Predict values from model stored in a column
Question
I have a large dataset (subset below) where I need to fit models to each subset of data (grouped by several factors: Round, Level, Temp), and then plot both the model fits and confidence intervals. The predict function works fine when the model is stored to a variable, rather than to a column.
I can nest the data based on the factors and fit each model, which is then stored to a column. But I can't quite figure out how to then take that model (stored in the column) and call it in the predict function.
This code throws:
Error: Problem with
mutate()columnpredicted. ipredicted = predict(model, data.frame(dose = Day)). x no applicable method for 'predict' applied to an object of class "list" Runrlang::last_error()to see
library(tidyverse)
library(drc)
b<- structure(list(Round = c("1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a", "1a",
"1a", "1a", "1a", "1a", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b", "1b",
"1b", "1b", "1b", "1b", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4"),
Temp...3 = c(10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10), Level = structure(c(1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("low",
"med", "high"), class = "factor"), PerMort = c(0, 0, 0, 0,
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9, 9, 9, 9, 9, 9, 9, 9, 9, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,
15, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 21, 21,
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 24, 24, 24,
24, 24, 24, 24, 24, 24, 24, 27, 27, 27, 27, 27, 27, 27, 27,
27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 15, 15, 15, 15, 15, 15,
15, 15, 15, 15, 15, 15, 17, 17, 17, 17, 17, 17, 17, 17, 17,
17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18,
21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24, 24,
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4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 16, 16,
16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17,
17, 17, 17, 17, 17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19,
19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,
20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 24, 24,
24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 26, 26, 26, 26, 26,
26, 26, 26, 26, 26, 26, 26, 28, 28, 28, 28, 28, 28, 28, 28,
28, 28, 28, 28)), row.names = c(NA, -480L), class = c("tbl_df",
"tbl", "data.frame"))
c<- b %>% filter(!is.na(Round)) %>%
dplyr::select(Round, Temp...3, Level, PerMort, Day) %>%
nest(dataset = c(-Round, -Temp...3, -Level) ) %>%
mutate(model = map(dataset, ~drc::drm(formula = .x$PerMort~.x$Day, fct = LL.3()) )) %>%
unnest(dataset) %>%
mutate(predicted = predict(model, data.frame(dose = Day )),
confidencedown = predict(model, data.frame(dose = Day ), interval ="confidence", level = 0.95)[,2],
confidenceup = predict(model, data.frame(dose = Day ), interval ="confidence", level = 0.95)[,3])
1 answers
solution1
2 ACCPTED 2022-03-03 22:34:42
You can use the {broom} package, the tidy() function. With the argument conf.int = TRUE returns by default the 95% confidence interval. Check the function help if you want to change the conf.level to another value
c<- b %>% filter(!is.na(Round)) %>%
dplyr::select(Round, Temp...3, Level, PerMort, Day) %>%
nest(dataset = c(-Round, -Temp...3, -Level) ) %>%
mutate(model = map(dataset, ~drc::drm(formula = .x$PerMort~.x$Day, fct = LL.3()) ),
results = map(model,~ broom::tidy(.x,conf.int = TRUE))) %>%
unnest(results)
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