R is designed to make working with statistical models fast, succinct, and reliable.
For instance building a model is a one-liner:
model <- lm(Petal.Length ~ Sepal.Length, data = iris)
And producing a detailed diagnostic summary of the model is also a one-liner:
summary(model) # Call: # lm(formula = Petal.Length ~ Sepal.Length, data = iris) # # Residuals: # Min 1Q Median 3Q Max # -2.47747 -0.59072 -0.00668 0.60484 2.49512 # # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) -7.10144 0.50666 -14.02 <2e-16 *** # Sepal.Length 1.85843 0.08586 21.65 <2e-16 *** # --- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # # Residual standard error: 0.8678 on 148 degrees of freedom # Multiple R-squared: 0.76, Adjusted R-squared: 0.7583 # F-statistic: 468.6 on 1 and 148 DF, p-value: < 2.2e-16
However, useful as the above is: it isn’t exactly presentation ready. To formally report the R-squared of our model we would have to cut and paste this information from the summary. That is a needlessly laborious and possibly error-prone step.
sigr package this can be made much easier:
library("sigr") Rsquared <- wrapFTest(model) print(Rsquared) #  "F Test summary: (R2=0.76, F(1,148)=468.6, p<1e-05)."
And this formal summary can be directly rendered into many formats (Latex, html, markdown, and ascii).
F Test summary: (R2=0.76, F(1,148)=468.6, p<1e-05).
sigr can help make your publication workflow much easier and more repeatable/reliable.
Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.