trend.ebam(siggenes)
trend.ebam()所属R语言包：siggenes
EBAM Analysis of Linear Trend
EBAM线性趋势分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Generates the required statistics for an Empirical Bayes Analysis of Microarrays for a linear trend in (ordinal) data.
经验Bayes分析微阵列技术为线性趋势（序）数据生成所需的统计资料。
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 ebam(..., method = trend.ebam).
不应直接调用，而是通过ebam（...，方法= trend.ebam）。
用法----------Usage----------
## Default S3 method:[默认方法]
trend.ebam(data, cl, catt = TRUE, approx = TRUE, n.interval = NULL,
df.dens = NULL, knots.mode = NULL, type.nclass = "wand",
B = 100, B.more = 0.1, B.max = 50000, n.subset = 10,
fast = FALSE, df.ratio = 3, rand = NA, ...)
## S3 method for class 'list'
trend.ebam(data, cl, catt = TRUE, approx = TRUE, n.interval = NULL,
df.dens = NULL, knots.mode = NULL, type.nclass = "wand", ...)
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：n.interval
the number of intervals used in the logistic regression with repeated observations for estimating the ratio f0/f (if approx = FALSE), or in the Poisson regression used to estimate the density of the observed z-values (if approx = TRUE). If NULL, n.interval is set to 139 if approx = FALSE, and estimated by the method specified by type.nclass if approx = TRUE.
用反复观察，在logistic回归估计比f0/f（如果approx = FALSE），或在使用泊松回归估计密度的观测z值的时间间隔的数目（如果approx = TRUE）。如果NULL，n.interval设置为139，如果approx = FALSE，type.nclass如果approx = TRUE指定的方法估计。
参数：df.dens
integer specifying the degrees of freedom of the natural cubic spline used in the Poisson regression to estimate the density of the observed z-values. Ignored if approx = FALSE. If NULL, df.dens is set to 3 if the degrees of freedom of the appromimated null distribution, i.e.\ the ChiSquare-distribution, are less than or equal to 2, and otherwise df.dens is set to 5.
整数，指定使用泊松回归估计观测到的z值的密度自然三次样条的自由程度。如果approx = FALSE忽略。如果NULL，df.dens设置为3，如果空分布的appromimated自由的程度，即\分布，ChiSquare小于或等于2，否则df.dens设置为5。
参数：knots.mode
if TRUE the df.dens - 1 knots are centered around the mode and not the median of the density when fitting the Poisson regression model. Ignored if approx = FALSE. If not specified, knots.mode is set to TRUE if the degrees of freedom of the approximated null distribution, i.e.\ tht ChiSquare-distribution, are larger than or equal to 3, and otherwise knots.mode is set to FALSE. For details on this density estimation, see denspr.
如果TRUEdf.dens - 1节围绕模式，而不是密度的中位数的泊松回归模型拟合。如果approx = FALSE忽略。如果没有指定，knots.mode设置为TRUE如果近似空分布的自由程度，即\，THTChiSquare分布，大于或等于3，否则 knots.mode设置FALSE。这个密度估计的详细信息，请参阅denspr。
参数：type.nclass
character string specifying the procedure used to compute the number of cells of the histogram. Ignored if approx = FALSE or n.interval is specified. Can be either "wand" (default), "scott", or "FD". For details, see denspr.
字符串指定的程序，用来计算直方图的单元数量。如果approx = FALSE或n.interval指定忽略。可以是"wand"（默认），"scott"或"FD"。有关详细信息，请参阅denspr。
参数：B
the number of permutations used in the estimation of the null distribution, and hence, in the computation of the expected z-values.
排列在空分布的估计使用人数，因此，在预期z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 in how many subsets the B permutations are divided when computing the permuted z-values. Please note that the meaning of n.subset differs between the SAM and the EBAM functions.
B排列分为许多子集时计算置换z值表示的数值。请注意，n.subset意义之间的SAM和EBAM的功能不同。
参数：fast
if FALSE the exact number of permuted test scores that are more extreme than a particular observed test score is computed for each of the variables/SNPs. If TRUE, a crude estimate of this number is used.
如果FALSE置换的测试成绩是超过一个特定的观察测试得分的极端的确切数目计算每个变量/单核苷酸多态性。如果TRUE，用于粗略估计这个数字。
参数：df.ratio
integer specifying the degrees of freedom of the natural cubic spline used in the logistic regression with repeated observations. Ignored if approx = TRUE.
整数，指定立方米用于在反复观察的logistic回归自然样条的自由程度。如果approx = TRUE忽略。
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 ebam.
列表包含ebam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----------
NJ. 2nd Edition.
Empirical Bayes Analysis of a Microarray Experiment, JASA, 96, 1151-1160.
参见----------See Also----------
EBAM-class,ebam, trend.stat, chisq.ebam
EBAM-class，ebam，trend.stat，chisq.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[其余20控件和值1，2，3]
# mat can be interpreted as scores for the different levels[可以解释为不同层次的分数垫]
# of the variables.[变量。]
cl <- rep(1:2, e=20)
# Then an EBAM analysis of linear trend can be done by[然后可以通过一个线性趋势EBAM的分析]
out <- ebam(mat, cl, method=trend.ebam)
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 EBAM analysis as above can then be [然后可以和上面的的相同EBAM分析]
# performed by [执行由]
out2 <- ebam(ltabs, method=trend.ebam)
out2
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注：
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发表于 2012-2-26 14:15:57
