# CI_g: Calculates a confidence interval for a standardized mean... In scdhlm: Estimating Hierarchical Linear Models for Single-Case Designs

## Description

Calculates a confidence interval given a `g_REML`, a `g_HPS`, or a `g_mlm` object using either a central t distribution (for a symmetric interval) or a non-central t distribution (for an asymmetric interval).

## Arguments

 `g` an estimated effect size object of class `g_REML`, class `g_HPS`, or class `g_mlm`. `cover` confidence level `bound` numerical tolerance for non-centrality parameter in `qt`. `symmetric` If `TRUE` (the default), use a symmetric confidence interval. If `FALSE`, use a non-central t approximation to obtain an asymmetric confidence interval.

## Value

A vector of upper and lower confidence bounds.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```data(Laski) Laski_RML <- lme(fixed = outcome ~ treatment, random = ~ 1 | case, correlation = corAR1(0, ~ time | case), data = Laski) Laski_g_REML <- suppressWarnings( g_REML(Laski_RML, p_const = c(0,1), r_const = c(1,0,1), returnModel = FALSE) ) CI_g(Laski_g_REML, symmetric = TRUE) CI_g(Laski_g_REML, symmetric = FALSE) Laski_HPS <- with(Laski, effect_size_MB(outcome, treatment, case, time)) CI_g(Laski_HPS, symmetric = FALSE) Laski_g_mlm <- g_mlm(Laski_RML, p_const = c(0,1), r_const = c(1,0,1), returnModel = TRUE) CI_g(Laski_g_mlm, symmetric = FALSE) ```

scdhlm documentation built on Jan. 13, 2021, 7:10 p.m.