stepfor(maSigPro)
stepfor()所属R语言包：maSigPro
Fitting a linear model by forward-stepwise regression
向前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----------
stepfor fits a linear regression model applying forward-stepwise strategy.
stepfor适合线性回归模型，采用逐步前进的战略。
用法----------Usage----------
stepfor(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 all the possible models with only one of the variables included in d. The most statistically significant variable (with the lowest p-value) is included in the model and then it is considered to introduce in the model another variable analysing all the possible models with two variables (the selected variable in the previous step plus a new variable). Again the most statistically significant variable (with lowest p-value) is included in the model. The process is repeated till there are no more statistically significant variables to include.
该战略开始分析所有可能的模型只包括在d的变量之一。最统计学意义的变量（与最低的p值）是包含在模型中，然后它被认为在模型中引入另一个变量分析所有可能的模式有两个变量（在上一步中选择的变量加一个新的变量）。再次包含在模型的最统计学意义的变量（p值最低）。这个过程反复进行，直到有没有更多的统计学意义的变量包括。
值----------Value----------
stepfor 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.
stepfor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, stepback, two.ways.stepback, two.ways.stepfor
lm，step，stepback，two.ways.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 <- stepfor(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 00:25:08
