# 17 Models are data

You might remember the episode of the Simpsons where Homer designs a car for ‘the average man’. It doesn’t end well. Traditional statistics packages are a bit like Homer’s car. They try to work for everyone, but in the process become bloated and difficult to use.

This is particularly true of the output of software like SPSS, which by default produces multiple pages of ‘results’ for even relatively simple statistical models. However, the problem is not just that SPSS is incredibly verbose.

The real issue is that SPSS views the results of a model as the end of a process, rather than the beginning. The model SPSS has is something like:

1. Collect data
2. Choose analysis from GUI
3. Select relevant figures from pages of output and publish.

This is a problem because in real life it just doesn’t work that way. In reality you will want to do things like:

• Run the same model for different outcomes
• Re-run similar models as part of a sensitivity analysis
• Compare different models and produce summaries of results from multiple models

All of this requires an iterative process, in which you may want to compare and visualise the results of multiple models. In a traditional GUI, this quickly becomes overwhelming.

However, if we treat modelling as a process which both consumes and produces data, R provides many helpful tools.

This is an important insight: in R, the results of analyses are not the end point — instead model results are themselves data, to be processed, visualised, and compared.

### Storing models in variables

This may seem obvious (and we have seen many examples in the sections above), but because R variables can contain anything, we can use them to store the results of our models.

This is important, because it means we can keep track of different versions of the models we run, and compare them.

### Extracting results from models

One of the nice things about R is that the summary() function will almost always provide a concise output of whatever model you send it, showing the key features of an model you have run.

However, this text output isn’t suitable for publication, and can even be too verbose for communicating with colleagues. Often, when communicating with others, you want to focus in on the important details from analyses and to do this you need to extract results from your models.

Thankfully, there is almost always a method to extract results to a dataframe. For example, if we run a linear model:

model.fit <- lm(mpg ~ wt + disp, data=mtcars)
summary(model.fit)

Call:
lm(formula = mpg ~ wt + disp, data = mtcars)

Residuals:
Min      1Q  Median      3Q     Max
-3.4087 -2.3243 -0.7683  1.7721  6.3484

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.96055    2.16454  16.151 4.91e-16 ***
wt          -3.35082    1.16413  -2.878  0.00743 **
disp        -0.01773    0.00919  -1.929  0.06362 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.917 on 29 degrees of freedom
Multiple R-squared:  0.7809,    Adjusted R-squared:  0.7658
F-statistic: 51.69 on 2 and 29 DF,  p-value: 2.744e-10

We can extract the parameter table from this model by saving the summary() of it, and then using the $ operator to access the coefficients table (actually a matrix), which is stored within the summary object. model.fit.summary <- summary(model.fit) model.fit.summary$coefficients
Estimate  Std. Error   t value     Pr(>|t|)
(Intercept) 34.96055404 2.164539504 16.151497 4.910746e-16
wt          -3.35082533 1.164128079 -2.878399 7.430725e-03
disp        -0.01772474 0.009190429 -1.928609 6.361981e-02

### ‘Poking around’ with $ and @ It’s a useful trick to learn how to ‘poke around’ inside R objects using the $ and @ operators (if you want the gory details see this guide).

In the video below, I use RStudio’s autocomplete feature to find results buried within a lm object:

For example, we could write the follwing to extract a table of coefficients, test statistics and p values from an lm() object (this is shown in the video:

model.fit.summary <- summary(model.fit)
model.fit.summary$coefficients %>% as_data_frame() Warning: as_data_frame() is deprecated, use as_tibble() (but mind the new semantics). This warning is displayed once per session. # A tibble: 3 x 4 Estimate Std. Error t value Pr(>|t|) <dbl> <dbl> <dbl> <dbl> 1 35.0 2.16 16.2 4.91e-16 2 -3.35 1.16 -2.88 7.43e- 3 3 -0.0177 0.00919 -1.93 6.36e- 2 ### Save time: use a broom The broom:: library is worth learning because it makes it really easy to turn model results into dataframes, which is almost always what we want when working with data. It takes a slightly different approach than simply poking around with$ and @, because it providing general methods to ‘clean up’ the output of many older R functions.

For example, the lm() or car::Anova functions display results in the console, but don’t make it easy to extract results as a dataframe. broom:: provides a consistent way of extracting the key numbers from most R objects.

Let’s say we have a regression model:

(model.1 <- lm(mpg ~ factor(cyl) + wt + disp, data=mtcars))

Call:
lm(formula = mpg ~ factor(cyl) + wt + disp, data = mtcars)

Coefficients:
(Intercept)  factor(cyl)6  factor(cyl)8            wt          disp
34.041673     -4.305559     -6.322786     -3.306751      0.001715  

We can extract model fit statistics — that is, attributes of the model as a whole — with glance(). This produces a dataframe:

glance(model.1)
# A tibble: 1 x 11
r.squared adj.r.squared sigma statistic  p.value    df logLik   AIC   BIC
<dbl>         <dbl> <dbl>     <dbl>    <dbl> <int>  <dbl> <dbl> <dbl>
1     0.838         0.813  2.60      34.8 2.73e-10     5  -73.3  159.  167.
# … with 2 more variables: deviance <dbl>, df.residual <int>

If we want to extract information about the model coefficients we can use tidy:

tidy(model.1, conf.int = T) %>%
pander
term estimate std.error statistic p.value conf.low conf.high
(Intercept) 34.04 1.963 17.34 3.662e-16 30.01 38.07
factor(cyl)6 -4.306 1.465 -2.939 0.006662 -7.311 -1.3
factor(cyl)8 -6.323 2.598 -2.433 0.02186 -11.65 -0.9913
wt -3.307 1.105 -2.992 0.005855 -5.574 -1.039
disp 0.001715 0.01348 0.1272 0.8997 -0.02595 0.02938

Which can then be plotted easily (adding the conf.int=T includes 95% confidence intervals for each parameter, which we can pass to ggplot):

tidy(model.1, conf.int = T) %>%
ggplot(aes(term, estimate, ymin=conf.low, ymax=conf.high)) +
geom_pointrange() +
geom_hline(yintercept = 0)

Finally, we can use the augment function to get information on individual rows in the modelled data: namely the fitted and residual values, plus common diagnostic metrics like Cooks distances:

augment(model.1) %>%
pander(split.tables=Inf)
.rownames mpg factor.cyl. wt disp .fitted .se.fit .resid .hat .sigma .cooksd .std.resid
Mazda RX4 21 6 2.62 160 21.35 1.058 -0.3468 0.1653 2.652 0.0008423 -0.1458
Mazda RX4 Wag 21 6 2.875 160 20.5 1.009 0.4964 0.1501 2.651 0.001512 0.2069
Datsun 710 22.8 4 2.32 108 26.56 0.7854 -3.755 0.09103 2.538 0.04586 -1.513
Hornet 4 Drive 21.4 6 3.215 258 19.55 1.355 1.853 0.2711 2.618 0.05169 0.8336
Hornet Sportabout 18.7 8 3.44 360 16.96 0.9784 1.739 0.1413 2.627 0.0171 0.7209
Valiant 18.1 6 3.46 225 18.68 1.059 -0.5806 0.1654 2.65 0.002363 -0.2442

Again these can be plotted:

augment(model.1) %>%
ggplot(aes(x=.fitted, y=.resid)) +
geom_point() +
geom_smooth()
geom_smooth() using method = 'loess' and formula 'y ~ x'

Because broom always returns a dataframe with a consistent set of column names we can also combine model results into tables for comparison. In this plot we see what happens to the regression coefficients in model 1 when we add disp, carb and drat in model 2. We plot the coefficients side by side for ease of comparison, and can see that the estimates for cyl1 and wt both shrink slightly with the addition of these variables:

# run a new model with more predictors
(model.2 <- lm(mpg ~ factor(cyl) + wt + disp + carb + drat, data=mtcars))

Call:
lm(formula = mpg ~ factor(cyl) + wt + disp + carb + drat, data = mtcars)

Coefficients:
(Intercept)  factor(cyl)6  factor(cyl)8            wt          disp
29.849209     -2.796142     -4.116561     -2.748229     -0.002826
carb          drat
-0.587422      1.056532

# make a single dataframe from both models
# addin a new model column with mutate to
# identify which coefficient came from which model
combined.results <- bind_rows(
tidy(model.1, conf.int = T) %>% mutate(model="1"),
tidy(model.2, conf.int = T) %>%  mutate(model="2"))
combined.results %>%
# remove the intercept to make plot scale more sane
filter(term != "(Intercept)") %>%
ggplot(aes(term, estimate, ymin=conf.low, ymax=conf.high, color=model)) +
geom_pointrange(position=position_dodge(width=.1)) +
geom_hline(yintercept = 0)