In neuropsychology/psycho.R: Efficient and Publishing-Oriented Workflow for Psychological Science

library(knitr)

Observed Data

library(tidyverse)
library(psycho)


df <- psycho::affective

plot <- df %>% 
  ggplot(aes(x=Adjusting, y=Life_Satisfaction)) +
  geom_count() +
  geom_smooth(method="loess", color="black", fill="black", alpha=0.1) +
  theme_classic()
plot

Linear Fit

Model

fit <- lm(Life_Satisfaction ~ Adjusting, data=df)

analyze(fit)

Visualize

data_linear <- df %>% 
  select(Adjusting) %>% 
  refdata(length.out=100) %>% 
  get_predicted(fit, .)

plot <- plot +
  geom_ribbon(data=data_linear, 
              aes(y=Life_Satisfaction_Predicted, ymin=Life_Satisfaction_CI_2.5, ymax=Life_Satisfaction_CI_97.5), 
              fill="red", 
              alpha=0.1) +
  geom_line(data=data_linear, 
            aes(y=Life_Satisfaction_Predicted), 
            color="red")
plot

Polynomial fit

fit <- lm(Life_Satisfaction ~ poly(Adjusting, 2), data=df)

analyze(fit)

Visualize

To add the predictions of this model, we use the exact same code as for the linear model.

data_poly <- df %>% 
  select(Adjusting) %>% 
  refdata(length.out=100) %>% 
  get_predicted(fit, .)

plot <- plot +
  geom_ribbon(data=data_poly, 
              aes(y=Life_Satisfaction_Predicted, ymin=Life_Satisfaction_CI_2.5, ymax=Life_Satisfaction_CI_97.5), 
              fill="blue", 
              alpha=0.1) +
  geom_line(data=data_poly, 
            aes(y=Life_Satisfaction_Predicted), 
            color="blue")
plot

Raw and Orthogonal Polynomials: Interpretation

data <- data.frame(x=seq(from=1, to=5, length.out=100),
                   y=4 - 0.6*x + 0.1*x^2 + 0.25*rnorm(100))

ggplot(data, aes(x, y)) + geom_point() + 
       geom_smooth(method = "lm", formula = y ~ poly(x, 2))

summary(lm(y ~ poly(x, 2, raw=TRUE)))
summary(lm(y ~ poly(x, 2)))

Use raw polynomials if you're interested in the coefficient and/or the regression equation.
Use orthogonal if you're interested in the general shape of the relationship.

Contribute

psycho is a young package in need of affection. Therefore, if you have any advices, opinions or such, we encourage you to either let us know by opening an issue, or by contributing to the code.

Credits

This package helped you? Don't forget to cite the various packages you used :)

You can cite psycho as follows:

Makowski, (2018). The psycho Package: An Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470. https://doi.org/10.21105/joss.00470