pool_scalar_RR: Rubin's Rules for scalar estimates

In miceafter: Data and Statistical Analyses after Multiple Imputation

Rubin's Rules for scalar estimates

Description

pool_scalar_RR Applies Rubin's pooling Rules for scalar estimates

Usage

pool_scalar_RR(
  est,
  se,
  logit_trans = FALSE,
  conf.level = 0.95,
  statistic = FALSE,
  dfcom = NULL,
  df_small = TRUE,
  approxim = "tdistr"
)

Arguments

est	a numerical vector of parameter estimates.
se	a numerical vector of standard error estimates.
logit_trans	If TRUE logit transformation of parameter values is applied before pooling, if FALSE (default), pooling is done on the original parameter scale.
conf.level	Confidence level of the confidence intervals.
statistic	if TRUE the test statistic and confidence interval are provided, if FALSE (default) these are not shown.
dfcom	The complete data analysis degrees of freedom.
df_small	if TRUE (default) the (Barnard & Rubin) small sample correction for the degrees of freedom is applied, if FALSE the old number of degrees of freedom is calculated.
approxim	if "tdistr" a t-distribution is used (default), if "zdistr" a z-distribution is used to derive a p-value according to the test statistic.

Details

The t-value is the quantile value of the t-distribution that can be used to calculate confidence intervals according to est_{pooled} +/- t_{1-α/2} * se_{pooled}. When statistic is TRUE the test statistic is calculated as statistic = est{pooled}/se{pooled}. The p-value is than derived using the t-distribution and adjusted degrees of freedom.

Value

A list object from which the following objects are extracted:

• pool_est the pooled parameter value.

• pool_se the pooled standard error value.

• t quantile of the t-distribution (to calculate confidence intervals).

• r the relative increase in variance due to missing data.

• dfcom complete data degrees of freedom.

• v_adj adjusted degrees of freedom (according to Barnard and Rubin 1999)

Author(s)

Martijn Heymans, 2021

Examples

est <- c(0.4, 0.6, 0.8)
se <- c(0.02, 0.05, 0.03)
res <- pool_scalar_RR(est, se, dfcom=500)
res

miceafter documentation built on Oct. 2, 2022, 5:08 p.m.