| Title: | Privacy-preserving Regression |
|---|---|
| Description: | Generalized linear modeling on vertically partitioned data using block coordinate descent. |
| Authors: | Erik-Jan van Kesteren |
| Maintainer: | Erik-Jan van Kesteren <[email protected]> |
| License: | GPL-3 |
| Version: | 0.9.5 |
| Built: | 2024-08-26 04:18:10 UTC |
| Source: | https://github.com/vankesteren/privreg |
Perform privacy-preserving regression modeling across different institutions. This class implements regression with gaussian and binomial responses using block coordinate descent.
an R6 object of class PrivReg
alice <- PrivReg$new( formula, data, family = "gaussian", name = "alice", verbose = FALSE, debug = FALSE, crypt_key = "testkey" ) alice$listen() alice$connect(127.0.0.1) alice$disconnect() alice$estimate() alice$calculate_se() alice$summary() alice$coef() alice$converged() alice$plot_paths() alice$elapsed()
formula model formula for the regression model at this institution
data data frame for the variables in the model formula
family response family as in glm. Currently only gaussian and binomial are supported
intercept whether to include the intercept. Always use this instead of + 0 in the model formula
name name of this institution
verbose whether to print information
debug whether to print debug statements
crypt_key pre-shared key used to encrypt communication
$new() instantiates and returns a new PrivReg object.
$listen() listens for incoming connections from a partner institution
$connect() connects to a listening partner institution
$disconnect() disconnects from the partner institution
$set_control() sets control parameters. See below for more info
$estimate() computes parameter estimates through block coordinate descent
$calculate_se() computes standard errors using projection method
$converged() test whether the algorithm has converged
$summary() displays a summary of the object, invisibly returns the coef matrix
$coef() returns the model coefficients
$plot_paths() plots the paths of the parameters over the estimation iterations
$elapsed() print information about the elapsed time
max_iter maximum number of iterations of the coordinate descent algorithm
tol PrivReg is converged if all beta changes are below tol.
se Whether to compute standard errors
## Not run: # generate some data set.seed(45) X <- matrix(rnorm(1000), 100) b <- runif(10, -1, 1) y <- X %*% b + rnorm(100, sd = sqrt(b %*% S %*% b)) # split into alice and bob institutions alice_data <- data.frame(y, X[, 1:5]) bob_data <- data.frame(y, X[, 6:10]) # create connection alice$listen() bob$connect("127.0.0.1") # if alice is on different computer, change ip # estimate alice$estimate() # ... # compare results to lm() summary(lm(y ~ X + 0)) alice$summary() bob$summary() ## End(Not run)## Not run: # generate some data set.seed(45) X <- matrix(rnorm(1000), 100) b <- runif(10, -1, 1) y <- X %*% b + rnorm(100, sd = sqrt(b %*% S %*% b)) # split into alice and bob institutions alice_data <- data.frame(y, X[, 1:5]) bob_data <- data.frame(y, X[, 6:10]) # create connection alice$listen() bob$connect("127.0.0.1") # if alice is on different computer, change ip # estimate alice$estimate() # ... # compare results to lm() summary(lm(y ~ X + 0)) alice$summary() bob$summary() ## End(Not run)
Perform privreg locally with two vertically partitioned datasets
privreg_local(y, Xa, Xb, family = gaussian(), se = TRUE, tol = 1e-12, maxit = 10000, debug = TRUE)privreg_local(y, Xa, Xb, family = gaussian(), se = TRUE, tol = 1e-12, maxit = 10000, debug = TRUE)
y |
outcome variable |
Xa |
alice model matrix |
Xb |
bob model matrix |
family |
response family (use family object!) |
se |
whether to compute the standard error |
tol |
tolerance |
maxit |
maximum iterations |
debug |
print debug information |