Making predictions

Introduction to Regression in R

Richie Cotton

Data Evangelist at DataCamp

The fish dataset: bream

bream <- fish %>% 
  filter(species == "Bream")

species	length_cm	mass_g
Bream	23.2	242
Bream	24.0	290
Bream	23.9	340
Bream	26.3	363
Bream	26.5	430
...	...	...

Plotting mass vs. length

ggplot(bream, aes(length_cm, mass_g)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)

A scatter plot of bream masses versus their lengths, with a linear trend line. The points all lie close to the trend line.

Running the model

mdl_mass_vs_length <- lm(mass_g ~ length_cm, data = bream)

Call:
lm(formula = mass_g ~ length_cm, data = bream)

Coefficients:
(Intercept)    length_cm  
   -1035.35        54.55

Data on explanatory values to predict

If I set the explanatory variables to these values,
what value would the response variable have?

library(dplyr)
explanatory_data <- tibble(length_cm = 20:40)

Call predict()

library(tibble)
explanatory_data <- tibble(length_cm = 20:40)

predict(mdl_mass_vs_length, explanatory_data)

         1          2          3          4          5          6 
  55.65205  110.20203  164.75202  219.30200  273.85198  328.40196 
         7          8          9         10         11         12 
 382.95194  437.50192  492.05190  546.60188  601.15186  655.70184 
        13         14         15         16         17         18 
 710.25182  764.80181  819.35179  873.90177  928.45175  983.00173 
        19         20         21 
1037.55171 1092.10169 1146.65167

Predicting inside a data frame

library(dplyr)
explanatory_data <- tibble(length_cm = 20:40)

prediction_data <- explanatory_data %>% 
  mutate(
    mass_g = predict(
      mdl_mass_vs_length, explanatory_data
    )
  )

# A tibble: 21 x 2
   length_cm mass_g
       <int>  <dbl>
 1        20   55.7
 2        21  110. 
 3        22  165. 
 4        23  219. 
 5        24  274. 
 6        25  328. 
 7        26  383. 
 8        27  438. 
 9        28  492. 
10        29  547. 
# ... with 11 more rows

Showing predictions

ggplot(bream, aes(length_cm, mass_g)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE) +
  geom_point(
    data = prediction_data, 
    color = "blue"
  )

The scatter plot of bream masses versus their lengths, with a linear trend line. the plot has been annotated with the points calculated using predict(). These points all follow the trend line exactly.

Extrapolating

Extrapolating means making predictions outside the range of observed data.

explanatory_little_bream <- tibble(length_cm = 10)
explanatory_little_bream %>% 
  mutate(
    mass_g = predict(
      mdl_mass_vs_length, explanatory_little_bream
    )
  )

# A tibble: 1 x 2
  length_cm mass_g
      <dbl>  <dbl>
1        10  -490.

Let's practice!

Introduction to Regression in R