Sentence examples for missing values replaced from inspiring English sources

The imputation process was used to create 10 datasets with missing values replaced by imputed candidates.

The first stage is to create multiple copies of the dataset, with the missing values replaced by imputed values.

The numbers of missing values replaced at baseline were: SBI, 3 (0.6%); TSK, 26 (5.6%); and MSBQ, 25 (5.4%).

The numbers of missing values replaced for the MSBQ were 28 (6.9%) at 1 year and 29 (7.6%) at 2 years.

To deal with missing values in our ITT sample, a multiple imputation technique will be used, in which a number of data sets are created, each with the missing values replaced by some plausible values.

Thirty copies of the original data set were generated with missing values replaced by values randomly generated from the predictive distribution, on the basis of the correlation between the variables.

Mean imputation, with the missing value replaced by the mean for that variable, was used to account for missing data for tumour size, tumour grade, HER2 status and KI67 status.

There are several strategies of handling missing data, for example delete all instances where there is at least one missing value, replacing missing attribute values by the attribute mean, or estimating each of the missing values using the values that are present in the dataset (interpolation) [6].

Partially missing data were imputed with the use of the last-observation-carried-forward (LOCF) method, whereby missing values were replaced by the last non-missing value.

Missing values were replaced with last value (baseline) carried forward, or if no baseline values were available, missing values were replaced with the mean value of the group the patient was randomised to.

Any missing values were replaced by a mean value.