Description

The function con_limit_deviations examines the admissibility or uncertainty of numerical study data according to the intervals defined in the metadata. The target values can be of type integer, float or datetime. Thus, con_limit_deviations is an implementation of the Inadmissible numerical values and Uncertain numerical values indicators, as well as the Inadmissible time-date values and Uncertain time-date values indicators. These belong to the Range and value violations domain in the Consistency dimension.

For more details, see the user’s manual and the source code.

Usage and arguments

con_limit_deviations(
  resp_vars = NULL,
  label_col = NULL,
  study_data = sd1,
  meta_data = md1,
  limits = c("HARD_LIMITS", "SOFT_LIMITS", "DETECTION_LIMITS")
)

The con_limit_deviations function has the following arguments:

  • resp_vars: the name of the continuous measurement variable
  • label_col: if labels should be used specify column of metadata containing the labels
  • limits: which limits should be investigated (HARD_LIMITS, SOFT_LIMITS, or DETECTION_LIMITS)
  • study_data: the name of the data frame that contains the measurements
  • meta_data: the name of the data frame that contains item-level metadata

This implementation makes no use of thresholds.

CAVEAT:

In the naming of the following function we deviate from other implementations. This is motivated by the generic use of a function that can process different types of limits, i.e. if SOFT_LIMITS or DETECTION_LIMITS. A necessary convention is the identical definition of limits as shown in the next example.

Example output

To illustrate the output, we use the example synthetic data and metadata that are bundled with the dataquieR package. See the introductory tutorial for instructions on importing these files into R, as well as details on their structure and contents.

For the con_limit_deviations function, the columns HARD_LIMITS, MISSING_LIST and JUMP_LIST in the metadata are particularly relevant.

HARD_LIMITS have to be defined as intervals:

  • \([0; 100]\): any value between 0 and 100, including 0 or 100

  • \((0; 100)\): any value between 0 and 100, not including 0 or 100

  • \([0; Inf)\): any positive numerical value, including 0

This table shows the metadata defined for the example data that required for this implementation:

VAR_NAMES LABEL MISSING_LIST JUMP_LIST HARD_LIMITS
4 v00003 AGE_0 NA NA [18;Inf)
39 v00030 MEDICATION_0 99980 | 99983 | 99988 | 99989 | 99990 | 99991 | 99993 | 99994 | 99995 NA [0;1]
1 v00000 CENTER_0 NA NA NA
34 v00025 SMOKE_SHOP_0 99980 | 99983 | 99988 | 99989 | 99990 | 99991 | 99993 | 99994 | 99995 NA [0;4]
23 v00016 DEV_NO_0 NA NA NA
43 v40000 PART_INTERVIEW NA NA NA
14 v00009 ARM_CIRC_0 99980 | 99981 | 99982 | 99983 | 99984 | 99985 | 99986 | 99987 | 99988 | 99989 | 99990 | 99991 | 99992 | 99993 | 99994 | 99995 NA [0;Inf)
18 v00012 USR_BP_0 99981 | 99982 NA NA
33 v00024 SMOKING_0 99980 | 99983 | 99988 | 99989 | 99990 | 99991 | 99993 | 99994 | 99995 NA [0;1]
21 v00014 CRP_0 99980 | 99981 | 99982 | 99983 | 99984 | 99985 | 99986 | 99988 | 99989 | 99990 | 99991 | 99992 | 99994 | 99995 NA [0;Inf)


However, this function can also be used with other columns of the metadata that contain limit definitions according to the conventions mentioned above. Currently, SOFT_LIMITS and DETECTION_LIMITS are also handled by the function.

For selected response variables

The function can be applied on selected variables using a vector of response variables. The output comprises two tables and plots for each selected variable. The function checks whether the respective limits are specified for each selected variable. If not, a warning is supplied.

limit_deviations_1 <- con_limit_deviations(resp_vars  = c("AGE_0", "SBP_0", "SEX_0"),
                                      label_col  = "LABEL",
                                      study_data = sd1,
                                      meta_data  = md1,
                                      limits     = "HARD_LIMITS")
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions_loc', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions_loc_ecdf', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions_only', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions_only_ecdf', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_distributions_prop', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_end_digits', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_loess', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_margins', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_multivariate_outlier', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_robust_univariate_outlier', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_shape_or_scale', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_univariate_outlier', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'acc_varcomp', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'com_item_missingness', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'com_qualified_item_missingness', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'com_qualified_segment_missingness', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'com_segment_missingness', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'com_unit_missingness', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'con_contradictions', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'con_contradictions_redcap', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'con_inadmissible_categorical', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'con_limit_deviations', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_all_datastructure_dataframe', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_all_datastructure_segment', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_datatype_matrix', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_duplicate_content', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_duplicate_ids', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_part_vars_structure', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_sts_element_dataframe', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_sts_element_segment', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_unexp_elements', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_unexp_records_dataframe', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_unexp_records_segment', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.
## Warning in rlang::call_name(rlang::caller_call(n)) %in% names(.indicator_or_descriptor): Internal error, sorry. Please report: For function 'int_unexp_records_set', the information, if it is an Indicator or a Descriptor is not correctly available in the Roxygen2 comments. Will classify it as a 'Descriptor', for now.

Output 1: FlaggedStudyData

The first table is related to the study data by a 1:1 relationship, i.e. for each observation is checked whether the value is below or above the limits. Call it with limit_deviations_1$FlaggedStudyData:


Output 2: SummaryData

The second table summarizes this information for each variable. Use limit_deviations_1$SummaryData to display it:

Variables Section Limits Number Percentage
AGE_0 below HARD_LIMITS 0 0
AGE_0 within HARD_LIMITS 2940 100
AGE_0 above HARD_LIMITS 0 0
SBP_0 below HARD_LIMITS 0 0
SBP_0 within HARD_LIMITS 2561 100
SBP_0 above HARD_LIMITS 0 0
SBP_0 below DETECTION_LIMITS 0 0
SBP_0 within DETECTION_LIMITS 2561 100
SBP_0 above DETECTION_LIMITS 0 0
SBP_0 below SOFT_LIMITS 0 0
SBP_0 within SOFT_LIMITS 2561 100
SBP_0 above SOFT_LIMITS 0 0

Output 3: SummaryPlotList

The plots for each variable are either a histogram (continuous) or a barplot (discrete) and all are added to a list which is accessed via MyValueLimits$SummaryPlotList.

Output 4: ModifiedStudyData

The fourth output object is a dataframe similar to the study data, however, limit deviations have been removed. Access it using limit_deviations_1$ModifiedStudyData.

Without specification of response variables

It is not necessary to specify variables. In this case the functions seeks for all numeric variables with defined limits. If the function identifies limit deviations, the respective values are removed in the dataframe of ModifiedStudyData.

limit_deviations_2 <- con_limit_deviations(label_col  = "LABEL",
                                      study_data = sd1,
                                      meta_data  = md1,
                                      limits     = "HARD_LIMITS")
## Did not find any 'SCALE_LEVEL' column in item-level meta_data. Predicting it from the data -- please verify these predictions, they may be wrong and lead to functions claiming not to be reasonably applicable to a variable.
## All variables for which limits are specified in the metadata are used.

Output 2: Summary data table

Variables Section Limits Number Percentage
AGE_0 below HARD_LIMITS 0 0.00
AGE_0 within HARD_LIMITS 2940 100.00
AGE_0 above HARD_LIMITS 0 0.00
AGE_1 below HARD_LIMITS 0 0.00
AGE_1 within HARD_LIMITS 2940 100.00
AGE_1 above HARD_LIMITS 0 0.00
SBP_0 below HARD_LIMITS 0 0.00
SBP_0 within HARD_LIMITS 2561 100.00
SBP_0 above HARD_LIMITS 0 0.00
SBP_0 below DETECTION_LIMITS 0 0.00
SBP_0 within DETECTION_LIMITS 2561 100.00
SBP_0 above DETECTION_LIMITS 0 0.00
SBP_0 below SOFT_LIMITS 0 0.00
SBP_0 within SOFT_LIMITS 2561 100.00
SBP_0 above SOFT_LIMITS 0 0.00
DBP_0 below HARD_LIMITS 0 0.00
DBP_0 within HARD_LIMITS 2544 100.00
DBP_0 above HARD_LIMITS 0 0.00
DBP_0 below DETECTION_LIMITS 0 0.00
DBP_0 within DETECTION_LIMITS 2544 100.00
DBP_0 above DETECTION_LIMITS 0 0.00
DBP_0 below SOFT_LIMITS 3 0.12
DBP_0 within SOFT_LIMITS 2470 97.09
DBP_0 above SOFT_LIMITS 71 2.79
GLOBAL_HEALTH_VAS_0 below HARD_LIMITS 0 0.00
GLOBAL_HEALTH_VAS_0 within HARD_LIMITS 2618 100.00
GLOBAL_HEALTH_VAS_0 above HARD_LIMITS 0 0.00
GLOBAL_HEALTH_VAS_0 below SOFT_LIMITS 257 9.82
GLOBAL_HEALTH_VAS_0 within SOFT_LIMITS 2090 79.83
GLOBAL_HEALTH_VAS_0 above SOFT_LIMITS 271 10.35
ASTHMA_0 below HARD_LIMITS 0 0.00
ASTHMA_0 within HARD_LIMITS 2641 100.00
ASTHMA_0 above HARD_LIMITS 0 0.00
ARM_CIRC_0 below HARD_LIMITS 0 0.00
ARM_CIRC_0 within HARD_LIMITS 2657 100.00
ARM_CIRC_0 above HARD_LIMITS 0 0.00
ARM_CIRC_0 below SOFT_LIMITS 0 0.00
ARM_CIRC_0 within SOFT_LIMITS 2657 100.00
ARM_CIRC_0 above SOFT_LIMITS 0 0.00
ARM_CIRC_DISC_0 below HARD_LIMITS 0 0.00
ARM_CIRC_DISC_0 within HARD_LIMITS 2633 100.00
ARM_CIRC_DISC_0 above HARD_LIMITS 0 0.00
ARM_CUFF_0 below HARD_LIMITS 0 0.00
ARM_CUFF_0 within HARD_LIMITS 2623 100.00
ARM_CUFF_0 above HARD_LIMITS 0 0.00
EXAM_DT_0 below HARD_LIMITS 0 0.00
EXAM_DT_0 within HARD_LIMITS 2940 100.00
EXAM_DT_0 above HARD_LIMITS 0 0.00
CRP_0 below HARD_LIMITS 0 0.00
CRP_0 within HARD_LIMITS 2699 100.00
CRP_0 above HARD_LIMITS 0 0.00
CRP_0 below DETECTION_LIMITS 5 0.19
CRP_0 within DETECTION_LIMITS 2694 99.81
CRP_0 above DETECTION_LIMITS 0 0.00
CRP_0 below SOFT_LIMITS 130 4.82
CRP_0 within SOFT_LIMITS 2561 94.89
CRP_0 above SOFT_LIMITS 8 0.30
BSG_0 below HARD_LIMITS 0 0.00
BSG_0 within HARD_LIMITS 2686 100.00
BSG_0 above HARD_LIMITS 0 0.00
BSG_0 below SOFT_LIMITS 92 3.43
BSG_0 within SOFT_LIMITS 2264 84.29
BSG_0 above SOFT_LIMITS 330 12.29
LAB_DT_0 below HARD_LIMITS 0 0.00
LAB_DT_0 within HARD_LIMITS 2940 100.00
LAB_DT_0 above HARD_LIMITS 0 0.00
EDUCATION_0 below HARD_LIMITS 0 0.00
EDUCATION_0 within HARD_LIMITS 2472 100.00
EDUCATION_0 above HARD_LIMITS 0 0.00
EDUCATION_1 below HARD_LIMITS 0 0.00
EDUCATION_1 within HARD_LIMITS 2422 99.88
EDUCATION_1 above HARD_LIMITS 3 0.12
MARRIED_0 below HARD_LIMITS 0 0.00
MARRIED_0 within HARD_LIMITS 2366 100.00
MARRIED_0 above HARD_LIMITS 0 0.00
N_CHILD_0 below SOFT_LIMITS 0 0.00
N_CHILD_0 within SOFT_LIMITS 2249 96.28
N_CHILD_0 above SOFT_LIMITS 87 3.72
EATING_PREFS_0 below HARD_LIMITS 0 0.00
EATING_PREFS_0 within HARD_LIMITS 2328 100.00
EATING_PREFS_0 above HARD_LIMITS 0 0.00
MEAT_CONS_0 below HARD_LIMITS 0 0.00
MEAT_CONS_0 within HARD_LIMITS 2302 100.00
MEAT_CONS_0 above HARD_LIMITS 0 0.00
SMOKING_0 below HARD_LIMITS 0 0.00
SMOKING_0 within HARD_LIMITS 2292 100.00
SMOKING_0 above HARD_LIMITS 0 0.00
SMOKE_SHOP_0 below HARD_LIMITS 0 0.00
SMOKE_SHOP_0 within HARD_LIMITS 782 97.02
SMOKE_SHOP_0 above HARD_LIMITS 24 2.98
N_INJURIES_0 below SOFT_LIMITS 0 0.00
N_INJURIES_0 within SOFT_LIMITS 2161 98.27
N_INJURIES_0 above SOFT_LIMITS 38 1.73
N_BIRTH_0 below SOFT_LIMITS 0 0.00
N_BIRTH_0 within SOFT_LIMITS 1098 99.91
N_BIRTH_0 above SOFT_LIMITS 1 0.09
PREGNANT_0 below HARD_LIMITS 0 0.00
PREGNANT_0 within HARD_LIMITS 1065 100.00
PREGNANT_0 above HARD_LIMITS 0 0.00
MEDICATION_0 below HARD_LIMITS 0 0.00
MEDICATION_0 within HARD_LIMITS 292 45.55
MEDICATION_0 above HARD_LIMITS 349 54.45
N_ATC_CODES_0 below HARD_LIMITS 0 0.00
N_ATC_CODES_0 within HARD_LIMITS 2058 100.00
N_ATC_CODES_0 above HARD_LIMITS 0 0.00
INT_DT_0 below HARD_LIMITS 0 0.00
INT_DT_0 within HARD_LIMITS 2940 100.00
INT_DT_0 above HARD_LIMITS 0 0.00
ITEM_1_0 below HARD_LIMITS 0 0.00
ITEM_1_0 within HARD_LIMITS 2248 100.00
ITEM_1_0 above HARD_LIMITS 0 0.00
ITEM_2_0 below HARD_LIMITS 0 0.00
ITEM_2_0 within HARD_LIMITS 2197 100.00
ITEM_2_0 above HARD_LIMITS 0 0.00
ITEM_3_0 below HARD_LIMITS 0 0.00
ITEM_3_0 within HARD_LIMITS 2184 100.00
ITEM_3_0 above HARD_LIMITS 0 0.00
ITEM_4_0 below HARD_LIMITS 0 0.00
ITEM_4_0 within HARD_LIMITS 2143 100.00
ITEM_4_0 above HARD_LIMITS 0 0.00
ITEM_5_0 below HARD_LIMITS 0 0.00
ITEM_5_0 within HARD_LIMITS 2074 100.00
ITEM_5_0 above HARD_LIMITS 0 0.00
ITEM_6_0 below HARD_LIMITS 0 0.00
ITEM_6_0 within HARD_LIMITS 2048 100.00
ITEM_6_0 above HARD_LIMITS 0 0.00
ITEM_7_0 below HARD_LIMITS 0 0.00
ITEM_7_0 within HARD_LIMITS 2068 100.00
ITEM_7_0 above HARD_LIMITS 0 0.00
ITEM_8_0 below HARD_LIMITS 0 0.00
ITEM_8_0 within HARD_LIMITS 2013 100.00
ITEM_8_0 above HARD_LIMITS 0 0.00
QUEST_DT_0 below HARD_LIMITS 9 0.31
QUEST_DT_0 within HARD_LIMITS 2931 99.69
QUEST_DT_0 above HARD_LIMITS 0 0.00


Output 3: Plot List

Here, only five plots are displayed. However, for each variable with limits, a plot has been generated.

Variables of type datetime

The con_limit_deviations function can also be applied to datetime variables:

limit_deviations_3 <- con_limit_deviations(resp_vars  = c("QUEST_DT_0"),
                                      label_col  = "LABEL",
                                      study_data = sd1,
                                      meta_data  = md1,
                                      limits     = "HARD_LIMITS")
## Did not find any 'SCALE_LEVEL' column in item-level meta_data. Predicting it from the data -- please verify these predictions, they may be wrong and lead to functions claiming not to be reasonably applicable to a variable.

Output 2: Summary Data

Variables Section Limits Number Percentage
QUEST_DT_0 below HARD_LIMITS 9 0.31
QUEST_DT_0 within HARD_LIMITS 2931 99.69
QUEST_DT_0 above HARD_LIMITS 0 0.00

Output 3: Plot List

Interpretation

The definition of HARD_LIMITS is a common issue in the data curation process. For example, values of a numeric rating scale (0 - 10) should not exceed these limits and values outside these limits must be removed or at least verified as they represent certain incorrect measurements. Nevertheless, there are measurements in which the definition of such limits is difficult. In this case the alternative definition of SOFT_LIMITS is recommended.

Algorithm of the implementation

  1. Remove missing codes from the study data (if defined in the metadata)
  2. Interpretation of variable specific intervals as supplied in the metadata.
  3. Identification of measurements outside defined limits. Therefore two output data frames are generated:
    • on the level of observation to flag each deviation, and
    • a summary table for each variable.
  4. A list of plots is generated for each variable examined for limit deviations. The histogram-like plots indicate respective limits as well as deviations.
  5. Values exceeding limits are removed in a data frame of modified study data

Concept relations