Sentence examples for missing data then from inspiring English sources

The idea behind imputation methods is that as we know how to analyze the data if there were no missing data (planned analysis), and if we could fill in (impute) the missing data, then we could just analyze this imputed dataset.

No drift and level shifts: if the level shifts are separated by missing data, then window width should be limited by the duration of the gaps of missing data to avoid adjacent levels influencing each other.

If the ZNF sequences for a particular primate had excessive missing data, then the primate was excluded from the study for that ZNF.

If these influential sites contain missing data, then the decision on whether to exclude or keep such sites may be the main determinant of the tree topology.

If, given the observed data, the probability of drop-out does not depend on missing data, then the data are MAR.

We first derive the likelihood function for a truncated bivariate normal distribution with missing data then describe an MI method for values < LOD.

The missing data were then imputed using Beagle v3.3 (Browning and Browning 2007).

All sequenced codon positions were included in the alignment, for introns and exons in arsB; however, every site containing gaps or missing data was then eliminated from the dataset.

GPS recordings of individual daily tracks were corrected for outliers and missing data and then converted from the original geographic coordinate system to a UTM grid projection (WGS1984, zone 38S) using ArcGIS 10.0 (ESRI, Redlands, CA, USA).

The missing data is then imputed by taking the mean value of all the non-missing measurements from the other stations at the missing time point, and modifying it based on the week, day and hour effect of the given station.

We therefore used mean substitution for missing data, and then applied this linear function using all 36 variables to our data set.