Metadata-Version: 1.1
Name: tirt
Version: 0.0.5
Summary: the simulation of Thurstone Item Response Theory, include fixed forced test and adaptive forced test. 
Home-page: https://github.com/inuyasha2012/tirt
Author: inuyasha2012
Author-email: inuyasha021@163.com
License: MIT
Description: 
        tirt
        ====
        
        the simulation of Thurstone Item Response Theory, include fixed forced
        test and adaptive forced test.
        模拟瑟斯顿项目反应理论，包括固定测验和自适应测验。
        
        瑟斯顿IRT模型简介和应用
        -----------------------
        
        瑟斯顿IRT模型主要应用于迫选式非认知测验（人格测验，动机测验，兴趣测验等）。
        
        固定测验模拟
        ------------
        
        模拟100个被试，30个维度，每个维度10个陈述，每道题3个陈述，所以下面这个陈述总共有100题
        
        ::
        
            from tirt import SimFixedTirt
        
            fixed_tirt = SimFixedTirt(subject_nums=100, trait_size=30, items_size_per_dim=10)
            theta_list = fixed_tirt.sim()
            score_list = fixed_tirt.scores
        
            for i, theta in enumerate(theta_list):
                print score_list[i]
                print theta
        
        自适应测验模拟
        --------------
        
        模拟1个被试，题库600道题，30个维度，首先随机抽10题，第二阶段抽合适的题40道题，总共50道题
        
        ::
        
            from tirt import SimAdaptiveTirt
        
            sat = SimAdaptiveTirt(subject_nums=1, item_size=600, trait_size=30, max_sec_item_size=40)
            sat.sim()
        
            for key, value in sat.thetas.items():
                print sat.scores[key]
                print value
        
        一致性
        ------
        
        迫选测验通常都没有测谎量表（迫选测验本身抗作假），而衡量被试是否认真作答有更好的一致性分数
        
        ::
        
            from tirt import irt_consistency_score, sim_scores, BayesProbitModel, gen_item_dict, SimFixedTirt
            from tirt.utils import random_params
        
            # 生成试题字典
            item_dict = gen_item_dict(30, 10, block_size=3)
            # 生成试题参数
            a, b = random_params(item_dict, 30, block_size=3)
            # 生成随机得分
            scores = sim_scores(30, 10, 10)
        
            for score in scores:
                model = BayesProbitModel(a, b, score=score)
                # 打印一致性
                print irt_consistency_score(model)
        
            model = SimFixedTirt(trait_size=30, items_size_per_dim=10, subject_nums=100, model='bayes_probit')
            model.sim()
            print model.get_consistency_scores()
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
