Adjusted standard errors for slope coefficients in an exploratory analysis

Computes an adjusted standard error in a general linear model after one or more predictor variables with nonsignificant slopes have been dropped from the model. The adjusted standard errors are then used to compute adjusted t-values, p-values, and confidence intervals. The mean square error and error degrees of freedom from the full model are used to compute the adjusted standard errors. These adjusted results are less susceptible to the negative effects of an exploratory model selection.

adj.se(alpha, mse1, mse2, dfe1, se, b)

Arguments

alpha: alpha level for 1-alpha confidence
mse1: mean squared error in full model
mse2: mean squared error in selected model
dfe1: error df in full model
se: vector of slope standard errors in selected model
b: vector of estimated slopes in selected model

Value

Returns adjusted standard error, t-statistic, p-value, and confidence interval for each slope coefficient

Examples

se <- c(1.57, 3.15, 0.982)
b <- c(3.78, 8.21, 2.99)
adj.se(.05, 10.26, 8.37, 114, se, b)
#>      Estimate   adj SE        t  df           p        LL        UL
#> [1,]     3.78 1.738243 2.174609 114 0.031725582 0.3365531  7.223447
#> [2,]     8.21 3.487559 2.354082 114 0.020279958 1.3011734 15.118827
#> [3,]     2.99 1.087233 2.750102 114 0.006930554 0.8362007  5.143799

# Should return:
#      Estimate   adj SE        t  df           p        LL        UL
# [1,]     3.78 1.738243 2.174609 114 0.031725582 0.3365531  7.223447
# [2,]     8.21 3.487559 2.354082 114 0.020279958 1.3011734 15.118827
# [3,]     2.99 1.087233 2.750102 114 0.006930554 0.8362007  5.143799