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The estimates of genetic differentiation were rather robust up to 90% of missing observations but became substantially biased when missing genotypes were imputed.
None of the methods used yields a uniform p-value distribution, most likely because the studied subset of SNPs is not only subject to missing observations but also to considerable genotyping error.
Second, the estimations of heterozygosity and inbreeding coefficient would become less accurate with larger numbers of missing observations, but the estimation biases were much smaller than those from genotype imputations.
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Were all observations included, there would be 684 observations, but because of missing observations, there are 507 observations in the data set.
*** In total 101 missing observations due to a missing value for one or more independent variables.
*Level of university education: 10 missing observations in Germany and 6 missing observations in Austria.
The observed reduction in risk was not due to missing observations.
Due to the high risk of missing observations in microarray experiments, it is fundamental to concentrate not only on optimal designs but also on designs which are robust against missing observations.
Third, all relevant variables are screened for missing observations.
The effects of missing observations on complete and partial diallel cross designs are examined.
Subset designs are studied for their robustness to missing observations in different experimental regions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com