two.ways.stepback(maSigPro)
two.ways.stepback()所属R语言包：maSigPro
Fitting a linear model by backward-stepwise regression
向后逐步回归拟合线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
two.ways.stepback fits a linear regression model applying backward-stepwise strategy.
two.ways.stepback适合线性回归模型，采用向后逐步战略。
用法----------Usage----------
two.ways.stepback(y = y, d = d, alfa = 0.05)
参数----------Arguments----------
参数：y
dependent variable
因变量
参数：d
data frame containing by columns the set of variables that could be in the selected model
帧数据列包含的变量，可能是在选定的模型集
参数：alfa
significance level to decide if a variable stays or not in the model
显着性水平，以决定是否变量住宿或模型
Details
详情----------Details----------
The strategy begins analysing a model with all the variables included in d. If all the variables are statistically significant (all the variables have a p-value less than alfa) this model will be the result. If not, the less statistically significant variable will be removed and the model is re-calculated. The process is repeated up to find a model with all the variables statistically significant (p-value < alpha). Each time that a variable is removed from the model, it is considered the possibility of one or more removed variables to come in again.
战略分析模型的所有变量包含在d开始。如果所有的变量统计学意义（所有的变量比阿尔法p值），这种模式将成为结果。如果没有，少统计学意义的变量将被删除，并重新计算模型。重复这个过程找到的所有变量统计学意义（p值<α）模型。每一个变量从模型中删除的时候，它被认为是删除一个或多个变量来再次的可能性。
值----------Value----------
two.ways.stepback returns an object of the class lm, where the model uses y as dependent variable and all the selected variables from d as independent variables.
two.ways.stepback返回一个类的对象lm，模型使用y作为因变量和d作为独立变量选定的所有变量。
The function summary are used to obtain a summary and analysis of variance table of the results. The generic accessor functions coefficients, effects, fitted.values and residuals extract various useful features of the value returned by lm.
函数summary用于获取了总结和方差分析结果表。通用存取功能coefficients，effects，fitted.values和residuals由lm返回的值中提取各种有用的功能。
作者（S）----------Author(s)----------
Ana Conesa, aconesa@ivia.es; Maria Jose Nueda, mj.nueda@ua.es
参考文献----------References----------
maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.
参见----------See Also----------
lm, step, stepfor, stepback, two.ways.stepfor
lm，step，stepfor，stepback，two.ways.stepfor
举例----------Examples----------
## create design matrix[＃创建一个设计矩阵]
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 = "")
dise <- make.design.matrix(edesign)
dis <- as.data.frame(dise$dis)
## expression vector[＃表达向量]
y <- c(0.082, 0.021, 0.010, 0.113, 0.013, 0.077, 0.068, 0.042, -0.056, -0.232, -0.014, -0.040,
-0.055, 0.150, -0.027, 0.064, -0.108, -0.220, 0.275, -0.130, 0.130, 1.018, 1.005, 0.931,
-1.009, -1.101, -1.014, -0.045, -0.110, -0.128, -0.643, -0.785, -1.077, -1.187, -1.249, -1.463)
s.fit <- two.ways.stepback(y = y, d = dis)
summary(s.fit)
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 00:25:35
