discrim(sensR)
discrim()所属R语言包：sensR
Sensory discrimination analysis
感官鉴别分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the probability of a correct answer (Pc), the probability of discrimination (Pd) and d-prime, their standard errors, confidence intervals and a p-value of a difference or similarity test for one of the four common discrimination protocols.
计算的概率（Pc）的一个正确的答案，歧视（Pd）的概率和d-素数，它们的标准误差，置信区间和的差异或相似性试验的四种常见歧视协议之一的p-值。
用法----------Usage----------
discrim(correct, total, pd0 = 0, conf.level = 0.95,
method = c("duotrio", "threeAFC", "twoAFC", "triangle"),
statistic = c("exact", "likelihood", "score", "Wald"),
test = c("difference", "similarity"), ...)
## S3 method for class 'discrim'
print(x, digits = getOption("digits"), ...)
参数----------Arguments----------
参数：correct
the number of correct answers; non-negativescalar integer
正确答案的数量;非negativescalar整数
参数：total
the total number of answers (the sample size); positive scalar integer
回答总次数（样本大小）;阳性标量整数
参数：pd0
the probability of discrimination under the null hypothesis; numerical scalar between zero and one
在零和1之间的数值标量歧视的零假设下的概率;
参数：conf.level
the confidence level for the confidence intervals
置信水平的置信区间
参数：method
the discrimination protocol. Four allowed values: "twoAFC", "threeAFC", "duotrio", "triangle"
协议的歧视。四个允许值：“twoAFC”，“threeAFC”，“duotrio”，“三角”
参数：test
the type of test
测试的类型
参数：statistic
the statistic to be used for hypothesis testing and confidence intervals
要用于假设检验和置信区间的统计
参数：x
an object of class "discrim"
一个对象的类"discrim"
参数：digits
number of digits in resulting table of results
导致的结果表中的数字位数
参数：...
not currently used
当前未使用
Details
详细信息----------Details----------
The p-value for the standard one-tailed difference test of "no difference" is obtained with pd0 = 0.
获得pd0 = 0p值的标准差异检验单尾“没有差别”。
The probability under the null hypothesis is given by pd0 + pg * (1 - pd0) where pg is the guessing probability which is defined by the discrimination protocol given in the method argument.
的零假设下的概率pd0 + pg * (1 - pd0)其中pg是猜测概率的定义给出的协议在method参数的歧视。
All estimates are restricted to their allowed ranges, e.g. Pc is always as least as large as the guessing probability. Similarly confidence limits are also restricted to the allowed range of the parameters.
所有的估计仅限于允许的范围内，例如PC总是猜测概率为至少一样大。同样的置信区间也仅限于允许的范围内的参数。
Standard errors are not defined when the parameter estimates are at the boundary of their allowed range, so these will be reported as NA in such cases.
标准差时定义的参数估计是在他们允许的范围内的边界，因此，这些将被报告为NA在这种情况下，。
The "Wald" statistic is *NOT* recommended for practical use—it is included here for completeness and to allow comparisons.
"Wald"统计数字是*不*建议在实际使用此处包含的完整性，并进行比较。
For statistic = "score", the confidence interval is computed from Wilson's score interval, while the p-value for the hypothesis test is based on Pearson's chi-square test, cf. prop.test.
对于statistic = "score"，置信区间的计算从威尔逊的评分区间，假设检验的p值是基于Pearson卡方检验，比照。 prop.test。
值----------Value----------
An object of class discrim with elements <table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> matrix of estimates, standard errors and confidence intervals</td></tr> </table> <table summary="R valueblock"> <tr valign="top"><td>data</td> <td> a named vector with the data supplied to the function</td></tr> <tr valign="top"><td>p.value</td> <td> the p-value of the hypothesis test</td></tr> <tr valign="top"><td>call</td> <td> the matched call</td></tr> <tr valign="top"><td>test</td> <td> the type of test</td></tr> <tr valign="top"><td>method</td> <td> the discrimination protocol</td></tr> <tr valign="top"><td>statistic</td> <td> the statistic used for confidence intervals and p-value</td></tr> <tr valign="top"><td>pd0</td> <td> the probability of discrimination under the null hypothesis</td></tr> <tr valign="top"><td>conf.level</td> <td> the confidence level</td></tr> <tr valign="top"><td>stat.value</td> <td> for statistic != "exact" the value of the test statistic used to calculate the p-value</td></tr> <tr valign="top"><td>df</td> <td> for statistic == "score" the number of degrees of freedom used for the Pearson chi-square test to calculate the p-value</td></tr> <tr valign="top"><td>profile</td> <td> for statistic == "likelihood" the profile likelihood on the scale of Pc</td></tr> </table>
一个对象的类discrim的元素的表summary="R valueblock"> <tr valign="top"> <TD> coefficients</ TD> <TD>矩阵的估计，标准误差和</ TD> </ TR> </ TABLE> summary="R valueblock"> <tr valign="top"> <TD> data </ TD> <td>一个名为矢量与置信区间所提供的数据的功能</ TD> </ TR> <tr valign="top"> <TD>p.value </ TD> <TD>的假设检验的p值</ TD> < / TR> <tr valign="top"> <TD>call </ TD> <TD>匹配的呼叫</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>的测试类型</ TD> </ TR> <tr valign="top"> <TD>test </ TD> <TD>的歧视，协议</ TD> </ TR> <tr valign="top"> <TD>method </ TD> <TD>使用的统计置信区间和p值</ TD> </ TR> <TR VALIGN =“”> <TD>statistic</ TD> <TD>歧视的零假设下的概率</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>的置信度</ TD> </ TR> <tr valign="top"> <TD>pd0 </ TD> <TD>conf.level检验统计量的值，用于计算p值</ TD> </ TR> <tr valign="top"> <TD> stat.value </ TD> <TD>statistic != "exact"数使用的自由度Pearson卡方检验计算p值</ TD> </ TR> <tr valign="top"> <TD>df</ TD> <TD> statistic == "score"的档案可能性的PC上规模</ TD> </ TR> </表>
（作者）----------Author(s)----------
Rune Haubo B Christensen and Per Bruun Brockhoff
参考文献----------References----------
models for sensory discrimination tests as generalized linear models.
参见----------See Also----------
triangle, twoAFC, threeAFC, duotrio, discrimPwr, discrimSim, discrimSS, samediff, AnotA, findcr, profile, plot.profile confint
triangle，twoAFC，threeAFC，duotrio，discrimPwr，discrimSim，discrimSS，samediff，<所述>，AnotA，findcr，profileplot.profile
实例----------Examples----------
## Running the simple discrimination (difference) tests:[＃运行简单的歧视（差别）的测试：]
discrim(10, 15, method = "twoAFC")
discrim(10, 15, method = "threeAFC", statistic = "likelihood")
discrim(10, 15, method = "duotrio", conf.level = 0.90)
discrim(10, 15, method = "triangle", statistic = "score")
## plot the distributions of sensory intensity:[＃图感觉强度的分布：]
m1 <- discrim(10, 15, method = "twoAFC")
plot(m1)
## A similarity test where less than chance successes are obtained:[＃A相似的测试，其中小于机会成功获得：]
discrim(20, 75, method = "triangle", pd0 = .2, test = "similarity")
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 01:07:03
