Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

First of all, is it heteros**k**edasticity or heteros**c**edasticity? According to
McCulloch (1985),
heteros**k**edasticity is the proper spelling, because when transliterating Greek words, scientists
use the Latin letter k in place of the Greek letter κ (kappa). κ sometimes is transliterated as
the Latin letter c, but only when these words entered the English language through French, such
as scepter.

Now that this is out of the way, we can get to the meat of this blogpost (foreshadowing pun). A random variable is said to be heteroskedastic, if its variance is not constant. For example, the variability of expenditures may increase with income. Richer families may spend a similar amount on groceries as poorer people, but some rich families will sometimes buy expensive items such as lobster. The variability of expenditures for rich families is thus quite large. However, the expenditures on food of poorer families, who cannot afford lobster, will not vary much. Heteroskedasticity can also appear when data is clustered; for example, variability of expenditures on food may vary from city to city, but is quite constant within a city.

To illustrate this, let’s first load all the packages needed for this blog post:

```
library(robustbase)
library(tidyverse)
library(sandwich)
library(lmtest)
library(modelr)
library(broom)
```

First, let’s load and prepare the data:

```
data("education")
education <- education %>%
rename(residents = X1,
per_capita_income = X2,
young_residents = X3,
per_capita_exp = Y,
state = State) %>%
mutate(region = case_when(
Region == 1 ~ "northeast",
Region == 2 ~ "northcenter",
Region == 3 ~ "south",
Region == 4 ~ "west"
)) %>%
select(-Region)
```

I will be using the `education`

data set from the `{robustbase}`

package. I renamed some columns
and changed the values of the `Region`

column. Now, let’s do a scatterplot of per capita expenditures
on per capita income:

```
ggplot(education, aes(per_capita_income, per_capita_exp)) +
geom_point() +
theme_dark()
```

It would seem that, as income increases, variability of expenditures increases too. Let’s look
at the same plot by `region`

:

```
ggplot(education, aes(per_capita_income, per_capita_exp)) +
geom_point() +
facet_wrap(~region) +
theme_dark()
```

I don’t think this shows much; it would seem that observations might be clustered, but there are not enough observations to draw any conclusion from this plot (in any case, drawing conclusions from only plots is dangerous).

Let’s first run a good ol’ linear regression:

```
lmfit <- lm(per_capita_exp ~ region + residents + young_residents + per_capita_income, data = education)
summary(lmfit)
```

```
##
## Call:
## lm(formula = per_capita_exp ~ region + residents + young_residents +
## per_capita_income, data = education)
##
## Residuals:
## Min 1Q Median 3Q Max
## -77.963 -25.499 -2.214 17.618 89.106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -467.40283 142.57669 -3.278 0.002073 **
## regionnortheast 15.72741 18.16260 0.866 0.391338
## regionsouth 7.08742 17.29950 0.410 0.684068
## regionwest 34.32416 17.49460 1.962 0.056258 .
## residents -0.03456 0.05319 -0.650 0.519325
## young_residents 1.30146 0.35717 3.644 0.000719 ***
## per_capita_income 0.07204 0.01305 5.520 1.82e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.88 on 43 degrees of freedom
## Multiple R-squared: 0.6292, Adjusted R-squared: 0.5774
## F-statistic: 12.16 on 6 and 43 DF, p-value: 6.025e-08
```

Let’s test for heteroskedasticity using the Breusch-Pagan test that you can find in the `{lmtest}`

package:

`bptest(lmfit)`

```
##
## studentized Breusch-Pagan test
##
## data: lmfit
## BP = 17.921, df = 6, p-value = 0.006432
```

This test shows that we can reject the null that the variance of the residuals is constant,
thus heteroskedacity is present. To get the correct standard errors, we can use the `vcovHC()`

function from the `{sandwich}`

package (hence the choice for the header picture of this post):

```
lmfit %>%
vcovHC() %>%
diag() %>%
sqrt()
```

```
## (Intercept) regionnortheast regionsouth regionwest
## 311.31088691 25.30778221 23.56106307 24.12258706
## residents young_residents per_capita_income
## 0.09184368 0.68829667 0.02999882
```

By default `vcovHC()`

estimates a heteroskedasticity consistent (HC) variance covariance
matrix for the parameters. There are several ways to estimate such a HC matrix, and by default
`vcovHC()`

estimates the “HC3” one. You can refer to Zeileis (2004)
for more details.

We see that the standard errors are much larger than before! The intercept and `regionwest`

variables
are not statistically significant anymore.

You can achieve the same in one single step:

`coeftest(lmfit, vcov = vcovHC(lmfit))`

```
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -467.402827 311.310887 -1.5014 0.14056
## regionnortheast 15.727405 25.307782 0.6214 0.53759
## regionsouth 7.087424 23.561063 0.3008 0.76501
## regionwest 34.324157 24.122587 1.4229 0.16198
## residents -0.034558 0.091844 -0.3763 0.70857
## young_residents 1.301458 0.688297 1.8908 0.06540 .
## per_capita_income 0.072036 0.029999 2.4013 0.02073 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

It’s is also easy to change the estimation method for the variance-covariance matrix:

`coeftest(lmfit, vcov = vcovHC(lmfit, type = "HC0"))`

```
##
## t test of coefficients:
##
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -467.402827 172.577569 -2.7084 0.009666 **
## regionnortheast 15.727405 20.488148 0.7676 0.446899
## regionsouth 7.087424 17.755889 0.3992 0.691752
## regionwest 34.324157 19.308578 1.7777 0.082532 .
## residents -0.034558 0.054145 -0.6382 0.526703
## young_residents 1.301458 0.387743 3.3565 0.001659 **
## per_capita_income 0.072036 0.016638 4.3296 8.773e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

As I wrote above, by default, the `type`

argument is equal to “HC3”.

Another way of dealing with heteroskedasticity is to use the `lmrob()`

function from the
`{robustbase}`

package. This package is quite interesting, and offers quite a lot of functions
for robust linear, and nonlinear, regression models. Running a robust linear regression
is just the same as with `lm()`

:

```
lmrobfit <- lmrob(per_capita_exp ~ region + residents + young_residents + per_capita_income,
data = education)
summary(lmrobfit)
```

```
##
## Call:
## lmrob(formula = per_capita_exp ~ region + residents + young_residents + per_capita_income,
## data = education)
## \--> method = "MM"
## Residuals:
## Min 1Q Median 3Q Max
## -57.1123 -15.0082 -0.8517 24.0570 174.3250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -157.22831 132.17406 -1.190 0.24075
## regionnortheast 19.71413 32.49408 0.607 0.54724
## regionsouth 9.78199 29.93815 0.327 0.74545
## regionwest 43.98580 33.15145 1.327 0.19157
## residents 0.03497 0.04339 0.806 0.42469
## young_residents 0.58273 0.25611 2.275 0.02793 *
## per_capita_income 0.04330 0.01434 3.019 0.00425 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Robust residual standard error: 26.79
## Multiple R-squared: 0.6166, Adjusted R-squared: 0.5631
## Convergence in 23 IRWLS iterations
##
## Robustness weights:
## observation 50 is an outlier with |weight| = 0 ( < 0.002);
## 7 weights are ~= 1. The remaining 42 ones are summarized as
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.07764 0.85620 0.94120 0.85630 0.98840 0.99780
## Algorithmic parameters:
## tuning.chi bb tuning.psi refine.tol
## 1.548e+00 5.000e-01 4.685e+00 1.000e-07
## rel.tol scale.tol solve.tol eps.outlier
## 1.000e-07 1.000e-10 1.000e-07 2.000e-03
## eps.x warn.limit.reject warn.limit.meanrw
## 1.071e-08 5.000e-01 5.000e-01
## nResample max.it best.r.s k.fast.s k.max
## 500 50 2 1 200
## maxit.scale trace.lev mts compute.rd fast.s.large.n
## 200 0 1000 0 2000
## psi subsampling cov
## "bisquare" "nonsingular" ".vcov.avar1"
## compute.outlier.stats
## "SM"
## seed : int(0)
```

This however, gives you different estimates than when fitting a linear regression model. The estimates should be the same, only the standard errors should be different. This is because the estimation method is different, and is also robust to outliers (at least that’s my understanding, I haven’t read the theoretical papers behind the package yet).

Finally, it is also possible to bootstrap the standard errors. For this I will use the
`bootstrap()`

function from the `{modelr}`

package:

```
resamples <- 100
boot_education <- education %>%
modelr::bootstrap(resamples)
```

Let’s take a look at the `boot_education`

object:

`boot_education`

```
## # A tibble: 100 x 2
## strap .id
## <list> <chr>
## 1 <S3: resample> 001
## 2 <S3: resample> 002
## 3 <S3: resample> 003
## 4 <S3: resample> 004
## 5 <S3: resample> 005
## 6 <S3: resample> 006
## 7 <S3: resample> 007
## 8 <S3: resample> 008
## 9 <S3: resample> 009
## 10 <S3: resample> 010
## # ... with 90 more rows
```

The column `strap`

contains resamples of the original data. I will run my linear regression
from before on each of the resamples:

```
(
boot_lin_reg <- boot_education %>%
mutate(regressions =
map(strap,
~lm(per_capita_exp ~ region + residents +
young_residents + per_capita_income,
data = .)))
)
```

```
## # A tibble: 100 x 3
## strap .id regressions
## <list> <chr> <list>
## 1 <S3: resample> 001 <S3: lm>
## 2 <S3: resample> 002 <S3: lm>
## 3 <S3: resample> 003 <S3: lm>
## 4 <S3: resample> 004 <S3: lm>
## 5 <S3: resample> 005 <S3: lm>
## 6 <S3: resample> 006 <S3: lm>
## 7 <S3: resample> 007 <S3: lm>
## 8 <S3: resample> 008 <S3: lm>
## 9 <S3: resample> 009 <S3: lm>
## 10 <S3: resample> 010 <S3: lm>
## # ... with 90 more rows
```

I have added a new column called `regressions`

which contains the linear regressions on each
bootstrapped sample. Now, I will create a list of tidied regression results:

```
(
tidied <- boot_lin_reg %>%
mutate(tidy_lm =
map(regressions, broom::tidy))
)
```

```
## # A tibble: 100 x 4
## strap .id regressions tidy_lm
## <list> <chr> <list> <list>
## 1 <S3: resample> 001 <S3: lm> <tibble [7 × 5]>
## 2 <S3: resample> 002 <S3: lm> <tibble [7 × 5]>
## 3 <S3: resample> 003 <S3: lm> <tibble [7 × 5]>
## 4 <S3: resample> 004 <S3: lm> <tibble [7 × 5]>
## 5 <S3: resample> 005 <S3: lm> <tibble [7 × 5]>
## 6 <S3: resample> 006 <S3: lm> <tibble [7 × 5]>
## 7 <S3: resample> 007 <S3: lm> <tibble [7 × 5]>
## 8 <S3: resample> 008 <S3: lm> <tibble [7 × 5]>
## 9 <S3: resample> 009 <S3: lm> <tibble [7 × 5]>
## 10 <S3: resample> 010 <S3: lm> <tibble [7 × 5]>
## # ... with 90 more rows
```

`broom::tidy()`

creates a data frame of the regression results. Let’s look at one of these:

`tidied$tidy_lm[[1]]`

```
## # A tibble: 7 x 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) -648. 138. -4.69 0.0000279
## 2 regionnortheast 50.0 24.6 2.03 0.0487
## 3 regionsouth 14.3 23.3 0.613 0.543
## 4 regionwest 34.7 24.9 1.39 0.171
## 5 residents -0.0931 0.0506 -1.84 0.0729
## 6 young_residents 1.85 0.342 5.40 0.00000273
## 7 per_capita_income 0.0801 0.0110 7.27 0.00000000524
```

This format is easier to handle than the standard `lm()`

output:

`tidied$regressions[[1]]`

```
##
## Call:
## lm(formula = per_capita_exp ~ region + residents + young_residents +
## per_capita_income, data = .)
##
## Coefficients:
## (Intercept) regionnortheast regionsouth
## -648.45429 49.99493 14.31032
## regionwest residents young_residents
## 34.73523 -0.09312 1.84805
## per_capita_income
## 0.08006
```

Now that I have all these regression results, I can compute any statistic I need. But first, let’s transform the data even further:

```
list_mods <- tidied %>%
pull(tidy_lm)
```

`list_mods`

is a list of the `tidy_lm`

data frames. I now add an index and
bind the rows together (by using `map2_df()`

instead of `map2()`

):

```
mods_df <- map2_df(list_mods,
seq(1, resamples),
~mutate(.x, resample = .y))
```

Let’s take a look at the final object:

`head(mods_df, 25)`

```
## # A tibble: 25 x 6
## term estimate std.error statistic p.value resample
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 (Intercept) -648. 138. -4.69 0.0000279 1
## 2 regionnortheast 50.0 24.6 2.03 0.0487 1
## 3 regionsouth 14.3 23.3 0.613 0.543 1
## 4 regionwest 34.7 24.9 1.39 0.171 1
## 5 residents -0.0931 0.0506 -1.84 0.0729 1
## 6 young_residents 1.85 0.342 5.40 0.00000273 1
## 7 per_capita_income 0.0801 0.0110 7.27 0.00000000524 1
## 8 (Intercept) -597. 145. -4.12 0.000170 2
## 9 regionnortheast 15.3 15.2 1.00 0.322 2
## 10 regionsouth 9.23 16.7 0.553 0.583 2
## # ... with 15 more rows
```

Now this is a very useful format, because I now can group by the `term`

column and compute any
statistics I need, in the present case the standard deviation:

```
(
r.std.error <- mods_df %>%
group_by(term) %>%
summarise(r.std.error = sd(estimate))
)
```

```
## # A tibble: 7 x 2
## term r.std.error
## <chr> <dbl>
## 1 (Intercept) 239.
## 2 per_capita_income 0.0235
## 3 regionnortheast 25.4
## 4 regionsouth 21.7
## 5 regionwest 22.7
## 6 residents 0.0705
## 7 young_residents 0.521
```

We can append this column to the linear regression model result:

```
lmfit %>%
broom::tidy() %>%
full_join(r.std.error) %>%
select(term, estimate, std.error, r.std.error)
```

`## Joining, by = "term"`

```
## # A tibble: 7 x 4
## term estimate std.error r.std.error
## <chr> <dbl> <dbl> <dbl>
## 1 (Intercept) -467. 143. 239.
## 2 regionnortheast 15.7 18.2 25.4
## 3 regionsouth 7.09 17.3 21.7
## 4 regionwest 34.3 17.5 22.7
## 5 residents -0.0346 0.0532 0.0705
## 6 young_residents 1.30 0.357 0.521
## 7 per_capita_income 0.0720 0.0131 0.0235
```

As you see, using the whole bootstrapping procedure is longer than simply using either one of the first two methods. However, this procedure is very flexible and can thus be adapted to a very large range of situations. Either way, in the case of heteroskedasticity, you can see that results vary a lot depending on the procedure you use, so I would advise to use them all as robustness tests and discuss the differences.

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