| ci.condslope.log {statpsych} | R Documentation |
Confidence interval for conditional (simple) slopes in a logistic model
Description
Computes confidence intervals and test statistics for population conditional slopes (simple slopes) in a logistic model that includes a predictor variable (x1), a moderator variable (x2), and a product predictor variable (x1*x2). Conditional slopes are computed at low and high values of the moderator variable.
Usage
ci.condslope.log(alpha, b1, b2, se1, se2, cov, lo, hi)
Arguments
alpha |
alpha level for 1-alpha confidence |
b1 |
estimated slope coefficient for predictor variable |
b2 |
estimated slope coefficient for product variable |
se1 |
standard error for predictor coefficient |
se2 |
standard error for product coefficient |
cov |
estimated covariance between predictor and product coefficients |
lo |
low value of moderator variable |
hi |
high value of moderator variable |
Value
Returns a 2-row matrix. The columns are:
Estimate - estimated conditional slope
exp(Estimate) - estimated exponentiated conditional slope
z - z test statistic
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Examples
ci.condslope.log(.05, .132, .154, .031, .021, .015, 5.2, 10.6)
# Should return:
# Estimate exp(Estimate) z p
# At low moderator 0.9328 2.541616 2.269824 0.023218266
# At high moderator 1.7644 5.838068 2.906507 0.003654887
# LL UL
# At low moderator 1.135802 5.687444
# At high moderator 1.776421 19.186357