About my previous answer, I should have taken advantage of glm() in place of
lm(), as the response is binomial.
--
GG
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.
I would tackle the problem in the following way:
lm.model <- lm(z~ x + y, data=m)
summary(lm.model)
Call:
lm(formula = z ~ x + y, data = m)
Residuals:
Min 1Q Median 3Q Max
-0.34476713 -0.09571506 -0.01786731 0.05225554 0.51693389
Coefficients:
Dear all,
I am trying to use neural networks to discriminate positive and
negative outcomes from a test; this outcome is given in the z variable
of the example I am providing. Each outcome is associated with a pair
of variables x and y and I was planning to identify the cut-offs that
could separate
3 matches
Mail list logo
