Logistic and Poisson regression models
Logistic and Poisson regression models.
glm_logistic(x, y, full = FALSE,tol = 1e-09, maxiters = 100) glm_poisson(x, y, full = FALSE,tol = 1e-09)
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). |
y |
The dependent variable; a numerical vector with two values (0 and 1) for the logistic regression or integer values, 0, 1, 2,... for the Poisson regression. |
full |
If this is FALSE, the coefficients and the deviance 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. |
The function is written in C++ and this is why it is very fast.
When full is FALSE a list including:
be |
The regression coefficients. |
devi |
The deviance of the model. |
When full is TRUE a list including:
info |
The regression coefficients, their standard error, their Wald test statistic and their p-value. |
devi |
The deviance. |
Manos Papadakis <papadakm95@gmail.com>
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Manos Papadakis <papadakm95@gmail.com>.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
## Not run: x <- matrix(rnorm(100 * 3), ncol = 3) y <- rbinom(100, 1, 0.6) ## binary logistic regression a1 <- glm_logistic(x, y, full = TRUE) a2 <- glm(y ~ x, binomial) x <- matrix(rnorm(100 * 3), ncol = 3) y <- rpois(100, 10) ## binary logistic regression b1 <- glm_poisson(x, y, full = TRUE) b2 <- glm(y ~ x, poisson) x<-y<-a1<-a2<-b1<-b2<-NULL ## End(Not run)
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