| ci.condslope {statpsych} | R Documentation |
Confidence interval for conditional (simple) slopes in a linear model
Description
Computes confidence intervals and test statistics for population conditional slopes (simple slopes) in a general linear model that includes a predictor variable (x1), a moderator variable (x2), and a product predictor variable (x1*x2). Conditional slopes are computed at specified low and high values of the moderator variable.
Usage
ci.condslope(alpha, b1, b2, se1, se2, cov, lo, hi, dfe)
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 |
dfe |
error degrees of freedom |
Value
Returns a 2-row matrix. The columns are:
Estimate - estimated conditional slope
t - t test statistic
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Examples
ci.condslope(.05, .132, .154, .031, .021, .015, 5.2, 10.6, 122)
# Should return:
# Estimate SE t df p
# At low moderator 0.9328 0.4109570 2.269824 122 0.024973618
# At high moderator 1.7644 0.6070517 2.906507 122 0.004342076
# LL UL
# At low moderator 0.1192696 1.746330
# At high moderator 0.5626805 2.966119