info_item {TestDesign} | R Documentation |
(C++) Calculate Fisher information
Description
info_*()
and array_info_*()
are functions for calculating Fisher information.
Usage
info_1pl(x, b)
info_2pl(x, a, b)
info_m_2pl(x, a, d)
dirinfo_m_2pl(x, a, d)
thisdirinfo_m_2pl(x, alpha_vec, a, d)
info_3pl(x, a, b, c)
info_m_3pl(x, a, d, c)
dirinfo_m_3pl(x, a, d, c)
thisdirinfo_m_3pl(x, alpha_vec, a, d, c)
info_pc(x, b)
info_gpc(x, a, b)
info_m_gpc(x, a, d)
dirinfo_m_gpc(x, a, d)
thisdirinfo_m_gpc(x, alpha_vec, a, d)
info_gr(x, a, b)
info_m_gr(x, a, d)
dirinfo_m_gr(x, a, d)
thisdirinfo_m_gr(x, alpha_vec, a, d)
array_info_1pl(x, b)
array_info_2pl(x, a, b)
array_info_m_2pl(x, a, d)
array_dirinfo_m_2pl(x, a, d)
array_thisdirinfo_m_2pl(x, alpha_vec, a, d)
array_info_3pl(x, a, b, c)
array_info_m_3pl(x, a, d, c)
array_dirinfo_m_3pl(x, a, d, c)
array_thisdirinfo_m_3pl(x, alpha_vec, a, d, c)
array_info_pc(x, b)
array_info_gpc(x, a, b)
array_info_m_gpc(x, a, d)
array_dirinfo_m_gpc(x, a, d)
array_thisdirinfo_m_gpc(x, alpha_vec, a, d)
array_info_gr(x, a, b)
array_info_m_gr(x, a, d)
array_dirinfo_m_gr(x, a, d)
array_thisdirinfo_m_gr(x, alpha_vec, a, d)
Arguments
x |
the theta value. The number of columns should correspond to the number of dimensions.
For |
b , d |
the difficulty parameter. |
a |
the a-parameter. |
alpha_vec |
the alpha angle vector. Used for directional information in |
c |
the c-parameter. |
Details
info_*()
functions accept a single theta value, and array_info_*
functions accept multiple theta values.
Supports unidimensional and multidimensional models.
info_1pl()
,array_info_1pl()
: 1PL modelsinfo_2pl()
,array_info_2pl()
: 2PL modelsinfo_3pl()
,array_info_3pl()
: 3PL modelsinfo_pc()
,array_info_pc()
: PC (partial credit) modelsinfo_gpc()
,array_info_gpc()
: GPC (generalized partial credit) modelsinfo_gr()
,array_info_gr()
: GR (graded response) modelsinfo_m_2pl()
,array_info_m_2pl()
: multidimensional 2PL modelsinfo_m_3pl()
,array_info_m_3pl()
: multidimensional 3PL modelsinfo_m_gpc()
,array_info_m_gpc()
: multidimensional GPC modelsinfo_m_gr()
,array_info_m_gr()
: multidimensional GR modelsDirectional information for a specific angle
thisdirinfo_m_2pl()
,array_thisdirinfo_m_2pl()
: multidimensional 2PL modelsthisdirinfo_m_3pl()
,array_thisdirinfo_m_3pl()
: multidimensional 3PL modelsthisdirinfo_m_gpc()
,array_thisdirinfo_m_gpc()
: multidimensional GPC modelsthisdirinfo_m_gr()
,array_thisdirinfo_m_gr()
: multidimensional GR models
References
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.
Lord, F. M. (1952). A theory of test scores (Psychometric Monograph No. 7). Richmond, VA: Psychometric Corporation.
Birnbaum, A. (1957). Efficient design and use of tests of mental ability for various decision-making problems (Series Report No. 58-16. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1958). On the estimation of mental ability (Series Report No. 15. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1958). Further considerations of efficiency in tests of a mental ability (Series Report No. 17. Project No. 7755-23). Randolph Air Force Base, TX: USAF School of Aviation Medicine.
Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In Lord, F. M., Novick, M. R. (eds.), Statistical Theories of Mental Test Scores, 397-479. Reading, MA: Addison-Wesley.
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47(2), 149-174.
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561-573.
Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm. Applied Psychological Measurement, 16(2), 159-176.
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, 17.
Examples
x <- 0.5
info_1pl(x, 1)
info_2pl(x, 1, 2)
info_3pl(x, 1, 2, 0.25)
info_pc(x, c(0, 1))
info_gpc(x, 2, c(0, 1))
info_gr(x, 2, c(0, 2))
x <- matrix(seq(0.1, 0.5, 0.1)) # three theta values, unidimensional
array_info_1pl(x, 1)
array_info_2pl(x, 1, 2)
array_info_3pl(x, 1, 2, 0.25)
array_info_pc(x, c(0, 1))
array_info_gpc(x, 2, c(0, 1))
array_info_gr(x, 2, c(0, 2))