Confidence interval for a linear contrast of regression coefficients in multiple group regression model

Source: R/statpsych2.R

Computes a confidence interval and test statistic for a linear contrast of population regression coefficients (e.g., a y-intercept or a slope coefficient) across groups in a multiple group regression model. Equality of error variances across groups is not assumed. A Satterthwaite adjustment to the degrees of freedom is used to improve the accuracy of the confidence interval.

ci.lc.reg(alpha, est, se, n, s, v)

Arguments

alpha

alpha level for 1-alpha confidence

est

vector of parameter estimates

se

vector of standard errors

n

vector of group sample sizes

s

number of predictor variables for each within-group model

v

vector of contrast coefficients

Value

Returns a 1-row matrix. The columns are:

  • Estimate - estimated linear contrast

  • SE - standard error

  • t - t test statistic

  • df - degrees of freedom

  • p - p-value

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

Examples

est <- c(1.74, 1.83, 0.482)
se <- c(.483, .421, .395)
n <- c(40, 40, 40)
v <- c(.5, .5, -1)
ci.lc.reg(.05, est, se, n, 4, v)
#>  Estimate        SE        t      df          p        LL       UL
#>     1.303 0.5085838 2.562016 78.8197 0.01231256 0.2906532 2.315347

# Should return:
# Estimate        SE        t      df          p        LL       UL
#    1.303 0.5085838 2.562016 78.8197 0.01231256 0.2906532 2.315347