pool_scalar_RR Applies Rubin's pooling Rules for scalar
estimates
pool_scalar_RR(
est,
se,
logit_trans = FALSE,
conf.level = 0.95,
statistic = FALSE,
dfcom = NULL,
df_small = TRUE,
approxim = "tdistr"
)a numerical vector of parameter estimates.
a numerical vector of standard error estimates.
If TRUE logit transformation of parameter values is applied before pooling, if FALSE (default), pooling is done on the original parameter scale.
Confidence level of the confidence intervals.
if TRUE the test statistic and confidence interval are provided, if FALSE (default) these are not shown.
The complete data analysis degrees of freedom.
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.
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.
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)
The t-value is the quantile value of the t-distribution that can be used to calculate confidence intervals according to \(est_{pooled} +/- t_{1-\alpha/2} * se_{pooled}\). 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.