betabin(sensR)
betabin()所属R语言包：sensR
Beta-binomial and chance-corrected beta-binomial models for
β-二项分布和机会修正β-二项式模型
译者：生物统计家园网 机器人LoveR
----------Description----------
Fits the beta-binomial model and the chance-corrected beta-binomial model to (over-dispersed) binomial data.
适用于β-二项式模型和修正的机会β-二项式模型（超分散）二项数据。
用法----------Usage----------
betabin(data, start = c(.5,.5),
method = c("duotrio", "threeAFC", "twoAFC", "triangle"),
vcov = TRUE, corrected = TRUE, gradTol = 1e-4, ...)
## S3 method for class 'betabin'
summary(object, level = 0.95, ...)
参数----------Arguments----------
参数：data
matrix or data.frame with two columns; first column contains the number of success and the second the total number of cases. The number of rows should correspond to the number of observations.
有两列，第一列的矩阵或数据框包含了很多成功的，第二个是总病例数。的行数应符合的若干意见。
参数：start
starting values to be used in the optimization
的初始值被用于在优化
参数：vcov
logical, should the variance-covariance matrix of the parameters be computed?
逻辑，应计算的方差 - 协方差矩阵的参数？
参数：method
the sensory discrimination protocol for which d-prime and its standard error should be computed
D-素数，应计算其标准错误的感觉辨别协议
参数：corrected
should the chance corrected or the standard beta binomial model be estimated?
应该纠正的机会或标准的Beta二项式模型来估计呢？
参数：gradTol
a warning is issued if max|gradient| < gradTol, where 'gradient' is the gradient at the values at which the optimizer terminates. This is not used as a termination or convergence criterion during model fitting.
会发出一个警告如果max |梯度| <gradTol，其中梯度是优化终止处的梯度的值。这是不是用模型拟合过程中终止或收敛准则。
参数：object
an object of class "betabin", i.e. the result of betabin().
一个的对象类“betabin”，即betabin()。
参数：level
the confidence level of the confidence intervals computed by the summary method
的简要方法所计算的置信区间的置信水平
参数：...
betabin: The only recognized (hidden) argument is doFit (boolean) which by default is TRUE. When FALSE betabin returns an environment which facilitates examination of the likelihood surface via the (hidden) functions sensR:::getParBB and sensR:::setParBB. Not used in summary.betabin.
betabin：唯一可识别的参数（隐藏）doFit（布尔值），默认情况下是TRUE。当FALSEbetabin返回一个有利的环境（隐藏）功能sensR:::getParBB和sensR:::setParBB通过审查的可能性表面。不使用summary.betabin。
Details
详细信息----------Details----------
The beta-binomial models are parameterized in terms of mu and gamma, where mu corresponds to a probability parameter and gamma measures over-dispersion. Both parameters are restricted to the interval (0, 1). The parameters of the standard (i.e. corrected = FALSE) beta-binomial model refers to the mean (i.e. probability) and dispersion on the scale of the observations, i.e. on the scale where we talk of a probability of a correct answer (Pc). The parameters of the chance corrected (i.e. corrected = TRUE) beta-binomial model refers to the mean and dispersion on the scale of the "probability of discrimination" (Pd). The mean parameter (mu) is therefore restricted to the interval from zero to one in both models, but they have different interpretations.
β-二项式模型进行参数化和γ万亩，亩对应的概率参数和γ措施过度分散。这两个参数被限制的时间间隔（0，1）。的参数标准（即校正= FALSE）β-二项式模型是指平均值（即概率）和分散的意见，即上规模上规模，这里我们要讨论的一个正确的答案（PC）的概率。参数修正的机会（即校正= TRUE）β-二项式模型是指规模概率“歧视”（PD）的平均和分散。 （亩）的平均参数，因此限制在两个模型中从零到一的时间间隔，但它们具有不同的解释。
The summary method use the estimate of mu to infer the parameters of the sensory experiment; Pc, Pd and d-prime. These are restricted to their allowed ranges, e.g. Pc is always as least as large as the guessing probability.
摘要方法使用的估计亩的感官实验来推断参数，Pc，Pd和D-黄金。这些限制在其允许的范围，例如PC总是猜测概率为至少一样大。
Confidens intervals are computed as Wald (normal-based) intervals on the mu-scale and the confidence limits are subsequently transformed to the Pc, Pd and d-prime scales. Confidence limits are restricted to the allowed ranges of the parameters, for example no confidence limits will be less than zero.
Confidens的时间间隔计算沃尔德（正常）的时间间隔上万亩的规模和置信界限，随后转化为PC，PD和D-首要尺度。置信界限被限制在允许的范围的参数，例如不信任限制将小于零。
Standard errors, and therefore also confidence intervals, are only available if the parameters are not at the boundary of their allowed range (parameter space). If parameters are close to the boundaries of their allowed range, standard errors, and also confidence intervals, may be misleading. The likelihood ratio tests are more accurate. More accurate confidence intervals such as profile likelihood intervals may be implemented in the future.
The summary method provides a likelihood ratio test of over-dispersion on one degree of freedom and a likelihood ratio test of association (i.e. where the null hypothesis is "no difference" and the alternative hypothesis is "any difference") on two degrees of freedom (chi-square tests). Since the gamma parameter is tested on the boundary of the parameter space, the correct degree of freedom for the first test is probably 1/2 rather than one, or somewhere in between, and the latter test is probably also on less than two degrees of freedom. Research is needed to determine the appropriate no. degrees of freedom to use in each case. The choices used here are believed to be conservative, so the stated p-values are probably a little too large.
摘要方法提供了一种过度分散的似然比检验的自由度和似然比检验协会（即原假设是“没有差别”，另一种假设是“任何区别”）两个自由度（卡方检验）。由于伽马参数测试的参数空间的边界上，第一次测试的正确的自由度可能是1/2，而不是一个，或介于两者之间，以及后者的试验也可能是在小于2度的自由。研究是必要的，以确定适当的没有。在每种情况下使用的自由度。这里使用的选择被认为是保守的，在规定的P值可能有点过大。
The log-likelihood of the standard beta-binomial model is
标准的β-二项式模型是对数似然
∑_{j=1}^N \log Beta(α + x_j, β - x_j + n_j)</i>
Σ_{J = 1} ^ N \ LOG测试（α+β - x_j，x_j + n_j）</ I>
and the log-likelihood of the chance corrected beta-binomial model is
和对数似然修正的机会β-二项式模型
Beta(α + i, n_j - x_j + β) )</i>
β（α+β+我，n_j - x_j））</ P>
where μ = α/(α + β), γ = 1/(α + β + 1), Beta is the Beta function, cf. beta, N is the number of independent binomial observations, i.e. the number of rows in data, and p_g is the guessing probability, pGuess.
μ = α/(α + β)，γ = 1/(α + β + 1)，Beta是β函数，比照。 beta，N是独立二项式观测的数量，也就是在data的行数，和p_g是猜测概率，pGuess的。
The variance-covariance matrix (and standard errors) is based on the inverted Hessian at the optimum. The Hessian is obtained with the hessian function from the numDeriv package.
方差 - 协方差矩阵（和标准误差）为基础上的倒置的Hessian矩阵在最适宜的。的Hessian获得hessian功能从numDeriv包的。
The gradient at the optimum is evaluated with gradient from the numDeriv package.
在最佳的梯度评估gradient，从numDeriv包。
The bounded optimization is performed with the "L-BFGS-B" optimizer in optim.
“L-BFGS-B”优化optim有界进行优化。
The following additional methods are implemented objects of class betabin: print, vcov and logLik.
下面的方法是实现类的对象betabin：print，vcov和logLik。
值----------Value----------
An object of class betabin with elements <table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> named vector of coefficients</td></tr> <tr valign="top"><td>vcov</td> <td> variance-covariance matrix of the parameter estimates if vcov = TRUE</td></tr> <tr valign="top"><td>data</td> <td> the data supplied to the function</td></tr> <tr valign="top"><td>call</td> <td> the matched call</td></tr> <tr valign="top"><td>logLik</td> <td> the value of the log-likelihood at the MLEs</td></tr> <tr valign="top"><td>method</td> <td> the method used for the fit</td></tr> <tr valign="top"><td>convergence</td> <td> 0 indicates convergence. For other error messages, see optim.</td></tr> <tr valign="top"><td>message</td> <td> possible error message - see optim for details</td></tr> <tr valign="top"><td>counts</td> <td> the number of iterations used in the optimization - see optim for details</td></tr> <tr valign="top"><td>corrected</td> <td> is the chance corrected model estimated?</td></tr> <tr valign="top"><td>logLikNull</td> <td> log-likelihood of the binomial model with prop = pGuess</td></tr> <tr valign="top"><td>logLikMu</td> <td> log-likelihood of a binomial model with prop = sum(x)/sum(n)</td></tr> </table>
类的一个对象betabin的元素的表summary="R valueblock"> <tr valign="top"> <TD>coefficients </ TD> <TD>命名为向量的系数</ TD > </ TR> <tr valign="top"> <TD> vcov </ TD> <TD>方差 - 协方差矩阵的参数估计，如果vcov = TRUE</ TD> </ TR> <tr valign="top"> <TD> data </ TD> <TD>提供的数据的功能</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>匹配的呼叫</ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD>值的log在极大似然估计的可能性</ TD> </ TR> <tr valign="top"> <TD>logLik </ TD> <TD>配合使用的方法</ TD> </ TR> < TR VALIGN =“顶”> <TD>method</ TD> <TD> 0表示收敛。对于其他错误讯息，请参阅convergence。</ TD> </ TR> <tr valign="top"> <TD> optim </ TD> <TD>可能出现的错误消息 - 请参阅message详细资料</ TD> </ TR> <tr valign="top"> <TD>optim</ TD> <TD>用于优化的迭代 - counts 的详细信息</ TD> </ TR> <tr valign="top"> <TD>optim </ TD> <TD>机会纠正模型估计？</ TD> </ TR> < TR VALIGN =“顶”> <TD>corrected </ TD> <TD>的二项式模型的对数似然道具= pGuess </ TD> </ TR> <tr valign="top"> < logLikNull TD> </ TD> <TD>对数似然的二项式模型道具= SUM（X）/和（N）</ TD> </ TR> </表>
（作者）----------Author(s)----------
Rune Haubo B Christensen
参考文献----------References----------
replications in difference tests. Food Quality and Preference, 14, pp. 405–417.
参见----------See Also----------
triangle, twoAFC, threeAFC, duotrio,
triangle，twoAFC，threeAFC，duotrio，
实例----------Examples----------
## Create data:[创建数据：]
x <- c(3,2,6,8,3,4,6,0,9,9,0,2,1,2,8,9,5,7)
n <- c(10,9,8,9,8,6,9,10,10,10,9,9,10,10,10,10,9,10)
dat <- data.frame(x, n)
(bb <- betabin(dat, method = "duotrio"))
(bb <- betabin(dat, corrected = FALSE, method = "duotrio"))
summary(bb)
vcov(bb)
logLik(bb)
AIC(bb)
coef(bb)
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 01:06:37
