Computes confidence intervals for exp(B) - 1 (as a percent) and exp(B)
where B is a population slope coefficient in a binary logit, ordinal
logit, or log-Poisson model. This function is useful with software that
does not have an option to compute exp(B) and exp(B) - 1.
Usage
expon.slope(alpha, b, se)
Arguments
- alpha
alpha level for 1-alpha confidence
- b
estimated slope coefficient
- se
slope standard error
Value
Returns a 2-row matrix. The first row gives the results for exp(B), and the
the second row gives the results for exp(B) - 1 (as a percent). The columns are:
Estimate - estimate of exp(B) or exp(B) - 1
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Examples
expon.slope(.05, .502, .0396)
#> Estimate LL UL
#> exp(B) 1.652022 1.528651 1.78535
#> 100[exp(B) - 1]% 65.202201 52.865066 78.53502
# Should return:
# Estimate LL UL
# exp(B) 1.652022 1.528651 1.78535
# 100[exp(B) - 1]% 65.202201 52.865066 78.53502