我正在为可以分组的数据(grp1
和grp2
(测试不同的线性回归模型。
library(rcompanion)
library(dplyr)
library(tidyr)
#some sample data
sampleDF <- data.frame(grp1=append(rep("foo",6),rep("bar",6)),
grp2=append(rep("A",3),rep("B",3)),
x=seq(3)-rnorm(3),
y=seq(3)*3+1)
#some models
model1 <- y ~ x
model2 <- y ~ x - 1
#do the fitting
sampleDF %>%
group_by(grp1, grp2) %>%
do(model1 = lm(model1, data = .),
model2 = lm(model2, data = .)) %>%
ungroup() -> fittedModels
这产生:
fittedModels
# A tibble: 4 x 4
grp1 grp2 model1 model2
* <fct> <fct> <list> <list>
1 bar A <lm> <lm>
2 bar B <lm> <lm>
3 foo A <lm> <lm>
4 foo B <lm> <lm>
使用broom
,模型可以很好地收集到数据帧中,如:
fittedModels %>% rowwise() %>% broom::tidy(model2)
# A tibble: 4 x 7
# Groups: grp1, grp2 [4]
grp1 grp2 term estimate std.error statistic p.value
<fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 bar A x 3.43 0.124 27.7 0.00130
2 bar B x 3.43 0.124 27.7 0.00130
3 foo A x 3.43 0.124 27.7 0.00130
4 foo B x 3.43 0.124 27.7 0.00130
我想用不同的方法来比较这些模型。库rcompanion
提供了方法compareLM()
和pairwiseModelAnova()
。
尝试:
fittedModels %>% summarise(compareLM(model1, model2))
> Error in formula.default(fits[[i]]) : invalid formula
rowwise(fittedModels) %>% summarise(compareLM(model1, model2))
> Error: Column `compareLM(model1, model2)` must be length 1 (a summary value), not 2
仔细检查发现:
fittedModels$model1[[1]]
Call:
lm(formula = y ~ x, data = .)
Coefficients:
(Intercept) x
0 3
m.1 <- fittedModels$model1[1]
m.2 <- fittedModels$model2[1]
compareLM(m.1,m.2)
> Error in formula.default(fits[[i]]) : invalid formula
哪一个可能指向管道问题?!
有办法解决这个问题吗?
我在您的数据中获得了一个错误,但当将随机数添加到sampleDF$x
时,它可以工作。
library(rcompanion)
library(dplyr)
library(tidyr)
library(broom)
library(purrr)
#some sample data
set.seed(1)
sampleDF <- data.frame(grp1=append(rep("foo",6),rep("bar",6)),
grp2=append(rep("A",3),rep("B",3)),
x=seq(3) - rnorm(3),
y=seq(3)*3+1)
#some models
model1 <- y ~ x
model2 <- y ~ x - 1
# Use purrr
sampleDF %>%
group_by(grp1, grp2) %>%
nest() %>%
mutate(model1 = purrr::map(data, ~lm(model1, data = .))) %>%
mutate(model2 = purrr::map(data, ~lm(model2, data = .))) %>%
mutate(tidy_model1 = purrr::map(model1, broom::tidy)) %>%
mutate(tidy_model2 = purrr::map(model2, broom::tidy)) %>%
mutate(compare_m1m2 = purrr::map2(model1, model2, ~compareLM(.x, .y)))
> # A tibble: 4 x 8
> # Groups: grp1, grp2 [4]
> grp1 grp2 data model1 model2 tidy_model1 tidy_model2 > compare_m1m2
> <chr> <chr> <list> <list> <list> <list> <list> > <list>
> 1 foo A <tibble [3 × 2]> <lm> <lm> <tibble [2 × 5]> <tibble [1 × 5]> <named list [2]>
> 2 foo B <tibble [3 × 2]> <lm> <lm> <tibble [2 × 5]> <tibble [1 × 5]> <named list [2]>
> 3 bar A <tibble [3 × 2]> <lm> <lm> <tibble [2 × 5]> <tibble [1 × 5]> <named list [2]>
> 4 bar B <tibble [3 × 2]> <lm> <lm> <tibble [2 × 5]> <tibble [1 × 5]> <named list [2]>