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我可以看到这已经几乎完成了，但我是 R 的新手，无法弄清楚。几乎，我有一个回归循环（请不要批评数据挖掘），我需要将每个循环中的一些内容报告到一个新的列表/数据框/最合适的内容中。这是我的代码：

#Required packages
require(lattice)
require(plyr)


ACDn <- "ACDn.csv"
x <- as.matrix(read.csv(ACDn, colClasses = "numeric"))

#To find which columns are which, in order to split up the overall dataset.
which( colnames(X)=="H95.H30" )
which( colnames(X)=="H99" )

#Here i split all the data into their different types, i.e. LHt = Lidar Heights. Please ignore
#those that are unpopulated, as i am waiting on data to run.

Yall <- x[,c(59:79)]                            #All "True Variables" - BA, MTH, etc.
Y <- Yall[,10]                                  #Specifies which columnn is the Y variable, BA = 10,
                                                #TopHt = 11, SPH = 12, Vol_live = 13, RecovVol = 14

X <- x[,c(1:58,80:95)]                          #All Lidar metrics and combinations.
LHt <- X[,c(28:41,59:74)]
LCv <- X[,c()]
LKu <- X[,c()]
LSk <- X[,c()]
L?? <- X[,c()]

#Create List file. I 

Optmod1 <- 

#Loop Creation, need dataset sizes. The ?? are caused by not knowing the exact sizes 
#of the relative datasets yet. Somewhere in here i would like the an entry for EACH model to be
#appended to a data.frame (or list, whatever is most appropriate), which would state the variables
# i.e. 'y', 'i', 'j', 'k', 'l', 'm', and the Adj. R-squared value (which i guess can be extracted
# through using 'summary(mod)$adj.r.squared). 

For(i in 1:30) {
  For(j in 1:??) {
    For(k in 1:??) {
      For(l in 1:??){
        For(m in 1:??){
          mod <- lm(Y ~ LHt[i] + LCv[j] + LKu[k] + LSk[l] + L??[m])
        }
      }
    }
  }
}

差不多了，每次运行“mod”之后，我只需要它抛出“Y”、“i”、“j”、“k”、“l”、“m”和 Adjusted.R-Squared （我猜是通过使用“summary(mod)$adj.r.squared”）到一个可提取的表格中。

抱歉，如果其中任何一个是不识字的，我对此很陌生，并且之前刚刚获得过规定的代码，因此我的基本理解是稀疏的。

谢谢你的时间！

PS随意提出任何问题 - 我会努力回答他们！

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1 回答 1

1

您的问题的简短答案是

Answers = list()
For(i in 1:30) {
  For(j in 1:??) {
    For(k in 1:??) {
      For(l in 1:??){
        For(m in 1:??){
          mod <- lm(Y ~ LHt[i] + LCv[j] + LKu[k] + LSk[l] + L??[m])
          Answers[[length(Answers)+1]] = list(i,j,k,l,m,summary(mod)$adj.r.squared)
        }
      }
    }
  }
}

它将您想要的信息存储在列表中。它的工作原理是创建一个空白列表，然后在每次循环运行回归模型时附加到该列表。但是，在循环中增长这样的列表是非常糟糕的 R 实践。

您最好先将所有可能的形式公式写入LHt[i] + LCv[j] + LKu[k] + LSk[l] + L??[m]列表，然后使用 lapply 进行回归......

首先用于expand.grid给出一个包含 5 列的数据框，每列包含来自每个类别的一个变量名称

LHT_names = lapply(1:30,function(i) paste("LHt[",i,"]",sep="")) #a list of names of LHT type variables for use in formula
LCv_names = lapply(1:?,function(i) paste("LCv[",i,"]",sep="")) #similar for LCv
LKu_names = ...
LSk_names = ...
L??_names = ...

temp = expand.grid(c(LHt_names, LCv_names, LKu_names, LSk_names, L??_names))

然后，使用 paste 和 lapply 获取公式列表：

list_of_formulas = lapply(seq_along(nrow(temp)), function(i) paste("Y~",paste(temp[i,],collapse="+"),sep = ""))

然后，使用 lapply 获取回归模型列表

list_of_models = lapply(list_of_formulas, function(x)  lm(x) )
于 2013-03-26T06:14:32.260 回答