Calculate y ~ sigmoid(a + b x) using iteratively re-weighted least squares. Zero indexed.
logistic_solve1(x, y, w, initial_link, i, j, skip)
x | NumericVector, expanatory variable. |
---|---|
y | NumericVector, 0/1 values to fit. |
w | NumericVector, weights (required, positive). |
initial_link, | initial link estimates (required, all zeroes is a good start). |
i | integer, first index (inclusive). |
j | integer, last index (inclusive). |
skip | integer, index to skip (-1 to not skip). |
vector of a and b.
set.seed(5) d <- data.frame( x = rnorm(10), y = sample(c(0,1), 10, replace = TRUE) ) weights <- runif(nrow(d)) m <- glm(y~x, data = d, family = binomial, weights = weights)#> Warning: non-integer #successes in a binomial glm!#> (Intercept) x #> 0.8821778 1.9689368#> [1] 0.8821778 1.9689368