In PsyTeachR/reprores-v2: Data Skills for Reproducible Research

# please do not alter this code chunk
knitr::opts_chunk$set(echo = TRUE,
                      message = FALSE, 
                      error = TRUE)

library(broom)
library(tidyverse)
library(reprores)

# install the class package reprores to access built-in data
# devtools::install_github("psyteachr/reprores-v2)
# or download data from the website
# https://psyteachr.github.io/reprores/data/data.zip

The personality_scores dataset

Load the dataset reprores::personality_scores.

data("personality_scores", package = "reprores")

Question 1

Use ggplot2 to visualise the relationship between extraversion (Ex) on the horizontal axis and neuroticism (Ne) on the vertical axis.

ggplot(personality_scores, aes(x = Ex, y = Ne)) +
  geom_density_2d_filled(show.legend = FALSE, alpha = 0.5) +
  geom_smooth(method = lm, formula = y~x)

Question 2

Run a regression model that predicts neuroticism from extraversion, and store the model object in the variable personality_mod. End the block by printing out the summary of the model.

personality_mod <- lm(Ne ~ Ex, data = personality_scores)

summary(personality_mod) #print out the model summary

Question 3

Make a histogram of the residuals of the model using ggplot2.

residuals <- residuals(personality_mod)

ggplot() +
  geom_histogram(aes(residuals), color = "black", binwidth = 0.25)

Question 4

Write code to predict the neuroticism score for the minimum, mean, and maximum extraversion scores. Store the vector of predictions in the variable personality_pred.

scores <-  data.frame(
  Ex = c(
    min(personality_scores$Ex, na.rm = TRUE),
    mean(personality_scores$Ex, na.rm = TRUE),
    max(personality_scores$Ex, na.rm = TRUE)
  ),
  # adding the row names makes the output of predict easier to read
  row.names = c("min", "mean", "max")
)

personality_pred <- predict(personality_mod, newdata = scores)

personality_pred # print the predicted values

Simulating data from the linear model

Question 5

NOTE: You can knit this file to html to see formatted versions of the equations below (which are enclosed in $ characters); alternatively, if you find it easier, you can hover your mouse pointer over the $ in the code equations to see the formatted versions.

Write code to randomly generate 10 Y values from a simple linear regression model with an intercept of 3 and a slope of -7. Recall the form of the linear model:

$Y_i = \beta_0 + \beta_1 X_i + e_i$

The residuals ($e_i$s) are drawn from a normal distribution with mean 0 and variance $\sigma^2 = 4$, and $X$ is the vector of integer values from 1 to 10. Store the 10 observations in the variable Yi below. (NOTE: the standard deviation is the square root of the variance, i.e. $\sigma$; rnorm() takes the standard deviation, not the variance, as its third argument).

X <- 1:10
err <- rnorm(10, sd = 2)
Yi <- 3 - 7 * X + err

Yi # print the values of Yi

PsyTeachR/reprores-v2 documentation built on Sept. 26, 2022, 10:06 a.m.