confint(model, level = 1 -0.15/length(coef(model)))

> confint(ACFL.m1, level = 1 -0.15/length(coef(ACFL.m1)))
               1.87 %    98.12 %
(Intercept)   1.5838374  3.2846837
PDCD          8.6257799 12.3346769
PMIX        -17.6815440  6.8408247
RTCL          0.2888126  0.6987631

> K <- cbind(0, diag(length(coef(ACFL.m1))-1))
> rownames(K) <- names(coef(ACFL.m1)) [-1]
> ACLF.mc <- glht(ACFL.m1, linfct = K)
> ACFL.ci <- confint(ACLF.mc, level = 0.85)
> ACFL.ci

Simultaneous Confidence Intervals

Fit: lm(formula = PINDX ~ PDCD + PMIX + RTCL, data = ACFL)

Quantile = 1.9021
85% family-wise confidence level


Linear Hypotheses:
           Estimate   lwr      upr
PDCD == 0  10.4802   8.7910  12.1695
PMIX == 0  -5.4204 -16.5895   5.7488
RTCL == 0   0.4938   0.3071   0.6805