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 <- lm(formula = Petal.Length ~ Sepal.Length, data = iris) (smod_lm <- summary(model_lm)) ## ## 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

and also in the `summary()`

object:

c(R2 = smod_lm$r.squared, F = smod_lm$fstatistic[1]) ## R2 F.value ## 0.7599546 468.5501535

Note, though, that while the summary *reports* the 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 statistic *and* simultaneously 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$isVersicolor <- iris$Species == "versicolor" model_glm <- glm( isVersicolor ~ Sepal.Length + Sepal.Width, data = iris, family = binomial) (smod_glm <- summary(model_glm)) ## ## Call: ## glm(formula = isVersicolor ~ Sepal.Length + Sepal.Width, family = binomial, ## data = iris) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.9769 -0.8176 -0.4298 0.8855 2.0855 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|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: 5

To get the significance of a logistic regression model, call `wrapr::wrapChiSqTest():`

library(sigr) (chi2Test <- wrapChiSqTest(model_glm)) ## [1] “Chi-Square Test summary: pseudo-R2=0.21 (X2(2,N=150)=39, p<1e-05).”

Notice 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 Test** summary: *pseudo- R^{2}*=0.21 (

*χ*(2,

^{2}*N*=150)=39,

*p*<1e-05).

By 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*.