1-hold out logistic regression predections. Zero indexed.
xlogistic_fits(x, y, w, i, j)
NumericVector, expanatory variable.
NumericVector, 0/1 values to fit.
NumericVector, weights (required, positive).
integer, first index (inclusive).
integer, last index (inclusive).
vector of predictions for interval.
set.seed(5)
d <- data.frame(x = rnorm(10))
d$y <- d$x + rnorm(nrow(d))>0
weights <- runif(nrow(d))
m <- glm(y~x, data = d, family = binomial, weights = weights)
#> Warning: non-integer #successes in a binomial glm!
d$pred1 <- predict(m, newdata = d, type = "link")
d$pred2 <- xlogistic_fits(d$x, d$y, weights, 0, nrow(d)-1)
d <- d[order(d$x), , drop = FALSE]
print(d)
#> x y pred1 pred2
#> 3 -1.25549186 FALSE -2.4292992 -2.167502e+00
#> 1 -0.84085548 TRUE -1.8751975 -1.511600e+308
#> 8 -0.63537131 FALSE -1.6005976 -1.581696e+00
#> 6 -0.60290798 FALSE -1.5572150 -1.394288e+00
#> 7 -0.47216639 FALSE -1.3824977 -1.150064e+00
#> 9 -0.28577363 FALSE -1.1334107 -8.816750e-01
#> 4 0.07014277 FALSE -0.6577798 -4.424278e-01
#> 10 0.13810822 FALSE -0.5669537 -2.757255e-01
#> 2 1.38435934 TRUE 1.0984811 8.595039e-01
#> 5 1.71144087 TRUE 1.5355784 -7.673982e-01