Sentence examples for a large proportion of missing values from inspiring English sources

Some adverse factors (and especially combinations of adverse factors, e.g., a large proportion of missing values and a high level of dependence of the missing data pattern on true person proficiency) led to biases in the range of −0.145 to 0.092 for the subpopulation differences and −0.041 to 0.061 for the regression coefficient β2.

Data quality is improved by filtering out users with a large proportion of missing values.

The bias was therefore rarely associated with a single factor (e.g., a large proportion of missing values alone) but rather with combinations of (adverse) conditions (e.g., a large proportion of missing values and a high level of dependence of the pattern of missing data on true person proficiencies).

Note that, for most compounds in the created dataset, there is a large proportion of missing values for the log Kt:p of most tissues (See Additional file 1).

Despite their promise, NGS technologies also suffer from remarkable limitations: high error rates, enrichment of rare variants, and a large proportion of missing values, as well as the fact that most current analytical methods are designed for population-based association studies.

We showed that a recently proposed machine-learning algorithm AODE is well suited to detect and rank functional protein associations being particularly valuable for scenarios characterized by a large proportion of missing values.

The overall accuracies are lower for this dataset as it comes from high-throughput experiments that tend to be noisy, and also because it has a larger proportion of missing values.

Generalizations from analysis of the race variable are problematic due to the large proportion of missing values, but at least 74% of male and 67% of female scan recipients were Caucasian.

They should also check whether important predictors are recorded in each study, and evaluate the amount of missing data for available predictors; though multiple imputation can limit these issues, studies with multiple missing predictors or large proportions of missing values might be best excluded for robustness (unless imputation assumptions can be justified).

However, the larger proportion of missing values for potential confounders (reproductive factors, cause of subfertility) among controls complicated our multivariable analyses.

The factor working conditions had the largest proportion of missing values, which is similar to results from previous research on the SAQ [ 22, 23].