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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.

Bu egzersiz, kursun bir parçasıdır

Dealing With Missing Data in R

Kursa Göz Atın

Egzersiz talimatları

  • 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

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# 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 <- "___"
Kodu Düzenle ve Çalıştır