed successfully. If we receive a ‘file not found’ error, double check the directory path to the file. You can use the dir command to view the entire contents of a directory. More information can be found here: https://pip.pypa.io/en/stable/installation.

Now, install required modules by running the following commands:

# Installing the 'pandas' module
pip3 install pandas

# Installing the 'numpy' module
pip3 install numpy

# Installing the 'rpy2' module
pip3 install rpy2

Now, you can start coding in Python using Python’s command-line interpreter or IDLE application. Go to Start and enter python in the search bar. You can see Python 3.x and IDLE which can be used for coding. Open one of them and follow the following steps.

Importing Python modules and functions

Use the following codes to import required Python modules:

# import 'pandas' package
import pandas as pd

# import 'numpy' package
import numpy as np                

# import 'rpy2' package
import rpy2                  

and using the following codes imports the required functions such as importr() and data():

from rpy2.robjects.packages import importr, data
from rpy2.robjects.vectors import StrVector
from rpy2.robjects import numpy2ri, pandas2ri
# activation of the automatic conversion of 'numpy' and 'pandas' objects into rpy2 objects
numpy2ri.activate()   
pandas2ri.activate()

Installing R packages from CRAN and GitHub

First load the packages preinstalled with R using importr() as follows:

# import R's "utils" package
utils = importr('utils')

# import R's "base" package
base = importr('base')

Then install the R packages stats and BFI from CRAN by typing

utils.chooseCRANmirror(ind=1)  # which selects the first mirror in the list
package_names = ('stats', 'BFI')
utils.install_packages(StrVector(package_names))

Now load the installed packages using

stats = importr('stats')
BFI = importr('BFI')

utils.chooseCRANmirror(ind=1)
utils.install_packages('devtools')
devtools = importr('devtools')

# Installing the 'BFI' package
devtools.install_github("hassanpazira/BFI", force = True)

# Loading the package
BFI = importr('BFI')

Data Simulation for two local centers

First generate 30 samples randomly from Gaussian distribution with 3 covariates:

# model parameters: 'theta' and 'p' are assumed to be the same for both centers:
theta = np.array([1, 2, 3, 4, 0.75])   # intercept is theta[0], sigma2 is theta[4]
p = 3     # number of regression parameters without intercept

# Center 1:
n1 = 30   # sample size of center 1
X1 = np.random.normal(size=(n1, p)) 
X1.shape   # dimension of X1
X1 = pd.DataFrame(X1, columns=['X1', 'X2', 'X3'])
mu1 = theta[0] + np.dot(X1, np.delete(theta, [0, 4]))  # for gaussian: \eta = \mu 
X1 = pandas2ri.py2rpy(X1)   # == base.as_data_frame(X1)
y1 = np.random.normal(loc=mu1, scale=np.sqrt(theta[4]))

Then generate randomly 50 samples from Gaussian distribution with 3 covariates:

# Center 2:
n2 = 50   # sample size of center 2
X2 = np.random.normal(size=(n2, p))
X2 = pd.DataFrame(X2, columns=['X1', 'X2', 'X3'])
mu2 = theta[0] + np.dot(X2, np.delete(theta, [0, 4]))
X2 = pandas2ri.py2rpy(X2)
y2 = np.random.normal(loc=mu2, scale=np.sqrt(theta[4]))

MAP estimates at the local centers

The following compute the Maximum A Posterior (MAP) estimators of the parameters for center 1:

# Creating an inverse covariance matrix for a Gaussian prior for center 1
Lambda1 = BFI.inv_prior_cov(X1, 0.01, 'gaussian')
fit1 = BFI.MAP_estimation(y1, X1, 'gaussian', Lambda1)
print(fit1)
theta_hat1 = fit1.rx2("theta_hat") # MAP estimates of the intercept and coefficients
A_hat1 = fit1.rx2("A_hat")         # minus the curvature matrix
summary_1 = BFI.summary_bfi(fit1, cur_mat = True)

Obtaining the MAP estimators of the parameters for center 2 using the following:

# Creating an inverse covariance matrix for Gaussian prior for center 2
Lambda2 = BFI.inv_prior_cov(X2, 0.01, 'gaussian')
# MAP estimates
fit2 = BFI.MAP_estimation(y2, X2, 'gaussian', Lambda2)
theta_hat2 = fit2.rx2("theta_hat") # MAP estimates of the parameters
A_hat2 = fit2.rx2("A_hat")         # minus the curvature matrix around 'theta_hat2'
summary_2 = BFI.summary_bfi(fit2, cur_mat = True)

BFI at central center

Now the main function bfi() can be used to obtain the BFI estimates:

theta_hats = base.list(theta_hat1, theta_hat2)
A_hats = base.list(A_hat1, A_hat2)
Lambda1 = pd.DataFrame(Lambda1, index=fit1.rx2("names"), columns=fit1.rx2("names"))
Lambda2 = pd.DataFrame(Lambda2, index=fit2.rx2("names"), columns=fit2.rx2("names"))
Lambdas = base.list(Lambda1, Lambda2)
fit_bfi = BFI.bfi(theta_hats, A_hats, Lambdas)
print(fit_bfi)
summary_bfi = BFI.summary_bfi(fit_bfi, cur_mat = True)

Datasets included in the BFI package

To find a list of all datasets included in the package run:

print(utils.data(package = "BFI"))

In order to use the datasets available from the BFI package, use the following codes:

Nurses = data(BFI).fetch('Nurses')['Nurses']  # is equivalent to BFI::Nurses in R
print("Dimension of the 'Nurses' data: \n", base.dim(Nurses))
print("Colnames of the 'Nurses' data: \n", base.colnames(Nurses))

trauma = data(BFI).fetch('trauma')['trauma']  # is equivalent to BFI::trauma in R
print("Dimension of the 'trauma' data: \n", base.dim(trauma))
print("Colnames of the 'trauma' data: \n", base.colnames(trauma))

At the end, use the following to deactivate automatic conversion:

numpy2ri.deactivate()
pandas2ri.deactivate()