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Evaluating the different parameters in the model

We are imputing our data for a reason - we want to analyze the data!

In this example, we are interested in predicting sea temperature, so we will build a linear model predicting sea temperature.

We will fit this model to each of the datasets we created and then explore the coefficients in the data.

The objects from the previous lesson (ocean_cc, ocean_imp_lm_wind, ocean_imp_lm_all, and bound_models) are loaded into the workspace.

Este exercício faz parte do curso

Dealing With Missing Data in R

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Instruções do exercício

  • Create the model summary for each dataset with columns for residuals using residuals, predict, and tidy.
  • Explore the coefficients in the model and put the model with the highest estimate for air_temp_c in the object best_model

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create the model summary for each dataset
model_summary <- bound_models %>% 
  group_by(imp_model) %>%
  nest() %>%
  mutate(mod = map(data, ~lm(sea_temp_c ~ air_temp_c + humidity + year, data = .)),
         res = map(mod, ___),
         pred = map(mod, ___),
         tidy = map(mod, ___))

# Explore the coefficients in the model
model_summary %>% 
	select(___,___) %>% 
	unnest()
best_model <- "___"
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