Augment predictions from many models in the original dataset
1
I want to run many models with all possible combinations of x and ys. I created the following code to do that.
library(tidyverse)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars)))
Now, I want to get all the predictions from all these models in the original dataset, here mtcars. How can I do that? Is there a way to use augment from broom?
r purrr broom
asked Nov 16 '18 at 0:27
GeetGeet
5981719
add a comment |
1
I want to run many models with all possible combinations of x and ys. I created the following code to do that.
library(tidyverse)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars)))
Now, I want to get all the predictions from all these models in the original dataset, here mtcars. How can I do that? Is there a way to use augment from broom?
r purrr broom
asked Nov 16 '18 at 0:27
GeetGeet
5981719
add a comment |
1
1
1
I want to run many models with all possible combinations of x and ys. I created the following code to do that.
library(tidyverse)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars)))
Now, I want to get all the predictions from all these models in the original dataset, here mtcars. How can I do that? Is there a way to use augment from broom?
r purrr broom
asked Nov 16 '18 at 0:27
GeetGeet
5981719
I want to run many models with all possible combinations of x and ys. I created the following code to do that.
library(tidyverse)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars)))
Now, I want to get all the predictions from all these models in the original dataset, here mtcars. How can I do that? Is there a way to use augment from broom?
r purrr broom
r purrr broom
asked Nov 16 '18 at 0:27
GeetGeet
5981719
asked Nov 16 '18 at 0:27
GeetGeet
5981719
asked Nov 16 '18 at 0:27
GeetGeet
5981719
asked Nov 16 '18 at 0:27
GeetGeet
5981719
asked Nov 16 '18 at 0:27
GeetGeet
5981719
5981719
add a comment |
add a comment |
1 Answer
1
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oldest
votes
1
You can use map and augment similar to the way you fit glm to each row.
library(tidyverse)
library(broom)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars))) %>%
mutate(Pred = map(model, augment))
The prediction is in the .fitted column in each dataframe from the Pred list.
models2 <- models %>%
select(Formula, Pred) %>%
unnest() %>%
select(`.rownames`, names(mtcars), Formula, `.fitted`) %>%
spread(Formula, `.fitted`)
answered Nov 16 '18 at 0:39
wwwwww
28k112343
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Ismodel2what you want?
– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,.rownames, names(mtcars),.fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys,.fitted) Can you put this in your answer for the benefit of other users?
– Geet
Nov 16 '18 at 1:28
add a comment |
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1
You can use map and augment similar to the way you fit glm to each row.
library(tidyverse)
library(broom)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars))) %>%
mutate(Pred = map(model, augment))
The prediction is in the .fitted column in each dataframe from the Pred list.
models2 <- models %>%
select(Formula, Pred) %>%
unnest() %>%
select(`.rownames`, names(mtcars), Formula, `.fitted`) %>%
spread(Formula, `.fitted`)
answered Nov 16 '18 at 0:39
wwwwww
28k112343
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Ismodel2what you want?
– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,.rownames, names(mtcars),.fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys,.fitted) Can you put this in your answer for the benefit of other users?
– Geet
Nov 16 '18 at 1:28
add a comment |
1
You can use map and augment similar to the way you fit glm to each row.
library(tidyverse)
library(broom)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars))) %>%
mutate(Pred = map(model, augment))
The prediction is in the .fitted column in each dataframe from the Pred list.
models2 <- models %>%
select(Formula, Pred) %>%
unnest() %>%
select(`.rownames`, names(mtcars), Formula, `.fitted`) %>%
spread(Formula, `.fitted`)
answered Nov 16 '18 at 0:39
wwwwww
28k112343
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Ismodel2what you want?
– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,.rownames, names(mtcars),.fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys,.fitted) Can you put this in your answer for the benefit of other users?
– Geet
Nov 16 '18 at 1:28
add a comment |
1
1
1
You can use map and augment similar to the way you fit glm to each row.
library(tidyverse)
library(broom)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars))) %>%
mutate(Pred = map(model, augment))
The prediction is in the .fitted column in each dataframe from the Pred list.
models2 <- models %>%
select(Formula, Pred) %>%
unnest() %>%
select(`.rownames`, names(mtcars), Formula, `.fitted`) %>%
spread(Formula, `.fitted`)
answered Nov 16 '18 at 0:39
wwwwww
28k112343
You can use map and augment similar to the way you fit glm to each row.
library(tidyverse)
library(broom)
y <- names(mtcars)
xs <- map(y, ~setdiff(names(mtcars), .x)) %>%
map(~paste0(.x, collapse = "+")) %>%
unlist()
ys <- names(mtcars)
models <- tibble(ys, xs) %>%
mutate(Formula = paste0(ys, " ~ ", xs)) %>%
mutate(model = map(Formula, ~glm(as.formula(.x), data = mtcars))) %>%
mutate(Pred = map(model, augment))
The prediction is in the .fitted column in each dataframe from the Pred list.
models2 <- models %>%
select(Formula, Pred) %>%
unnest() %>%
select(`.rownames`, names(mtcars), Formula, `.fitted`) %>%
spread(Formula, `.fitted`)
answered Nov 16 '18 at 0:39
wwwwww
28k112343
edited Nov 16 '18 at 1:06
answered Nov 16 '18 at 0:39
wwwwww
28k112343
answered Nov 16 '18 at 0:39
wwwwww
28k112343
answered Nov 16 '18 at 0:39
wwwwww
28k112343
28k112343
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Ismodel2what you want?
– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,.rownames, names(mtcars),.fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys,.fitted) Can you put this in your answer for the benefit of other users?
– Geet
Nov 16 '18 at 1:28
add a comment |
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Ismodel2what you want?
– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,.rownames, names(mtcars),.fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys,.fitted) Can you put this in your answer for the benefit of other users?
– Geet
Nov 16 '18 at 1:28
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Ok, Thanks! How can I pull those predictions from the Pred list then so that every prediction stays in the main mtcars dataset?
– Geet
Nov 16 '18 at 0:52
Please see my update. Is
model2 what you want?– www
Nov 16 '18 at 0:58
Please see my update. Is
model2 what you want?– www
Nov 16 '18 at 0:58
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
Almost! I actually want the predictions in columns eg. mpg_pred, cyl_pred, ...carb_pred with 32 rows. I guess, tidyr::spread could be leveraged to do that?
– Geet
Nov 16 '18 at 1:02
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
You are right. Please see my update again.
– www
Nov 16 '18 at 1:07
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,
.rownames, names(mtcars), .fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys, .fitted) Can you put this in your answer for the benefit of other users?– Geet
Nov 16 '18 at 1:28
Wow...fantastic! To make the predicted variable names, I made one small change and used it: models2 <- models %>% select(ys, pred) %>% unnest() %>% select(ys,
.rownames, names(mtcars), .fitted) %>% mutate(ys = paste0(ys, "_pred")) %>% spread(ys, .fitted) Can you put this in your answer for the benefit of other users?– Geet
Nov 16 '18 at 1:28
add a comment |
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