Rfast2: sclr – R documentation

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sclr

Scaled logistic regression

Scaled logistic regression.

sclr(y, x, full = FALSE, tol = 1e-07, maxiters = 100)

`y`	The dependent variable; a numerical vector with two values (0 and 1).
`x`	A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. This can be a matrix or a data.frame (with factors).
`full`	If this is FALSE, the coefficients and the log-likelihood will be returned only. If this is TRUE, more information is returned.
`tol`	The tolerance value to terminate the Newton-Raphson algorithm.
`maxiters`	The max number of iterations that can take place in each regression.

When full is FALSE a list including:

`theta`	The estimated theta parameter.
`be`	The estimated regression coefficients.
`loglik`	The log-likelihood of the model.
`iters`	The number of iterations required by Newton-Raphson.

When full is TRUE a list including:

`info`	The estimated theta, regression coefficients, their standard error, their Wald test statistic and their p-value.
`loglik`	The log-likelihood.
`iters`	The number of iterations required by Newton-Raphson.

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr>.

Dunning AJ (2006). A model for immunological correlates of protection. Statistics in Medicine, 25(9): 1485-1497. https://doi.org/10.1002/sim.2282.

x <- matrix(rnorm(100 * 2), ncol = 2)
y <- rbinom(100, 1, 0.6)   ## binary logistic regression
a <- sclr(y, x)

A Collection of Efficient and Extremely Fast R Functions II

v0.0.9

GPL (>= 2.0)

Authors

Manos Papadakis, Michail Tsagris, Stefanos Fafalios and Marios Dimitriadis.

Initial release

2021-03-21