Many univariate simple poisson regressions
It performs very many univariate simple poisson regressions.
quasi.poisson_only(x, y, tol = 1e-09, maxiters = 100)
x |
A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. Currently only continuous variables are allowed. |
y |
The dependent variable; a numerical vector with many discrete values (count data). |
maxiters |
The maximum number of iterations after which the Newton-Raphson algorithm is terminated. |
tol |
The tolerance value to terminate the Newton-Raphson algorithm. |
The function is written in C++ and this is why it is very fast. It can accept thousands of predictor variables. It is usefull for univariate screening. We provide no p-value correction (such as fdr or q-values); this is up to the user.
A matrix with the deviance and the estimated phi parameter (dispersion parameter) of each simple poisson regression model for each predictor variable.
Manos Papadakis <papadakm95@gmail.com> and Stefanos Fafalios <stefanosfafalios@gmail.com>
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr>, Manos Papadakis <papadakm95@gmail.com> and Stefanos Fafalios <stefanosfafalios@gmail.com>.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
## 200 variables, hence 200 univariate regressions are to be fitted x <- matrix( rnorm(100 * 200), ncol = 200 ) y <- rpois(100, 10) system.time( poisson_only(x, y) ) b1 <- poisson_only(x, y) b2 <- quasi.poisson_only(x, y) b1<-b2<-x<-y<-NULL
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