check_dgf {iotarelr}

Check for Different Guidance Functioning (DGF)

Description

Function for checking if the coding scheme is the same for different sub-groups.

Usage

check_dgf(
  data,
  splitcr,
  random_starts = 300,
  max_iterations = 5000,
  cr_rel_change = 1e-12,
  con_step_size = 1e-04,
  con_random_starts = 10,
  con_max_iterations = 5000,
  con_rel_convergence = 1e-12,
  b_min = 0.01,
  trace = FALSE,
  con_trace = FALSE,
  fast = TRUE
)

Arguments

data

Data for which the elements should be estimated. Data must be an object of type data.frame or matrix with cases in the rows and raters in the columns. Please note that no additional variables are allowed in this object.

splitcr

Vector containing the assignments of coding units to groups. The vector must have the same length as the number of rows of object data.

random_starts

An integer for the number of random starts for the EM algorithm.

max_iterations

An integer for the maximum number of iterations within the EM algorithm.

cr_rel_change

Positive numeric value for defining the convergence of the EM algorithm.

con_step_size

Double for specifying the size for increasing or decreasing the probabilities during the conditioning stage of estimation. This value should not be less than 1e-3.

con_random_starts

Integer for the number of random starts within the condition stage.

con_max_iterations

Integer for the maximum number of iterations during the condition stage.

con_rel_convergence

Double for determining the convergence criterion during condition stage. The algorithm stops if the relative change is smaller than this criterion.

b_min

Value ranging between 0 and 1 determining the minimal size of the categories for checking if boundary values occurred. The algorithm tries to select solutions that are not considered to be boundary values.

trace

TRUE for printing progress information on the console. FALSE if this information is not to be printed.

con_trace

TRUE for printing progress information on the console during estimations in the condition stage. FALSE if this information is not to be printed.

fast

Bool If TRUE a fast estimation is applied during the condition stage. This option ignores all parameters beginning with "con_". If FALSE the estimation described in Berding and Pargmann (2022) is used. Default is TRUE.

Value

Returns an object of class iotarelr_iota2_dif. For each group, the results of the estimation are saved separately. The structure within each group is similar to the results from compute_iota2(). Please check that documentation.

References

Florian Berding and Julia Pargmann (2022).Iota Reliability Concept of the Second Generation. Measures for Content Analysis Done by Humans or Artificial Intelligences. Berlin:Logos. https://doi.org/10.30819/5581