T.fit(maSigPro)
T.fit()所属R语言包：maSigPro
Makes a stepwise regression fit for time series gene expression experiments
使得时间序列的基因表达实验的逐步回归拟合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
T.fit selects the best regression model for each gene using stepwise regression.
T.fit选择最佳回归模型为每个使用逐步回归的基因。
用法----------Usage----------
T.fit(data, design = data$dis, step.method = "backward",
min.obs = data$min.obs, alfa = data$Q, nvar.correction = FALSE)
参数----------Arguments----------
参数：data
can either be a p.vector object or a matrix containing expression data with the same requirements as for the p.vector function
可以是p.vector对象或矩阵含有p.vector函数表达同样的要求，数据
参数：design
design matrix for the regression fit such as that generated by the make.design.matrix function. If data is a p.vector object, the same design matrix is used by default
设计矩阵的回归适应如make.design.matrix函数产生的。如果数据是一个p.vector对象，默认情况下使用相同的设计矩阵
参数：step.method
argument to be passed to the step function. Can be either "backward", "forward", "two.ways.backward" or "two.ways.forward"
要传递给阶跃函数的参数。可以要么"backward"，"forward"，"two.ways.backward"或"two.ways.forward"
参数：min.obs
genes with less than this number of true numerical values will be excluded from the analysis
与真正的数值比这少的基因将被排除在分析
参数：alfa
significance level used for variable selection in the stepwise regression
显着性水平在逐步回归变量选择
参数：nvar.correction
argument for correcting T.fit significance level. See details
参数纠正T.fit意义水平。查看详情
Details
详情----------Details----------
In the maSigPro approach p.vector and T.fit are subsequent steps, meaning that significant genes are first selected on the basis of a general model and then the significant variables for each gene are found by step-wise regression.
在maSigPro方法p.vector和T.fit是后续步骤，这意味着显着的基因是第一个选定的一般模型的基础上，然后逐步回归发现每个基因的重大变数。
The step regression can be "backward" or "forward" indicating whether the step procedure starts from the model with all or none variables. With the "two.ways.backward" or "two.ways.forward" options the variables are both allowed to get in and out. At each step the p-value of each variable is computed and variables get in/out the model when this p-value is lower or higher than given threshold alfa. When nva.correction is TRUE the given significance level is corrected by the number of variables in the model
逐步回归可以是"backward"或"forward"指示是否所有或无变量模型的步骤程序，从开始的。与"two.ways.backward"或"two.ways.forward"变量都允许和选项。每个变量的p值在每一步计算和变量/模型得到时，P-值低于或高于给定的阈值阿尔法。为TRUE nva.correction时给定的显着性水平纠正模型中的变量的数目由
值----------Value----------
参数：sol
matrix for summary results of the stepwise regression. For each selected gene the following values are given:
矩阵的逐步回归结果摘要。对于每个选定的基因有下列值：
p-value of the regression ANOVA
P-值的回归方差分析
R-squared of the model
R平方模型
p-value of the regression coefficients of the selected variables
所选变量的回归系数的P-值
参数：sig.profiles
expression values for the genes contained in sol
为sol中包含的基因表达值
参数：coefficients
matrix containing regression coefficients for the adjusted models
调整模型回归系数矩阵包含
参数：groups.coeffs
matrix containing the coefficients of the impiclit models of each experimental group
包含各实验组的impiclit模型系数矩阵
参数：variables
variables in the complete regression model
完整的回归模型中的变量
参数：G
total number of input genes
输入基因总数
参数：g
number of genes taken in the regression fit
数量的基因在回归拟合
参数：dat
input analysis data matrix
输入分析数据矩阵
参数：dis
regression design matrix
回归设计矩阵
参数：step.method
imputed step method for stepwise regression
估算步骤逐步回归方法
参数：edesign
matrix of experimental design
实验设计矩阵
参数：influ.info
data frame of genes containing influencial data
含有期影响数据的基因数据框
作者（S）----------Author(s)----------
Ana Conesa, <a href="mailto:aconesa@ivia.es">aconesa@ivia.es</a>; Maria Jose Nueda,
<a href="mailto:mj.nueda@ua.es">mj.nueda@ua.es</a>
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102
参见----------See Also----------
p.vector, step
p.vector，step
举例----------Examples----------
#### GENERATE TIME COURSE DATA[＃＃＃生成时间的课程资料]
## generate n random gene expression profiles of a data set with [＃生成N个随机设置数据基因表达谱]
## one control plus 3 treatments, 3 time points and r replicates per time point.[＃一个控制加3个疗程，3个时间点和r每时间点复制。]
tc.GENE <- function(n, r,
var11 = 0.01, var12 = 0.01,var13 = 0.01,
var21 = 0.01, var22 = 0.01, var23 =0.01,
var31 = 0.01, var32 = 0.01, var33 = 0.01,
var41 = 0.01, var42 = 0.01, var43 = 0.01,
a1 = 0, a2 = 0, a3 = 0, a4 = 0,
b1 = 0, b2 = 0, b3 = 0, b4 = 0,
c1 = 0, c2 = 0, c3 = 0, c4 = 0)
{
tc.dat <- NULL
for (i in 1:n) {
Ctl <- c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13)) # Ctl group[CTL组]
Tr1 <- c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23)) # Tr1 group[TR1组]
Tr2 <- c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33)) # Tr2 group[TR2组]
Tr3 <- c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43)) # Tr3 group[TR3组]
gene <- c(Ctl, Tr1, Tr2, Tr3)
tc.dat <- rbind(tc.dat, gene)
}
tc.dat
}
## Create 270 flat profiles[＃创建270平剖面]
flat <- tc.GENE(n = 270, r = 3)
## Create 10 genes with profile differences between Ctl and Tr1 groups[＃创建10个基因与CTL和TR1组之间的轮廓差异]
twodiff <- tc.GENE (n = 10, r = 3, b2 = 0.5, c2 = 1.3)
## Create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups[＃创建10个基因与CTL，TR2，TR3组之间的轮廓差异]
threediff <- tc.GENE(n = 10, r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
## Create 10 genes with profile differences between Ctl and Tr2 and different variance[＃创建10个基因与CTL和TR2和不同方差之间的轮廓差异]
vardiff <- tc.GENE(n = 10, r = 3, a3 = 0.7, b3 = 1, c3 = 1.2, var32 = 0.03, var33 = 0.03)
## Create dataset[＃创建数据集]
tc.DATA <- rbind(flat, twodiff, threediff, vardiff)
rownames(tc.DATA) <- paste("feature", c(1:300), sep = "")
colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")
tc.DATA [sample(c(1 300*36)), 300)] <- NA # introduce missing values[引进缺失值]
#### CREATE EXPERIMENTAL DESIGN[＃＃＃创建一个实验设计]
Time <- rep(c(rep(c(1:3), each = 3)), 4)
Replicates <- rep(c(1:12), each = 3)
Control <- c(rep(1, 9), rep(0, 27))
Treat1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
Treat2 <- c(rep(0, 18), rep(1, 9), rep(0,9))
Treat3 <- c(rep(0, 27), rep(1, 9))
edesign <- cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
rownames(edesign) <- paste("Array", c(1:36), sep = "")
## run T.fit from a p.vector object[＃运行从p.vector对象T.fit]
tc.p <- p.vector(tc.DATA, design = make.design.matrix(edesign), Q = 0.01)
tc.tstep <- T.fit(data = tc.p , alfa = 0.05)
## run T.fit from a data matrix and a design matrix[＃运行T.fit的数据矩阵和设计矩阵]
dise <- make.design.matrix(edesign)
tc.tstep <- T.fit (data = tc.DATA[271:300,], design = dise$dis,
step.method = "two.ways.backward", min.obs = 10, alfa = 0.05)
tc.tstep$sol # gives the p.values of the significant [给出显著p.values的]
# regression coefficients of the optimized models[优化模型的回归系数]
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 00:25:25
