Partial Correlation can be used when we want to look at the association between two variables while controlling for scores on another, related, variable. We can ask: what’s the association between a person’s exam score and anxiety, regardless the number of hours revising?
The general form of pcor() is: pc <- pcor(c(“var1”, “var2”, “control1”, “control2” etc.), var(dataframe))
where, the output is saved as an object called “pc” (you can call in anything you want). You’ll have to install the package “ggm”.
library(ggm)
Create a tibble that includes the necessary variables.
examdata2 <- examdata %>% select(exam, anxiety, revise)
Run the partial correlation.
pc <- pcor(c("exam", "anxiety", "revise"), var(examdata2))
Test the significance of the new partial correlation coefficient.
pcor.test(pc, 1, 103)
## $tval
## [1] -2.545307
##
## $df
## [1] 100
##
## $pvalue
## [1] 0.01244581
| Exam and Anxiety Controlling for Revisions | |
|---|---|
| Partial Correlation | ❓ |
| R^2 | ❓ |
| p value | ❓ |
| Correlation w/o Controlling for Revise | ❓ |
Formatting and working with Correlations from the R BLOG
a. Import the data
df <- as.tibble(iris) # Load the iris dataset
df
# A tibble: 150 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# ... with 140 more rows
b. Compute and Store the Results
cor_results <- cor.test(df$Sepal.Length, df$Petal.Length)
cor_results
Pearson's product-moment correlation
data: df$Sepal.Length and df$Petal.Length
t = 21.646, df = 148, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.8270363 0.9055080
sample estimates:
cor
0.8717538
c. Install and load the formatting package from GitHub.
devtools::install_github("neuropsychology/psycho.R") # only once!
Load the package
library(psycho)
d. Examine the result. Nice that is provides a 95% CI.
psycho::analyze(cor_results)
e. Produce as a table
results <- analyze(cor_results)
summary(results)
effect statistic df p CI_lower CI_higher
1 0.8717538 21.64602 148 1.038667e-47 0.8270363 0.905508
Use kable from library(knitr)
knitr::kable(summary((results)))
and get this in R:
| effect| statistic| df| p| CI_lower| CI_higher|
|---------:|---------:|---:|--:|---------:|---------:|
| 0.8717538| 21.64602| 148| 0| 0.8270363| 0.905508|
…which looks this when rendered in Markdown:
| effect | statistic | df | p | CI_lower | CI_higher |
|---|---|---|---|---|---|
| 0.8717538 | 21.64602 | 148 | 0 | 0.8270363 | 0.905508 |
Nice!
You can learn more at this site that introduces you to kableExtra by Hao Zhu