twoAC(sensR)
twoAC()所属R语言包：sensR
2-AC Discrimination Protocol
2-AC歧视协议
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes estimates and standard errors of d-prime and tau for the two alternative (2-AC) protocol. A confidence interval and significance test for d-prime is also provided. The 2-AC protocol is equivalent to a 2-AFC protocol with a "no-difference" option.
计算估计和标准错误的D-黄金和tau（2-AC）两种可供选择的协议。还提供了D-黄金置信区间和显着性检验。 2-AC协议是相当于一个2-AFC协议与一个“无差异”选项。
用法----------Usage----------
twoAC(data, d.prime0 = 0, conf.level = 0.95,
statistic = c("likelihood", "Wald"),
alternative = c("two.sided", "less", "greater"), ...)
参数----------Arguments----------
参数：data
a non-negative numeric vector of length 3 with the number of observations in the three response categories. If the third element is larger than the first element, then the estimate of d-prime will be positive.
的长度为3的三个回应组别中的观测值的数目与一个非负的数值向量。如果第三个元素是大于第一个元素，则d的素数的估计将是正的。
参数：d.prime0
the value of d-prime under the null hypothesis for the significance test.
D-黄金的显着性检验的零假设下的价值。
参数：conf.level
the confidence level.
的置信水平。
参数：statistic
the statistic to use for confidence level and significance test.
使用的统计置信水平和显着性检验。
参数：alternative
the type of alternative hypothesis.
的类型，备择假设。
参数：...
not currently used.
当前未使用。
Details
详细信息----------Details----------
confint, profile, logLik, vcov, and print methods are implemented for twoAC objects.
confint，profile，logLik，vcov和printtwoAC对象方法的实现。
Power computations for the 2-AC protocol is implemented in twoACpwr.
功率计算为2-AC协议实现在twoACpwr。
值----------Value----------
An object of class twoAC with elements <table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> 2 by 2 coefficient matrix with estimates and standard errors of d-prime and tau. If the variance-covariance matrix of the parameters is not defined, the standard errors are NA. </td></tr> <tr valign="top"><td>vcov</td> <td> variance-covariance matrix of the parameter estimates. Only present if defined for the supplied data. </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 maximum likelihood estimates. </td></tr> <tr valign="top"><td>alternative</td> <td> the name of the alternative hypothesis for the significance test.</td></tr> <tr valign="top"><td>statistic</td> <td> the name of the test statistic used for the significance test.</td></tr> <tr valign="top"><td>conf.level</td> <td> the confidence level for the confidence interval for d-prime.</td></tr> <tr valign="top"><td>d.prime0</td> <td> the value of d-prime under the null hypothesis in the significance test.</td></tr> <tr valign="top"><td>p.value</td> <td> p-value of the significance test.</td></tr> <tr valign="top"><td>confint</td> <td> two-sided condfidence interval for d-prime. This is only available if the standard errors are defined, which may happen in boundary cases. Use profile and confint methods to get confidence intervals instead; see the examples.</td></tr> </table>
一个对象的类twoAC的元素表summary="R valueblock"> <tr valign="top"> <TD> coefficients </ TD> <TD> 2的系数矩阵的估计D-黄金和tau和标准错误。如果没有被定义的参数的方差 - 协方差矩阵，标准误差是NA。 </ TD> </ TR> <tr valign="top"> <TD> vcov </ TD> <TD>方差 - 协方差矩阵的参数估计。如果提供的数据定义。 </ TD> </ TR> <tr valign="top"> <TD>data</ TD> <TD>提供的数据的功能。 </ TD> </ TR> <tr valign="top"> <TD>call</ TD> <TD>匹配的呼叫。 </ TD> </ TR> <tr valign="top"> <TD> logLik</ TD> <TD>最大似然估计的对数似然值。 </ TD> </ TR> <tr valign="top"> <TD>alternative</ TD> <TD>的名称替代假设的显着性检验。</ TD> </ TR> <tr valign="top"> <TD> statistic </ TD> <TD>检验统计量的显着性检验的名称。</ TD> </ TR> <TR VALIGN =“顶” > <TD> conf.level </ TD> <TD>的置信水平的置信区间为D-黄金。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>值的显着性检验的零假设下的D-黄金。</ TD> </ TR> <tr valign="top"> <TD>d.prime0</ TD> <TD>的显着性检验的p值</ TD> </ TR> <tr valign="top"> <TD>p.value </ TD> <TD>双面condfidence的时间间隔D-贷。如果这是唯一可用的标准误差的定义，可能发生的边界情况。使用confint和profile方法得到的置信区间，而不是看到的例子。</ TD> </ TR> </表>
（作者）----------Author(s)----------
Rune Haubo B Christensen
参考文献----------References----------
Christensen R.H.B., Lee H-S and Brockhoff P.B. (2011). Estimation of the Thurstonian model for the 2-AC protocol. Submitted to Food Quality and Preference.
参见----------See Also----------
clm2twoAC, twoACpwr
clm2twoAC，twoACpwr
实例----------Examples----------
## Simple:[＃简单：]
fit <- twoAC(c(2,2,6))
fit
## Typical discrimination-difference test: [＃典型的歧视差测试：]
(fit <- twoAC(data = c(2, 5, 8), d.prime0 = 0, alternative = "greater"))
## Typical discrimination-similarity test: [＃典型的歧视相似的测试：]
(fit <- twoAC(data = c(15, 15, 20), d.prime0 = .5, alternative = "less"))
## Typical preference-difference test:[＃典型的偏好差异的测试：]
(fit <- twoAC(data = c(3, 5, 12), d.prime0 = 0,
alternative = "two.sided"))
## Typical preference (non-)inferiority test:[＃典型偏好（非）劣性检验：]
(fit <- twoAC(data = c(3, 5, 12), d.prime0 = 0,
alternative = "greater"))
## For preference equivalence tests (two-sided) use CI with alpha/2:[＃偏好等价测试（双面）与α/ 2使用CI：]
## declare equivalence at the 5% level if 90% CI does not contain,[＃申报等价的，在5％的水平，如果不包含90％CI，]
## e.g, -1 or 1: [＃例如，-1或1：]
(fit <- twoAC(data = c(15, 10, 10), d.prime0 = 0, conf.level = .90))
## The var-cov matrix and standard errors of the parameters are not[＃VAR病毒的矩阵和参数的标准误差是不]
## defined in all situations. If standard errors are not[＃定义在所有情况下。如果标准错误是不]
## defined, then confidence intervals are not provided directly:[＃define定义，然后置信区间不提供直接：]
(fit <- twoAC(c(5, 0, 15)))
## We may use profile and confint methods to get confidence intervals[＃我们可以使用配置文件和confint方法得到的置信区间]
## never the less: [＃从来不：]
pr <- profile(fit, range = c(-1, 3))
confint(pr)
plot(pr)
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注：
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发表于 2012-9-30 01:08:51
