Confidence intervals for conditional (simple) slopes in a logistic model

Source: R/statpsych3.R

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.

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 - two-sided p-value

  • LL - lower limit of the exponentiated confidence interval

  • UL - upper limit of the exponentiated confidence interval

Examples

ci.condslope.log(.05, .132, .154, .031, .021, .015, 5.2, 10.6)
#>                   Estimate exp(Estimate)        z           p       LL
#> At low moderator    0.9328      2.541616 2.269824 0.023218266 1.135802
#> At high moderator   1.7644      5.838068 2.906507 0.003654887 1.776421
#>                          UL
#> At low moderator   5.687444
#> At high moderator 19.186357

# 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