ci.condslope.log: Confidence intervals for conditional (simple) slopes in a...

In statpsych: Statistical Methods for Psychologists

Confidence intervals 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 - 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)

# 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

statpsych documentation built on Sept. 11, 2024, 7:42 p.m.