Sentence examples for implement imputation from inspiring English sources
The study was motivated by the need to implement imputation from low-density marker panels into routine genomic evaluation of Australian sheep, which comprises multiple breeds and crossbreds.
Our findings thus clearly stress the importance of the availability of high-quality physical (Beagle algorithm) and genetic maps (IMPUTE2 and FImpute algorithms) for crops in order to implement imputation in plant breeding.
We also implemented imputation on randomly excluded empirical data for mimicking GBS-derived marker data.
In summary, we have described the methodology used to genotype, impute, and analyze data for association with phenotypes in the multiethnic Generation R Study, addressing a number of practical issues that arise in implementing imputation-based association for a multiethnic cohort.
We used MOLGENIS compute to implement the imputation pipeline, run the 8835 imputation chunks in parallel on a PBS compute cluster, and keep track of the 15 imputations (five for each population).
In order to implement multiple imputation in practice, we first need to specify the predictor variables.
Researchers have developed a variety of default routines to implement multiple imputation; however, there has been limited research comparing the performance of these methods, particularly for categorical data.
We implemented multiple imputation using the sequential regression multivariate imputation approach (SRMI), also referred to as Fully Conditional Specification (FCS) and Multiple Imputation by Chained Equations (MICE): this method allows for efficient imputation by fitting a model to each variable, conditional on all others, and imputing one variable at a time [ 50, 51].
To handle missing data, we implemented multiple imputation by chained equations using classification and regression trees (MICE-CART) as the conditional models for imputation [ 26].
Complete R code implementing our imputation model is provided in Additional file 1.
We implemented multiple imputation of missing data, which yielded five data sets.
