trend.stat(siggenes)
trend.stat()所属R语言包：siggenes
SAM Analysis of Linear Trend
SAM分析线性趋势
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generates the required statistics for a Significance Analysis of Microarrays for a linear trend in (ordinal) data.
生成意义的微阵列技术为线性趋势（序）数据分析所需的统计资料。
In the two-class case, the Cochran-Armitage trend statistic is computed. Otherwise, the statistic for the general test of trend described on page 87 of Agresti (2002) is determined.
在两个阶级的情况下，计算的的科克伦-Armitage趋势统计。否则，一般测试趋势的统计描述Agresti 87页（2002）确定。
Should not be called directly, but via sam(..., method = trend.stat).
不应该被称为直接，但通过SAM（...，方法= trend.stat）。
用法----------Usage----------
## Default S3 method:[默认方法]
trend.stat(data, cl, catt = TRUE, approx = TRUE, B = 100,
B.more = 0.1, B.max = 50000, n.subset = 10, rand = NA, ...)
## S3 method for class 'list'
trend.stat(data, cl, catt = TRUE, approx = TRUE, B = 100,
B.more = 0.1, B.max = 50000, n.subset = 10, rand = NA, ...)
参数----------Arguments----------
参数：data
either a numeric matrix or data frame, or a list. If a matrix or data frame, then each row must correspond to a variable (e.g., a SNP), and each column to a sample (i.e.\ an observation). The values in the matrix or data frame are interpreted as the scores for the different levels of the variables. If the number of observations is huge it is better to specify data as a list consisting of matrices, where each matrix represents one group and summarizes how many observations in this group show which level at which variable. The row and column names of all matrices must be identical and in the same order. The column names must be interpretable as numeric scores for the different levels of the variables. These matrices can, e.g., be generated using the function rowTables from the package scrime. (It is recommended to use this function, as trend.stat has been made for using the output of rowTables.) For details on how to specify this list, see the examples section on this man page, and the help for rowChisqMultiClass in the package scrime.
无论是数字矩阵或数据框，或一个列表。如果一个矩阵或数据框，然后每行必须对应一个变量（例如，一个SNP），每个样品的列（即\观察）。在矩阵或数据框的值被解释变量的不同水平的分数。如果观测的数量是巨大的，最好是指定data作为一个列表组成的矩阵，每个矩阵代表一组，并总结在这组节目多少观测水平的变量。所有矩阵的行和列名必须相同，并以相同的顺序。列名必须是解释变量的不同水平的数字分数。例如，可以生成这些矩阵可以使用的功能rowTables包scrime。 （建议使用此功能，trend.stat已使用的rowTables输出。）有关如何指定此列表的详细信息，请参阅本手册页上一节的例子，和帮助rowChisqMultiClass包scrime。
参数：cl
a numeric vector of length ncol(data) indicating to which classes the samples in the matrix or data frame data belongs. The values in cl must be interpretable as scores for the different classes. Must be specified if data is a matrix or a data frame, whereas cl can but must not be specified if data is a list. If specified in the latter case, cl must have length data, i.e.\ one score for each of the matrices, and thus for each of the groups. If not specified, cl will be set to the integers between 1 and c, where c is the number of classes/matrices.
数值向量的长度ncol(data)表示矩阵或数据框data属于类样品。 cl值必须为不同类别的分数解释。必须指定如果data是一个矩阵或一个数据框，而cl可以，但不能指定data如果是一个列表。如果指定在后一种情况下，cl必须有长度data，即\每个矩阵的一分，从而为每个组。如果没有指定，cl将被设置为整数1至c，其中c类/矩阵的数量。
参数：catt
should the Cochran-Armitage trend statistic be computed in the two-class case? If FALSE, the trend statistic described on page 87 of Agresti (2002) is determined which differs by the factor (n - 1) / n from the Cochran-Armitage trend statistic.
应在两个阶级的情况下计算的的科克伦-Armitage趋势统计？如果FALSE，Agresti 87页所述的趋势统计（2002）决定因素不同(n - 1) / n从的科克伦-Armitage趋势统计。
参数：approx
should the null distribution be approximated by the Chisquare-distribution with one degree of freedom? If FALSE, a permutation method is used to estimate the null distribution. If data is a list, approx must currently be TRUE.
空分布近似Chisquare分布与一个自由度？如果FALSE，置换的方法被用来估计空分布。如果data是一个列表，approx目前必须是TRUE。
参数：B
the number of permutations used in the estimation of the null distribution, and hence, in the computation of the expected d-values.
排列在空分布的估计使用人数，因此，在预期d值的计算。
参数：B.more
a numeric value. If the number of all possible permutations is smaller than or equal to (1+B.more)*B, full permutation will be done. Otherwise, B permutations are used.
一个数值。如果所有可能的排列数小于或等于（1 +B.more）*B，全置换将完成。否则，使用B排列。
参数：B.max
a numeric value. If the number of all possible permutations is smaller than or equal to B.max, B randomly selected permutations will be used in the computation of the null distribution. Otherwise, B random draws of the group labels are used.
一个数值。如果所有可能的排列数小于或等于B.max的，B随机选择的排列将在空分布的计算。否则，B随机组标签提请使用。
参数：n.subset
a numeric value indicating how many permutations are considered simultaneously when computing the expected d-values.
一个数值，表示多少排列计算预期d值时，同时考虑。
参数：rand
numeric value. If specified, i.e. not NA, the random number generator will be set into a reproducible state.
数值。如果指定，即不NA，随机数发生器将被设置成一个可重复的状态。
参数：...
ignored.
忽略。
值----------Value----------
A list containing statistics required by sam.
列表包含sam需要的统计数据。
作者（S）----------Author(s)----------
Holger Schwender, <a href="mailto:holger.schw@gmx.de">holger.schw@gmx.de</a>
参考文献----------References----------
applied to the ionizing radiation response. PNAS, 98, 5116-5121.
参见----------See Also----------
SAM-class,sam, chisq.stat, trend.ebam
SAM-class，sam，chisq.stat，trend.ebam
举例----------Examples----------
# Generate a random 1000 x 40 matrix consisting of the values[生成一个随机的1000×40的值组成的矩阵]
# 1, 2, and 3, and representing 1000 variables and 40 observations.[1，2，3，相当于1000个变量和40意见。]
mat <- matrix(sample(3, 40000, TRUE), 1000)
# Assume that the first 20 observations are cases, and the[假设前20个观测情况下，和]
# remaining 20 are controls, and that the values 1, 2, 3 in mat[其余20个是控件和值1，2，3垫]
# can be interpreted as scores for the different levels[可以理解为不同层次的分数]
# of the variables represented by the rows of mat.[垫行所代表的变量。]
cl <- rep(1:2, e=20)
# Then an SAM analysis of linear trend can be done by[然后可以通过SAM的线性趋势分析]
out <- sam(mat, cl, method=trend.stat)
out
# The same results can also be obtained by employing[也可以得到相同的结果，由用人]
# contingency tables, i.e. by specifying data as a list.[应急表，即由一个列表指定的数据。]
# For this, we need to generate the tables summarizing[对于这一点，我们需要生成的表总结]
# groupwise how many observations show which level at[GroupWise的观测表明多少级]
# which variable. These tables can be obtained by[哪些变量。这些表可以得到]
library(scrime)
cases <- rowTables(mat[, cl==1])
controls <- rowTables(mat[, cl==2])
ltabs <- list(cases, controls)
# And the same SAM analysis as above can then be [与上述相同的SAM分析，然后可以]
# performed by [执行由]
out2 <- sam(ltabs, method=trend.stat, approx=TRUE)
out2
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 2012-2-26 14:16:07
