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sigr

Concise formatting of significances in R.

Please see Adding polished significance summaries to papers using R for some discussion.

See also:

• “The prevalence of statistical reporting errors in psychology (1985–2013)”, Nuijten, M.B., Hartgerink, C.H.J., van Assen, M.A.L.M. et al., Behav Res (2015), doi:10.3758/s13428-015-0664-2
• Reporting Statistics in APA Style
• Publication Manual of the American Psychological Association, Seventh Edition
• Proofing statistics in papers
• apa
• bootstrap
• broom
• achetverikov/APAstats
• pwr
• ggstatsplot
• “Why Most Published Research Findings Are False”, John P. A. Ioannidis PLOS Medicine, August 30, 2005
• “The garden of forking paths”, Andrew Gelman and Eric Loken, 14 Nov 2013

sigr is a small package that concentrates on computing summary statistics and reporting in an appropriate format.

For example here is formatting the quality of a logistic regression.

d <- data.frame(x=c(1,2,3,4,5,6,7,7),
      y=c(TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE))
model <- glm(y~x,data=d,family=binomial)
summary(model)

## 
## Call:
## glm(formula = y ~ x, family = binomial, data = d)
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -0.7455     1.6672  -0.447    0.655
## x             0.1702     0.3429   0.496    0.620
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 11.090  on 7  degrees of freedom
## Residual deviance: 10.837  on 6  degrees of freedom
## AIC: 14.837
## 
## Number of Fisher Scoring iterations: 4

library("sigr")

cat(render(wrapChiSqTest(model),
           pLargeCutoff=1, format='markdown'))

Chi-Square Test summary: pseudo-R²=0.02282 (χ²(1,N=8)=0.2531, p=0.6149).

To install, from inside R please run:

install.packages("sigr")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.