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cA missing value is imputed five times, resulting in five complete datasets including all imputed and not-imputed variables [24].
With this method, each missing value is imputed randomly from a set of nearest observed values in the dataset.
These four ad hoc methods also overstate the precision by underestimating the standard error set because the same value is imputed repeatedly.
Doing so, however, underestimates the variability of the data (because a single value is imputed for multiple points) and subsequently produces liberal estimates of statistical significance.
When either x i or y i is MAR and the other variable is < LOD, the < LOD value is imputed based on the estimated marginal distribution (a truncated univariate normal).
Each missing value is imputed 10 times to account for the uncertainty underlying the missing values using "aregImpute" function in the "Hmisc" package in R based on additive regression, bootstrapping, and predictive mean matching [ 32].
Similar(52)
A small amount of data was missing and its value was imputed.
Each missing value was imputed in this way.
If both current and most recent values were missing, then no value was imputed.
For these cases, a zero value was imputed for statistical analysis.
Missing values were imputed using multiple imputations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com