**R – Win-Vector Blog**, and kindly contributed to R-bloggers)

If you’ve read our previous R Tip on using sigr with linear models, you might have noticed that the `lm()`

summary object does in fact carry the R-squared and F statistics, both in the printed form:

model_lm |t|) ## (Intercept) -7.10144 0.50666 -14.02and also in the

`summary()`

object:c(R2 = smod_lm$r.squared, F = smod_lm$fstatistic[1]) ## R2 F.value ## 0.7599546 468.5501535Note, though, that while the summary

reportsthe model’s significance, it does not carry it as a specific`summary()`

object item.`sigr::wrapFTest()`

is a convenient way to extract the model’s R-squared and F statisticandsimultaneously calculate the model significance, as is required by many scientific publications.

`sigr`

is even more helpful for logistic regression, via`glm()`

, which reports neither the model’s chi-squared statistic nor its significance.iris$isVersicolor |z|) ## (Intercept) 8.0928 2.3893 3.387 0.000707 *** ## Sepal.Length 0.1294 0.2470 0.524 0.600247 ## Sepal.Width -3.2128 0.6385 -5.032 4.85e-07 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 190.95 on 149 degrees of freedom ## Residual deviance: 151.65 on 147 degrees of freedom ## AIC: 157.65 ## ## Number of Fisher Scoring iterations: 5To get the significance of a logistic regression model, call

`wrapr::wrapChiSqTest():`

library(sigr) (chi2TestNotice that the fit summary also reports a pseudo-R-squared. You can extract the values directly off the

`sigr`

object, as well:str(chi2Test) ## List of 10 ## $ test : chr "Chi-Square test" ## $ df.null : int 149 ## $ df.residual : int 147 ## $ null.deviance : num 191 ## $ deviance : num 152 ## $ pseudoR2 : num 0.206 ## $ pValue : num 2.92e-09 ## $ sig : num 2.92e-09 ## $ delta_deviance: num 39.3 ## $ delta_df : int 2 ## - attr(*, "class")= chr [1:2] "sigr_chisqtest" "sigr_statistic"And of course you can render the

`sigr`

object into one of several formats (Latex, html, markdown, and ascii) for direct inclusion in a report or publication.render(chi2Test, format = "html")

Chi-Square Testsummary:pseudo-=0.21 (R^{2}χ(2,^{2}N=150)=39,pBy the way, if you are interested, we give the explicit formula for calculating the significance of a logistic regression model in

Practical Data Science with R.

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