R 拆分因子、sapply和lm

我想将lm（）应用于按主题分组的观察，但无法计算出sapply语法。最后，我想要一个数据帧，每个主题有一行，截距和斜率（即：sub，lm$coverties[1]lm$coverties[2]的行）

---

听上去，这可能就是你想要做的：

sapply(s, FUN= function(x)
  lm(x[ ,"dep"] ~ x[,"ind"])$coefficients[c(1, 2)])
#                      a          b          c
# (Intercept) 0.71379430 -0.6817331  0.5717372
# x[, "ind"]  0.07125591  1.1452096 -1.0303726

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其他选择，如果这是你想要的 我注意到，一般来说，如果您正在拆分，然后使用

s/lappy

，您通常可以直接跳到

by

并跳过

split

步骤：

do.call(rbind, 
        by(data = df, INDICES=df$subj, FUN=function(x) 
          lm(x[, "dep"] ~ x[, "ind"])$coefficients[c(1, 2)]))
#   (Intercept)  x[, "ind"]
# a   0.7137943  0.07125591
# b  -0.6817331  1.14520962
# c   0.5717372 -1.03037257

或者，您可以使用其中一个软件包更方便地进行此类计算，如“data.table”：

库（data.table）
DT那么
nlme:：lmList
呢

library(nlme)

coef(lmList(dep~ind|subj,df))
##   (Intercept)         ind
## a   0.7137943  0.07125591
## b  -0.6817331  1.14520962
## c   0.5717372 -1.03037257

如果需要，您可以将其转置。
+1以提供可复制的示例。提供示例输出会使问题变得更好。非常好。我还不太喜欢“do.call（rbind，…）”电话；我注意到它经常出现。
head(a)
                                                            a
coefficients                             0.1233519, 0.4610505
residuals        0.4471916, -0.3060402, 0.4460895, -0.5872409
effects          -0.6325478, 0.6332422, 0.5343949, -0.7429069
rank                                                        2
fitted.values 0.74977179, 0.09854505, -0.05843569, 0.47521446
assign                                                   0, 1
                                                             b
coefficients                              1.1220840, 0.2024222
residuals     -0.04461432, 0.02124541, 0.27103003, -0.24766112
effects           -2.0717363, 0.2228309, 0.2902311, -0.2302195
rank                                                         2
fitted.values       1.1012775, 0.8433366, 1.1100777, 1.0887808
assign                                                    0, 1
                                                         c
coefficients                          0.2982019, 0.1900459
residuals     -0.5606330, 1.0491990, 0.3908486, -0.8794147
effects       -0.6742600, 0.2271767, 1.1273566, -1.0345665
rank                                                     2
fitted.values   0.3718773, 0.2193339, 0.5072572, 0.2500516
assign                                                0, 1

sapply(s, FUN= function(x)
  lm(x[ ,"dep"] ~ x[,"ind"])$coefficients[c(1, 2)])
#                      a          b          c
# (Intercept) 0.71379430 -0.6817331  0.5717372
# x[, "ind"]  0.07125591  1.1452096 -1.0303726

do.call(rbind, 
        by(data = df, INDICES=df$subj, FUN=function(x) 
          lm(x[, "dep"] ~ x[, "ind"])$coefficients[c(1, 2)]))
#   (Intercept)  x[, "ind"]
# a   0.7137943  0.07125591
# b  -0.6817331  1.14520962
# c   0.5717372 -1.03037257

library(data.table)
DT <- data.table(df)
DT[, list(Int = lm(dep ~ ind)$coefficients[1],
          Slo = lm(dep ~ ind)$coefficients[2]), by = subj]
#    subj        Int         Slo
# 1:    a  0.7137943  0.07125591
# 2:    b -0.6817331  1.14520962
# 3:    c  0.5717372 -1.03037257

library(nlme)

coef(lmList(dep~ind|subj,df))
##   (Intercept)         ind
## a   0.7137943  0.07125591
## b  -0.6817331  1.14520962
## c   0.5717372 -1.03037257