如何在一个循环中拟合多个交互模型？ 结果

问题描述

让我们说我有3个响应变量A，C和M，我想为所有可能的模型拟合模型，即拟合Y〜A，Y〜C，Y〜M，Y〜A * C，Y〜A * M，Y〜C * M等。是否有一种快速的方法来执行此操作，而无需每次都手动指定交互？

我不想写

M1 = glm(Y ~ A,data = subs,family = "poisson")
M2 = glm(Y ~ C,family = "poisson")
M3 = glm(Y ~ M,family = "poisson")
M4 = glm(Y ~ A*C,family = "poisson")
...

实际上，我有3个以上的变量，并且想要某种循环，这甚至有可能。 谢谢

解决方法

这应该有效：

glmulti::glmulti(
  Y = "Y",xr = c("A","C","M"),data = subs,filename = "my_results",family = "poisson"
) 

它将创建一个文本文件my_results.txt，其中包含有关每个模型的信息。

您也可以与其他软件包leaps，bestglm，甚至可能与其他软件包作为一个整体使用。

,

将所有RHS变量放在v扇区中，并使用combn获得一和二的组合（使用lapply）。我们从reformulate和paste获得的公式与*折叠。在combn之后，我们需要另一个lapply来遍历组合。

v <- c("X1","X2","X3")
res <- lapply(1:2,function(n) {
  .env <- environment()
  cb <- combn(c("X1","X3"),n,function(x) paste(x,collapse=" * "))
  lapply(cb,function(cb) lm(reformulate(cb,"y",env=.env),dat))
})

结果

res
# [[1]]
# [[1]][[1]]
# 
# Call:
#   lm(formula = reformulate(cb,env = .env),data = dat)
# 
# Coefficients:
#   (Intercept)           X1  
#       -0.3433       0.3382  
# 
# 
# [[1]][[2]]
# 
# Call:
#   lm(formula = reformulate(cb,data = dat)
# 
# Coefficients:
#   (Intercept)           X2  
#      0.008104     1.017076  
# 
# 
# [[1]][[3]]
# 
# Call:
#   lm(formula = reformulate(cb,data = dat)
# 
# Coefficients:
#   (Intercept)           X3  
#       0.02774      1.04382  
# 
# 
# 
# [[2]]
# [[2]][[1]]
# 
# Call:
#   lm(formula = reformulate(cb,data = dat)
# 
# Coefficients:
#   (Intercept)           X1           X2        X1:X2  
#        -0.577        1.408        1.157        0.296  
# 
# 
# [[2]][[2]]
# 
# Call:
#   lm(formula = reformulate(cb,data = dat)
# 
# Coefficients:
#   (Intercept)           X1           X3        X1:X3  
#        0.7378      -0.6141       1.3707      -1.1076  
# 
# 
# [[2]][[3]]
# 
# Call:
#   lm(formula = reformulate(cb,data = dat)
# 
# Coefficients:
#   (Intercept)           X2           X3        X2:X3  
#      0.257141     1.148571     1.290523    -0.009836  

---

数据：

dat <- structure(list(X1 = c(1.37095844714667,-0.564698171396089,0.363128411337339,0.63286260496104,0.404268323140999,-0.106124516091484,1.51152199743894,-0.0946590384130976,2.01842371387704,-0.062714099052421),X2 = c(1.30486965422349,2.28664539270111,-1.38886070111234,-0.278788766817371,-0.133321336393658,0.635950398070074,-0.284252921416072,-2.65645542090478,-2.44046692857552,1.32011334573019),X3 = c(-0.306638594078475,-1.78130843398,-0.171917355759621,1.2146746991726,1.89519346126497,-0.4304691316062,-0.25726938276893,-1.76316308519478,0.460097354831271,-0.639994875960119
),y = c(2.8246396305329,0.645476124553837,-0.162546123564699,0.959822161909057,2.67109557131028,-1.61765192870095,0.185540684874441,-5.36518513868917,-2.37615350981384,0.653526977609908)),row.names = c(NA,-10L),class = "data.frame")

,

这是一种功能编程方法。 创建数据后，只要您的Y是第一列，此代码就会使用所有其余变量（无论多少）并在其组合上构造模型。

最后，由于您已在此框架中完成此操作，因此可以调用扫帚的​​tidy和confint_tidy将结果提取到易于过滤的数据集中。

DF <- data_frame(Y = rpois(100,5),A = rnorm(100),C = rnorm(100),M = rnorm(100))

formula_frame <- bind_rows(data_frame(V1 = names(DF[,-1])),as_data_frame(t(combn(names(DF[,-1]),2)))) %>%
  rowwise() %>%
  mutate(formula_text = paste0("Y ~",if_else(is.na(V2),V1,paste(V1,V2,sep = "*"))),formula_obj = list(as.formula(formula_text))) %>%
  ungroup()

formula_frame %>% 
mutate(fits = map(formula_obj,~glm(.x,family = "poisson",data = DF) %>%
(function(X)bind_cols(broom::tidy(X),broom::confint_tidy((X)))))) %>%
 unnest(fits) %>%
 select(-formula_obj)

# A tibble: 18 x 10
   V1    V2    formula_text term        estimate std.error statistic   p.value conf.low conf.high
   <chr> <chr> <chr>        <chr>          <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
 1 A     NA    Y ~A         (Intercept)  1.63       0.0443    36.8   6.92e-297   1.54      1.72  
 2 A     NA    Y ~A         A            0.0268     0.0444     0.602 5.47e-  1  -0.0603    0.114 
 3 C     NA    Y ~C         (Intercept)  1.63       0.0443    36.8   5.52e-296   1.54      1.72  
 4 C     NA    Y ~C         C            0.0326     0.0466     0.699 4.84e-  1  -0.0587    0.124 
 5 M     NA    Y ~M         (Intercept)  1.63       0.0454    35.8   1.21e-280   1.53      1.71  
 6 M     NA    Y ~M         M           -0.0291     0.0460    -0.634 5.26e-  1  -0.119     0.0615
 7 A     C     Y ~A*C       (Intercept)  1.62       0.0446    36.4   5.64e-290   1.54      1.71  
 8 A     C     Y ~A*C       A            0.00814    0.0459     0.178 8.59e-  1  -0.0816    0.0982
 9 A     C     Y ~A*C       C            0.0410     0.0482     0.850 3.96e-  1  -0.0532    0.136 
10 A     C     Y ~A*C       A:C          0.0650     0.0474     1.37  1.70e-  1  -0.0270    0.158 
11 A     M     Y ~A*M       (Intercept)  1.62       0.0458    35.5   1.21e-275   1.53      1.71  
12 A     M     Y ~A*M       A            0.0232     0.0451     0.514 6.07e-  1  -0.0653    0.112 
13 A     M     Y ~A*M       M           -0.0260     0.0464    -0.561 5.75e-  1  -0.116     0.0655
14 A     M     Y ~A*M       A:M         -0.00498    0.0480    -0.104 9.17e-  1  -0.0992    0.0887
15 C     M     Y ~C*M       (Intercept)  1.60       0.0472    34.0   1.09e-253   1.51      1.70  
16 C     M     Y ~C*M       C            0.0702     0.0506     1.39  1.65e-  1  -0.0291    0.169 
17 C     M     Y ~C*M       M           -0.0333     0.0479    -0.695 4.87e-  1  -0.127     0.0611
18 C     M     Y ~C*M       C:M          0.0652     0.0377     1.73  8.39e-  2  -0.0102    0.138