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fix: broken latex rendering #14

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fix: broken latex rendering #14

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17 changes: 12 additions & 5 deletions ... Adapting to Varied Data Types/Regression-Analysis-in-R-Adapting-to-Varied-Data-Types.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -688,12 +688,19 @@ summary(model.log)$coefficients
Using our estimated regression coefficients we can write our fitted regression model as

```{r, echo = FALSE}
logEq<- paste0("$\frac{\text{ln(p(TypeIIDiabetes))}}{(1-p(TypeIIDiabetes))}", signif(coef(model.log)[1],2), " + ", signif(coef(model.log)[2],2), " * ExerciseHours + ",
signif(coef(model.log)[3],2), " * AlcoholUnits$")

pretty_number <- function(number, significant_figures = 2, show_sign = TRUE) {
polarity <- sign(number)
polarity <- ifelse(polarity == 1, "+", "-")
output <- ifelse(show_sign, polarity, "")
output <- paste0(output, signif(abs(number), significant_figures))
return(output)
}
coef_one <- pretty_number(coef(model.log)[1], show_sign = FALSE)
coef_two <- pretty_number(coef(model.log)[2])
coef_three <- pretty_number(coef(model.log)[3])
```

`r logEq`.
$$ln\bigg(\frac{p(TypeII\;Diabetes)}{1-p(TypeII\;Diabetes)}\bigg) = `r coef_one` `r coef_two` * ExerciseHours `r coef_three` * AlcoholUnits$$.

Let's say we have a new observation we want to make a prediction for, we know that they exercise for on average 4 hours a week and consume 10 units of alcohol per week. We can input these values into our equation to estimate the log odds of the this individual having type 2 diabetes.

Expand Down Expand Up @@ -829,4 +836,4 @@ As a consequence we have drawn out the link between regression and

In summary regression is a modular framework that can be used to test a endless range of analytical questions.

## Extras
## Extras