discrimR(sensR)
discrimR()所属R语言包：sensR
Replicated Thurstonian Model for discrimination analysis
复制的Thurstonian判别分析模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The model is a synthesis of a mixture and a mixed effect model. The random effect distribution for the cluster term (often individuals) is a point mass for delta = 0 and a continuous distribution for delta > 0.
该模型是一种合成的混合物和混合效应模型。聚类术语（通常是个人）的随机效应分布是δ= 0和点质量的Δ> 0的连续分布。
The function fits the model and computes d-prime for an average subject, 2) the variance among subjects, 3) the "posterior" probability of a subject being a discriminator (with delta > 0), 4) the "posterior" expectation on the random effect (ie. the subject-specific delta) and 5) the probability that a randomly chosen individual is a discriminator (ie. the probability mass at delta = 0 in the random effects distribution)
该功能适用的模型，并计算平均为主题，2）D-贷科目之间的差异，3）“后路”概率的题目的鉴别（Delta> 0），4）“后路”的期望上的随机效应（即特定主题的增量）和5）的概率是一个随机选择的个体是一个鉴别器（即在δ= 0的随机效应分布的概率质量）
Warning: This function is preliminary; see the details for further information.
警告：此功能是初步的详细信息，查看详细信息。
用法----------Usage----------
discrimR(formula, data, weights, cluster, start, subset, na.action,
contrasts = NULL, hess = FALSE, ranef = FALSE, zi = FALSE,
method = c("duotrio", "probit", "threeAFC", "triangle",
"twoAFC"), ...)
参数----------Arguments----------
参数：formula
A formula where the lhs is the binomial response. An indicator vector or a matrix with two column; successes and failures like in a call to glm with a binomial family. The rhs should be 1; no other predictors are currently allowed, but extending this is ongoing work.
的LHS了一个公式，二项式响应。两列指标向量或矩阵，喜欢在调用glm一个二项式家庭的成功经验和失败教训。右边应该是1，没有任何其他的预测，目前允许的，但延续，这是正在进行的工作。
参数：data
The data.frame in which to look for variables.
data.frame在其中寻找变量。
参数：weights
Possible weights
可能的权重
参数：cluster
The clustering variable; should be a factor.
聚类变量;应该是一个因素。
参数：start
Optional starting values; recommended in the current implementation
建议在目前的实现可选的初始值;
参数：subset
...
...
参数：na.action
...
...
参数：contrasts
...
...
参数：hess
Should the hessian of the parameters be computed?
如麻的参数计算？
参数：ranef
Should the random effect estimates be computed?
如果随机效应估计计算？
参数：zi
Should the posterior probabilities of a subject being a discriminator be computed?
的主题是鉴别的后验概率计算？
参数：method
Should correspond to the actual test applied.
应该对应于应用的实际测试。
参数：...
Additional arguments to optim. control=list(trace=TRUE, REPORT=1) is recommended, so the reduction in deviance and convergence can be followed.
其他参数optim。 control=list(trace=TRUE, REPORT=1)建议，以便之后可以减少偏差和收敛。
Details
详细信息----------Details----------
This function is preliminary and improving it is ongoing work. The computational methods are expected to change completely. This will hopefully facilitate methods for more general rhs-formulae with additional predictors.
此功能是初步的，并加以改善，是正在进行的工作。的计算方法，有望彻底改变。希望这将促进RHS-公式更普遍的额外的预测方法。
Currently no methods or extractor functions have been written, so the user will have to select the relevant elements from the fitted object (see below). Implementation of methods and extractor functions will occur in due course.
目前没有任何方法或提取功能已被写入，因此用户将有选择的拟合对象的有关内容（见下文）。实施的方法和提取功能将在适当的时候出现。
值----------Value----------
A list with the following elements: <table summary="R valueblock"> <tr valign="top"><td>fpar</td> <td> The fixed effect parameter, ie. delta (for an average individual)</td></tr> <tr valign="top"><td>rpar</td> <td> A vector with two elements: The first element is the variance component (standard deviation) on the log-scale, where optimization is performed. The second element is the variance component (standard deviation) on the original scale.</td></tr> <tr valign="top"><td>deviance</td> <td> Deviance for the model</td></tr> <tr valign="top"><td>se</td> <td> standard errors for 1) the fixed effect parameter and 2) the variance component on the log-scale</td></tr> <tr valign="top"><td>convergence</td> <td> Convergence message from optim</td></tr> <tr valign="top"><td>lli</td> <td> Log-likelihood contributions from each of the observations.</td></tr> <tr valign="top"><td>ranef</td> <td> The random effect estimates for the levels of the clustering factor (often individual)</td></tr> <tr valign="top"><td>zi</td> <td> posterior probabilities of a subject being a discriminator</td></tr> <tr valign="top"><td>p</td> <td> The probability that a randomly chosen individual is a discriminator (ie. the probability mass for delta > 0 in the random effects distribution)</td></tr> <tr valign="top"><td>fitted</td> <td> Fitted values</td></tr> <tr valign="top"><td>Y</td> <td> The scaled response vector on which optimization is performed.</td></tr> <tr valign="top"><td>call</td> <td> the matched call</td></tr> </table>
列表包含下列元素：<table summary="R valueblock"> <tr valign="top"> <TD>fpar </ TD> <TD>，即固定效应参数。Delta（平均个体）</ TD> </ TR> <tr valign="top"> <TD> rpar</ TD> <td>一个向量的两个要素：第一个要素是差异（标准偏差）成分上的log规模，执行最优化的地方。第二个因素是原有规模的方差分量（标准差）。</ TD> </ TR> <tr valign="top"> <TD>deviance </ TD> <TD>越轨模型</ TD> </ TR> <tr valign="top"> <TD> se </ TD> <TD>标准误差为1）固定效应参数和2）的方差分量的log规模</ TD> </ TR> <tr valign="top"> <TD>convergence </ TD> <TD>的收敛消息从optim </ TD> </ TR> < TR VALIGN =“”> <TD>lli </ TD> <TD>对数似然从每个观察。</ TD> </ TR> <tr valign="top"> <TD >ranef</ TD> <TD>随机效应估计的水平簇因子（通常是个人）</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>后验概率的主体鉴别</ TD> </ TR> <tr valign="top"> <TD> zi</ TD> <TD>的概率随机选择的个人是鉴别（即随机效应分布在Delta> 0的概率质量）</ TD> </ TR> <tr valign="top"> <TD>p / TD> <TD>的拟合值</ TD> </ TR> <tr valign="top"> <TD> fitted</ TD> <TD>比例的响应向量进行优化。 / TD> </ TR> <tr valign="top"> <TD>Y </ TD> <TD>匹配的呼叫</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Rune Haubo B Christensen
参见----------See Also----------
triangle, twoAFC, threeAFC, duotrio, discrimPwr, discrimSim, discrimSS, samediff, AnotA, findcr
triangle，twoAFC，threeAFC，duotrio，discrimPwr，discrimSim，discrimSS，samediff，AnotA，findcr
实例----------Examples----------
## Not run: [＃不运行：]
freq <- c(10,8,10,9,8,9,9,1,10,10,8,2,6,7,6,7,6,4,5,5,3,3,9,9,5,5,8,8,9,9)
tmp <- data.frame(id = factor(1:30), n = rep(10, 30), freq = freq)
head(tmp)
str(tmp)
fm <- discrimR(cbind(freq, n - freq) ~ 1, tmp, cluster = id,
start = c(.5, .5), method = "twoAFC",
ranef = TRUE, zi = TRUE, hess = TRUE,
control=list(trace=TRUE, REPORT=1))
names(fm)
fm[1:4]
## End(Not run)[＃（不执行）]
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 2012-9-30 01:07:15
