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liboscats Reference Manual | ![]() |
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Top | Description | Object Hierarchy | Properties |
"Dprior" GGslVector* : Read / Write "Nposterior" guint : Read / Write / Construct "Sigma" GGslMatrix* : Read / Write "independent" gboolean : Read / Write "modelKey" gchar* : Read / Write "mu" GGslVector* : Read / Write "posterior" gboolean : Read / Write "thetaKey" gchar* : Read / Write "tol" gdouble : Read / Write / Construct
struct OscatsAlgEstimate;
Statistics algorithm ("administered"). Update the examinee's latent IRT ability estimate.
"Dprior"
property"Dprior" GGslVector* : Read / Write
Prior distribution for discrete dimensions as a vector of probabilities
for all Prod_i n_i patterns, where n_i is the number of categories for
discrete dimension i. The values should be ordered so that the lowest
numbered binary dimension increases fasted, and the ordinal dimensions
follow binary dimensions. The probabilities should sum to 1. Default:
uniform. Note, this is used only when "posterior" is
TRUE
.
"Nposterior"
property"Nposterior" guint : Read / Write / Construct
For the first N items, use Expected A Posteriori (EAP) estimation for continuous dimensions and Maximum A Posteriori (MAP) estimation for discrete dimensions. Switch to Maximum Likelihood (MLE) estimation after N items have been recorded. Note, EAP/MAP is always used as a fallback if the MLE fails to converge, e.g. for perfect response patterns.
Default value: 0
"Sigma"
property"Sigma" GGslMatrix* : Read / Write
Population covariance matrix for the normal prior under EAP. (Note: The value is copied.) Default: identity.
"independent"
property"independent" gboolean : Read / Write
Continuous and discrete dimensions are independent.
Default value: TRUE
"modelKey"
property"modelKey" gchar* : Read / Write
The key indicating which model to use for estimation. A NULL
value or
empty string indicates the item's default model.
Default value: NULL
"mu"
property"mu" GGslVector* : Read / Write
Population mean for the normal prior under EAP. (Note: The value is copied.) Default: 0.
"posterior"
property"posterior" gboolean : Read / Write
Always use Expected A Posteriori (EAP) estimation for continuous dimensions and Maximum A Posteriori (MAP) estimation for discrete dimensions, instead of Maximum Likelihood (MLE). Note, EAP/MAP is always used as a fallback if the MLE fails to converge, e.g. for perfect response patterns.
Default value: FALSE
"thetaKey"
property"thetaKey" gchar* : Read / Write
The key indicating which latent variable to use for estimation. A NULL
value or empty string indicates the examinee's default estimation theta.
Default value: NULL
"tol"
property"tol" gdouble : Read / Write / Construct
Tolerance for Newton-Raphson maximization (algorithm iterates until largest change in any dimension is less than tolerance).
Allowed values: >= G_MINDOUBLE
Default value: 1e-06