Yes, the correlation function from the psycho package.
devtools::install_github("neuropsychology/psycho.R") # Install the newest version
library(psycho)
library(tidyverse)
cor <- psycho::affective %>%
correlation()This function automatically select numeric variables and run a correlation analysis. It returns apsychobject.
We can then extract a formatted table that can be saved and pasted into reports and manuscripts by using the summary function.
summary(cor)
# write.csv(summary(cor), "myformattedcortable.csv")| Age | Life_Satisfaction | Concealing | Adjusting | |
|---|---|---|---|---|
| Age | ||||
| Life_Satisfaction | 0.03 | |||
| Concealing | -0.05 | -0.06 | ||
| Adjusting | 0.03 | 0.36*** | 0.22*** | |
| Tolerating | 0.03 | 0.15*** | 0.07 | 0.29*** |
It integrates a plot done with ggcorplot.
plot(cor)
It also includes a pairwise correlation printing method.
print(cor)Pearson Full correlation (p value correction: holm):
- Age / Life_Satisfaction: Results of the Pearson correlation showed a non significant and weak negative association between Age and Life_Satisfaction (r(1249) = 0.030, p > .1).
- Age / Concealing: Results of the Pearson correlation showed a non significant and weak positive association between Age and Concealing (r(1249) = -0.050, p > .1).
- Life_Satisfaction / Concealing: Results of the Pearson correlation showed a non significant and weak positive association between Life_Satisfaction and Concealing (r(1249) = -0.063, p > .1).
- Age / Adjusting: Results of the Pearson correlation showed a non significant and weak negative association between Age and Adjusting (r(1249) = 0.027, p > .1).
- Life_Satisfaction / Adjusting: Results of the Pearson correlation showed a significant and moderate negative association between Life_Satisfaction and Adjusting (r(1249) = 0.36, p < .001***).
- Concealing / Adjusting: Results of the Pearson correlation showed a significant and weak negative association between Concealing and Adjusting (r(1249) = 0.22, p < .001***).
- Age / Tolerating: Results of the Pearson correlation showed a non significant and weak negative association between Age and Tolerating (r(1249) = 0.031, p > .1).
- Life_Satisfaction / Tolerating: Results of the Pearson correlation showed a significant and weak negative association between Life_Satisfaction and Tolerating (r(1249) = 0.15, p < .001***).
- Concealing / Tolerating: Results of the Pearson correlation showed a non significant and weak negative association between Concealing and Tolerating (r(1249) = 0.074, p = 0.05°).
- Adjusting / Tolerating: Results of the Pearson correlation showed a significant and weak negative association between Adjusting and Tolerating (r(1249) = 0.29, p < .001***).
You can also cutomize the type (pearson, spearman or kendall), the p value correction method(holm (default), bonferroni, fdr, none…) and run partial, semi-partial or glasso correlations.
psycho::affective %>%
correlation(method = "pearson", adjust="bonferroni", type="partial") %>%
summary()| Age | Life_Satisfaction | Concealing | Adjusting | |
|---|---|---|---|---|
| Age | ||||
| Life_Satisfaction | 0.01 | |||
| Concealing | -0.06 | -0.16*** | ||
| Adjusting | 0.02 | 0.36*** | 0.25*** | |
| Tolerating | 0.02 | 0.06 | 0.02 | 0.24*** |
In order to prevent people for running many uncorrected correlation tests (promoting p-hacking and result-fishing), we included the i_am_cheating parameter. If FALSE (default), the function will help you finding interesting results!
df_with_11_vars <- data.frame(replicate(11, rnorm(1000)))
cor <- correlation(df_with_11_vars, adjust="none")## Warning in correlation(df_with_11_vars, adjust = "none"): We‘ve detected that you are running a lot (> 10) of correlation tests without adjusting the p values. To help you in your p-fishing, we‘ve added some interesting variables: You never know, you might find something significant!
## To deactivate this, change the ‘i_am_cheating‘ argument to TRUE.
summary(cor)| X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | X11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | |||||||||||
| X2 | -0.04 | ||||||||||
| X3 | -0.04 | -0.02 | |||||||||
| X4 | 0.02 | 0.05 | -0.02 | ||||||||
| X5 | -0.01 | -0.02 | 0.05 | -0.03 | |||||||
| X6 | -0.03 | 0.03 | 0.08* | 0.02 | 0.02 | ||||||
| X7 | 0.03 | -0.01 | -0.02 | -0.04 | -0.03 | -0.04 | |||||
| X8 | 0.01 | -0.07* | 0.04 | 0.02 | -0.01 | -0.01 | 0.00 | ||||
| X9 | -0.02 | 0.03 | -0.03 | -0.02 | 0.00 | -0.04 | 0.03 | -0.02 | |||
| X10 | -0.03 | 0.00 | 0.00 | 0.01 | 0.01 | -0.01 | 0.01 | -0.02 | 0.02 | ||
| X11 | 0.01 | 0.01 | -0.03 | -0.05 | 0.00 | 0.05 | 0.01 | 0.00 | -0.01 | 0.07* | |
| Local_Air_Density | 0.26*** | -0.02 | -0.44*** | -0.15*** | -0.25*** | -0.50*** | 0.57*** | -0.11*** | 0.47*** | 0.06 | 0.01 |
| Reincarnation_Cycle | -0.03 | -0.02 | 0.02 | 0.04 | 0.01 | 0.00 | 0.05 | -0.04 | -0.05 | -0.01 | 0.03 |
| Communism_Level | 0.58*** | -0.44*** | 0.04 | 0.06 | -0.10** | -0.18*** | 0.10** | 0.46*** | -0.50*** | -0.21*** | -0.14*** |
| Alien_Mothership_Distance | 0.00 | -0.03 | 0.01 | 0.00 | -0.01 | -0.03 | -0.04 | 0.01 | 0.01 | -0.02 | 0.00 |
| Schopenhauers_Optimism | 0.11*** | 0.31*** | -0.25*** | 0.64*** | -0.29*** | -0.15*** | -0.35*** | -0.09** | 0.08* | -0.22*** | -0.47*** |
| Hulks_Power | 0.03 | 0.00 | 0.02 | 0.03 | -0.02 | -0.01 | -0.05 | -0.01 | 0.00 | 0.01 | 0.03 |
As we can see, Schopenhauer’s Optimism is strongly related to many variables!!!
This package was useful? You can cite psycho as follows:
转自：https://neuropsychology.github.io/psycho.R//2018/05/20/correlation.html
Beautiful and Powerful Correlation Tables in R
原文：https://www.cnblogs.com/payton/p/9070525.html