corAR1(nlme)
corAR1()所属R语言包：nlme
AR(1) Correlation Structure
AR（1）相关结构
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function is a constructor for the corAR1 class, representing an autocorrelation structure of order 1. Objects created using this constructor must later be initialized using the appropriate Initialize method.
此功能一个corAR1类较1阶自相关结构，构造。创建的对象，使用此构造稍后必须初始化使用适当的Initialize方法。
用法----------Usage----------
corAR1(value, form, fixed)
参数----------Arguments----------
参数：value
the value of the lag 1 autocorrelation, which must be between -1 and 1. Defaults to 0 (no autocorrelation).
滞后1自相关的价值，它必须是-1和1之间。默认为0（无自相关）。
参数：form
a one sided formula of the form ~ t, or ~ t | g, specifying a time covariate t and, optionally, a grouping factor g. A covariate for this correlation structure must be integer valued. When a grouping factor is present in form, the correlation structure is assumed to apply only to observations within the same grouping level; observations with different grouping levels are assumed to be uncorrelated. Defaults to ~ 1, which corresponds to using the order of the observations in the data as a covariate, and no groups.
一个双面公式的形式~ t或~ t | g，指定时间协t“，可选的分组因素g。一个协为此相关结构必须是整数值。当一个分组的因素是在form目前，相关结构假设只适用于在同一分组级别的观察，观察不同的分组级别被假定为不相关。默认~ 1，这相当于使用秩序的意见作为协变量的数据，并没有组。
参数：fixed
an optional logical value indicating whether the coefficients should be allowed to vary in the optimization, or kept fixed at their initial value. Defaults to FALSE, in which case the coefficients are allowed to vary.
一个可选的逻辑值，指明系数是否应允许不同的优化，或保持固定在其初始值。默认FALSE，在这种情况下，系数允许不同。
值----------Value----------
an object of class corAR1, representing an autocorrelation structure of order 1.
类corAR1较1阶自相关结构，对象。
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----------
Analysis: Forecasting and Control", 3rd Edition, Holden-Day.
in S and S-PLUS", Springer, esp. pp. 235, 397.
参见----------See Also----------
ACF.lme, corARMA, corClasses, Dim.corSpatial, Initialize.corStruct, summary.corStruct
ACF.lme，corARMA，corClasses，Dim.corSpatial，Initialize.corStruct，summary.corStruct
举例----------Examples----------
## covariate is observation order and grouping factor is Mare[＃协变量是观察顺序和分组因素是母马]
cs1 <- corAR1(0.2, form = ~ 1 | Mare)
# Pinheiro and Bates, p. 236[皮涅伊罗和贝茨，P。 236]
cs1AR1 <- corAR1(0.8, form = ~ 1 | Subject)
cs1AR1. <- Initialize(cs1AR1, data = Orthodont)
corMatrix(cs1AR1.)
# Pinheiro and Bates, p. 240[皮涅伊罗和贝茨，P。 240]
fm1Ovar.lme <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
data = Ovary, random = pdDiag(~sin(2*pi*Time)))
fm2Ovar.lme <- update(fm1Ovar.lme, correlation = corAR1())
# Pinheiro and Bates, pp. 255-258: use in gls[皮涅伊罗和贝茨，第255-258：在GLS]
fm1Dial.gls <-
gls(rate ~(pressure + I(pressure^2) + I(pressure^3) + I(pressure^4))*QB,
Dialyzer)
fm2Dial.gls <- update(fm1Dial.gls,
weights = varPower(form = ~ pressure))
fm3Dial.gls <- update(fm2Dial.gls,
corr = corAR1(0.771, form = ~ 1 | Subject))
# Pinheiro and Bates use in nlme: [皮涅伊罗贝茨使用nlme：]
# from p. 240 needed on p. 396[从p。 240页上的需要。 396]
fm1Ovar.lme <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
data = Ovary, random = pdDiag(~sin(2*pi*Time)))
fm5Ovar.lme <- update(fm1Ovar.lme,
corr = corARMA(p = 1, q = 1))
# p. 396[页。 396]
fm1Ovar.nlme <- nlme(follicles~
A+B*sin(2*pi*w*Time)+C*cos(2*pi*w*Time),
data=Ovary, fixed=A+B+C+w~1,
random=pdDiag(A+B+w~1),
start=c(fixef(fm5Ovar.lme), 1) )
# p. 397[页。 397]
fm2Ovar.nlme <- update(fm1Ovar.nlme,
corr=corAR1(0.311) )
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注：
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发表于 2012-2-16 21:27:25
