library(devtools) # Tools to Make Developing R Packages Easier
::install_github("Watjoa/CaviR") devtools
The package
Perform fancy statistics by yourself. Correct and fast.
Installation
Correlations
Using the coRtable
function to have a clear, informative correlation table with descriptions
coRtable(data[,c("Extra","Agree","Con","Neur","Open")])
 M  SD  1.  2.  3.  4.  5. 

1. Extra  3.58  0.52  
2. Agree  3.66  0.53  .22*  
3. Con  3.43  0.54  .06  .42***  
4. Neur  2.76  0.8  .06  .19  .28**  
5. Open  3.41  0.58  .44***  .22*  .27**  .04  
Note. *** p <.001, ** p <. 01, * p < .05 
or use the multicoR
function to have a correlation table for withingroup and betweengroup level. Descriptives are based on the betweengroup level.
multicoR(datamultilevel[, c(
"ID","Integration","Suppression","Dysregulation")])
 M  SD  ICC  1.  2.  3. 

1. Integration  3.29  0.66  0.64  .08***  .23***  
2. Suppression  2.32  0.76  0.63  .25***  .15***  
3. Dysregulation  2.26  0.73  0.59  .18***  .35***  
Note. *** p <.001, ** p <. 01, * p < .05 
In R, you can select the whole output (CTRL+A or CMD+A) and paste the output in excel to provide further modifications.
(M)ANOVA
Using the manovaR
function, a descriptive overview is displayed for a comparison between groups, including univariate, multivariate and tukey posthoc analyses.
manovaR(data[,c('Group','Autonomy','Vitality','Persistence')],
stand=TRUE, sign = 0.05, tukey = TRUE)
[1] "Grouping variable has only 2 levels. Tukey not applicable"
variables  Group 1  Group 2  Fvalue  pvalue 
 etasquared 

Autonomy  3.37 (±0.92)  2.31 (±1.09)  57.48  <.001  ***  0.37 
Vitality  3.37 (±0.92)  2.31 (±1.09)  41.78  <.001  ***  0.29 
Persistence  3.37 (±0.92)  2.31 (±1.09)  28.18  <.001  ***  0.22 
Wilks Lambda = 0.607,F(3,95) = 20.479 , p = <.001 
Regression
Using the summaRy
function, an overview is presented with information regarding your linear (mixed) model
< lm(Persistence~ Condition.d * Indecisiveness.c, data=data)
model summaRy(model)
Predictors  coefficients  β  std. error  tvalue  pvalue 


(Intercept)  1.22  0.00  0.32  3.82  <.001  *** 
Condition.d  1.10  0.49  0.2  5.56  <.001  *** 
Indecisiveness.c  1.18  0.64  0.52  2.29  0.02  * 
Condition.d:Indecisiveness.c  0.83  0.72  0.32  2.57  0.01  ** 
ANOVA:  
Sumsq  Meansq  (df)  F stat  partial η2  VIF  
Condition.d  28.41  28.41  1  31.11, p = <.001  0.25  1.01 
Indecisiveness.c  0.21  0.21  1  0.23, p = 0.63  0.00  10.21 
Condition.d:Indecisiveness.c  6.01  6.01  1  6.58, p = 0.01  0.07  10.20 
Residuals  83.092  0.913  91  
Info: 95 observations, (9) missing obs. deleted  
Fit: F(3,91) = 12.64, p = <.001  
R2= 0.29, Adj. R2 = 0.27 
Significant twoway interaction effects could be displayed using the inteRplot
function.
inteRplot(model,
pred = 'Condition.d',
mod = 'Indecisiveness.c',
outcome = 'Persistence',
xaxis = 'Condition',
moderator = 'Indecisiveness',
miny = 1,
maxy = 5,
xlabels=c('No choice','Choice'))
Clustering
The clusteRs
function of the CaviR
package provides four figures showing a particular type of validation for a number of clusters. Indeed, we want to have all four of them, as we want to make a considered decision on how many clusters are in our dataset. This does not mean that all four types of validations will point towards the same number of clusters (sometimes it does, indicating strong evidence for a particular number). Therefore, you need to consider all types and explain in your reporting why you choose for a particular number of clusters.
 Elbow method: the number of clusters with both a minimum of withincluster variation and a maximum of betweencluster variation
 the Average Silhouette method: the number of clusters with the highest average silhouette, indicating the best quality of clustering
 the Gap statistic method: the number of clusters with the highest Gapstatistic [@tibshirani2001]
 Majority rule: a summary of 30 indices reporting the most optimal number of clusters using the ‘NbClust’ function, including the CH index.
clusteRs(df_clust)