detect_outliers.RdDetection of observations outliers based on residuals values according to two different methods: Grubb's test or a non-parametric method.
detect_outliers(run, compartment = NULL, residuals = NULL, method = "grubbs", grubbs_pvalue_threshold = 0.05, boxplot_coefficient = 3, keep_time_zero = FALSE)
| run |
|
|---|---|
| compartment | integer. Number of the compartment of the observations of interest. |
| residuals | character. Column name of the residuals in the output tables. |
| method | character. One of |
| grubbs_pvalue_threshold | numeric. p-value threshold for Grubb's test. |
| boxplot_coefficient | numeric. k coefficient for non-parametric test. |
| keep_time_zero | logical. If |
A list with the following structure:
method: character string of outlier detection method.
residuals: character string of the type of residuals.
source: tibble of the data source.
outliers: tibble of the outliers with 5 columns: ID, TIME, CMT, DV and
"residuals".
EXAMPLERUN %>% detect_outliers(compartment = 2, residuals = "CWRES", method = "grubbs")#> $method #> [1] "grubbs" #> #> $residuals #> [1] "CWRES" #> #> $source #> # A tibble: 4,408 x 6 #> ID TIME CMT DV PRED CWRES #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 0.125 2 0.863 53.2 0 #> 2 1 0.208 2 2.04 52.3 0 #> 3 1 0.375 2 5.15 50.6 0 #> 4 1 1.04 2 12.8 45.0 0 #> 5 1 2.04 2 21.0 38.7 0 #> 6 1 3.04 2 23.8 34.3 -0.869 #> 7 1 8.02 2 16.7 23.0 -0.410 #> 8 1 10.0 2 13.4 20.0 -0.479 #> 9 1 21.0 2 2.82 7.99 -1.34 #> 10 1 28.0 2 0.993 4.21 -1.23 #> # … with 4,398 more rows #> #> $outliers #> # A tibble: 13 x 7 #> ID TIME CMT DV PRED CWRES dataset_row_index #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 524 6.99 2 555. 94.6 7.19 11443 #> 2 422 88.0 2 59.0 0.0425 5.77 8851 #> 3 525 70.0 2 358. 70.7 5.63 11484 #> 4 306 70.0 2 165. 40.5 5.48 6864 #> 5 471 56.0 2 183. 28.8 5.28 10060 #> 6 492 98 2 153. 9.98 5.01 10625 #> 7 97 0.417 2 99.3 21.2 4.84 2839 #> 8 414 165. 2 148. 1.93 4.64 8698 #> 9 416 112. 2 67.6 1.00 4.63 8730 #> 10 205 138. 2 2.66 0.0424 4.53 4990 #> 11 150 15.0 2 73.3 30.9 4.46 3972 #> 12 480 56 2 42.0 23.5 4.29 10298 #> 13 386 224. 2 2.85 0.0846 3.55 8153 #>EXAMPLERUN %>% detect_outliers(compartment = 2, residuals = "CWRES", method = "grubbs", grubbs_pvalue_threshold = 0.10)#> $method #> [1] "grubbs" #> #> $residuals #> [1] "CWRES" #> #> $source #> # A tibble: 4,408 x 6 #> ID TIME CMT DV PRED CWRES #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 0.125 2 0.863 53.2 0 #> 2 1 0.208 2 2.04 52.3 0 #> 3 1 0.375 2 5.15 50.6 0 #> 4 1 1.04 2 12.8 45.0 0 #> 5 1 2.04 2 21.0 38.7 0 #> 6 1 3.04 2 23.8 34.3 -0.869 #> 7 1 8.02 2 16.7 23.0 -0.410 #> 8 1 10.0 2 13.4 20.0 -0.479 #> 9 1 21.0 2 2.82 7.99 -1.34 #> 10 1 28.0 2 0.993 4.21 -1.23 #> # … with 4,398 more rows #> #> $outliers #> # A tibble: 13 x 7 #> ID TIME CMT DV PRED CWRES dataset_row_index #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 524 6.99 2 555. 94.6 7.19 11443 #> 2 422 88.0 2 59.0 0.0425 5.77 8851 #> 3 525 70.0 2 358. 70.7 5.63 11484 #> 4 306 70.0 2 165. 40.5 5.48 6864 #> 5 471 56.0 2 183. 28.8 5.28 10060 #> 6 492 98 2 153. 9.98 5.01 10625 #> 7 97 0.417 2 99.3 21.2 4.84 2839 #> 8 414 165. 2 148. 1.93 4.64 8698 #> 9 416 112. 2 67.6 1.00 4.63 8730 #> 10 205 138. 2 2.66 0.0424 4.53 4990 #> 11 150 15.0 2 73.3 30.9 4.46 3972 #> 12 480 56 2 42.0 23.5 4.29 10298 #> 13 386 224. 2 2.85 0.0846 3.55 8153 #>EXAMPLERUN %>% detect_outliers(compartment = 2, residuals = "CWRES", method = "boxplot")#> $method #> [1] "boxplot" #> #> $residuals #> [1] "CWRES" #> #> $source #> # A tibble: 4,408 x 6 #> ID TIME CMT DV PRED CWRES #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 0.125 2 0.863 53.2 0 #> 2 1 0.208 2 2.04 52.3 0 #> 3 1 0.375 2 5.15 50.6 0 #> 4 1 1.04 2 12.8 45.0 0 #> 5 1 2.04 2 21.0 38.7 0 #> 6 1 3.04 2 23.8 34.3 -0.869 #> 7 1 8.02 2 16.7 23.0 -0.410 #> 8 1 10.0 2 13.4 20.0 -0.479 #> 9 1 21.0 2 2.82 7.99 -1.34 #> 10 1 28.0 2 0.993 4.21 -1.23 #> # … with 4,398 more rows #> #> $outliers #> # A tibble: 12 x 7 #> ID TIME CMT DV PRED CWRES dataset_row_index #> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 524 6.99 2 555. 94.6 7.19 11443 #> 2 422 88.0 2 59.0 0.0425 5.77 8851 #> 3 525 70.0 2 358. 70.7 5.63 11484 #> 4 306 70.0 2 165. 40.5 5.48 6864 #> 5 471 56.0 2 183. 28.8 5.28 10060 #> 6 492 98 2 153. 9.98 5.01 10625 #> 7 97 0.417 2 99.3 21.2 4.84 2839 #> 8 414 165. 2 148. 1.93 4.64 8698 #> 9 416 112. 2 67.6 1.00 4.63 8730 #> 10 205 138. 2 2.66 0.0424 4.53 4990 #> 11 150 15.0 2 73.3 30.9 4.46 3972 #> 12 480 56 2 42.0 23.5 4.29 10298 #>