1

我正在尝试在分组数据中进行线性拟合。

但是，我还想在整个拟合过程中添加一些条件，但是当我对某些条件进行子集化时没有这样做。

set.seed(183)
library(dplyr)
V <- rep(seq(1,8),3)
value = c(c(sort(runif(5,0.001,1)),rep(0,3)),c(sort(runif(5,0.001,1)),rep(0,2),runif(1,0.001,1)),c(sort(runif(5,0.001,1)),rep(0,2),runif(1,0.001,1)))
group=rep(letters[1:3],each=8)

df <- data.frame(group,V,value)

#    > df
#   group V      value
#1      a 1 0.15087459
#2      a 2 0.35408406
#3      a 3 0.47339320
#4      a 4 0.67614665
#5      a 5 0.98273932
#6      a 6 0.00000000
#7      a 7 0.00000000
#8      a 8 0.00000000
#9      b 1 0.32821476
#10     b 2 0.35737009
#11     b 3 0.58821689
#12     b 4 0.81088053
#13     b 5 0.99122633
#14     b 6 0.00000000
#15     b 7 0.00000000
#16     b 8 0.03697432
#17     c 1 0.12940226
#18     c 2 0.41918905
#19     c 3 0.66020739
#20     c 4 0.84124155
#21     c 5 0.95052213
#22     c 6 0.00000000
#23     c 7 0.00000000
#24     c 8 0.15071444

我在每个组内的条件是

1）如果所有最后 3 个value==0仅在V>=4&V<=5

2）如果最后 2value>0只适合V>=7.

这是我为执行此操作而编写的函数

   get_slope <- function(df){
  if (tail(df$value,3)==0)
    slp = coef(lm(value~V, data=subset(df,V>=4&V<=5)))[2]
    else 
    if (any(tail(df$value,3)>=0))
        slp = coef(lm(value ~ V, data=subset(df,V>=7)))[2]
    return(slp)
  }

df_slope <- df%>%
  group_by(group)%>%
  do(.,slope=get_slope(df))

Warning messages:
1: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used
2: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used
3: In if (tail(df$value, 3) == 0) slp = coef(lm(value ~ V, data = subset(df,  :
  the condition has length > 1 and only the first element will be used

最后我想得到每组的斜率值。

是否有捷径可寻？

提前谢谢了！

4

2 回答 2

1

该条件tail(df$value,3)==0将给出 3 个 T/F 值。我在下面的函数中交换它，并使用 split 和 plyr::ldply 而不是 dplyr。

df1<-split(df, df$group)
get_slope <- function(df){
  if (any(!tail(df$value,3)==0)) coef(lm(value ~ V, data=subset(df,V>=7)))[2]
  else  coef(lm(value~V, data=subset(df,V>=4&V<=5)))[2]
}
library(plyr)
ldply(df1, get_slope)

  .id          V
1   a 0.06940913
2   b 0.20794964
3   c 0.84607397

dplyr 方法按要求：

df_slope <- df %>% 
               group_by(group) %>% 
               do(.,slope=get_slope(.))

df_slope$slope <- df_slope$slope %>% unlist %>% as.numeric
df_slope

Source: local data frame [3 x 2]
Groups: <by row>

   group      slope
  (fctr)      (dbl)
1      a 0.06940913
2      b 0.20794964
3      c 0.84607397
于 2016-06-13T08:48:26.320 回答
1

我想到的是循环所有级别并应用该功能。

1.设置一个数组来接收所有结果

    slp = c()

2.循环组中的所有级别，如果条件满足则执行操作。 

for（组中唯一的（df$group））{
     if(sum(tail(df$value[which(df$group==group)],3) == c(0,0,0))==3){
       slp = c(slp,coef(lm(值~V, 数据=子集(df,V>=4&V<=5)))[2])
      }else if(any(tail(df$value[which(df$group==group)],2)>=0)){
   slp = c(slp, coef(lm(value ~ V, data=subset(df,V>=7)))[2])
     }}
3.打印结果 
slp
         美国广播公司
0.06448301 0.55057826 0.55057826

我的解决方案可能不像预期的那样容易，但应该很容易理解，我希望它会有所帮助。

于 2016-06-13T09:53:28.273 回答