Package 'dataquieR'

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 - e2

Arguments

Value

result

Description

Get a subset of a dataquieR dq_report2 report

Usage

## S3 method for class 'dataquieR_resultset2'
x[row, col, res, drop = FALSE, els = row, as_raw = FALSE]

Arguments

Value

a list with results, depending on drop and the number of results, the list may contain all requested results in sub-lists. The order of the results follows the order of the row/column/result-names given

Description

Get a single result from a ⁠dataquieR 2⁠ report

Usage

## S3 method for class 'dataquieR_resultset2'
x[[el]]

Arguments

Value

the dataquieR result object

Description

Set a single result from a ⁠dataquieR 2⁠ report

Usage

## S3 replacement method for class 'dataquieR_resultset2'
x[[el]] <- value

Arguments

Value

the dataquieR result object

Description

Overwriting of elements only list-wise supported

Usage

## S3 replacement method for class 'dataquieR_resultset2'
x[...] <- value

Arguments

Value

nothing, stops

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 * e2

Arguments

Value

result

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 / e2

Arguments

Value

result

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 %/% e2

Arguments

Value

result

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 %% e2

Arguments

Value

result

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 ^ e2

Arguments

Value

result

Description

Operator caring for units

Usage

## S3 method for class 'numeric_with_unit'
e1 + e2

Arguments

Value

result

Access single results from a dataquieR_resultset2 report

Description

Access single results from a dataquieR_resultset2 report

Usage

## S3 method for class 'dataquieR_resultset2'
x$el

Arguments

Value

the dataquieR result object

Write single results from a dataquieR_resultset2 report

Description

Write single results from a dataquieR_resultset2 report

Usage

## S3 replacement method for class 'dataquieR_resultset2'
x$el <- value

Arguments

Value

the dataquieR result object

Description

This function creates distribution plots for categorical variables.

Descriptor

Usage

acc_cat_distributions(
  resp_vars = NULL,
  group_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2,
  n_cat_max = getOption("dataquieR.max_cat_resp_var_levels_in_plot",
    dataquieR.max_cat_resp_var_levels_in_plot_default),
  n_group_max = getOption("dataquieR.max_group_var_levels_in_plot",
    dataquieR.max_group_var_levels_in_plot_default),
  n_data_min = getOption("dataquieR.min_time_points_for_cat_resp_var",
    dataquieR.min_time_points_for_cat_resp_var_default)
)

Arguments

Details

To complete

Value

A list with:

• SummaryPlot: ggplot2::ggplot for the response variable in resp_vars.

See Also

Online Documentation

Description

Data quality indicator checks "Unexpected location" and "Unexpected proportion" with histograms.

Indicator

Usage

acc_distributions(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  check_param = c("any", "location", "proportion"),
  plot_ranges = TRUE,
  flip_mode = "noflip",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Unexpected location" (FLG_acc_ud_loc) and "Unexpected proportion" (FLG_acc_ud_prop) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for "Unexpected location" and / or "Unexpected proportion" for a report

• SummaryPlotList: list of ggplot2::ggplots for each response variable in resp_vars.

Algorithm of this implementation:

• If no response variable is defined, select all variables of type float or integer in the study data.

• Remove missing codes from the study data (if defined in the metadata).

• Remove measurements deviating from (hard) limits defined in the metadata (if defined).

• Exclude variables containing only NA or only one unique value (excluding NAs).

• Perform check for "Unexpected location" if defined in the metadata (needs a LOCATION_METRIC (mean or median) and LOCATION_RANGE (range of expected values for the mean and median, respectively)).

• Perform check for "Unexpected proportion" if defined in the metadata (needs PROPORTION_RANGE (range of expected values for the proportions of the categories)).

• Plot histogram(s).

See Also

Online Documentation

Description

Data quality indicator checks "Unexpected location" and "Unexpected proportion" if a grouping variable is included: Plots of empirical cumulative distributions for the subgroups.

Descriptor

Usage

acc_distributions_ecdf(
  resp_vars = NULL,
  group_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2,
  n_group_max = getOption("dataquieR.max_group_var_levels_in_plot",
    dataquieR.max_group_var_levels_in_plot_default),
  n_obs_per_group_min = getOption("dataquieR.min_obs_per_group_var_in_plot",
    dataquieR.min_obs_per_group_var_in_plot_default)
)

Arguments

Value

A list with:

• SummaryPlotList: list of ggplot2::ggplots for each response variable in resp_vars.

See Also

Online Documentation

Description

Data quality indicator checks "Unexpected location" and "Unexpected proportion" with histograms.

Indicator

Usage

acc_distributions_loc(
  resp_vars = NULL,
  study_data,
  label_col = VAR_NAMES,
  item_level = "item_level",
  check_param = "location",
  plot_ranges = TRUE,
  flip_mode = "noflip",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Unexpected location" (FLG_acc_ud_loc) and "Unexpected proportion" (FLG_acc_ud_prop) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for "Unexpected location" and / or "Unexpected proportion" for a report

• SummaryPlotList: list of ggplot2::ggplots for each response variable in resp_vars.

Algorithm of this implementation:

• If no response variable is defined, select all variables of type float or integer in the study data.

• Remove missing codes from the study data (if defined in the metadata).

• Remove measurements deviating from (hard) limits defined in the metadata (if defined).

• Exclude variables containing only NA or only one unique value (excluding NAs).

• Perform check for "Unexpected location" if defined in the metadata (needs a LOCATION_METRIC (mean or median) and LOCATION_RANGE (range of expected values for the mean and median, respectively)).

• Perform check for "Unexpected proportion" if defined in the metadata (needs PROPORTION_RANGE (range of expected values for the proportions of the categories)).

• Plot histogram(s).

See Also

• acc_distributions

• Online Documentation

Description

Descriptor

Usage

acc_distributions_only(
  resp_vars = NULL,
  study_data,
  label_col = VAR_NAMES,
  item_level = "item_level",
  flip_mode = "noflip",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Unexpected location" (FLG_acc_ud_loc) and "Unexpected proportion" (FLG_acc_ud_prop) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for "Unexpected location" and / or "Unexpected proportion" for a report

• SummaryPlotList: list of ggplot2::ggplots for each response variable in resp_vars.

Algorithm of this implementation:

• If no response variable is defined, select all variables of type float or integer in the study data.

• Remove missing codes from the study data (if defined in the metadata).

• Remove measurements deviating from (hard) limits defined in the metadata (if defined).

• Exclude variables containing only NA or only one unique value (excluding NAs).

• Perform check for "Unexpected location" if defined in the metadata (needs a LOCATION_METRIC (mean or median) and LOCATION_RANGE (range of expected values for the mean and median, respectively)).

• Perform check for "Unexpected proportion" if defined in the metadata (needs PROPORTION_RANGE (range of expected values for the proportions of the categories)).

• Plot histogram(s).

See Also

• acc_distributions

• Online Documentation

Description

Data quality indicator checks "Unexpected location" and "Unexpected proportion" with histograms.

Indicator

Usage

acc_distributions_prop(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  check_param = "proportion",
  plot_ranges = TRUE,
  flip_mode = "noflip",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Unexpected location" (FLG_acc_ud_loc) and "Unexpected proportion" (FLG_acc_ud_prop) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for "Unexpected location" and / or "Unexpected proportion" for a report

• SummaryPlotList: list of ggplot2::ggplots for each response variable in resp_vars.

Algorithm of this implementation:

• If no response variable is defined, select all variables of type float or integer in the study data.

• Remove missing codes from the study data (if defined in the metadata).

• Remove measurements deviating from (hard) limits defined in the metadata (if defined).

• Exclude variables containing only NA or only one unique value (excluding NAs).

• Perform check for "Unexpected location" if defined in the metadata (needs a LOCATION_METRIC (mean or median) and LOCATION_RANGE (range of expected values for the mean and median, respectively)).

• Perform check for "Unexpected proportion" if defined in the metadata (needs PROPORTION_RANGE (range of expected values for the proportions of the categories)).

• Plot histogram(s).

See Also

• acc_distributions

• Online Documentation

Extension of acc_shape_or_scale to examine uniform distributions of end digits

Description

This implementation contrasts the empirical distribution of a measurement variables against assumed distributions. The approach is adapted from the idea of rootograms (Tukey (1977)) which is also applicable for count data (Kleiber and Zeileis (2016)).

Indicator

Usage

acc_end_digits(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

a list with:

• SummaryTable: data.frame with the columns Variables and FLG_acc_ud_shape

• SummaryPlot: ggplot2 distribution plot comparing expected with observed distribution

ALGORITHM OF THIS IMPLEMENTATION:

• This implementation is restricted to data of type float or integer.

• Missing codes are removed from resp_vars (if defined in the metadata)

• The user must specify the column of the metadata containing probability distribution (currently only: normal, uniform, gamma)

• Parameters of each distribution can be estimated from the data or are specified by the user

• A histogram-like plot contrasts the empirical vs. the technical distribution

See Also

Online Documentation

Description

The following R implementation executes calculations for quality indicator "Unexpected location" (see here. Local regression (LOESS) is a versatile statistical method to explore an averaged course of time series measurements (Cleveland, Devlin, and Grosse 1988). In context of epidemiological data, repeated measurements using the same measurement device or by the same examiner can be considered a time series. LOESS allows to explore changes in these measurements over time.

Descriptor

Usage

acc_loess(
  resp_vars,
  group_vars = NULL,
  time_vars,
  co_vars = NULL,
  study_data,
  label_col = VAR_NAMES,
  item_level = "item_level",
  min_obs_in_subgroup = getOption("dataquieR.acc_loess.min_obs_in_subgroup",
    dataquieR.acc_loess.min_obs_in_subgroup_default),
  resolution = 80,
  comparison_lines = list(type = c("mean/sd", "quartiles"), color = "grey30", linetype =
    2, sd_factor = 0.5),
  mark_time_points = getOption("dataquieR.acc_loess.mark_time_points",
    dataquieR.acc_loess.mark_time_points_default),
  plot_observations = getOption("dataquieR.acc_loess.plot_observations",
    dataquieR.acc_loess.plot_observations_default),
  plot_format = getOption("dataquieR.acc_loess.plot_format",
    dataquieR.acc_loess.plot_format_default),
  meta_data = item_level,
  meta_data_v2,
  n_group_max = getOption("dataquieR.max_group_var_levels_in_plot",
    dataquieR.max_group_var_levels_in_plot_default),
  enable_GAM = getOption("dataquieR.GAM_for_LOESS", dataquieR.GAM_for_LOESS_default),
  exclude_constant_subgroups =
    getOption("dataquieR.acc_loess.exclude_constant_subgroups",
    dataquieR.acc_loess.exclude_constant_subgroups_default),
  min_bandwidth = getOption("dataquieR.acc_loess.min_bw",
    dataquieR.acc_loess.min_bw_default),
  min_proportion = getOption("dataquieR.acc_loess.min_proportion",
    dataquieR.acc_loess.min_proportion_default)
)

Arguments

Details

If mark_time_points or plot_observations is selected, but would result in plotting more than 400 points, only a sample of the data will be displayed.

Limitations

The application of LOESS requires model fitting, i.e. the smoothness of a model is subject to a smoothing parameter (span). Particularly in the presence of interval-based missing data, high variability of measurements combined with a low number of observations in one level of the group_vars may distort the fit. Since our approach handles data without knowledge of such underlying characteristics, finding the best fit is complicated if computational costs should be minimal. The default of LOESS in R uses a span of 0.75, which provides in most cases reasonable fits. The function acc_loess adapts the span for each level of the group_vars (with at least as many observations as specified in min_obs_in_subgroup and with at least three time points) based on the respective number of observations. LOESS consumes a lot of memory for larger datasets. That is why acc_loess switches to a generalized additive model with integrated smoothness estimation (gam by mgcv) if there are 1000 observations or more for at least one level of the group_vars (similar to geom_smooth from ggplot2).

Value

a list with:

• SummaryPlotList: list with two plots if plot_format = "BOTH", otherwise one of the two figures described below:

  • Loess_fits_facets: The plot contains LOESS-smoothed curves for each level of the group_vars in a separate panel. Added trend lines represent mean and standard deviation or quartiles (specified in comparison_lines) for moving windows over the whole data.

  • Loess_fits_combined: This plot combines all curves into one panel. Given a low number of levels in the group_vars, this plot eases comparisons. However, if the number increases this plot may be too crowded and unclear.

See Also

Online Documentation

Description

A standard tool to calculate Mahalanobis distance. In this approach the squared Mahalanobis distance is calculated for ordinal variables (treated as continuous) to identify inattentive responses. It calculates the distance for each observational unit from the sample mean. The greater the distance, the atypical the responses.

Indicator

Usage

acc_mahalanobis(
  variable_group = NULL,
  study_data,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_cross_item = "cross-item_level",
  label_col = VAR_NAMES,
  meta_data_v2,
  cross_item_level,
  `cross-item_level`,
  mahalanobis_threshold =
    suppressWarnings(as.numeric(getOption("dataquieR.MAHALANOBIS_THRESHOLD",
    dataquieR.MAHALANOBIS_THRESHOLD_default)))
)

Arguments

Value

a list with:

• SummaryTable: data.frame underlying the plot

• SummaryData: data.frame underlying the plot with speaking column labels

• SummaryPlot: ggplot2::ggplot2 Q-Q plot of squared Mahalanobis distances vs. a theoretical chi-squared distribution showing outliers.

• FlaggedStudyData: data.frame contains the original data frame of the variables used to calculate the squared Mahalanobis distances with the additional column, containing the squared Mahalanobis distance, and a column called MD_outliers, that contains 1 if the observational unit is considered a multivariate outlier.

ALGORITHM OF THIS IMPLEMENTATION:

• Implementation is restricted to variables of type integer

• Remove missing codes from the study data (if defined in the metadata)

• The covariance matrix is estimated for all variables from variable_group

• The Mahalanobis distance of each observation is calculated $MD^2_i = (x_i - \mu)^T \Sigma^{-1} (x_i - \mu)$

• The default to consider a value an outlier is to use the 0.975 quantile of a theoretical chi-square distribution with degrees of freedom equals to the number of variables used to calculate the Mahalanobis distance (⁠Mayrhofer and Filzmoser⁠, 2023)

See Also

Online Documentation

Description

Please use instead the function acc_mahalanobis()

Indicator

Usage

acc_mahalanobis_ratio(
  resp_vars = NULL,
  study_data,
  label_col = VAR_NAMES,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2,
  meta_data_cross_item = "cross-item_level",
  cross_item_level,
  `cross-item_level`
)

Arguments

Details

Value

a list with:

• SummaryData: data.frame underlying the plot with user friendly caption

• SummaryTable: data.frame underlying the plot

• SummaryPlot: ggplot2::ggplot2 Q-Q plot of squared Mahalanobis distances vs. a theoretical chi-squared distribution showing outliers.

• FlaggedStudyData data.frame contains the original data frame of the variables used to calculate the squared Mahalanobis distances with an additional column indicating if for a group of variables if the observational unit is a multivariate outlier.

ALGORITHM OF THIS IMPLEMENTATION:

• Implementation is restricted to variables of type integer

• Remove missing codes from the study data (if defined in the metadata)

• The covariance matrix is estimated for all variables of resp_vars

• The Mahalanobis distance of each observation is calculated $MD^2_i = (x_i - \mu)^T \Sigma^{-1} (x_i - \mu)$

• The default to consider a value an outlier is to use the 0.975 quantile of a theoretical chi-square distribution with degrees of freedom equals to the number of variables used to calculate the Mahalanobis distance (⁠Mayrhofer and Filzmoser⁠, 2023)

See Also

Online Documentation

Estimate marginal means, see emmeans::emmeans

Description

This function examines the impact of so-called process variables on a measurement variable. This implementation combines a descriptive and a model-based approach. Process variables that can be considered in this implementation must be categorical. It is currently not possible to consider more than one process variable within one function call. The measurement variable can be adjusted for (multiple) covariables, such as age or sex, for example.

Marginal means rests on model-based results, i.e. a significantly different marginal mean depends on sample size. Particularly in large studies, small and irrelevant differences may become significant. The contrary holds if sample size is low.

Indicator

Usage

acc_margins(
  resp_vars = NULL,
  group_vars = NULL,
  co_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  threshold_type = "empirical",
  threshold_value,
  min_obs_in_subgroup = 5,
  min_obs_in_cat = 5,
  dichotomize_categorical_resp = TRUE,
  cut_off_linear_model_for_ord = 10,
  meta_data = item_level,
  meta_data_v2,
  sort_group_var_levels = getOption("dataquieR.acc_margins_sort",
    dataquieR.acc_margins_sort_default),
  include_numbers_in_figures = getOption("dataquieR.acc_margins_num",
    dataquieR.acc_margins_num_default),
  n_violin_max = getOption("dataquieR.max_group_var_levels_with_violins",
    dataquieR.max_group_var_levels_with_violins_default),
  no_overall_in_bin = getOption("dataquieR.no_overall_in_bin",
    dataquieR.no_overall_in_bin_default),
  no_geom_count_in_bin = getOption("dataquieR.no_geom_count_in_bin",
    dataquieR.no_geom_count_in_bin_default)
)

Arguments

Details

Limitations

Selecting the appropriate distribution is complex. Dozens of continuous, discrete or mixed distributions are conceivable in the context of epidemiological data. Their exact exploration is beyond the scope of this data quality approach. The present function uses the help function util_dist_selection, the assigned SCALE_LEVEL and the DATA_TYPE to discriminate the following cases:

• continuous data

• binary data

• count data with <= 20 distinct values

• count data with > 20 distinct values (treated as continuous)

• nominal data

• ordinal data

Continuous data and count data with more than 20 distinct values are analyzed by linear models. Count data with up to 20 distinct values are modeled by a Poisson regression. For binary data, the implementation uses logistic regression. Nominal response variables will either be transformed to binary variables or analyzed by multinomial logistic regression models. The latter option is only available if the argument dichotomize_categorical_resp is set to FALSE and if the package nnet is installed. The transformation to a binary variable can be user-specified using the metadata columns RECODE_CASES and/or RECODE_CONTROL. Otherwise, the most frequent category will be assigned to cases and the remaining categories to control. For ordinal response variables, the argument cut_off_linear_model_for_ord controls whether the data is analyzed in the same way as continuous data: If every level of the variable has at least as many observations as specified in the argument, the data will be analyzed by a linear model. Otherwise, the data will be modeled by a ordered regression, if the package ordinal is installed.

Value

a list with:

• SummaryTable: data.frame underlying the plot

• ResultData: data.frame

• SummaryPlot: ggplot2::ggplot() margins plot

See Also

Online Documentation

Description

A standard tool to detect multivariate outliers is the Mahalanobis distance. This approach is very helpful for the interpretation of the plausibility of a measurement given the value of another. In this approach the Mahalanobis distance is used as a univariate measure itself. We apply the same rules for the identification of outliers as in univariate outliers:

• the classical approach from Tukey: $1.5 * IQR$ from the 1st ($Q_{25}$) or 3rd ($Q_{75}$) quartile.

• the 3SD approach, i.e. any measurement of the Mahalanobis distance not in the interval of $\bar{x} \pm 3*\sigma$ is considered an outlier.

• the approach from Hubert for skewed distributions which is embedded in the R package robustbase

• a completely heuristic approach named $\sigma$-gap.

For further details, please see the vignette for univariate outlier.

Indicator

Usage

acc_multivariate_outlier(
  variable_group = NULL,
  id_vars = NULL,
  label_col = VAR_NAMES,
  study_data,
  item_level = "item_level",
  n_rules = 4,
  max_non_outliers_plot = 10000,
  criteria = c("tukey", "3sd", "hubert", "sigmagap"),
  meta_data = item_level,
  meta_data_v2,
  scale = getOption("dataquieR.acc_multivariate_outlier.scale",
    dataquieR.acc_multivariate_outlier.scale_default),
  multivariate_outlier_check = TRUE
)

Arguments

Value

a list with:

• SummaryTable: data.frame underlying the plot

• SummaryPlot: ggplot2::ggplot2 outlier plot

• FlaggedStudyData data.frame contains the original data frame with the additional columns tukey, ⁠3SD⁠, hubert, and sigmagap. Every observation is coded 0 if no outlier was detected in the respective column and 1 if an outlier was detected. This can be used to exclude observations with outliers.

ALGORITHM OF THIS IMPLEMENTATION:

• Implementation is restricted to variables of type float

• Remove missing codes from the study data (if defined in the metadata)

• The covariance matrix is estimated for all variables from variable_group

• The Mahalanobis distance of each observation is calculated $MD^2_i = (x_i - \mu)^T \Sigma^{-1} (x_i - \mu)$

• The four rules mentioned above are applied on this distance for each observation in the study data

• An output data frame is generated that flags each outlier

• A parallel coordinate plot indicates respective outliers

List function.

See Also

Online Documentation

Description

A classical but still popular approach to detect univariate outlier is the boxplot method introduced by Tukey 1977. The boxplot is a simple graphical tool to display information about continuous univariate data (e.g., median, lower and upper quartile). Outliers are defined as values deviating more than $1.5 \times IQR$ from the 1st (Q25) or 3rd (Q75) quartile. The strength of Tukey's method is that it makes no distributional assumptions and thus is also applicable to skewed or non mound-shaped data Marsh and Seo, 2006. Nevertheless, this method tends to identify frequent measurements which are falsely interpreted as true outliers.

A somewhat more conservative approach in terms of symmetric and/or normal distributions is the 3SD approach, i.e. any measurement not in the interval of $mean(x) +/- 3 * \sigma$ is considered an outlier.

Both methods mentioned above are not ideally suited to skewed distributions. As many biomarkers such as laboratory measurements represent in skewed distributions the methods above may be insufficient. The approach of Hubert and Vandervieren 2008 adjusts the boxplot for the skewness of the distribution. This approach is implemented in several R packages such as robustbase::mc which is used in this implementation of dataquieR.

Another completely heuristic approach is also included to identify outliers. The approach is based on the assumption that the distances between measurements of the same underlying distribution should homogeneous. For comprehension of this approach:

• consider an ordered sequence of all measurements.

• between these measurements all distances are calculated.

• the occurrence of larger distances between two neighboring measurements may than indicate a distortion of the data. For the heuristic definition of a large distance $1 * \sigma$ has been been chosen.

Note, that the plots are not deterministic, because they use ggplot2::geom_jitter.

Indicator

Usage

acc_robust_univariate_outlier(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  exclude_roles,
  n_rules = length(unique(criteria)),
  max_non_outliers_plot = 10000,
  criteria = c("tukey", "3sd", "hubert", "sigmagap"),
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Hint: The function is designed for unimodal data only.

Value

a list with:

• SummaryTable: data.frame with the columns Variables, Mean, SD, Median, Skewness, Tukey (N), ⁠3SD (N)⁠, Hubert (N), Sigma-gap (N), NUM_acc_ud_outlu, ⁠Outliers, low (N)⁠, ⁠Outliers, high (N)⁠ Grading

  • SummaryData: data.frame with the columns Variables, Mean, SD, Median, Skewness, Tukey (N), ⁠3SD (N)⁠, Hubert (N), Sigma-gap (N), Outliers (N), ⁠Outliers, low (N)⁠, ⁠Outliers, high (N)⁠

  • SummaryPlotList: ggplot2::ggplot univariate outlier plots

ALGORITHM OF THIS IMPLEMENTATION:

• Select all variables of type float in the study data

• Remove missing codes from the study data (if defined in the metadata)

• Remove measurements deviating from limits defined in the metadata

• Identify outliers according to the approaches of Tukey (Tukey 1977), 3SD (Saleem et al. 2021), Hubert (Hubert and Vandervieren 2008), and SigmaGap (heuristic)

• An output data frame is generated which indicates the no. possible outliers, the direction of deviations (Outliers, low; Outliers, high) for all methods and a summary score which sums up the deviations of the different rules

• A scatter plot is generated for all examined variables, flagging observations according to the no. violated rules (step 5).

See Also

acc_univariate_outlier

Description

This implementation contrasts the empirical distribution of a measurement variables against assumed distributions. The approach is adapted from the idea of rootograms (Tukey 1977) which is also applicable for count data (Kleiber and Zeileis 2016).

Indicator

Usage

acc_shape_or_scale(
  resp_vars,
  study_data,
  label_col,
  item_level = "item_level",
  dist_col,
  guess,
  par1,
  par2,
  end_digits,
  flip_mode = "noflip",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

a list with:

• ResultData: data.frame underlying the plot

• SummaryPlot: ggplot2::ggplot2 probability distribution plot

• SummaryTable: data.frame with the columns Variables and FLG_acc_ud_shape

ALGORITHM OF THIS IMPLEMENTATION:

• This implementation is restricted to data of type float or integer.

• Missing codes are removed from resp_vars (if defined in the metadata)

• The user must specify the column of the metadata containing probability distribution (currently only: normal, uniform, gamma)

• Parameters of each distribution can be estimated from the data or are specified by the user

• A histogram-like plot contrasts the empirical vs. the technical distribution

See Also

Online Documentation

Description

A classical but still popular approach to detect univariate outlier is the boxplot method introduced by Tukey 1977. The boxplot is a simple graphical tool to display information about continuous univariate data (e.g., median, lower and upper quartile). Outliers are defined as values deviating more than $1.5 \times IQR$ from the 1st (Q25) or 3rd (Q75) quartile. The strength of Tukey's method is that it makes no distributional assumptions and thus is also applicable to skewed or non mound-shaped data Marsh and Seo, 2006. Nevertheless, this method tends to identify frequent measurements which are falsely interpreted as true outliers.

A somewhat more conservative approach in terms of symmetric and/or normal distributions is the 3SD approach, i.e. any measurement not in the interval of $mean(x) +/- 3 * \sigma$ is considered an outlier.

Both methods mentioned above are not ideally suited to skewed distributions. As many biomarkers such as laboratory measurements represent in skewed distributions the methods above may be insufficient. The approach of Hubert and Vandervieren 2008 adjusts the boxplot for the skewness of the distribution. This approach is implemented in several R packages such as robustbase::mc which is used in this implementation of dataquieR.

Another completely heuristic approach is also included to identify outliers. The approach is based on the assumption that the distances between measurements of the same underlying distribution should homogeneous. For comprehension of this approach:

• consider an ordered sequence of all measurements.

• between these measurements all distances are calculated.

• the occurrence of larger distances between two neighboring measurements may than indicate a distortion of the data. For the heuristic definition of a large distance $1 * \sigma$ has been been chosen.

Note, that the plots are not deterministic, because they use ggplot2::geom_jitter.

Indicator

Usage

acc_univariate_outlier(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  exclude_roles,
  n_rules = length(unique(criteria)),
  max_non_outliers_plot = 10000,
  criteria = c("tukey", "3sd", "hubert", "sigmagap"),
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Hint: The function is designed for unimodal data only.

Value

a list with:

• SummaryTable: data.frame with the columns Variables, Mean, SD, Median, Skewness, Tukey (N), ⁠3SD (N)⁠, Hubert (N), Sigma-gap (N), NUM_acc_ud_outlu, ⁠Outliers, low (N)⁠, ⁠Outliers, high (N)⁠ Grading

  • SummaryData: data.frame with the columns Variables, Mean, SD, Median, Skewness, Tukey (N), ⁠3SD (N)⁠, Hubert (N), Sigma-gap (N), Outliers (N), ⁠Outliers, low (N)⁠, ⁠Outliers, high (N)⁠

  • SummaryPlotList: ggplot2::ggplot univariate outlier plots

ALGORITHM OF THIS IMPLEMENTATION:

• Select all variables of type float in the study data

• Remove missing codes from the study data (if defined in the metadata)

• Remove measurements deviating from limits defined in the metadata

• Identify outliers according to the approaches of Tukey (Tukey 1977), 3SD (Saleem et al. 2021), Hubert (Hubert and Vandervieren 2008), and SigmaGap (heuristic)

• An output data frame is generated which indicates the no. possible outliers, the direction of deviations (Outliers, low; Outliers, high) for all methods and a summary score which sums up the deviations of the different rules

• A scatter plot is generated for all examined variables, flagging observations according to the no. violated rules (step 5).

See Also

• acc_robust_univariate_outlier

• Online Documentation

Description

This function is still under construction. It is designed to run for any statistical data type as follows:

• Variables with only two distinct values will be modeled by mixed effects logistic regression.

• Nominal variables will be transformed to binary variables. This can be user-specified using the metadata columns RECODE_CASES and/or RECODE_CONTROL. Otherwise, the most frequent category will be assigned to cases and the remaining categories to control. As for other binary variables, the ICC will be computed using a mixed effects logistic regression.

• Ordinal variables will be analyzed by linear mixed effects models, if every level of the variable has at least as many observations as specified in the argument cut_off_linear_model_for_ord. Otherwise, the data will be modeled by a mixed effects ordered regression, if the package ordinal is available.

• Metric variables with integer values are analyzed by linear mixed effects models.

• For variables with data type float, the existing implementation acc_varcomp is called, which also uses linear mixed effects models.

Indicator

Usage

acc_varcomp(
  resp_vars = NULL,
  group_vars = NULL,
  co_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  min_obs_in_subgroup = 10,
  min_subgroups = 5,
  cut_off_linear_model_for_ord = 10,
  threshold_value = lifecycle::deprecated(),
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Not yet described

Value

The function returns two data frames, 'SummaryTable' and 'SummaryData', that differ only in the names of the columns.

Description

dataquieR's translated texts featuring access to the language keys, still.

Usage

## S3 method for class 'dataquieR_translated'
as.character(x, ...)

Arguments

Value

character with only the translated entries

See Also

base::as.character

Description

such objects, for now, only occur in RECCap rules, so this function is meant for internal use, mostly – for now.

Usage

## S3 method for class 'interval'
as.character(x, ...)

Arguments

Value

interval as character

See Also

base::as.character

Description

Deprecated

Usage

## S3 method for class 'dataquieR_resultset'
as.data.frame(x, ...)

Arguments

Value

Deprecated

Description

Deprecated

Usage

## S3 method for class 'dataquieR_resultset'
as.list(x, ...)

Arguments

Value

Deprecated

inefficient way to convert a report to a list. try prep_set_backend()

Description

inefficient way to convert a report to a list. try prep_set_backend()

Usage

## S3 method for class 'dataquieR_resultset2'
as.list(x, ...)

Arguments

Value

list

Description

The allowable direction of an association. The input is a string that can be either "positive" or "negative".

Usage

ASSOCIATION_DIRECTION

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

The allowable form of association. The string specifies the form based on a selected list.

Usage

ASSOCIATION_FORM

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

The metric underlying the association in ASSOCIATION_RANGE. The input is a string that specifies the analysis algorithm to be used.

Usage

ASSOCIATION_METRIC

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Specifies the allowable range of an association. The inclusion of the endpoints follows standard mathematical notation using round brackets for open intervals and square brackets for closed intervals. Values must be separated by a semicolon.

Usage

ASSOCIATION_RANGE

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Specifies the unique IDs for cross-item level metadata records

Usage

CHECK_ID

Details

if missing, dataquieR will create such IDs

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Specifies the unique labels for cross-item level metadata records

Usage

CHECK_LABEL

Details

if missing, dataquieR will create such labels

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Two versions exist, the newer one is used by con_contradictions_redcap and is described here., the older one used by con_contradictions is described here.

See Also

meta_data_cross

Description

types of value codes

Usage

CODE_CLASSES

Description

Default Name of the Table featuring Code Lists

Metadata sheet name containing VALUE_LABEL_TABLES This metadata sheet can contain both value labels of several VALUE_LABEL_TABLE and also Missing and JUMP tables

Usage

CODE_LIST_TABLE

CODE_LIST_TABLE

Description

Only existence is checked, order not yet used

Usage

CODE_ORDER

Description

Item-Missingness (also referred to as item nonresponse (De Leeuw et al. 2003)) describes the missingness of single values, e.g. blanks or empty data cells in a data set. Item-Missingness occurs for example in case a respondent does not provide information for a certain question, a question is overlooked by accident, a programming failure occurs or a provided answer were missed while entering the data.

Indicator

Usage

com_item_missingness(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  show_causes = TRUE,
  cause_label_df,
  include_sysmiss = TRUE,
  threshold_value,
  suppressWarnings = FALSE,
  assume_consistent_codes = TRUE,
  expand_codes = assume_consistent_codes,
  drop_levels = FALSE,
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  pretty_print = lifecycle::deprecated(),
  meta_data = item_level,
  meta_data_v2
)

Arguments

Value

a list with:

• SummaryTable: data frame about item missingness per response variable

• SummaryData: data frame about item missingness per response variable formatted for user

• SummaryPlot: ggplot2 heatmap plot, if show_causes was TRUE

• ReportSummaryTable: data frame underlying SummaryPlot

ALGORITHM OF THIS IMPLEMENTATION:

• Lists of missing codes and, if applicable, jump codes are selected from the metadata

• The no. of system missings (NA) in each variable is calculated

• The no. of used missing codes is calculated for each variable

• The no. of used jump codes is calculated for each variable

• Two result dataframes (1: on the level of observations, 2: a summary for each variable) are generated

• OPTIONAL: if show_causes is selected, one summary plot for all resp_vars is provided

See Also

Online Documentation

Description

Indicator

Usage

com_qualified_item_missingness(
  resp_vars,
  study_data,
  label_col = NULL,
  item_level = "item_level",
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  meta_data = item_level,
  meta_data_v2,
  meta_data_segment,
  segment_level
)

Arguments

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Non-response rate" (PCT_com_qum_nonresp) and "Refusal rate" (PCT_com_qum_refusal) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for “Non-response rate” and "Refusal rate" for a report

Description

Indicator

Usage

com_qualified_segment_missingness(
  label_col = NULL,
  study_data,
  item_level = "item_level",
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  meta_data = item_level,
  meta_data_v2,
  meta_data_segment,
  segment_level
)

Arguments

Value

A list with:

• SegmentTable: data.frame containing data quality checks for "Non-response rate" (PCT_com_qum_nonresp) and "Refusal rate" (PCT_com_qum_refusal) for each segment.

• SegmentData: a data.frame containing data quality checks for "Unexpected location" and "Unexpected proportion" per segment for a report

Description

This implementation can be applied in two use cases:

1. participation in study segments is not recorded by respective variables, e.g. a participant's refusal to attend a specific examination is not recorded.

2. participation in study segments is recorded by respective variables.

Use case (1) will be common in smaller studies. For the calculation of segment missingness it is assumed that study variables are nested in respective segments. This structure must be specified in the static metadata. The R-function identifies all variables within each segment and returns TRUE if all variables within a segment are missing, otherwise FALSE.

Use case (2) assumes a more complex structure of study data and metadata. The study data comprise so-called intro-variables (either TRUE/FALSE or codes for non-participation). The column PART_VAR in the metadata is filled by variable-IDs indicating for each variable the respective intro-variable. This structure has the benefit that subsequent calculation of item missingness obtains correct denominators for the calculation of missingness rates.

Descriptor

Usage

com_segment_missingness(
  study_data,
  item_level = "item_level",
  strata_vars = NULL,
  group_vars = NULL,
  label_col,
  threshold_value,
  direction,
  color_gradient_direction,
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  exclude_roles = c(VARIABLE_ROLES$PROCESS),
  meta_data = item_level,
  meta_data_v2,
  segment_level,
  meta_data_segment
)

Arguments

Details

Implementation and use of thresholds

This implementation uses one threshold to discriminate critical from non-critical values. If direction is above than all values below the threshold_value are normal (displayed in dark blue in the plot and flagged with GRADING = 0 in the dataframe). All values above the threshold_value are considered critical. The more they deviate from the threshold the displayed color shifts to dark red. All critical values are highlighted with GRADING = 1 in the summary data frame. By default, highest values are always shown in dark red irrespective of the absolute deviation.

If direction is below than all values above the threshold_value are normal (displayed in dark blue, GRADING = 0).

Hint

This function does not support a resp_vars argument but exclude_roles to specify variables not relevant for detecting a missing segment.

List function.

Value

a list with:

• ResultData: data frame about segment missingness

• SummaryPlot: ggplot2 heatmap plot: a heatmap-like graphic that highlights critical values depending on the respective threshold_value and direction.

• ReportSummaryTable: data frame underlying SummaryPlot

See Also

Online Documentation

Description

This implementation examines a crude version of unit missingness or unit-nonresponse (Kalton and Kasprzyk 1986), i.e. if all measurement variables in the study data are missing for an observation it has unit missingness.

The function can be applied on stratified data. In this case strata_vars must be specified.

Descriptor

Usage

com_unit_missingness(
  id_vars = NULL,
  strata_vars = NULL,
  label_col,
  study_data,
  item_level = "item_level",
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

This implementations calculates a crude rate of unit-missingness. This type of missingness may have several causes and is an important research outcome. For example, unit-nonresponse may be selective regarding the targeted study population or technical reasons such as record-linkage may cause unit-missingness.

It has to be discriminated form segment and item missingness, since different causes and mechanisms may be the reason for unit-missingness.

Hint

This function does not support a resp_vars argument but id_vars, which have a roughly inverse logic behind: id_vars with values do not prevent a row from being considered missing, because an ID is the only hint for a unit that elsewise would not occur in the data at all.

List function.

Value

A list with:

• FlaggedStudyData: data.frame with id-only-rows flagged in a column Unit_missing

• SummaryData: data.frame with numbers and percentages of unit missingness

See Also

Online Documentation

Description

Cross-item level metadata attribute name

Usage

COMPUTATION_RULE

See Also

meta_data_computation

Description

• MAXIMUM_LONG_STRING Social Science: Computed Indicator Variable, maximum long string

• IRV Social Science: Computed Indicator Variable, IRV

• TOTRESPT Social Science: Computed Indicator Variable, TOTRESPT

• RESPT_PER_ITEM Social Science: Computed Indicator Variable, RESPT_PER_ITEM

• RELCOMPL_SPEED Social Science: Computed Indicator Variable, RELCOMPL_SPEED

• MISS_RESP Social Science: Computed Indicator Variable, MISS_RESP

• NA Social Science: Computed Indicator Variable – N/A

See Also

VARIABLE_ROLES

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, CONTRADICTION_TERM, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Other SSI: IRV, MAHALANOBIS_RATIO, MAXIMUM_LONG_STRING, MISS_RESP, RELCOMPL_SPEED, RESPT_PER_ITEM, TOTRESPT

Description

This approach considers a contradiction if impossible combinations of data are observed in one participant. For example, if age of a participant is recorded repeatedly the value of age is (unfortunately) not able to decline. Most cases of contradictions rest on comparison of two variables.

Important to note, each value that is used for comparison may represent a possible characteristic but the combination of these two values is considered to be impossible. The approach does not consider implausible or inadmissible values.

Descriptor

Usage

con_contradictions(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  threshold_value,
  check_table,
  summarize_categories = FALSE,
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Algorithm of this implementation:

• Select all variables in the data with defined contradiction rules (static metadata column CONTRADICTIONS)

• Remove missing codes from the study data (if defined in the metadata)

• Remove measurements deviating from limits defined in the metadata

• Assign label to levels of categorical variables (if applicable)

• Apply contradiction checks on predefined sets of variables

• Identification of measurements fulfilling contradiction rules. Therefore two output data frames are generated:

  • on the level of observation to flag each contradictory value combination, and

  • a summary table for each contradiction check.

• A summary plot illustrating the number of contradictions is generated.

List function.

Value

If summarize_categories is FALSE: A list with:

• FlaggedStudyData: The first output of the contradiction function is a data frame of similar dimension regarding the number of observations in the study data. In addition, for each applied check on the variables an additional column is added which flags observations with a contradiction given the applied check.

• SummaryTable: The second output summarizes this information into one data frame. This output can be used to provide an executive overview on the amount of contradictions. This output is meant for automatic digestion within pipelines.

• SummaryData: The third output is the same as SummaryTable but for human readers.

• SummaryPlot: The fourth output visualizes summarized information of SummaryData.

if summarize_categories is TRUE, other objects are returned: one per category named by that category (e.g. "Empirical") containing a result for contradictions within that category only. Additionally, in the slot all_checks a result as it would have been returned with summarize_categories set to FALSE. Finally, a slot SummaryData is returned containing sums per Category and an according ggplot2::ggplot in SummaryPlot.

See Also

Online Documentation

Description

This approach considers a contradiction if impossible combinations of data are observed in one participant. For example, if age of a participant is recorded repeatedly the value of age is (unfortunately) not able to decline. Most cases of contradictions rest on comparison of two variables.

Important to note, each value that is used for comparison may represent a possible characteristic but the combination of these two values is considered to be impossible. The approach does not consider implausible or inadmissible values.

Indicator

Usage

con_contradictions_redcap(
  study_data,
  item_level = "item_level",
  label_col,
  threshold_value,
  meta_data_cross_item = "cross-item_level",
  use_value_labels,
  summarize_categories = FALSE,
  meta_data = item_level,
  cross_item_level,
  `cross-item_level`,
  meta_data_v2
)

Arguments

Details

Algorithm of this implementation:

• Remove missing codes from the study data (if defined in the metadata)

• Remove measurements deviating from limits defined in the metadata

• Assign label to levels of categorical variables (if applicable)

• Apply contradiction checks (given as REDCap-like rules in a separate metadata table)

• Identification of measurements fulfilling contradiction rules. Therefore two output data frames are generated:

  • on the level of observation to flag each contradictory value combination, and

  • a summary table for each contradiction check.

• A summary plot illustrating the number of contradictions is generated.

List function.

Value

If summarize_categories is FALSE: A list with:

• FlaggedStudyData: The first output of the contradiction function is a data frame of similar dimension regarding the number of observations in the study data. In addition, for each applied check on the variables an additional column is added which flags observations with a contradiction given the applied check.

• VariableGroupData: The second output summarizes this information into one data frame. This output can be used to provide an executive overview on the amount of contradictions.

• VariableGroupTable: A subset of VariableGroupData used within the pipeline.

• SummaryPlot: The third output visualizes summarized information of SummaryData.

If summarize_categories is TRUE, other objects are returned: A list with one element Other, a list with the following entries: One per category named by that category (e.g. "Empirical") containing a result for contradiction checks within that category only. Additionally, in the slot all_checks, a result as it would have been returned with summarize_categories set to FALSE. Finally, in the top-level list, a slot SummaryData is returned containing sums per Category and an according ggplot2::ggplot in SummaryPlot.

See Also

Online Documentation for the function meta_data_cross Online Documentation for the required cross-item-level metadata

Description

For each categorical variable, value lists should be defined in the metadata. This implementation will examine, if all observed levels in the study data are valid.

Indicator

Usage

con_inadmissible_categorical(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  threshold_value = 0,
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Algorithm of this implementation:

• Remove missing codes from the study data (if defined in the metadata)

• Interpretation of variable specific VALUE_LABELS as supplied in the metadata.

• Identification of measurements not corresponding to the expected categories. Therefore two output data frames are generated:

  • on the level of observation to flag each undefined category, and

  • a summary table for each variable.

• Values not corresponding to defined categories are removed in a data frame of modified study data

Value

a list with:

• SummaryData: data frame summarizing inadmissible categories with the columns:

  • Variables: variable name/label

  • OBSERVED_CATEGORIES: the categories observed in the study data

  • DEFINED_CATEGORIES: the categories defined in the metadata

  • NON_MATCHING: the categories observed but not defined

  • NON_MATCHING_N: the number of observations with categories not defined

  • NON_MATCHING_N_PER_CATEGORY: the number of observations for each of the unexpected categories

• SummaryTable: data frame for the dataquieR pipeline reporting the number and percentage of inadmissible categorical values

• ModifiedStudyData: study data having inadmissible categories removed

• FlaggedStudyData: study data having cases with inadmissible categories flagged

See Also

Online Documentation

Description

For each categorical variable, value lists should be defined in the metadata. This implementation will examine, if all observed levels in the study data are valid.

Indicator

Usage

con_inadmissible_vocabulary(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  threshold_value = 0,
  meta_data = item_level,
  meta_data_v2
)

Arguments

Details

Algorithm of this implementation:

• Remove missing codes from the study data (if defined in the metadata)

• Interpretation of variable specific VALUE_LABELS as supplied in the metadata.

• Identification of measurements not corresponding to the expected categories. Therefore two output data frames are generated:

  • on the level of observation to flag each undefined category, and

  • a summary table for each variable.

• Values not corresponding to defined categories are removed in a data frame of modified study data

Value

a list with:

• SummaryData: data frame summarizing inadmissible categories with the columns:

  • Variables: variable name/label

  • OBSERVED_CATEGORIES: the categories observed in the study data

  • DEFINED_CATEGORIES: the categories defined in the metadata

  • NON_MATCHING: the categories observed but not defined

  • NON_MATCHING_N: the number of observations with categories not defined

  • NON_MATCHING_N_PER_CATEGORY: the number of observations for each of the unexpected categories

  • GRADING: indicator TRUE/FALSE if inadmissible categorical values were observed (more than indicated by the threshold_value)

• SummaryTable: data frame for the dataquieR pipeline reporting the number and percentage of inadmissible categorical values

• ModifiedStudyData: study data having inadmissible categories removed

• FlaggedStudyData: study data having cases with inadmissible categories flagged

See Also

Online Documentation

Examples

## Not run: 
sdt <- data.frame(DIAG = c("B050", "B051", "B052", "B999"),
                  MED0 = c("S01XA28", "N07XX18", "ABC", NA), stringsAsFactors = FALSE)
mdt <- tibble::tribble(
~ VAR_NAMES, ~ DATA_TYPE, ~ STANDARDIZED_VOCABULARY_TABLE, ~ SCALE_LEVEL, ~ LABEL,
"DIAG", "string", "<ICD10>", "nominal", "Diagnosis",
"MED0", "string", "<ATC>", "nominal", "Medication"
)
con_inadmissible_vocabulary(NULL, sdt, mdt, label_col = LABEL)
prep_load_workbook_like_file("meta_data_v2")
il <- prep_get_data_frame("item_level")
il$STANDARDIZED_VOCABULARY_TABLE[[11]] <- "<ICD10GM>"
il$DATA_TYPE[[11]] <- DATA_TYPES$INTEGER
il$SCALE_LEVEL[[11]] <- SCALE_LEVELS$NOMINAL
prep_add_data_frames(item_level = il)
r <- dq_report2("study_data", dimensions = "con")
r <- dq_report2("study_data", dimensions = "con",
     advanced_options = list(dataquieR.non_disclosure = TRUE))
r

## End(Not run)

Description

Inadmissible numerical values can be of type integer or float. This implementation requires the definition of intervals in the metadata to examine the admissibility of numerical study data.

This helps identify inadmissible measurements according to hard limits (for multiple variables).

Indicator

Usage

con_limit_deviations(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  meta_data_cross_item = "cross-item_level",
  limits = NULL,
  flip_mode = "noflip",
  return_flagged_study_data = FALSE,
  return_limit_categorical = TRUE,
  meta_data = item_level,
  cross_item_level,
  `cross-item_level`,
  meta_data_v2,
  show_obs = TRUE
)

Arguments

Details

Algorithm of this implementation:

• Remove missing codes from the study data (if defined in the metadata)

• Interpretation of variable specific intervals as supplied in the metadata.

• 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.

• A list of plots is generated for each variable examined for limit deviations. The histogram-like plots indicate respective limits as well as deviations.

• Values exceeding limits are removed in a data frame of modified study data

Value

a list with:

• FlaggedStudyData data.frame 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. Optional, see return_flagged_study_data.

• SummaryTable data.frame summarizing limit deviations for each variable.

• SummaryData data.frame summarizing limit deviations for each variable for a report.

• SummaryPlotList list of ggplot2::ggplots The plots for each variable are either a histogram (continuous) or a barplot (discrete).

• ReportSummaryTable: heatmap-like data frame about limit violations

See Also

• Online Documentation

Description

description of the contradiction functions

Usage

contradiction_functions_descriptions

Description

Note: in some prep_-functions, this field is named RULE

Usage

CONTRADICTION_TERM

Details

Specifies a contradiction rule. Use REDCap like syntax, see online vignette

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TYPE, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Specifies the type of a contradiction. According to the data quality concept, there are logical and empirical contradictions, see online vignette

Usage

CONTRADICTION_TYPE

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, DATA_PREPARATION, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

For contradiction rules, the required pre-processing steps that can be given. Note: MISSING_LABEL, MISSING_INTERPRET may not work for non-factor variables

Usage

DATA_PREPARATION

Details

LABEL LIMITS MISSING_NA MISSING_LABEL MISSING_INTERPRET

See Also

meta_data_cross

Other meta_data_cross: ASSOCIATION_DIRECTION, ASSOCIATION_FORM, ASSOCIATION_METRIC, ASSOCIATION_RANGE, CHECK_ID, CHECK_LABEL, COMPUTED_VARIABLE_ROLES, CONTRADICTION_TERM, CONTRADICTION_TYPE, GOLDSTANDARD, IRV, MAHALANOBIS_RATIO, MAHALANOBIS_THRESHOLD, MAXIMUM_LONG_STRING, MISS_RESP, MULTIVARIATE_OUTLIER_CHECK, MULTIVARIATE_OUTLIER_CHECKTYPE, RELCOMPL_SPEED, REL_VAL, RESPT_PER_ITEM, SCALE_ACRONYM, SCALE_NAME, TOTRESPT, VARIABLE_LIST, VARIABLE_LIST_ORDER, meta_data_computation, meta_data_cross

Description

Data Types of Study Data

In the metadata, the following entries are allowed for the variable attribute DATA_TYPE:

Usage

DATA_TYPES

Details

• integer for integer numbers

• string for text/string/character data

• float for decimal/floating point numbers

• datetime for timepoints

• time for time of day

Data Types of Function Arguments

As function arguments, dataquieR uses additional type specifications:

• numeric is a numerical value (float or integer), but it is not an allowed DATA_TYPE in the metadata. However, some functions may accept float or integer for specific function arguments. This is, where we use the term numeric.

• enum allows one element out of a set of allowed options similar to match.arg

• set allows a subset out of a set of allowed options similar to match.arg with several.ok = TRUE.

• variable Function arguments of this type expect a character scalar that specifies one variable using the variable identifier given in the metadata attribute VAR_NAMES or, if label_col is set, given in the metadata attribute given in that argument. Labels can easily be translated using prep_map_labels

• ⁠variable list⁠ Function arguments of this type expect a character vector that specifies variables using the variable identifiers given in the metadata attribute VAR_NAMES or, if label_col is set, given in the metadata attribute given in that argument. Labels can easily be translated using prep_map_labels

See Also

integer string

Description

All available data types, mapped from their respective R types

Usage

DATA_TYPES_OF_R_TYPE

See Also

prep_dq_data_type_of

Internal constructor for the internal class dataquieR_resultset.

Description

creates an object of the class dataquieR_resultset.

Usage

dataquieR_resultset(...)

Arguments

Details

The class features the following methods:

• as.data.frame.dataquieR_resultset, * as.list.dataquieR_resultset, * print.dataquieR_resultset, * summary.dataquieR_resultset

Value

an object of the class dataquieR_resultset.

See Also

dq_report

Description

Deprecated

Usage

dataquieR_resultset_verify(...)

Arguments

Value

Deprecated

Class dataquieR_resultset2.

Description

Class dataquieR_resultset2.

See Also

dq_report2

Description

If this option is set to TRUE, time course plots will only show subgroups with more than one distinct value. This might improve the readability of the figure.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Description

TODO

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Description

The value should be greater than 0 and less than or equal to 1. In general, increasing the bandwidth leads to a smoother trend line.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Minimum observations for a level to be included by acc_loess()

Description

specify the minimum number of observations required for each of the subgroups. Subgroups with fewer observations are excluded. The default number is 30.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Description

The value should be greater than 0 and less than 0.4. If the proportion of cases or controls is lower than the specified value, the LOESS figure will not be created for the specified binary outcome.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

default for Plot-Format in acc_loess()

Description

TODO

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Description

TODO

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_margins_num, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt

Description

If this option is set to FALSE, the figures created by acc_margins will not include the number of observations for each level of the grouping variable. This can be used to obtain clean static plots.

See Also

Other options: dataquieR, dataquieR.CONDITIONS_LEVEL_TRHESHOLD, dataquieR.CONDITIONS_WITH_STACKTRACE, dataquieR.ELEMENT_MISSMATCH_CHECKTYPE, dataquieR.ERRORS_WITH_CALLER, dataquieR.GAM_for_LOESS, dataquieR.MAHALANOBIS_THRESHOLD, dataquieR.MAX_LABEL_LEN, dataquieR.MAX_LONG_LABEL_LEN, dataquieR.MAX_VALUE_LABEL_LEN, dataquieR.MESSAGES_WITH_CALLER, dataquieR.MULTIVARIATE_OUTLIER_CHECK, dataquieR.VALUE_LABELS_htmlescaped, dataquieR.WARNINGS_WITH_CALLER, dataquieR.acc_loess.exclude_constant_subgroups, dataquieR.acc_loess.mark_time_points, dataquieR.acc_loess.min_bw, dataquieR.acc_loess.min_obs_in_subgroup, dataquieR.acc_loess.min_proportion, dataquieR.acc_loess.plot_format, dataquieR.acc_loess.plot_observations, dataquieR.acc_margins_sort, dataquieR.acc_multivariate_outlier.scale, dataquieR.acc_shape_or_scale_ci, dataquieR.col_con_con_empirical, dataquieR.col_con_con_logical, dataquieR.convert_to_list_for_lapply, dataquieR.debug, dataquieR.des_summary_hard_lim_remove, dataquieR.dontwrapresults, dataquieR.droplevels_ReportSummaryTable, dataquieR.dt_adjust, dataquieR.fix_column_type_on_read, dataquieR.flip_mode, dataquieR.force_item_specific_missing_codes, dataquieR.force_label_col, dataquieR.grading_formats, dataquieR.grading_rulesets, dataquieR.guess_character, dataquieR.guess_missing_codes, dataquieR.ignore_empty_vars, dataquieR.lang, dataquieR.lazy_plots, dataquieR.lazy_plots_cache, dataquieR.lazy_plots_gg_compatibility, dataquieR.locale, dataquieR.max_cat_resp_var_levels_in_plot, dataquieR.max_group_var_levels_in_plot, dataquieR.max_group_var_levels_with_violins, dataquieR.min_obs_per_group_var_in_plot, dataquieR.min_time_points_for_cat_resp_var, dataquieR.no_geom_count_in_bin, dataquieR.no_overall_in_bin, dataquieR.non_disclosure, dataquieR.old_factor_handling, dataquieR.old_type_adjust, dataquieR.precomputeStudyData, dataquieR.print_block_load_factor, dataquieR.progress_fkt_default, dataquieR.progress_msg_fkt_default, dataquieR.resume_checkpoint, dataquieR.resume_print, dataquieR.scale_level_heuristics_control_binaryrecodelimit, dataquieR.scale_level_heuristics_control_metriclevels, dataquieR.study_data_cache_max, dataquieR.study_data_cache_metrics, dataquieR.study_data_cache_metrics_env, dataquieR.study_data_cache_quick_fill, dataquieR.study_data_colnames_case_sensitive, dataquieR.testdebug, dataquieR.traceback, dataquieR.type_adjust_parallel, progress_init_fkt