Confidence interval for a linear contrast of general linear model parameters

Source: R/statpsych2.R

Computes the estimate, standard error, and confidence interval for a linear contrast of parameters in a general linear model using coef(object) and vcov(object) where "object" is a fitted model object from the lm function.

ci.lc.glm(alpha, n, b, V, q)

Arguments

alpha

alpha for 1 - alpha confidence

n

sample size

b

vector of parameter estimates from coef(object)

V

covariance matrix of parameter estimates from vcov(object)

q

vector of coefficients

Value

Returns a 1-row matrix. The columns are:

  • Estimate - estimate of linear function

  • 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

y <- c(43, 62, 49, 60, 36, 79, 55, 42, 67, 50)
x1 <- c(3, 6, 4, 6, 2, 7, 4, 2, 7, 5)
x2 <- c(4, 6, 3, 7, 1, 9, 3, 3, 8, 4)
out <- lm(y ~ x1 + x2)
b <- coef(out)
V <- vcov(out)
n <- length(y)
q <- c(0, .5, .5)
b
#> (Intercept)          x1          x2 
#>   26.891111    3.648889    2.213333 
ci.lc.glm(.05, n, b, V, q)
#>  Estimate        SE       t df           p       LL       UL
#>  2.931111 0.4462518 6.56829  7 0.000313428 1.875893 3.986329

#  Should return:
# (Intercept)          x1          x2 
#   26.891111    3.648889    2.213333 
#
# Estimate        SE       t df           p       LL       UL
# 2.931111 0.4462518 6.56829  7 0.000313428 1.875893 3.986329