exactRLRT(RLRsim)
exactRLRT()所属R语言包：RLRsim
Restricted Likelihood Ratio Tests for additive and linear mixed models
添加剂和混合线性模型的限制似然比检验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function provides an (exact) restricted likelihood ratio test based on simulated values from the finite sample distribution for testing whether the variance of a random effect is 0 in a linear mixed model with known correlation structure of the tested random effect and i.i.d. errors.
此函数提供了一个限制似然比检验（精确的）的基础上从有限的用于测试的样本分布的方差的随机效应是否是0线性混合模型与已知的相关结构的测试的随机效应的模拟值和独立同分布的错误。
用法----------Usage----------
exactRLRT(m, mA = NULL, m0 = NULL, seed = NA, nsim = 10000,
log.grid.hi = 8, log.grid.lo = -10, gridlength = 200)
参数----------Arguments----------
参数：m
The fitted model under the alternative or, for testing in models with multiple variance components, the reduced model containing only the random effect to be tested (see Details), an lme, lmer or spm object
拟合模型下的替代品，在多个方差分量的模型进行测试，简化模型只包含随机效应进行测试（见详情），lme，lmer或spm 对象
参数：mA
The full model under the alternative for testing in models with multiple variance components
完整的测试模型中的多个方差分量模型下的替代
参数：m0
The model under the null for testing in models with multiple variance components
测试模型中的多个方差分量模型下的空
参数：seed
input for set.seed
输入set.seed
参数：nsim
Number of values to simulate
参数值的数量来模拟
参数：log.grid.hi
Lower value of the grid on the log scale. See exactRLRT.
较低的值的log规模上的网格。见exactRLRT。
参数：log.grid.lo
Lower value of the grid on the log scale. See exactRLRT.
较低的值的log规模上的网格。见exactRLRT。
参数：gridlength
Length of the grid. See exactLRT.
网格的长度。见exactLRT。
Details
详细信息----------Details----------
Testing in models with only a single variance component require only the first argument m. For testing in models with multiple variance components, the fitted model m must contain only the random effect set to zero under the null hypothesis, while mA and m0 are the models under the alternative and the null, respectively. For models with a single variance component, the simulated distribution is exact if the number of parameters (fixed and random) is smaller than the number of observations. Extensive simulation studies (see second reference below) confirm that the application of the test to models with multiple variance components is safe and the simulated distribution is correct as long as the number of parameters (fixed and random) is smaller than the number of observations and the nuisance variance components are not superfluous or very small. We use the finite sample distribution of the restricted likelihood ratio test statistic as derived by Crainiceanu & Ruppert (2004).
只有一个单一的方差分量模型中测试，只需要在第一个参数m。测试模型中的多个方差分量的拟合模型m必须设置为0的零假设下只包含随机效应，而mA和m0的模式是根据替代和空。对于与一个单一的方差分量的模型，模拟分布是精确的，如果参数的数目（固定的和随机的）是小于观测值的数量。大量的模拟研究（见下面第二个参考）确认，多个方差分量模型的应用测试是安全的，模拟分布是正确数量的参数（固定和随机）只要是小于的若干意见方差分量的滋扰是不是多余的或非常小的。我们用有限的样本分布的限制似然比检验统计量，推导出Crainiceanu鲁珀特（2004年）。
值----------Value----------
A list of class htest containing the following components:
列表类htest包含以下组件：
参数：statistic
the observed restricted likelihood ratio
所观察到的限制似然比
参数：p
p-value for the observed test statistic
所观察到的检验统计量的p-值
参数：method
a character string indicating what type of test was performed and how many values were simulated to determine the critical value
一个字符串，表示什么类型的测试执行和多少个值进行了模拟，以确定临界值
参数：sample
the samples from the null distribution returned by RLRTSim
空分布的样品返回RLRTSim
（作者）----------Author(s)----------
Fabian Scheipl, bug fixes by Andrzej Galecki, updates for <span class="pkg">lme4.0</span>-compatibility by Ben Bolker
参考文献----------References----------
Likelihood ratio tests in linear mixed models with one variance component, Journal of the Royal Statistical Society: Series B,66,165–185.
Restricted Likelihood Ratio Testing for Zero Variance Components in Linear Mixed Models, Journal of Computational and Graphical Statistics, 17 (4): 870–891.
Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Computational Statistics & Data Analysis, 52(7):3283–3299.
参见----------See Also----------
RLRTSim for the underlying simulation algorithm;
RLRTSim模拟算法的基础;
实例----------Examples----------
library(lme4)
data(sleepstudy)
mA <- lmer(Reaction ~ I(Days-4.5) + (1|Subject) + (0 + I(Days-4.5)|Subject), sleepstudy)
m0 <- update(mA, . ~ . - (0 + I(Days-4.5)|Subject))
m.slope <- update(mA, . ~ . - (1|Subject))
#test for subject specific slopes:[测试特定主题的斜坡：]
exactRLRT(m.slope, mA, m0)
library(mgcv)
data(trees)
#test quadratic trend vs. smooth alternative[测试的二次趋势与光滑替代]
m.q<-gamm(I(log(Volume)) ~ Height + s(Girth, m = 3), data = trees, method = "REML")$lme
exactRLRT(m.q)
#test linear trend vs. smooth alternative[测试线性趋势与光滑替代]
m.l<-gamm(I(log(Volume)) ~ Height + s(Girth, m = 2), data = trees, method = "REML")$lme
exactRLRT(m.l)
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-26 23:31:28
