变异一列模型：“错误：‘Mutate ()’not `model`. x not`model‘的问题必须是向量，而不是’be‘对象。”
变异一列模型：“错误：‘Mutate ()’not `model`. x not`model‘的问题必须是向量，而不是’be‘对象。”
提问于 2020-09-02 01:23:04
我有一个dataframe，它包含一个模型公式定义作为列。我想突变一个新列，其中每一行都是基于相应的行模型定义的模型。
一些数据：
# Set up
library(tidyverse)
library(lubridate)
# Create data
mydf <- data.frame(
cohort = seq(ymd('2019-01-01'), ymd('2019-12-31'), by = '1 days'),
n = rnorm(365, 1000, 50) %>% round,
cohort_cost = rnorm(365, 800, 50)
) %>%
crossing(tenure_days = 0:365) %>%
mutate(activity_date = cohort + days(tenure_days)) %>%
mutate(daily_revenue = rnorm(nrow(.), 20, 1)) %>%
group_by(cohort) %>%
arrange(activity_date) %>%
mutate(cumulative_revenue = cumsum(daily_revenue)) %>%
arrange(cohort, activity_date) %>%
mutate(payback_velocity = round(cumulative_revenue / cohort_cost, 2)) %>%
select(cohort, n, cohort_cost, activity_date, tenure_days, everything())
## wider data
mydf_wide <- mydf %>%
select(cohort, n, cohort_cost, tenure_days, payback_velocity) %>%
group_by(cohort, n, cohort_cost) %>%
pivot_wider(names_from = tenure_days, values_from = payback_velocity, names_prefix = 'velocity_day_')现在，最后一个问题代码块。它在最后一行失败了：
models <- data.frame(
from = mydf$tenure_days %>% unique,
to = mydf$tenure_days %>% unique
) %>%
expand.grid %>%
filter(to > from) %>%
filter(from > 0) %>%
arrange(from) %>%
mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>%
mutate(model = lm(as.formula(mod_formula), data = mydf_wide))错误：
mutate()输入model的问题。X输入model必须是向量，而不是lm对象。输入model为lm(as.formula(mod_formula), data = mydf_wide)。
如果我运行最后一个代码块减去最后一行，并查看生成的数据框架“模型”，它如下所示：
models %>% head
from to mod_formula
1 1 2 velocity_day_2 ~ velocity_day_1
2 1 3 velocity_day_3 ~ velocity_day_1
3 1 4 velocity_day_4 ~ velocity_day_1
4 1 5 velocity_day_5 ~ velocity_day_1
5 1 6 velocity_day_6 ~ velocity_day_1
6 1 7 velocity_day_7 ~ velocity_day_1我试着把它作为一个列表列，但为了做到这一点，我意识到我需要分组。但在这种情况下，我需要按每件事进行分组。我修改了最后一个代码块：
models <- data.frame(
from = mydf$tenure_days %>% unique,
to = mydf$tenure_days %>% unique
) %>%
expand.grid %>%
filter(to > from) %>%
filter(from > 0) %>%
arrange(from) %>%
mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>%
group_by_all() %>%
nest() %>%
mutate(model = lm(as.formula(mod_formula), data = mydf_wide))但是，这会导致相同的错误。
我如何在“模型”中添加一个新列，其中包含基于字段‘mod_式’中的公式的每一行的线性模型？
回答 1
Stack Overflow用户
回答已采纳
发布于 2020-09-02 01:29:25
lm不是矢量化的。添加rowwise为每一行创建一个模型。
library(dplyr)
models <- data.frame(
from = mydf$tenure_days %>% unique,
to = mydf$tenure_days %>% unique
) %>%
expand.grid %>%
filter(to > from) %>%
filter(from > 0) %>%
arrange(from) %>%
mutate(mod_formula = paste0('velocity_day_', to, ' ~ velocity_day_', from)) %>%
rowwise() %>%
mutate(model = list(lm(as.formula(mod_formula), data = mydf_wide)))
models
# from to mod_formula model
# <int> <int> <chr> <list>
#1 1 2 velocity_day_2 ~ velocity_day_1 <lm>
#2 1 3 velocity_day_3 ~ velocity_day_1 <lm>
#3 1 4 velocity_day_4 ~ velocity_day_1 <lm>
#4 1 5 velocity_day_5 ~ velocity_day_1 <lm>
#5 1 6 velocity_day_6 ~ velocity_day_1 <lm>
#6 1 7 velocity_day_7 ~ velocity_day_1 <lm>
#...
#...您还可以使用map而不是rowwise。
mutate(model = purrr::map(mod_formula, ~lm(.x, data = mydf_wide))) 页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/63697426
复制相关文章
相似问题
腾讯云开发者
