Simple linear regressions coefficients
Simple linear regressions coefficients.
allbetas(y, x, pvalue = FALSE, logged = FALSE)
y |
A numerical vector with the response variable. |
x |
A matrix with the data, where rows denotes the observations and the columns contain the independent variables. |
pvalue |
If you want a hypothesis test that each slope (beta coefficient) is equal to zero set this equal to TRUE. It will also produce all the correlations between y and x. |
logged |
A boolean variable; it will return the logarithm of the pvalue if set to TRUE. |
A matrix with the constant (alpha) and the slope (beta) for each simple linear regression. If the p-value is set to TRUE, the correlation of each y with the x is calculated along with the relevant test statistic and its associated p-value.
Michail Tsagris
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Manos Papadakis <papadakm95@gmail.com>.
x <- matrix( rnorm(100 * 50), ncol = 50 ) y <- rnorm(100) r <- cor(y, x) ## correlation of y with each of the xs a <- allbetas(y, x) ## the coefficients of each simple linear regression of y with x x <- NULL
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