ci.lc.glm {statpsych} | R Documentation |
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)
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 |
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
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 ci.lc.glm(.05, n, b, V, q) # Should return: # (Intercept) x1 x2 # 26.891111 3.648889 2.213333 # > ci.lc.glm(.05, n, b, V, q) # Estimate SE t df p LL UL # [1,] 2.931111 0.4462518 6.56829 7 0.000313428 1.875893 3.986329