nlme.nlsList(nlme)
nlme.nlsList()所属R语言包：nlme
NLME fit from nlsList Object
从nlsList对象NLME适合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
If the random effects names defined in random are a subset of the lmList object coefficient names, initial estimates for the covariance matrix of the random effects are obtained (overwriting any values given in random). formula(fixed) and the data argument in the calling sequence used to obtain fixed are passed as the fixed and data arguments to nlme.formula, together with any other additional arguments in the function call. See the documentation on nlme.formula for a description of that function.
如果随机效果的名称randomlmList对象系数名称的一个子集的定义，协方差矩阵的随机效应的初步估计得到（覆盖random给定的任何值） 。 formula(fixed)和data参数用于获取fixedfixed和data参数nlme.formula，连同通过调用序列任何其他额外的参数在函数调用。功能的描述文件nlme.formula。
用法----------Usage----------
## S3 method for class 'nlsList'
nlme(model, data, fixed, random, groups, start, correlation, weights,
subset, method, na.action, naPattern, control, verbose)
参数----------Arguments----------
参数：model
an object inheriting from class nlsList, representing a list of nls fits with a common model.
对象类继承nlsList，代表名单nls适合一个共同的模式。
参数：data
this argument is included for consistency with the generic function. It is ignored in this method function.
这种说法是包括通用功能的一致性。在此方法的功能被忽略。
参数：fixed
this argument is included for consistency with the generic function. It is ignored in this method function.
这种说法是包括通用功能的一致性。在此方法的功能被忽略。
参数：random
an optional one-sided linear formula with no conditioning expression, or a pdMat object with a formula attribute. Multiple levels of grouping are not allowed with this method function. Defaults to a formula consisting of the right hand side of formula(fixed).
一个可选的双面线性公式，没有空调的表达，或pdMat与formula属性的对象。多层次的分组不允许用这种方法的功能。默认为公式右侧formula(fixed)组成。
参数：groups
an optional one-sided formula of the form ~g1 (single level of nesting) or ~g1/.../gQ (multiple levels of nesting), specifying the partitions of the data over which the random effects vary. g1,...,gQ must evaluate to factors in data. The order of nesting, when multiple levels are present, is taken from left to right (i.e. g1 is the first level, g2 the second, etc.).
可选片面公式的形式~g1（单级嵌套）或~g1/.../gQ（多层次的嵌套），指定的随机效应不同的数据分区。 g1,...,gQ必须评估在data因素。嵌套的顺序，多层次，采取从左至右（即g1是一级g2第二，等）。
参数：start
an optional numeric vector, or list of initial estimates for the fixed effects and random effects. If declared as a numeric vector, it is converted internally to a list with a single component fixed, given by the vector. The fixed component is required, unless the model function inherits from class selfStart, in which case initial values will be derived from a call to nlsList. An optional random component is used to specify initial values for the random effects and should consist of a matrix, or a list of matrices with length equal to the number of grouping levels. Each matrix should have as many rows as the number of groups at the corresponding level and as many columns as the number of random effects in that level.
一个可选的数字向量，或固定效应和随机效应的初步估计。如果声明为一个数值向量，它被转换成内部列表一个组件fixed向量。 fixed组件是必需的，除非该模型的功能从类继承selfStart，在这种情况下，初始值将被从调用nlsList派生。一个可选的random组件用于指定初始值的随机效应，并应组成一个矩阵，或等于分组级别的数量与长度的矩阵列表。每个矩阵应该有许多行，如在相应级别的群体数量，并在该级别中随机效应的多列。
参数：correlation
an optional corStruct object describing the within-group correlation structure. See the documentation of corClasses for a description of the available corStruct classes. Defaults to NULL, corresponding to no within-group correlations.
一个可选的corStruct对象，它描述的组内相关结构。可用corClasses类的描述，请参阅文件corStruct。 NULL默认值，相应的组内没有相关。
参数：weights
an optional varFunc object or one-sided formula describing the within-group heteroscedasticity structure. If given as a formula, it is used as the argument to varFixed, corresponding to fixed variance weights. See the documentation on varClasses for a description of the available varFunc classes. Defaults to NULL, corresponding to homoscedastic within-group errors.
一个可选的varFunc对象或片面的公式描述组内异方差结构。如果给出一个公式，它是用来作为的varFixed，相应的固定方差权重的参数。可用varClasses类的描述，请参阅文件varFunc。默认NULL，对应于同方差组内的错误。
参数：subset
an optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
一个可选的表达式表示data，应适合使用的行的子集。这可以是一个逻辑向量，或一个数字的向量，被列入观察数字或字符向量的行名被列入。所有的意见，包括默认情况下。
参数：method
a character string. If "REML" the model is fit by maximizing the restricted log-likelihood. If "ML" the log-likelihood is maximized. Defaults to "ML".
一个字符串。如果"REML"模型适合最大限度地限制日志的可能性。如果"ML"日志的可能性最大化。 "ML"默认。
参数：na.action
a function that indicates what should happen when the data contain NAs. The default action (na.fail) causes nlme to print an error message and terminate if there are any incomplete observations.
一个函数，它表示数据时，包含NA的，应该发生什么。默认动作（na.fail）导致nlme打印一个错误消息并终止，如果有任何不完整的意见。
参数：naPattern
an expression or formula object, specifying which returned values are to be regarded as missing.
表达或公式对象，指定返回值将被视为失踪。
参数：control
a list of control values for the estimation algorithm to replace the default values returned by the function nlmeControl. Defaults to an empty list.
估计算法的控制值的列表来替换默认的返回值的函数nlmeControl。默认为一个空列表。
参数：verbose
an optional logical value. If TRUE information on the evolution of the iterative algorithm is printed. Default is FALSE.
一个可选的逻辑值。如果TRUE迭代算法打印演化的信息。默认FALSE。
值----------Value----------
an object of class nlme representing the linear mixed-effects model fit. Generic functions such as print, plot and summary have methods to show the results of the fit. See nlmeObject for the components of the fit. The functions resid, coef, fitted, fixed.effects, and random.effects can be used to extract some of its components.
一个类的对象nlme代表线性混合效应模型拟合。通用功能，如print，plot和summary方法来显示合适的结果。看到nlmeObject适合的组成部分。职能resid，coef，fitted，fixed.effects，random.effects可以用来提取其组成部分。
作者（S）----------Author(s)----------
Jose Pinheiro and Douglas Bates <a href="mailto:bates@stat.wisc.edu">bates@stat.wisc.edu</a>
参考文献----------References----------
M.J. and Bates, D.M. (1988). The model formulation is described in Laird, N.M. and Ware, J.H. (1982). The variance-covariance parametrizations are described in < inheiro, J.C. and Bates., D.M. (1996). The different correlation structures available for the <code>correlation</code> argument are described in Box, G.E.P., Jenkins, G.M., and Reinsel G.C. (1994), Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996), and Venables, W.N. and Ripley, B.D. (1997). The use of variance functions for linear and nonlinear mixed effects models is presented in detail in Davidian, M. and Giltinan, D.M. (1995).
Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
for Repeated Measurement Data", Chapman and Hall.
Longitudinal Data", Biometrics, 38, 963-974.
Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data", Journal of the American Statistical Association, 83, 1014-1022.
"SAS Systems for Mixed Models", SAS Institute.
Parametrizations for Variance-Covariance Matrices", Statistics and Computing, 6, 289-296.
S-plus", 2nd Edition, Springer-Verlag.
参见----------See Also----------
nlme, lmList,
nlme，lmList
举例----------Examples----------
fm1 <- nlsList(SSasymp, data = Loblolly)
fm2 <- nlme(fm1, random = Asym ~ 1)
summary(fm1)
summary(fm2)
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 2012-2-16 20:11:08
