Fits a model of Iota2 to the data
Usage
compute_iota2(
data,
random_starts = 10,
max_iterations = 5000,
cr_rel_change = 1e-12,
con_step_size = 1e-04,
con_rel_convergence = 1e-12,
con_max_iterations = 5000,
con_random_starts = 5,
b_min = 0.01,
fast = TRUE,
trace = TRUE,
con_trace = FALSE
)
Arguments
- data
Data for which the elements should be estimated. Data must be an object of type
data.frame
ormatrix
with cases in the rows and raters in the columns.- 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_rel_convergence
Double
for determining the convergence criterion during the conditioning stage. The algorithm stops if the relative change is smaller than this criterion.- con_max_iterations
Integer
for the maximum number of iterations during the conditioning stage.- con_random_starts
Integer
for the number of random starts within the conditioning stage.- 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.
- fast
Bool
IfTRUE
a fast estimation is applied during the condition stage. This option ignores all parameters beginning with "con_". IfFALSE
the estimation described in Berding and Pargmann (2022) is used. Default isTRUE
.- 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 conditioning stage.FALSE
if this information is not to be printed.
Value
Returns a list
with the following three components:
The first component estimates_categorical_level
comprises all
elements that describe the ratings on a categorical level. The elements are
sub-divided into raw estimates and chance-corrected estimates.
raw_estimates
-
alpha_reliability:
A vector containing the Alpha Reliabilities for each category. These values represent probabilities.
beta_reliability:
A vector containing the Beta Reliabilities for each category. These values represent probabilities.
assignment_error_matrix:
Assignment Error Matrix containing the conditional probabilities for assigning a unit of category i to categories 1 to n.
iota:
A vector containing the Iota values for each category.
iota_error_1:
A vector containing the Iota Error Type I values for each category.
iota_error_2:
A vector containing the Iota Error Type II values for each category.
elements_chance_corrected
-
alpha_reliability:
A vector containing the chance-corrected Alpha Reliabilities for each category.
beta_reliability:
A vector containing the chance-corrected Beta Reliabilities for each category.
The second component estimates_scale_level contains elements for
describing the quality of the ratings on a scale level. It comprises the
following elements:
The third component information contains important information
regarding the parameter estimation. It comprises the following elements: