dataquieR (version 2.1.0)

acc_varcomp: Estimates variance components

Description

Variance based models and intraclass correlations (ICC) are approaches to examine the impact of so-called process variables on the measurements. This implementation is model-based.

NB: The term ICC is frequently used to describe the agreement between different observers, examiners or even devices. In respective settings a good agreement is pursued. ICC-values can vary between [-1;1] and an ICC close to 1 is desired (Koo and Li 2016, Müller and Büttner 1994).

However, in multi-level analysis the ICC is interpreted differently. Please see Snijders et al. (Sniders and Bosker 1999). In this context the proportion of variance explained by respective group levels indicate an influence of (at least one) level of the respective group_vars. An ICC close to 0 is desired.

Indicator

Usage

acc_varcomp(
  resp_vars = NULL,
  group_vars,
  co_vars = NULL,
  min_obs_in_subgroup = 30,
  min_subgroups = 5,
  label_col = NULL,
  threshold_value = 0.05,
  study_data,
  meta_data
)

Value

a list with:

• SummaryTable: data frame with ICCs per rvs

• SummaryData: data frame with ICCs per rvs

• ScalarValue_max_icc: maximum variance contribution value by group_vars

• ScalarValue_argmax_icc: variable with maximum variance contribution by group_vars

Arguments

resp_vars

variable list the names of the continuous measurement variables

group_vars

variable list the names of the resp. observer, device or reader variables

co_vars

variable list a vector of covariables, e.g. age and sex for adjustment

min_obs_in_subgroup

integer from=0. optional argument if a "group_var" is used. This argument specifies the minimum no. of observations that is required to include a subgroup (level) of the "group_var" in the analysis. Subgroups with fewer observations are excluded. The default is 30.

min_subgroups

integer from=0. optional argument if a "group_var" is used. This argument specifies the minimum no. of subgroups (levels) included "group_var". If the variable defined in "group_var" has fewer subgroups it is not used for analysis. The default is 5.

label_col

variable attribute the name of the column in the metadata with labels of variables

threshold_value

numeric from=0 to=1. a numerical value ranging from 0-1

study_data

data.frame the data frame that contains the measurements

meta_data

data.frame the data frame that contains metadata attributes of study data

ALGORITHM OF THIS IMPLEMENTATION:

• This implementation is yet restricted to data of type float.

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

• Deviations from limits, as defined in the metadata, are removed

• A linear mixed-effects model is estimated for resp_vars using co_vars and group_vars for adjustment.

• An output data frame is generated for group_vars indicating the ICC.

See Also

Online Documentation

Examples

if (FALSE) {
# runs spuriously slow on rhub
load(system.file("extdata/study_data.RData", package = "dataquieR"))
load(system.file("extdata/meta_data.RData", package = "dataquieR"))
co_vars <- c("SEX_0", "AGE_0")
min_obs_in_subgroup <- 30
min_subgroups <- 3
label_col <- LABEL
rvs <- c("DBP_0", "SBP_0")
group_vars <- prep_map_labels(rvs, meta_data = meta_data, from = label_col,
  to = VAR_NAMES)
group_vars <- prep_map_labels(group_vars, meta_data = meta_data,
  to = GROUP_VAR_OBSERVER)
group_vars <- prep_map_labels(group_vars, meta_data = meta_data)
acc_varcomp(
  resp_vars = rvs, group_vars = group_vars, co_vars = co_vars,
  min_obs_in_subgroup = min_obs_in_subgroup,
  min_subgroups = min_subgroups, label_col = label_col,
  study_data = study_data, meta_data = meta_data
)
}