firstpass(SAGx)
firstpass()所属R语言包：SAGx
First pass description of GeneChip data
基因芯片数据的第一道关口描述
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Does a first-pass analysis for a comparative experiment. This includes the calculation of means and confidence intervals for the groups, and finally
做了一个对比实验的第一通分析。这包括群体的手段和置信区间的计算，最后
用法----------Usage----------
firstpass(data = D, probes = probes , g, log = FALSE, present = NULL, labels = NULL, output.data = FALSE)
参数----------Arguments----------
参数：data
A data frame with one array in each column
一个数组中的每个列的数据框
参数：probes
a vector containing the names of the probes in the same order as rows in D
一个向量中含有探针的名称相同的顺序在D行
参数：g
A vector with the groups for the arrays, eg. TREATMENT and CONTROL
一个阵列组的向量，如。治疗和控制
参数：present
A dataframe with the Present calls, 3 = P, 2 = M, 1 = A.
一个与现状调用dataframe，3 = 2 = M 1 = A。
参数：log
if TRUE then data are log transformed through t(x) = log(1+x) and geometric means are calculated
如果TRUE，那么数据是通过T（X）转化的log记录（1 + x）和计算几何方法
参数：labels
a vector of labels given the group means
向量组的标签意味着
参数：output.data
if T the raw data are included in the output
如果T的原始数据都包含在输出
Details
详情----------Details----------
A speed-up for Wilcoxon based on Kronecker products was put in place with SAGx v.1.4.5. Ties are currently not taken into account in Wilcoxon.
基于Kronecker积的一个秩提速到位SAGx v.1.4.5。关系目前不考虑在秩。
值----------Value----------
A dataframe with the coumns PROBES, followed by group means and sd's, lower confidence intervals and then, upper confidence interval (confidence level 95%), and followed a Kruskal-Wallis p-value, and finally the input data,. If present names a dataframe holding the present calls the proportion present is calculated. Furthermore, if there are two groups the difference in group means is added.
一个dataframe与coumns组的手段和SD的，较低的置信区间，然后上置信区间（置信度95％），和探针，其次1克鲁斯卡尔 - 沃利斯p值，最后输入数据，。如果目前的名称目前持有1 dataframe要求的比例目前计算。此外，如果有两组组方式的差异增加。
举例----------Examples----------
## Not run: [＃无法运行：]
# not run[不运行]
g <- c(rep(1,4),rep(2,4)); labs <- c("Mean Diet","Mean Control"); probes <- paste("Probe",1:1000)
firstpass(data = utmat[1:2,], probes = probes[1:2], g, log = FALSE, labels = labs)
# Probesets Mean Diet Mean Control LCL.1 LCL.2 UCL.1 UCL.2 pval[probesets平均饮食平均控制LCL.1 LCL.2 UCL.1 UCL.2的pval]
#1 Probe 1 -12.3444460036497 -11.7495704973055 -12.9047961446666 -12.2832657957485 -11.7840958626327 -11.2158751988625 0.0433081428107922[1探针1 -12.3444460036497 -11.7495704973055 -12.9047961446666 -12.2832657957485 -11.7840958626327 -11.2158751988625 0.0433081428107922]
#2 Probe 2 -7.99773926405627 -8.02799133391929 -8.47704512876227 -8.19487551919835 -7.51843339935028 -7.86110714864023 0.772829992684449[2探针2 -7.99773926405627 -8.02799133391929 -8.47704512876227 -8.19487551919835 -7.51843339935028 -7.86110714864023 0.772829992684449]
# Difference Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Subject 7 Subject 8[区别主题1主题2主题3除4除5除6除7除8]
#1 -0.594875506344176 -12.345150 -11.805071 -12.776232 -12.451332 -11.595748 -12.320430 -11.482349 -11.599755[1 -0.594875506344176 -12.345150 -11.805071 -12.776232 -12.451332 -11.595748 -12.320430 -11.482349 -11.599755]
#2 0.0302520698630131 -7.660097 -8.157944 -8.404433 -7.768484 -7.979951 -8.017327 -8.197361 -7.917326[2 0.0302520698630131 -7.660097 -8.157944 -8.404433 -7.768484 -7.979951 -8.017327 -8.197361 -7.917326]
## End(Not run)[＃结束（不运行）]
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。
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发表于 2012-2-26 13:46:45
