These kinds of details aren't the purview of broom, however, as broom is focused on tidying the results of a model for further analysis (particularly with respect to comparing slightly varying models). There are too many decimal places, the p-value employ scientific notation, and column titles like "statistic" don't specify what type of statistic. It has been observed by some, however, that even this summary isn't quite ready for publication. The broom package provides the summary in tidy format that, serendipitously, it a lot closer to what we would want for formal reports.
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While the summary is informative and useful, it is full of "stats-speak" and isn't necessarily in a format that is suitable for publication or submission to a client. We build the model and generate the standard summary.įit #> Call: #> lm(formula = mpg ~ qsec + factor(am) + wt + factor(gear), data = mtcars) #> #> Residuals: #> Min 1Q Median 3Q Max #> -3.5064 -1.5220 -0.7517 1.3841 4.6345 #> #> Coefficients: #> Estimate Std. To demonstrate, let's look at a simple linear model. In the remaining columns, an 'x' indicates that the sprinkle is already implemented for the output format an 'o' indicates that implementation is planned but not yet completed and a blank cell indicates that the sprinkle will not be implemented (usually because the output format doesn't support the option). In the "implemented" column, an 'x' indicates a customization that has been implemented, while a blank cell suggests that the customization is planned but has not yet been implemented. The table below shows the currently planned and implemented sprinkles. Tables can be customized by row, column, or even by a single cell by adding sprinkles to the dust object. Sprinkle( col = 5, fn = quote(pvalString( value))) % >% Fit Additional documentation is being constructed at