创建模型和扩充数据而不会丢失dplyr / broom中的其他列 [英] Creating models and augmenting data without losing additional columns in dplyr/broom
问题描述
请考虑以下数据/示例。每个数据集包含多个样本，这些样本具有一个观测值和一个估计值：
Consider the following data / example. Each dataset contains a number of samples with one observation and one estimate:
library(tidyverse)
library(broom)
data = read.table(text = '
dataset sample_id observation estimate
A A1 4.8 4.7
A A2 4.3 4.5
A A3 3.1 2.9
A A4 2.1 2
A A5 1.1 1
B B1 4.5 4.3
B B2 3.9 4.1
B B3 2.9 3
B B4 1.8 2
B B5 1 1.2
', header = TRUE)
我想计算线性每个数据集建模，以消除观测值和估计值之间的任何线性偏差，并让拟合值靠近原始值：
I want to calculate a linear model per dataset to remove any linear bias between observation and estimate, and get the fitted values next to the original ones:
data %>%
group_by(dataset) %>%
do(lm(observation ~ estimate, data = .) %>% augment)
但是，这样做是删除 sample_id 列，我需要保留该列以继续该数据集根据唯一ID进行的计算：
What this does, however, is remove the sample_id column, which I need to keep in order to continue calculations with this dataset based on that unique ID:
# A tibble: 10 x 10
# Groups: dataset [2]
dataset observation estimate .fitted .se.fit .resid .hat .sigma .cooksd .std.resid
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 A 4.80 4.70 4.68 0.107 0.115 0.478 0.152 0.491 1.04
2 A 4.30 4.50 4.49 0.0996 -0.193 0.416 0.0609 0.957 -1.64
3 A 3.10 2.90 2.97 0.0693 0.135 0.201 0.156 0.120 0.976
4 A 2.10 2.00 2.11 0.0849 -0.00583 0.303 0.189 0.000444 -0.0452
5 A 1.10 1.00 1.15 0.120 -0.0508 0.602 0.180 0.206 -0.521
6 B 4.50 4.30 4.31 0.109 0.191 0.468 0.0597 1.20 1.65
7 B 3.90 4.10 4.09 0.100 -0.193 0.396 0.0844 0.798 -1.56
8 B 2.90 3.00 2.91 0.0713 -0.00630 0.201 0.195 0.000247 -0.0443
9 B 1.80 2.00 1.83 0.0898 -0.0275 0.319 0.193 0.0103 -0.210
10 B 1.00 1.20 0.964 0.125 0.0355 0.616 0.191 0.104 0.360
我该如何保留我原始数据集中的其他列？
我见过这个答案之前使用 nest 折叠数据，但是我仍然只能使用这种方法来获取模型参数。我想我可以提取每个数据集的参数：
I have seen this answer which uses nest to collapse the data before, but I still only get the model parameters using this approach. I guess I could extract the parameters per dataset:
data %>%
group_by(dataset) %>%
nest() %>%
mutate(
mod = map(data, linear_adj_model),
pars = map(mod, tidy)
) %>%
unnest(pars) %>%
select(dataset, term, estimate) %>%
spread(term, estimate)
…这给了我：
# A tibble: 2 x 3
dataset `(Intercept)` estimate
* <fct> <dbl> <dbl>
1 A 0.196 0.955
2 B -0.330 1.08
...然后离开-将其与原始数据连接起来，然后每个变异每个估计来获得线性调整后的值，但这似乎太复杂了。
… and then left-join that with the original data, and then mutate each estimate to get the linearly-adjusted one, but that seems much too complicated.
我发现的另一个丑陋之处包括将列作为虚拟变量添加到模型中：
Another ugly hack I've found consists in adding the column as a dummy variable to the model:
data %>%
group_by(dataset) %>%
do(lm(observation ~ estimate + 0 * sample_id, data = .) %>% augment)
是否有一个更简单（整洁）的解决方案，无需手动指定我想保留的变量？
Is there an easier (tidy) solution that does not involve manually specifying the variables I want to keep?
推荐答案
您可以使用 broom :: augment_columns ，而不是增补。我们需要的函数的两个参数是 x -模型-和 data -原始
You could use broom::augment_columns instead of augment. The two arguments of the function we need are x -- "a model" -- and data -- "original data onto which columns should be added".
library(tidyverse)
library(broom)
split(data, data$dataset) %>%
map(., ~lm(formula = observation ~ estimate, data = .)) %>%
map2(.x = ., .y = split(data, f = data$dataset), .f = ~augment_columns(x = .x, data = .y)) %>%
bind_rows() %>%
select(-.rownames)
# dataset sample_id observation estimate .fitted .se.fit .resid .hat .sigma .cooksd .std.resid
#1 A A1 4.8 4.7 4.6845093 0.10675590 0.115490737 0.4781238 0.15157780 0.4911635931 1.03547990
#2 A A2 4.3 4.5 4.4934963 0.09956065 -0.193496255 0.4158455 0.06089193 0.9570799385 -1.63978525
#3 A A3 3.1 2.9 2.9653922 0.06929022 0.134607804 0.2014190 0.15623754 0.1200409795 0.97563873
#4 A A4 2.1 2.0 2.1058337 0.08491818 -0.005833662 0.3025227 0.18902495 0.0004439221 -0.04524332
#5 A A5 1.1 1.0 1.1507686 0.11979870 -0.050768624 0.6020891 0.18032220 0.2055920869 -0.52129162
#6 B B1 4.5 4.3 4.3087226 0.10879087 0.191277434 0.4679235 0.05965705 1.1954021471 1.64881395
#7 B B2 3.9 4.1 4.0929657 0.10006757 -0.192965672 0.3958920 0.08438937 0.7984863377 -1.56105324
#8 B B3 2.9 3.0 2.9063028 0.07128455 -0.006302757 0.2009004 0.19471901 0.0002470587 -0.04433279
#9 B B4 1.8 2.0 1.8275183 0.08983650 -0.027518289 0.3190771 0.19335019 0.0103015495 -0.20968503
#10 B B5 1.0 1.2 0.9644907 0.12484420 0.035509285 0.6162071 0.19051943 0.1042741368 0.36040302
这个想法是按照数据集 split 拟合模型到列表的每个组件，然后使用 map2 遍历模型和用于模型构建的（完整）数据，即 split的结果（数据，f = data $ dataset）。
The idea is to split the data by dataset, fit a model to each component of the list and then use map2 to iterate over the models and the (complete) data used for model building, i.e. the outcome of split(data, f = data$dataset) in parallel.
augment_columns 添加 .rownames 列，因此在最后一行中选择。
编辑
相同的解决方案，但希望更易于阅读。
The same solution but hopefully easier to read.
data_split <- split(data, data$dataset)
models <- map(data_split, ~lm(formula = observation ~ estimate, data = .))
map2(.x = models, .y = data_split, .f = ~augment_columns(x = .x, data = .y)) %>%
bind_rows() %>%
select(-.rownames)
第一个代码块具有以下功能四个参数： df ， split_var ， dependend_var 和 explanatory_var 。
augment_df <- function(df, split_var, dependend_var, explanatory_var) {
require(tidyverse)
require(broom)
split(df, df[split_var]) %>%
map(., ~lm(formula = as.formula(paste0(dependend_var, " ~ ", explanatory_var)), data = .)) %>%
map2(.x = ., .y = split(df, df[split_var]), .f = ~augment_columns(x = .x, data = .y)) %>%
bind_rows() %>%
select(-.rownames)
}
augment_df(df = data, split_var = "dataset", dependend_var = "observation", explanatory_var = "estimate")
这篇关于创建模型和扩充数据而不会丢失dplyr / broom中的其他列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
