SimCiDiff(SimComp)
SimCiDiff()所属R语言包：SimComp
Simultaneous Confidence Intervals for Differences of Means of Multiple Endpoints
同时置信区间为多个端点的手段差异
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Simultaneous confidence intervals for general contrasts (linear functions) of normal means (e.g., "Dunnett", "Tukey", "Williams" ect.) when there is more than one primary response variable (endpoint). The procedure of Hasler and Hothorn (2011) is applied for differences of means of normally distributed data. The covariance matrices (containing the covariances between the endpoints) may be assumed to be equal or possibly unequal for the different groups. For the case of only a single endpoint and unequal covariance matrices (variances), the procedure coincides with the PI procedure of Hasler and Hothorn (2008).
同时置信区间为一般对比（线性函数）的正常手段（例如，“邓尼特”，“杜克”，“威廉斯”等。），当有一个以上的主要因变量（终点）。应用程序的哈斯勒和Hothorn的（2011）正态分布的数据的差异。的协方差矩阵（包含端点之间的协方差），可以假定为等于或可能不相等，为不同的组。程序的情况下，只有一个单一的端点和不平等的协方差矩阵（方差），恰逢，与PI程序的哈斯勒和Hothorn的（2008）。
用法----------Usage----------
SimCiDiff(data, grp, resp = NULL, type = "Dunnett", base = 1, ContrastMat = NULL,
alternative = "two.sided", covar.equal = FALSE, conf.level = 0.95)
参数----------Arguments----------
参数：data
a data frame containing a grouping variable and the endpoints as columns
数据框包含一个分组变量和作为列的端点
参数：grp
a character string with the name of the grouping variable
的名称的字符串的分组变量
参数：resp
a vector of character strings with the names of the endpoints; if resp=NULL (default), all column names of the data frame without the grouping variable are chosen automatically
resp=NULL如果（默认），所有列名的分组变量的数据框没有选择自动的向量端点的名称的字符串;
参数：type
a character string, defining the type of contrast, with the following options:
一个字符串，定义的类型的对比度，用下列选项：
"Dunnett": many-to-one comparisons
“邓尼特”：一比较
"Tukey": all-pair comparisons
“杜克”：所有对比较
"Sequen": comparisons of consecutive groups
“顺序”：连续组比较
"AVE": comparison of each group with average of all others
“AVE”：各组比较平均的所有其他
"GrandMean": comparison of each group with grand mean of all groups
“GrandMean”：比较各组与盛大意味着所有群体
"Changepoint": differences of averages of groups of higher order to averages of groups of lower order
“Changepoint的”组低阶高阶组的平均值的平均差异
"Marcus": Marcus contrasts
“马库斯”：马库斯对比
"McDermott": McDermott contrasts
“麦克德莫特”：麦克德莫特对比
"Williams": Williams trend tests
“威廉姆斯：威廉姆斯趋势检验
"UmbrellaWilliams": Umbrella-protected Williams trend tests
“UmbrellaWilliams”：伞保护的威廉姆斯趋势的测试
note that type is ignored if ContrastMat is specified by the user (see below)
注意type被忽略，如果ContrastMat是由用户指定的（见下文）
参数：base
a single integer specifying the control group for Dunnett contrasts, ignored otherwise
指定一个整数邓尼特对比对照组，否则将被忽略
参数：ContrastMat
a contrast matrix, where columns correspond to groups and rows correspond to contrasts
一个对比矩阵，其中列对应的行组和对应的对比
参数：alternative
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less"
一个字符串，指定其他假设，必须有一个"two.sided"（默认），"greater"或"less"
参数：covar.equal
a logical variable indicating whether to treat the covariance matrices (containing the covariances between the endpoints) for the different groups as being equal; if TRUE then the pooled covariance matrix is used, otherwise the Satterthwaite approximation to the degrees of freedom is used according to Hasler and Hothorn (2008)
指示是否要治疗的协方差矩阵（包含端点之间的协方差）为不同的组为等于一个逻辑变量;若TRUE然后汇集的协方差矩阵被使用，否则萨特思韦特近似的自由度是使用根据（哈斯勒和Hothorn的）
参数：conf.level
a numeric value defining the simultaneous confidence level
一个数值定义的同时置信水平
Details
详细信息----------Details----------
The interest is in simultaneous confidence intervals for several linear combinations (contrasts) of treatment means in a one-way ANOVA model, and simultaneously for multiple endpoints. For example, corresponding intervals for the all-pair comparison of Tukey (1953) and the many-to-one comparison of Dunnett (1955) are implemented, but allowing for multiple endpoints. Also, the user is free to create other interesting problem-specific contrasts. An approximate multivariate t-distribution is used to calculate lower and upper limits (see Hasler and Hothorn, 2011). Simultaneous tests based on these intervals control the familywise error rate in an admissible range and in the strong sense. The covariance matrices of the treatment groups (containing the covariances between the endpoints) can be assumed to be equal (covar.equal=TRUE) or unequal (covar.equal=FALSE). If being equal, the pooled covariance matrix is used, otherwise Satterthwaite approximations to the degrees of freedom are used according to Hasler and Hothorn (2008). Unequal covariance matrices occure if variances or correlations of some endpoints differ depending on the treatment groups.
该权益是在同步置信区间为几个线性组合（反差）的治疗装置，在一个单向方差分析模型，并同时为多个端点。例如，相应的时间间隔以Tukey（1953）的所有成对比较Dunnett法（1955年）和许多到一个比较来实现，但允许有多个端点。此外，用户可以自由创建其他有趣的问题，具体的对比。一个近似的多元t分布来计算的上限和下限（见哈斯勒和Hothorn的，2011）。同时根据这些间隔的测试族群误差率控制在允许的范围内，在强烈的责任感。治疗组（包含端点之间的协方差）的协方差矩阵可以被假定为等于（covar.equal=TRUE）或不相等的（covar.equal=FALSE）。如果是平等的，汇集协方差矩阵使用，否则萨特思韦特近似的自由度，根据哈斯勒和Hothorn（2008）的使用。不平等的协方差矩阵occure如果不同而有所不同治疗组的差异或相关的某些端点。
值----------Value----------
An object of class SimCi containing: <table summary="R valueblock"> <tr valign="top"><td>estimate</td> <td> a matrix of estimated differences </td></tr> <tr valign="top"><td>lower.raw</td> <td> a matrix of raw (unadjusted) lower limits </td></tr> <tr valign="top"><td>upper.raw</td> <td> a matrix of raw (unadjusted) upper limits </td></tr> <tr valign="top"><td>lower</td> <td> a matrix of lower limits adjusted for multiplicity </td></tr> <tr valign="top"><td>upper</td> <td> a matrix of upper limits adjusted for multiplicity </td></tr> <tr valign="top"><td>CorrMatDat</td> <td> either the estimated common correlation matrix of the data (covar.equal=TRUE) or the list of the different (one for each treatment) estimated correlation matrices of the data (covar.equal=FALSE) </td></tr> <tr valign="top"><td>CorrMatComp</td> <td> the estimated correlation matrix to be used for the multivariate t-distribution </td></tr> <tr valign="top"><td>degr.fr</td> <td> either a single degree of freedom (covar.equal=TRUE) or a vector of degrees of freedom (covar.equal=FALSE) related to the comparisons </td></tr> </table>
对象的类SimCi的含表summary="R valueblock"> <tr valign="top"> <TD>estimate </ TD> <td>一个矩阵的估计差异</ TD> </ TR> <tr valign="top"> <TD> lower.raw </ TD> <td>一个矩阵的原料（未经调整）的下限</ TD> </ TR> <tr valign="top"> <TD> upper.raw </ TD> <td>一个矩阵的原料（未经调整）的上限</ TD> </ TR> <tr valign="top"> <TD>lower</ TD> <td>一个矩阵的下限调整为多重</ TD> </ TR> <tr valign="top"> <TD> upper</ TD> <td>一个矩阵的上限调整多重</ TD> </ TR> <tr valign="top"> <TD>CorrMatDat </ TD> <TD>估计常见的相关矩阵的数据（covar.equal=TRUE）或列表中的不同（每次治疗）估计的相关矩阵的数据（covar.equal=FALSE）</ TD> </ TR> <tr valign="top"> <TD>CorrMatComp</ TD> <TD>估计的相关矩阵用于多元t分布</ TD> </ TR> <tr valign="top"> <TD>degr.fr </ TD> <TD>无论是一个单自由度（covar.equal=TRUE）或向量的自由度（covar.equal=FALSE）相关的比较</ TD> </ TR> </表>
注意----------Note----------
All measurement objects of each treatment group must have values for each endpoint. If there are missing values then the procedure stops. If covar.equal=TRUE, then the number of endpoints must not be greater than the total sample size minus the number of treatment groups. If covar.equal=FALSE, the number of endpoints must not be greater than the minimal sample size minus 1. Otherwise the procedure stops.
各治疗组的所有测量对象必须为每个端点的值。如果有缺失值，则程序将停止。如果covar.equal=TRUE，然后端点的数量不得大于总样本数减去治疗组。如果covar.equal=FALSE，端点的数量不得大于最小的样本大小减去1。否则，程序将停止。
All the intervals have the same direction for all comparisons and endpoints (alternative="..."). In case of doubt, use "two.sided".
的时间间隔具有相同的方向，所有的比较和端点（alternative="..."）的。在有疑问的情况下，使用"two.sided"。
（作者）----------Author(s)----------
Mario Hasler
参考文献----------References----------
Hasler, M. and Hothorn, L.A. (2011): A Dunnett-type procedure for multiple endpoints. The International Journal of Biostatistics 7, Article 3.
Hasler, M. and Hothorn, L.A. (2008): Multiple contrast tests in the presence of heteroscedasticity. Biometrical Journal 50, 793-800.
参见----------See Also----------
SimCiRat, SimTestDiff,
SimCiRat，SimTestDiff，
实例----------Examples----------
# Example 1:[实施例1：]
# Simultaneous confidence intervals related to a Dunnett-test for the groups[同时置信区间的组到邓尼特测试]
# B and H against the standard S, on the (single) endpoint Thromb.count,[对标准的S，B和H（单）端点Thromb.count，]
# assuming unequal variances for the groups. These are the well-known[假设方差不齐的组。这些是公知的]
# Dunnett-intervals but in the presence of heteroscedasticity.[邓尼特区间，但存在异方差。]
data(coagulation)
interv1 <- SimCiDiff(data=coagulation, grp="Group", resp="Thromb.count", type="Dunnett",
base=3, alternative="greater", covar.equal=FALSE)
interv1
# Example 2:[实施例2：]
# Simultaneous confidence intervals related to a Dunnett-test for the groups[同时置信区间的组到邓尼特测试]
# B and H against the standard S, simultaneously on all endpoints, assuming[对标准的S，B和H同时在所有端点上，假设]
# unequal covariance matrices for the groups.[不平等的协方差矩阵的群体。]
data(coagulation)
interv2 <- SimCiDiff(data=coagulation, grp="Group", resp=c("Thromb.count","ADP","TRAP"), type="Dunnett",
base=3, alternative="greater", covar.equal=FALSE)
summary(interv2)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-30 02:33:26
