如何在一个循环中拟合多个交互模型? [英] How to fit multiple interaction models in a loop?

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?

Lets say i have 3 response variables A,C and M and i want to fit a model for all possible models ie fit Y ~ A, Y ~ C, Y ~ M, Y ~ A * C, Y ~ A * M, Y ~ C * M, etc. Is there a quick way to do this without manually specifiying the interactions each time?

我不想写

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

In reality i have more than 3 variables and would like some sort of loop, is this even possible. Thanks

推荐答案

这是一种功能编程方法.创建数据后，只要 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.

Here is a sort of functional programming approach. You create your data, and as long as your Y is the first column, this code would take all the rest of the variables (no matter how many) and construct models on their combinations.

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.

Finally, since you've done it in this framework, you can call broom's tidy and confint_tidy to extract the results into an easy to filter dataset.

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 

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