Sentence examples for missing values b from inspiring English sources

This particular method was chosen due to its significant advantages: (a) dealing with missing values, (b) requiring data scaling, (c) implying a computationally efficient variant of gradient boosting algorithm [27], (d) providing satisfactory results in ML competitions [28] and was successfully used in other studies and domains (see [29, 30]).

a: indicates missing values; b: chi squared test for heterogeneity; c: linear trend.

MissVIA is built up based on the easy-to-use principle, so every imputation task could be completed with only three steps: (a) upload the dataset with missing values, (b) choose the imputation algorihtms and (c) click the "Submit" button.

Effects of the dummy variables indicating missing values on 1-2 working condition items (NS) and indicating 3-5 missing values (b = 0.07, p < 0.001), respectively, are not included in the table.

a Failure of category counts to add up to this number, or of percentages to add up to 100 denotes missing values b Mean (SD) and p-value of independent t-test.

aSubgroups may not total to 3998 because of missing values b No distal colorectal neoplasia diagnosed at FS cIncluding 8 cases of screening detected CRC dAll variables are specified in the Additional file 1 aAdjusted for gender, age, bmi, smoking habits, total score for exercise, total consumption of vegetables, fruit and berries, boiled potatoes, poultry, other meat than poultry and fatty fish.

B: attributes whose values are only available for the subjects in the original data set ((B_{obs}): available values of B attributes; (B_{mis}): missing values of B attributes).

We also conducted analyses a) before imputation of the missing values and b) after removing subjects with very dilute urine (specific gravity < 1.007, n = 15) or concentrated urine (specific gravity > 1.03, n = 7); results were comparable to those presented here.

Collinearity between potential predictor variables were assessed by variance inflation factors (VIF) above 10 [ 46] in which case the variable with a) least missing values or b) providing the best model fit was retained.

We choose the PAM algorithm because, compared to the k-means approach, it: (a) accepts a dissimilarity matrix (missing values allowed), (b) is more robust as it minimizes a sum of dissimilarities instead of a sum of squared Euclidean distances, (c) provides a graphical display, the silhouette plot, which allows the user to select the optimal number of clusters [ 110].

aThese analyses are based on n = 3189 patients; missing values were imputed b p values marked in bold are statistically significant on the basis of a significance level of 0.05.