AnotA(sensR)
AnotA()所属R语言包：sensR
Analysis of A-not-A tests
A-A测试分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computation of dprime and it's uncertainty for the monadic A-not-A test together with the one-tailed P-value of the difference test (Fisher's Exact test).
计算的单子A--A测试的单尾P-值的差异测试（Fisher精确检验）dprime和它的不确定性。
用法----------Usage----------
AnotA(x1, n1, x2, n2, ...)
## S3 method for class 'anota'
confint(object, parm, level = 0.95, ...)
## S3 method for class 'anota'
plot(x, main = TRUE, length = 1000, ...)
参数----------Arguments----------
参数：x1
the number of (correct) A-answers on A-samples
答案A-A-样品的数量（正确）
参数：n1
the total number of A-samples
A-样品的总数
参数：x2
the number of A-answers on not-A-samples
A-答案-A-样品的数量
参数：n2
the number of not-A-samples
-A-样品的数量
参数：object
an anota object
anota对象
参数：parm
currently not used
目前未使用
参数：level
the desired confidence level
所需的置信水平
参数：x
an anota object
anota对象
参数：main
should the plot have a main title?
图应该有一个主标题？
参数：length
the discretization of the curves
曲线的离散化
参数：...
additional arguments passed to glm for AnotA; not used for confint and plot
额外的参数传递给glm为AnotA不使用confint和plot“
Details
详细信息----------Details----------
The AnotA function uses the glm and fisher.test functions of the stats package. Note that all arguments have to be positive integers.
AnotA功能使用glm和fisher.test功能的stats包。请注意，所有的参数都为正整数。
值----------Value----------
For AnotA an object of class anota (which has a print method). This is a list with elements <table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> named vector of coefficients (d-prime)</td></tr> <tr valign="top"><td>res.glm</td> <td> the glm-object from the fitting process</td></tr> <tr valign="top"><td>vcov</td> <td> variance-covariance matrix of the coefficients</td></tr> <tr valign="top"><td>se</td> <td> named vector with standard error of the coefficients (standard error of d-prime</td></tr> <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> one-sided p-value from Fisher's exact test (fisher.test)</td></tr> <tr valign="top"><td>test</td> <td> a string with the name of the test (A-Not A) for the print method</td></tr> <tr valign="top"><td>call</td> <td> the matched call</td></tr>
对于AnotA类的一个对象anota（其中有一个print方法）。这是一个列表的元素<table summary="R valueblock"> <tr valign="top"> <TD> coefficients</ TD> <TD>命名的系数向量（D素数）</ TD > </ TR> <tr valign="top"> <TD>res.glm </ TD> <TD>装修过程的的GLM对象从</ TD> </ TR> <TR VALIGN =“顶部“<TD> vcov </ TD> <TD>方差 - 协方差矩阵的系数</ TD> </ TR> <tr valign="top"> <TD>se</ <TD>名为系数（D-黄金标准误差标准误差向量与TD> </ TD> </ TR> <tr valign="top"> <TD>data </ TD> <TD一个名为矢量提供的数据的功能</ TD> </ TR> <tr valign="top"> <TD>p.value </ TD> <TD>片面的P-值从费舍尔的精确检验（fisher.test）</ TD> </ TR> <tr valign="top"> <TD> test</ TD> <td>一个字符串的名称测试（ A-Not A）的打印方法</ TD> </ TR> <tr valign="top"> <TD>call</ TD> <TD>匹配的呼叫</ TD> </ TR >
</table> For plot a figure of the distributions of sensory intensity is produced, and for confint a 2-by-2 matrix of confidence intervals is returned.
</ TABLE> plot感觉强度的分布图制作，confint一个2×2矩阵的置信区间，则返回。
（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----------
print.discrim, discrim, discrimPwr, discrimSim,
print.discrim，discrim，discrimPwr，discrimSim，
实例----------Examples----------
# data: 10 of the A-samples were judged to be A[数据：10的A-样品被判定为甲]
# 20 A-samples in total[20 A-样品中总]
# 3 of the not-A samples were judged to be A[3不的样品被判定为甲]
# 20 not-A-samples in total[20-A-样品中总]
AnotA(10, 20, 3, 20)
(m1 <- AnotA(10, 20, 3, 20))
## plot distributions of sensory intensity:[＃图的感觉强度分布：]
plot(m1)
## likelihood based confidence intervals:[似然置信区间：]
confint(m1)
## Extended example plotting the profile likelihood[＃扩展的例子策划的档案可能性]
xt <- cbind(c(3, 10), c(20 - 3, 20 - 10))
lev <- gl(2, 1)
summary(res <- glm(xt ~ lev,
family = binomial(link = probit)))
N <- 100
dev <- double(N)
level <- c(0.95, 0.99)
delta <- seq(1e-4, 5, length = N)
for(i in 1:N)
dev[i] <- glm(xt ~ 1 + offset(c(0, delta[i])),
family = binomial(probit))$deviance
plot(delta, exp(-dev/2), type = "l",
xlab = expression(delta),
ylab = "Normalized Profile Likelihood")
## Add Normal approximation:[＃添加师范大学近似：]
lines(delta, exp(-(delta - coef(res)[2])^2 /
(2 * vcov(res)[2,2])), lty = 2)
## Add confidence limits:[＃添加的置信区间：]
lim <- sapply(level, function(x)
exp(-qchisq(x, df=1)/2) )
abline(h = lim, col = "grey")
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。
|
发表于 2012-9-30 01:06:26
