Ts.estim(TwoStepCLogit)
Ts.estim()所属R语言包：TwoStepCLogit
Two-step estimator
两个步骤的估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function that computes the two-step estimator proposed in Craiu et al. (2011) and its print method.
函数，计算的两个步骤的估计在Craiu等提出。 （2011年）和它的print方法。
用法----------Usage----------
Ts.estim(formula, data, random, all.m.1 = FALSE,
D = "UN(1)", itermax = 2000, tole = 1e-06)
## S3 method for class 'Ts.estim'
print(x, ...)
参数----------Arguments----------
参数：formula
A formula object, with the response on the left of a ~ operator, and the covariates on the right. The right hand side of the model must also include two special terms: a strata and a cluster term (ex. formula = Y ~ X1 + X2 + X3 + strata(var_strata) + cluster(var_cluster)). The strata and cluster functions (from the package survival) are used to identify the stratification and the cluster variables, respectively.
一个公式对象，在左侧的一个~运营商，并在右边的协变量的响应。右手边的模型还必须包括两个特殊条件：一个strata和cluster术语（例如formula = Y ~ X1 + X2 + X3 + strata(var_strata) + cluster(var_cluster)）。 strata和cluster功能（从包survival）被用来识别的分层和聚类变量，分别。
参数：data
A data frame (or object coercible by as.data.frame to a data frame) containing the variables in the model.
一个数据框（或对象强制转换通过as.data.frame的数据框），其中包含在模型中的变量。
参数：random
A formula object, with a blank on the left of a ~ operator, and, on the right, the covariates with random coefficients among the covariate listed in the model formula (ex. random = ~ X1 + X3). The default is to add random coefficients for every covariates listed in the model formula.
一个公式对象，一个空白的~运营商在左侧，和，在右边，具有随机系数的协变量之间的协变量中列出的模型formula（如random = ~ X1 + X3 ）。默认情况下是增加每个协变量随机系数模型中的formula上市。
参数：all.m.1
TRUE if sum of Y's in all strata is 1, FALSE otherwise (the default). When in doubt use FALSE (always works, but slower than necessary if all stratum sums are 1).
TRUEY的各阶层的总和为1，FALSE其他方式（默认值）。当怀疑使用FALSE（总是，但慢于必要的，如果所有阶层金额为1）。
参数：D
The form of the between-cluster variance-covariance matrix of the regression coefficients (matrix D) : either "UN" for unstructured matrix D or "UN(1)" (the default) for diagonal matrix D.
类间方差 - 协方差矩阵的回归系数（矩阵D）的形式：要么"UN"非结构化矩阵D或"UN(1)"（默认）为对角矩阵D。
参数：itermax
maximal number of EM iterations (default = 2000)
EM迭代的最大数量（默认值= 2000）
参数：tole
maximal distance between successive EM iterations tolerated before declaring convergence (default = 0.000001)
连续EM迭代之间的最大距离的耐受性在宣布收敛（默认值= 0.000001）
参数：x
An object, produced by the Ts.estim function, to print.
打印，产生的Ts.estim功能，对象。
参数：...
Further arguments to be passed to print.default.
进一步的参数被传递给print.default。
Details
详细信息----------Details----------
Calls coxph from the package survival.
调用coxph的包survival。
值----------Value----------
参数：beta
A vector: the regression coefficients.
一个向量回归系数。
参数：se
A vector: the regression coefficients' standard errors.
一个向量回归系数的标准误差。
参数：D
A matrix: estimate of the between-cluster variance-covariance matrix of the regression coefficients (matrix D).
一个矩阵：类间方差 - 协方差矩阵（矩阵D）的回归系数的估计。
参数：r.effect
The random effect estimates.
随机效应估计。
参数：coxph.warn
A list of character string vectors. If the coxph function generates one or more warnings when fitting the Cox model to a cluster, a copy of these warnings are stored in coxph.warn$Cluster_name where Cluster_name is the identification value for the cluster in the data set. A NULL list element means that coxph did not produce any warnings for that cluster.
列表字符串向量。如果coxph功能Cox模型拟合到聚类时，会产生一个或多个警告，这些警告的副本存储在coxph.warn$Cluster_nameCluster_name是标识值中的聚类数据集。 ANULL列表中的元素意味着这coxph不产生任何警告该聚类。
参数：Call
The function call.
函数调用。
（作者）----------Author(s)----------
Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie
Baillargeon
参考文献----------References----------
(2011), Conditional Logistic Regression with Longitudinal Follow-up and Individual-Level Random Coefficients: A Stable and Efficient Two-Step Estimation Method, Journal of Computational and Graphical Statistics. 20(3), 767-784.
参见----------See Also----------
ddim
ddim
实例----------Examples----------
data(bison)
# Two ways for specifying the same model[指定的同一型号的两种方式]
# Model: covariates forest, biomass and pmeadow[型号：协变量的森林，生物量和pmeadow]
# Random effects in front of forest and biomass[在前面的森林和生物量的随机效应]
# Main diagonal covariance structure for D (the default)[主对角线协方差结构D（默认的）]
way1 <- Ts.estim(formula = Y ~ forest + biomass + pmeadow +
strata(Strata) + cluster(Cluster), data = bison,
random = ~ forest + biomass)
way1
way2 <- Ts.estim(formula = bison[,3] ~ as.matrix(bison[,c(6,8:9)]) +
strata(bison[,2]) + cluster(bison[,1]), data = bison,
random = ~ as.matrix(bison[,c(6,8)]))
way2
# Unstructured covariance for D[非结构化协方差为D]
Fit <- Ts.estim(formula = Y ~ forest + biomass + pmeadow +
strata(Strata) + cluster(Cluster), data = bison,
random = ~ forest + biomass, D="UN")
Fit
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 2012-10-1 13:16:06
