344  penguins-lm

Schlüsselwörter

Aufgaben, Statistik, Prognose, Modellierung, R, Datenanalyse, Regression

344.1 Exercise

Consider the dataset penguins. Compute a linear model with body mass as output variable (DV) and flipper length as input (IV).

1. Report the coefficients and interpret them.
2. Plot the model and the coefficients.
3. Report the model fit (R squared).
4. BONUS: predict() the weight of an average flipper-sized animal. Check out the internet for examples of how to do so in case you need support.

344.2 Solution

344.2.1 Setup

library(tidyverse)
library(easystats)
library(ggpubr)

# import data:
penguins <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/palmerpenguins/penguins.csv")

344.2.2 Let’s go

lm1 <- lm(body_mass_g ~ flipper_length_mm, data = penguins)

Plot the model:

plot(estimate_relation(lm1))

Alternative plotting method:

ggscatter(penguins,
          x = "flipper_length_mm",
          y = "body_mass_g",
          add ="reg.line")

Coefficients (parameters):

parameters(lm1)

Plot the coefficients:

plot(parameters(lm1))

Model fit (explained variance by model):

r2(lm1)
## # R2 for Linear Regression
##        R2: 0.759
##   adj. R2: 0.758

Predict weight of average animal:

penguins |> 
  summarise(flipper_length_mm_avg = 
              mean(flipper_length_mm, na.rm = TRUE))

344.2.3 For average flipper length, what’s the expected weight?

predict(lm1, newdata = data.frame(flipper_length_mm = 200))
##    1 
## 4156

Around 4 kgs.

344.2.4 Centering the data

Center the data:

penguins_c <-
  penguins |> 
  mutate(flipper_length_mm_c = center(flipper_length_mm))

Now the mean value is (nearly) zero:

mean(penguins_c$flipper_length_mm_c, na.rm = TRUE)
## [1] -1.2e-14

Run the model again:

lm2 <- lm(body_mass_g ~ flipper_length_mm_c, data = penguins_c)

parameters(lm2)