I am trying to perform the following task: take a dataset, and feed it through multiple pre-designed models so that I can see how the predictions differ based on the different models. The following is what the data generally looks like:
data_1:
Year day station1 station2 station3 hour minute avtemp
1 2020 1 1.124 1.018 0.852 00 30 0.998
2 2020 1 1.123 1.020 0.848 01 0 0.997
3 2020 1 1.119 1.013 0.842 01 30 0.991
4 2020 1 1.124 1.016 0.845 02 0 0.995
5 2020 1 1.124 1.016 0.842 02 30 0.994
6 2020 1 1.124 1.017 0.840 03 0 0.994
Then, models were generated using a separate dataset that is structured very similarly (except for the fact that they are divided by "stand", the experimental unit for which I need to have a model for each, hence the multiple models), using the following code:
models_temp <- data_2 %>%
group_by(stand) %>%
do(modeltt = lm(projectedtemp ~ avtemp, data = .)) %>%
ungroup()
As you can see, the independent variable in the model matches a column in data_1, so hypothetically it should read cleanly. This code then generates a dataset with two columns: one with stand, and one with the model for each stand, with a lot of data stored in a list() format for each model, as is shown here:
stand model
trees list(coefficients = c(`(Intercept)` = 0.66135577718....)
shrubs list(coefficients = c(`(Intercept)` = 0.6468382809102...)
I tried to then use various versions of add_predictions, such as below, to use these models in a list to generate predictions from the dataset:
data_3 <- spread_predictions(data = data_1, models = models_temp)
Alas, I get the following error:
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "list"
I searched StackOverflow and couldn't find specific examples of people trying to do this, at least not without having to dramatically restructure their models. Does anyone know of maybe a better function to make what I see as a relatively simple task work, a better way to structure my models/data, or a simple fix to this error I am getting? Thank you all so much in advance.
All libraries loaded are as follows, and I believe most of this stuff relies on the "modelr" package:
library(dplyr)
library(ggplot2)
library(tidyverse)
library(gridExtra)
We can loop over the list with map and apply the function
library(purrr)
map(models_temp$modeltt, ~ spread_predictions(data = data_1, models = .x))