ChiSquaredLRTest(SEMModComp)
ChiSquaredLRTest()所属R语言包：SEMModComp
Conduct Chi-Squared Likelihood Ratio Test for Mean and Covariance Structure Models
进行卡方似然比检验的均值和方差结构模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
ChiSquaredLRTest performs the chi-squared likelihood ratio test for comparing hierarchically related mean and covariance structure models. Computations follow those in Levy, R., & Hancock, G. R. (2007). A framework of statistical tests for comparing mean and covariance structure models. Multivariate Behavioral Research, 42, 33-36.
ChiSquaredLRTest层次比较相关的均值和方差结构模型进行卡方似然比检验。计算遵循征费，R.，＆汉考克，GR（2007）。统计检验比较均值和方差结构模型的框架。多元行为研究，42，33-36。
用法----------Usage----------
ChiSquaredLRTest(x, model.1.mean.vector, model.1.cov.matrix,
model.1.df, model.2.mean.vector,
model.2.cov.matrix, model.2.df)
参数----------Arguments----------
参数：x
The raw data arranged with subjects as rows and measured variables as columns.
安排的主题行和测量变量列的原始数据。
参数：model.1.mean.vector
The model-implied mean vector from model 1, the more restricted (nested) model. If no value is supplied, will employ the mean vector from the sample.
该模型隐含的模型1，更受限制的（嵌套）模型的均值向量。如果未提供任何值，将采用从样本的平均向量。
参数：model.1.cov.matrix
The model-implied covariance matrix from model 1, the more restricted (nested) model. If no value is supplied, will employ the covariance matrix from the sample.
该模型隐含的协方差矩阵模型1，较受限制的模型（嵌套）。如果未提供任何值，将采用从样本协方差矩阵。
参数：model.1.df
The degrees of freedom for model 1, the more restricted (nested) model. If no value is supplied, will set equal to 0.
程度的自由，更受限制的（嵌套）模型模型1。如果未提供任何值，将设置为0。
参数：model.2.mean.vector
The model-implied mean vector from model 2, the more general model. If no value is supplied, will employ the mean vector from the sample.
该模型隐含的意思矢量模型2，更一般的模型。如果未提供任何值，将采用从样本的平均向量。
参数：model.2.cov.matrix
The model-implied covariance matrix from model 2, the more general model. If no value is supplied, will employ the covariance matrix from the sample.
模型2模型隐含的协方差矩阵，更一般的模型。如果未提供任何值，将采用从样本协方差矩阵。
参数：model.2.df
The degrees of freedom for model 2, the more general model. If no value is supplied, will set equal to 0.
对于模型2中，更一般的模型的自由度。如果未提供任何值，将设置为0。
Details
详细信息----------Details----------
The test is used to test for distinguishability and difference in fit between two models that are hierarchically related (i.e., nested) via referring -2* the likelihood ratio to a central chi-squared distribution with degrees of freedom equal to the difference in degrees of freedom for the two models. The test may also be used to conduct the chi-squared test of model fit for an individual model by not employing arguments for model 2. The test is also used (twice) to test for distinguishability in partially overlapping models See Levy, R., & Hancock, G. R. (2007). A framework of statistical tests for comparing mean and covariance structure models. Multivariate Behavioral Research, 42, 33-36. The function will perform listwise deletion in order to analyze a data set with no missing data. If the mean vector(s) and/or covariance matrix(ces) are not supplied for the models, the program will employ the estimates from the sample. If the number of degrees of freedom is not supplied for a model the program will employ a value of 0.
通过指-2 *似然比卡方分布到中央与程度的自由平等程度的差异，该测试是用来测试的区分度和分层的两种模式之间的差异在适合的（即嵌套）两款车型的自由。测试也可能被用来进行卡方检验模型的拟合为一个单独的模型，通过不使用参数模型2。该测试也使用（两次）测试部分重叠模型查看征费，R.，＆汉考克，GR（2007）区分。统计检验比较均值和方差结构模型的框架。多元行为研究，42，33-36。该函数将执行列表删除，以分析数据没有丢失的数据。如果平均向量（S）和/或协方差矩阵（CES）上不提供的模式，该计划将采用的估计，从样品。如果不提供一个模型的自由度数，该计划将采用的值为0。
值----------Value----------
a list containing:
一个列表，其中包含：
<table summary="R valueblock"> <tr valign="top"><td>N </td> <td> Total number of subjects in the data set </td></tr> <tr valign="top"><td>N.complete.data </td> <td> Number of subjects with complete data used in the analysis </td></tr> <tr valign="top"><td>LR </td> <td> The value of the likelihood ratio statistic </td></tr> <tr valign="top"><td>chi.sq.stat </td> <td> The test statistic. Under the null hypothesis of no difference in fit </td></tr> <tr valign="top"><td>df.test </td> <td> The degrees of freedom of the test, evaluated as (model.1.df-model.2.df) </td></tr> <tr valign="top"><td>p </td> <td> The p-value for the observed test statistic </td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> N </ TD> <TD>总数的主题数据集</ TD> </ TR> <TR VALIGN =“”> <TD>N.complete.data </ TD> <TD>完整的数据分析中使用的科目</ TD> </ TR> <tr valign="top"> <TD> LR </ TD> <TD>的似然比统计值</ TD> </ TR> <tr valign="top"> <TD>chi.sq.stat </ TD> <TD>检验统计量。在合适的无差异的零假设下</ TD> </ TR> <tr valign="top"> <TD>df.test </ TD> <TD>程度的测试，评估的自由（model.1.df model.2.df）</ TD> </ TR> <tr valign="top"> <TD> p </ TD> <TD>的p-值观测到的检验统计量</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Roy Levy <a href="mailto:Roy.Levy@asu.edu">Roy.Levy@asu.edu</a>
参考文献----------References----------
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-30 00:41:19
