loglin.smooth(SNSequate)
loglin.smooth()所属R语言包：SNSequate
Pre-smoothing using log-linear models.
预平滑，使用对数线性模型。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function fits log-linear models to score data and provides estimates of the (vector of) score probabilities as well as the C matrix decomposition of their covariance matrix, according to the specified equating design (see Details).
此功能适合的得分数据的对数线性模型，并提供（矢量）得分概率的估计，以及C矩阵分解它们的协方差矩阵，根据指定的等值的设计（请参阅详细情况）。
用法----------Usage----------
参数----------Arguments----------
参数：
Note that depending on the specified equating design, not all arguments are necessary as detailed below.
请注意，根据指定的等值设计，并非所有的参数是必要的，详情如下。
参数：scores
If the "EG" design is specified, a vector containing the raw sample frequencies coming from one group taking the test. If the "SG" design is specified, a matrix containing the (joint) bivariate sample frequencies for X (raws) and Y (columns). If the "CB" design is specified, a two column matrix containing the observed scores of the sample taking test X first, followed by test Y. The scores2 argument is then used for the scores of the sample taking test Y first followed by test X. If either the "NEAT_CB" or "NEAT_PSE" design is selected, a two column matrix containing the observed scores on test X (first column) and the observed scores on the anchor test A (second column). The scores2 argument is then used for the observed scores on test Y.
如果“EG”的设计，一个向量，包含原始采样频率从一组参加考试。如果指定的“SG”设计，矩阵中的（联合）二元采样频率为X（原糖）和Y（列）。如果“CB”的设计，一个两列的矩阵，包含的样本的观察分数测试X第一，其次是测试Y。 scores2参数，然后用于测试Xy首先其次是样品测试的分数。如果任一被选择时，“NEAT_CB”或“NEAT_PSE”设计一个两列的矩阵含有试验X（第一列上所观察到的分数）和观察分数上锚定测试A（第二列）。然后用所观察到的分数测试scores2Y参数。
参数：degree
Either a number or vector indicating the number of power moments to be fitted to the marginal distributions, or the number or cross moments to be fitted to the joint distributions, respectively. For the "EG" design it will be a number (see Details).
一个数字或矢量的边缘分布，或数或交叉的时间来安装的联合分布，分别被安装到权力的时刻。对于“EG”的设计，这将是一个数（见详情）。
参数：design
A character string indicating the equating design (one of "EG", "SG", "CB", "NEAT_CE", "NEAT_PSE")
一个字符串，指示的等值设计（之一“EG”，“SG”，“CB”中，“NEAT_CE”，“NEAT_PSE”）
参数：scores2
Only used for the "CB", "NEAT_CE" and "NEAT_PSE" designs. See the description of scores.
仅用于“CB”，“NEAT_CE”和“NEAT_PSE”设计。见的描述scores。
参数：degreeXA
A vector indicating the number of power moments to be fitted to the marginal distributions X and A, and the number or cross moments to be fitted to the joint distribution (X,A) (see details). Only used for the "NEAT_CE" and "NEAT_PSE" designs.
一个向量，表示电源瞬间被安装到的边缘分布的数量X和A，并数或交叉的瞬间被安装到联合分布(X,A)（见详情）。仅用于“NEAT_CE”和“NEAT_PSE”设计。
参数：degreeYA
Only used for the "NEAT_CE" and "NEAT_PSE" designs (see the description for degreeXA)
仅用于“NEAT_CE”和“NEAT_PSE的”设计（见说明degreeXA）
参数：J
The number of possible X scores. Only needed for "CB", "NEAT_CB" and "NEAT_PSE" designs
的数目可能X分数。只需要为“CB”，“NEAT_CB”和“NEAT_PSE”的设计
参数：K
The number of possible Y scores. Only needed for "CB", "NEAT_CB" and "NEAT_PSE" designs
的数目可能Y分数。只需要为“CB”，“NEAT_CB”和“NEAT_PSE”的设计
参数：L
The number of possible A scores. Needed for "NEAT_CB" and "NEAT_PSE" designs
的数目可能A分数。所需的“NEAT_CB”和“NEAT_PSE”设计
参数：wx
A number that satisfies 0<=w_x<=1 indicating the weight put on the data that is not subject to order effects. Only used for the "CB" design.
一个数字，满足0<=w_x<=1表示的重量把上的数据是没有顺序的影响。仅用于“CB”的设计。
参数：wy
A number that satisfies 0<=w_y<=1 indicating the weight put on the data that is not subject to order effects. Only used for the "CB" design.
一个数字，满足0<=w_y<=1表示的重量把上的数据是没有顺序的影响。仅用于“CB”的设计。
参数：w
A number that satisfies 0<=w<=1 indicating the weight given to population P. Only used for the "NEAT" design.
一个数字，满足0<=w<=1表示权重考虑到人口P。仅用于“干净”的设计。
参数：...
Further arguments currently not used.
目前没有进一步的论据。
Details
详细信息----------Details----------
This function fits loglinear models as described in Holland and Thayer (1987), and Von Davier et al. (2004). The following general equation can be used to represent the models according to the different designs used, in which the vector o (or matrix) of (marginal or bivariate) score probabilities satisfies the log-linear model:
此功能适合对数线性模型在荷兰和Thayer（1987），和冯Davier的等。 （2004）。可以使用下列等式来表示的模型，根据使用的不同的设计，其中向量o（或矩阵）（边缘或二元）得分概率满足对数线性模型：
where Z_m(z_g)=∑_{i=1}^{T_{Zm}}β_{zmi}(z_g)^i, W_m(w_h)=∑_{i=1}^{T_{Wm}}β_{Wmi}(w_h)^i, and, ZW_m(z_g,w_h)=∑_{i=1}^{I_{Zm}}∑_{i'=1}^{I_{Wm}}β_{ZWmii'}(z_g)^i(w_h)^{i'}.
Z_m(z_g)=∑_{i=1}^{T_{Zm}}β_{zmi}(z_g)^i，W_m(w_h)=∑_{i=1}^{T_{Wm}}β_{Wmi}(w_h)^i，，ZW_m(z_g,w_h)=∑_{i=1}^{I_{Zm}}∑_{i'=1}^{I_{Wm}}β_{ZWmii'}(z_g)^i(w_h)^{i'}。
The symbols will vary according to the different equating designs specified. Possible values are: o=p_{(12)}, p_{(21)}, p, q; Z=X, Y; W=Y, A; z=x, y; w=y, a; m=(12), (21), P, Q; g=j, k; h=l, k.
的符号会有所不同，根据不同的指定的等值设计。可能的值是：o=p_{(12)}, p_{(21)}, p, q，Z=X, YW=Y, Az=x, y; w=y, am=(12), (21), P, Q; g=j, kh=l, k 。
Particular cases of this general equation for each of the equating designs can be found in Von Davier et al (2004) (e.g., Equations (7.1) and (7.2) for the "EG" design, Equation (8.1) for the "SG" design, Equations (9,1) and (9.2) for the "CB" design).
特定情况下，可以发现在本一般方程为每个等同设计冯Davier等人（2004）（例如，方程（7.1）和（7.2），公式（8.1），用于为“EG”设计“ SG“的设计，方程组（9,1）和（9.2）的”CB“设计）。
值----------Value----------
参数：sp.est
The estimated score probabilities
估计得分概率
参数：C
The C matrix which is so that Sigma=CC^t </table>
C矩阵，这是使Sigma=CC^t</表>
（作者）----------Author(s)----------
Jorge Gonzalez B.
参考文献----------References----------
discrete probability distributions. Research Report 87-31, Princeton NJ: Educational Testing Service.
参见----------See Also----------
glm, ker.eq
glm，ker.eq
实例----------Examples----------
#Table 7.4 from Von Davier et al. (2004)[表7.4冯Davier等。 （2004）]
data(Math20EG)
rj<-loglin.smooth(scores=Math20EG[,1],degree=2,design="EG")$sp.est
sk<-loglin.smooth(scores=Math20EG[,2],degree=3,design="EG")$sp.est
score<-0:20
Table7.4<-cbind(score,rj,sk)
Table7.4
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 11:16:55
