Question

我可以看到这几乎已经完成，但我是R的新手，无法弄明白。差不多，我有一个回归循环（请不要批评数据挖掘），我需要从每个循环报告一些新的列表/数据框/最适合的东西。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])
        }
      }
    }
  }
}

所以，在'mod'每次运行之后，我只需要它抛出'Y'，'i'，'j'，'k'，'l'，'m'和调整后的。 R-Squared（我猜通过使用“summary（mod）$ adj.r.squared”）到一个可提取的表中。

很抱歉，如果这是r-illiterate，我是新手，并且刚刚获得了规定的代码，因此我的基本理解很少。

谢谢你的时间！

P.S。随意提出任何问题 - 我会尽力回答它们！

Answer 1

您问题的简短回答是

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)
        }
      }
    }
  }
}

将把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，然后每次在循环中运行回归模型时都会附加到该列表中。但是，在循环中增加这样的列表是非常糟糕的R实践。

首先将表单LHt[i] + LCv[j] + LKu[k] + LSk[l] + L??[m]的所有可能公式写入列表，然后使用lapply进行回归可能会更好......

首先使用expand.grid为数据框提供5列，每列包含每个类别中的一个变量名称

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，使用粘贴和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) )

每个回归的循环回归提取

1 个答案: