View source: R/pool_scalar_RR.R
pool_scalar_RR | R Documentation |
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" )
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. |
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.
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)
Martijn Heymans, 2021
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
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