lazy.irt {lazy.irt} | R Documentation |

The following are the classes of the functions.

uIRT: Item Parameter Estimation of Unidimensional IRT

cala: Calibration of Independently Estimated Sets of Item Parameters by Minimizing the LS Criterion Defined in terms of Parameter Values

calr Calibration of Independently Estimated Sets of Item Parameters by Minimizing the LS Criterion Defined in terms of Probability Values

est_theta: Estimation of Theta

ordinal_reg: Oridinal Regression (used in the m-step of uIRT)

irf Calculation of Item Response Function

icrfB: Calculation of Item Response Function of Binary Logisticitems

icrfG: Calculation of Item Response Function of Graded Responseitems

icrfN: Calculation of Item Response Function of Nominalitems (N.A.)

icrfP: Calculation of Item Response Function of Partial Credititems

icrfPN: Calculation of Item Response Function of Partial Credititems in Nominal Model Format

icrfPN0: Calculation of Partial Credit ICRF in Nominal format

dirf: Calculation of Derivative of Item Response Function

dicrfB: Calculation of the Derivative of Item Response Function of Binary Logistic Items

dicrfG: Calculation of the Derivative of Item Response Function of Graded Response Items

dicrfN: Calculation of the Derivative of Item Response Function of Nominal Items (N.A.)

dicrfP: Calculation of the Derivative of Item Response Function of Partial Credit Items

dicrfPN: Calculation of the Derivative of Item Response Function of Partial Credit Items in Nominal Format

dicrfPN0: Calculation of the Derivative of Partial Credit ICRF in Nominal format

dicrf_num: Numerical Derivative of Item Response Function using JacobianMat

dirf_p: Calculation of the Derivative of Item Response Function with respect to Item Parameters

obscore: Calculation of Observed Score Distribution and Posterior Distribution of Theta with Various Information Functions

smn: Parameter Estimation of Parametric (Normal pdf) Scored Multinomial Distributions

sumsmnw: Distribution of the Weighted Sum of Several Independent Scored Multinomial Distributions

sumsmnw12: Distribution of the Weighted Sum of Two Independent Scored Multinomial Distributions

rel_irt: Calculation of Test Reliability and Average SEM under IRT model

info_func: Calculation of Various Information Functions

graded_info: Calculation of the Information Function associated with the Graded Observed Score.

flatten_SEM: Find the Transformation of the Observed Score such that the Resulting Score has a Flat SEM almost everywhere.

flatten_SEM_theta: Find the Transformation of the Thetahat Based Observed Score such that the Resulting Score has a Flat SEM almost everywhere.

fitG2P: Approximate Conversion of Partial Credit Items to Graded Response Items Using Logit Transformation

fitP2G: Approximate Conversion of Graded Response Items to Partial Credit Items Using Logit Transformation

fitG2P_ls: Conversion of Partial Credit Items to Graded Response Items

fitP2G_ls: Conversion of Graded Response Items to Partial Credit Items

fit223_ls: Conversion of 3PLM Items to 2PLM Items

convP2N: Convert Partial Credit Item Parameters to in Nominal Format

convP2PN: Convert Partial Credit Item Parameters in Standard Format to Partial Credit Parameters in Nominal Format

convPN2P: Convert Partial Credit Item Parameters in Nominal Format to Standard Format

read.param: Reading Parameter File

read.weight: Reading Weight File

checkparam: Checking Parameter Data Frame

gendataIRT: Generation of Simulated Item Response Data

find_mode: Find the Mode of icrf

find_intersection: Find the Intersection of icrfs

gen_icrfnames: Generate the row names of vec(icrf)

graded_prob; Calculation of Probability Contents associated with Graded Score

paramA1: Set of All Types of Items # 1. (10 items)

paramB1: Binary Item Parameter Data Frame # 1. (18 2PLM items)

paramB2: Binary Item Parameter Data Frame # 2. (18 3PLM items)

paramS1: Small Item Parameter Data Frame # 1. (3 mixed type items)

paramS2: Small Item Parameter Data Frame # 2. (8 mixed type items)

paramS3: Small Item Parameter Data Frame # 2. (20 mixed type items)

weightS1: Small Item Weight Data Frame # 1 To be used in conjunction with paramS1.

weightS11: Small Item Weight Data Frame # 2. To be used in conjunction with paramS1.

weightS12: Small Item Weight Data Frame # 3. To be used in conjunction with paramS1.

weightS2: Small Item Weight Data Frame # 4. To be used in conjunction with paramS2.

weightS21: Small Item Weight Data Frame # 5. To be used in conjunction with paramS2.

weightS22: Small Item Weight Data Frame # 6. To be used in conjunction with paramS2.

weightS3: Small Item Weight Data Frame # 7. To be used in conjunction with paramS3.

paramCal1: Small Item Parameter Data Frame for Testing cala and calr #1.

paramCal2: Small Item Parameter Data Frame for Testing cala and calr #2.

uLRT: Item Parameter Estimation of Unidimensional LRT

with strict monotonicity restrictions.est_rank: Estmation of LRT latent rank for each person.

fitI2L: Approxiamte Conversion of LRT model to 2PLM IRT model

fitI2L_ls: LS Conversion of LRT model to 2PLM IRT model

[Package *lazy.irt* version 0.1.3 Index]