9.5 Presenting results

The direct software output always tells you more information than what you are wanting to share with an audience. Here are some ways to “prettify” your regression output.

Using tidy and kable

library(broom)
tidy(mlr.dad.model) |> kable(digits=3)

term	estimate	std.error	statistic	p.value
(Intercept)	-2.761	1.138	-2.427	0.016
FAGE	-0.027	0.006	-4.183	0.000
FHEIGHT	0.114	0.016	7.245	0.000

Using gtsummary

library(gtsummary)
tbl_regression(mlr.dad.model)

Characteristic	Beta	95% CI¹	p-value
FAGE	-0.03	-0.04, -0.01	<0.001
FHEIGHT	0.11	0.08, 0.15	<0.001
¹ CI = Confidence Interval

Using dwplot from the dotwhisker package to create a forest plot.

library(dotwhisker)
dwplot(mlr.dad.model)

Improvement on dwplot - extract the point estimate & CI into a data table, then add it as a geom_text layer.

text <- data.frame(                               # create a data frame
  estimate = coef(mlr.dad.model),                 # by extracting the coefficients,
  CI.low = confint(mlr.dad.model)[,1],            # with their lower
  CI.high = confint(mlr.dad.model)[,2]) %>%       # and upper confidence interval values
  round(2)                                        # round digits

# create the string for the label
text$label <- paste0(text$estimate, "(", text$CI.low, ", " , text$CI.high, ")")

text                                              # view the results to check for correctness
##             estimate CI.low CI.high               label
## (Intercept)    -2.76  -5.01   -0.51 -2.76(-5.01, -0.51)
## FAGE           -0.03  -0.04   -0.01 -0.03(-0.04, -0.01)
## FHEIGHT         0.11   0.08    0.15    0.11(0.08, 0.15)
text <- text[-1, ]                                # drop the intercept row

# ---- create plot ------
mlr.dad.model %>%                                  # start with a model
  tidy() %>%                                       # tidy up the output
  relabel_predictors("(Intercept)" = "Intercept",  # convert to sensible names
                     FAGE = "Age", 
                     FHEIGHT = "Height") %>% 
  filter(term != "Intercept") %>%                  # drop the intercept 
  dwplot() +                                       # create the ggplot 
  geom_text(aes(x=text$estimate, y = term,         # add the estimates and CI's
                label = text$label), 
            nudge_y = .1) +                        # move it up a smidge
  geom_vline(xintercept = 0, col = "grey",         # add a reference line at 0
             lty = "dashed", size=1.2) +           # make it dashed and a little larger
  scale_x_continuous(limits = c(-.15, .15))        # expand the x axis limits for readability