Many univariate simple linear regressions
It performs very many univariate simple linear regressions with or without categorical variables.
regression(x, y, poia = NULL, logged = FALSE)
x |
A data.frame or a matrix with the data, where the rows denote the samples (and the two groups) and the columns
are the variables. A data frame is expected if you have categorical predictor variables. If you only have continuous
predictor variables you should the function |
y |
The dependent variable; a numerical vector. |
poia |
If the "x" is a data.frame and you know the indices of the columns which are categorical variables supply it here. |
logged |
Do you want the logarithm of the p-values be returned? The default value is FALSE. |
Some parts of the function will be transferred in C++. 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 two columns, the test statistic value and its corresponding (logged) p-value.
Manos Papadakis <papadakm95@gmail.com>
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Manos Papadakis <papadakm95@gmail.com>.
Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
y <- rnorm(150) a <- regression(iris, y) a summary(lm(y ~ iris[, 5]) ) ## check the F-test
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.