The goal of supernova is to create ANOVA tables in the
format used by Judd, McClelland, and Ryan (2017, ISBN: 978-1138819832)
in their introductory textbook, Data Analysis: A Model Comparison
Approach to Regression, ANOVA, and Beyond (book website).*
These tables include proportional reduction in error, a useful measure
for teaching the underlying concepts of ANOVA and regression, and
formatting to ease the transition between the book and R.
* Note: we are NOT affiliated with the authors or their institution.
In keeping with the approach in Judd, McClelland, and Ryan (2017), the ANOVA tables in this package are calculated using a model comparison approach that should be understandable given a beginner’s understanding of base R and the information from the book (so if you have these and don’t understand what is going on in the code, let us know because we are missing the mark!). Here is an explanation of how the tables are calculated for fully independent predictor variables (i.e. between-subjects designs):
The “Total” row is calculated by updating the model passed to an
empty model. For example,
lm(mpg ~ hp * disp, data = mtcars) is updated to
lm(mpg ~ NULL, data = mtcars). From this empty model, the
sum of squares and df can be calculated.
If there is at least one predictor in the model, the overall
model row and the error row are calculated. In the vernacular of the
book, the compact model is represented by the updated empty model from 1
above, and the augmented model is the original model passed to
supernova(). From these models the SSE(A) is calculated by
sum(resid(null_model) ^ 2), the SSR is calculated by SSE(C)
- SSE(A), the PRE for the overall model is extracted from the fit
(r.squared), the df for the error row is extracted from the
lm() fit (df.residuals).
If there are more than one predictors, the single term deletions
are computed using drop1(). For the model
y ~ a * b (which expands to y ~ a + b + a:b,
where a:b is the interaction of a and
b), drop1() essentially creates three models
each with one term removed: y ~ a + a:b,
y ~ b + a:b, and y ~ a + b. These models are
considered the compact models which do not include the tested terms
a, b, and a:b, respectively.
drop1() computes the SSR (Sum Sq) and SSE(C)
(RSS) for each of these augmented and compact model pairs,
and these values are used to compute the SSR and PRE for each.
Finally, the MS (SS / df),
F (MSR / MSE), and p columns are
calculated from already-computed values in the table.
The following models are explicitly tested and supported by
supernova(), for independent samples (between-subjects)
data only. For these models, there is also support for datasets
with missing or unbalanced data.
y ~ NULLy ~ ay ~ a + by ~ a * bAdditionally, a subset of within-subjects designs are supported and
explicitly tested. To accommodate these models supernova()
can accept models fit via lmer() as in the Examples below. Only models like those included in
those examples have been tested for within-subjects designs.
Anything not included above is not (yet) explicitly tested and may yield errors or incorrect statistics. This includes, but is not limited to
In addition to the ANOVA table provided by supernova(),
the supernova package provides some useful functions for
teaching ANOVA and pairwise comparisons:
Generate models: Generate the models that were
compared to create each row of an ANOVA table using
generate_models(). This can be done for each of the
different SS Types as described in Using Different SS Types below.
Pairwise comparisons: Test each categorical group in
a model against the others using pairwise(). This function
supports Tukey and Bonferroni corrections. See the Pairwise Comparisons section below.
You can install the released version of supernova from CRAN with:
install.packages("supernova")Alternatively you can download the package directly from this
repository using remotes:
library(remotes)
install_github("UCLATALL/supernova")Here are some basic examples of the code and output for this package:
supernova(lm(mpg ~ NULL, data = mtcars))
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ NULL
#>
#> SS df MS F PRE p
#> ----- --------------- | -------- --- ------ --- --- ---
#> Model (error reduced) | --- --- --- --- --- ---
#> Error (from model) | --- --- --- --- --- ---
#> ----- --------------- | -------- --- ------ --- --- ---
#> Total (empty model) | 1126.047 31 36.324supernova(lm(mpg ~ hp, data = mtcars))
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ hp
#>
#> SS df MS F PRE p
#> ----- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 678.373 1 678.373 45.460 0.6024 .0000
#> Error (from model) | 447.674 30 14.922
#> ----- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324supernova(lm(mpg ~ hp + disp, data = mtcars))
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ hp + disp
#>
#> SS df MS F PRE p
#> ----- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 842.554 2 421.277 43.095 0.7482 .0000
#> hp | 33.665 1 33.665 3.444 0.1061 .0737
#> disp | 164.181 1 164.181 16.795 0.3667 .0003
#> Error (from model) | 283.493 29 9.776
#> ----- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324supernova(lm(mpg ~ hp * disp, data = mtcars))
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ hp * disp
#>
#> SS df MS F PRE p
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 923.189 3 307.730 42.475 0.8198 .0000
#> hp | 113.393 1 113.393 15.651 0.3586 .0005
#> disp | 188.449 1 188.449 26.011 0.4816 .0000
#> hp:disp | 80.635 1 80.635 11.130 0.2844 .0024
#> Error (from model) | 202.858 28 7.245
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324supernova(lm(mpg ~ hp * disp, data = mtcars), verbose = FALSE)
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ hp * disp
#>
#> SS df MS F PRE p
#> ------- | -------- -- ------- ------ ------ -----
#> Model | 923.189 3 307.730 42.475 0.8198 .0000
#> hp | 113.393 1 113.393 15.651 0.3586 .0005
#> disp | 188.449 1 188.449 26.011 0.4816 .0000
#> hp:disp | 80.635 1 80.635 11.130 0.2844 .0024
#> Error | 202.858 28 7.245
#> ------- | -------- -- ------- ------ ------ -----
#> Total | 1126.047 31 36.324First let’s load up lme4 which gives us
lmer(), the function we will use to fit within-subjects
models. Additionally, install and load the JMRData package
which has some short datasets with non-independent observations, and
load dplyr and tidyr so that we can tidy the
data.
# Run this line if you do not have the JMRData package
# remotes::install_github("UCLATALL/JMRData")
library(lme4)
library(tidyr)
library(dplyr)
simple_crossed <- JMRData::ex11.9 %>%
gather(condition, puzzles_completed, -subject) %>%
mutate_at(vars(subject, condition), as.factor)
multiple_crossed <- JMRData::ex11.17 %>%
gather(condition, recall, -Subject) %>%
separate(condition, c("type", "time"), -1) %>%
mutate(across(c(Subject, type, time), as.factor))Fitting the simple_crossed data with lm()
would ignore the non-independence due to observations coming from the
same subject. Compare this output with the following output
where the model was fit with lmer() and the specification
of subject as a random factor:
simple_crossed %>%
lm(puzzles_completed ~ condition, data = .) %>%
supernova(verbose = FALSE)
#> Analysis of Variance Table (Type III SS)
#> Model: puzzles_completed ~ condition
#>
#> SS df MS F PRE p
#> ----- | ------ -- ----- ----- ------ -----
#> Model | 2.250 1 2.250 1.518 0.0978 .2382
#> Error | 20.750 14 1.482
#> ----- | ------ -- ----- ----- ------ -----
#> Total | 23.000 15 1.533
# use lmer() to specify the non-independence
simple_crossed %>%
lmer(puzzles_completed ~ condition + (1|subject), data = .) %>%
supernova()
#> Analysis of Variance Table (Type III SS)
#> Model: puzzles_completed ~ condition + (1 | subject)
#>
#> SS df MS F PRE p
#> ---------------------- | ------ -- ----- ----- ------ -----
#> Between Subjects |
#> Total | 18.000 7 2.571
#> ---------------------- | ------ -- ----- ----- ------ -----
#> Within Subjects |
#> condition | 2.250 1 2.250 5.727 0.4500 .0479
#> Error | 2.750 7 0.393
#> Total | 5.000 8 0.625
#> ---------------------- | ------ -- ----- ----- ------ -----
#> Total | 23.000 15 1.533Here is another example like the previous, but here multiple
variables (time, type) and their interaction
have been specified:
# fitting this with lm would ignore the non-independence due to Subject
multiple_crossed %>%
lm(recall ~ type * time, data = .) %>%
supernova(verbose = FALSE)
#> Analysis of Variance Table (Type III SS)
#> Model: recall ~ type * time
#>
#> SS df MS F PRE p
#> --------- | ------- -- ------ ----- ------ -----
#> Model | 85.367 5 17.073 2.791 0.3677 .0400
#> type | 12.100 1 12.100 1.978 0.0761 .1724
#> time | 44.400 2 22.200 3.629 0.2322 .0420
#> type:time | 1.867 2 0.933 0.153 0.0126 .8593
#> Error | 146.800 24 6.117
#> --------- | ------- -- ------ ----- ------ -----
#> Total | 232.167 29 8.006
# using lmer() we can specify the non-independence
multiple_crossed %>%
lmer(recall ~ type * time + (1|Subject) + (1|type:Subject) + (1|time:Subject), data = .) %>%
supernova()
#> Analysis of Variance Table (Type III SS)
#> Model: recall ~ type * time + (1 | Subject) + (1 | type:Subject) + (1 | time:Subject)
#>
#> SS df MS F PRE p
#> ---------------------- | ------- -- ------ ------ ------ -----
#> Between Subjects |
#> Total | 131.000 4 32.750
#> ---------------------- | ------- -- ------ ------ ------ -----
#> Within Subjects |
#> type | 17.633 1 17.633 11.376 0.7399 .0280
#> Error | 6.200 4 1.550
#> time | 65.867 2 32.933 29.939 0.8821 .0002
#> Error | 8.800 8 1.100
#> type:time | 1.867 2 0.933 9.333 0.7000 .0081
#> Error | 0.800 8 0.100
#> Total | 101.167 25 4.047
#> ---------------------- | ------- -- ------ ------ ------ -----
#> Total | 232.167 29 8.006In this example, each person in a group of three generates a rating.
If we fit the data with lm() that would ignore the
non-independence due to the people being in the same group.
Compare this output with the following output where the
group is specified as a random factor.
simple_nested <- JMRData::ex11.1 %>%
gather(id, value, starts_with("score")) %>%
mutate(across(c(group, instructions, id), as.factor))
# fitting this with lm would ignore the non-independence due to group
simple_nested %>%
lm(value ~ instructions, data = .) %>%
supernova(verbose = FALSE)
#> Analysis of Variance Table (Type III SS)
#> Model: value ~ instructions
#>
#> SS df MS F PRE p
#> ----- | ------ -- ------ ------ ------ -----
#> Model | 12.500 1 12.500 12.500 0.4386 .0027
#> Error | 16.000 16 1.000
#> ----- | ------ -- ------ ------ ------ -----
#> Total | 28.500 17 1.676
# using lmer() we can specify the non-independence
simple_nested %>%
lmer(value ~ instructions + (1|group), data = .) %>%
supernova()
#> Analysis of Variance Table (Type III SS)
#> Model: value ~ instructions + (1 | group)
#>
#> SS df MS F PRE p
#> ---------------------- | ------ -- ------ ----- ------ -----
#> Between Subjects |
#> instructions | 12.500 1 12.500 4.687 0.5396 .0963
#> Error | 10.667 4 2.667
#> Total | 23.167 5 4.633
#> ---------------------- | ------ -- ------ ----- ------ -----
#> Within Subjects |
#> Total | 5.333 12 0.444
#> ---------------------- | ------ -- ------ ----- ------ -----
#> Total | 28.500 17 1.676In this example, each person in heterosexual marriage generates a
rating of satisfaction. Additionally, these couples were chosen such
that they either have children or not, and have been married 15 vs. 30
years. If we fit the data with lm() that would ignore the
non-independence due to the people being in the same
couple. Compare this output with the following output where
the group is specified as a random factor.
complex_nested <- JMRData::ex11.22 %>%
gather(sex, rating, Male, Female) %>%
mutate(across(c(couple, children, sex, yearsmarried), as.factor))
# fitting this with lm would ignore the non-independence due to group
complex_nested %>%
lm(rating ~ sex * yearsmarried * children, data = .) %>%
supernova(verbose = FALSE)
#> Analysis of Variance Table (Type III SS)
#> Model: rating ~ sex * yearsmarried * children
#>
#> SS df MS F PRE p
#> ------------------------- | ------ -- ----- ------ ------ -----
#> Model | 26.500 7 3.786 5.345 0.6092 .0009
#> sex | 0.000 1 0.000 0.000 0.0000 .0000
#> yearsmarried | 1.125 1 1.125 1.588 0.0621 .2197
#> children | 2.000 1 2.000 2.824 0.1053 .1059
#> sex:yearsmarried | 2.250 1 2.250 3.176 0.1169 .0874
#> sex:children | 0.062 1 0.062 0.088 0.0037 .7690
#> yearsmarried:children | 7.563 1 7.563 10.676 0.3079 .0033
#> sex:yearsmarried:children | 0.500 1 0.500 0.706 0.0286 .4091
#> Error | 17.000 24 0.708
#> ------------------------- | ------ -- ----- ------ ------ -----
#> Total | 43.500 31 1.403
# using lmer() we can specify the non-independence
complex_nested %>%
lmer(rating ~ sex * yearsmarried * children + (1|couple), data = .) %>%
supernova()
#> Analysis of Variance Table (Type III SS)
#> Model: rating ~ sex * yearsmarried * children + (1 | couple)
#>
#> SS df MS F PRE p
#> --------------------------- | ------ -- ------ ----- ------ -----
#> Between Subjects |
#> yearsmarried | 10.125 1 10.125 9.529 0.4426 .0094
#> children | 0.500 1 0.500 0.471 0.0377 .5058
#> yearsmarried:children | 10.125 1 10.125 9.529 0.4426 .0094
#> Error | 12.750 12 1.063
#> Total | 33.500 15 2.233
#> --------------------------- | ------ -- ------ ----- ------ -----
#> Within Subjects |
#> sex | 3.125 1 3.125 8.824 0.4237 .0117
#> sex:yearsmarried | 2.000 1 2.000 5.647 0.3200 .0350
#> sex:children | 0.125 1 0.125 0.353 0.0286 .5635
#> sex:yearsmarried:children | 0.500 1 0.500 1.412 0.1053 .2577
#> Error | 4.250 12 0.354
#> Total | 10.000 16 0.625
#> --------------------------- | ------ -- ------ ----- ------ -----
#> Total | 43.500 31 1.403supernova(lm(mpg ~ hp * disp, data = mtcars))
#> Analysis of Variance Table (Type III SS)
#> Model: mpg ~ hp * disp
#>
#> SS df MS F PRE p
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 923.189 3 307.730 42.475 0.8198 .0000
#> hp | 113.393 1 113.393 15.651 0.3586 .0005
#> disp | 188.449 1 188.449 26.011 0.4816 .0000
#> hp:disp | 80.635 1 80.635 11.130 0.2844 .0024
#> Error (from model) | 202.858 28 7.245
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324These are equivalent to the above:
supernova(lm(mpg ~ hp * disp, data = mtcars), type = 3)
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "III")
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "orthogonal")supernova(lm(mpg ~ hp * disp, data = mtcars), type = 1)
#> Analysis of Variance Table (Type I SS)
#> Model: mpg ~ hp * disp
#>
#> SS df MS F PRE p
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 923.189 3 307.730 42.475 0.8198 .0000
#> hp | 678.373 1 678.373 93.634 0.7698 .0000
#> disp | 164.181 1 164.181 22.661 0.4473 .0001
#> hp:disp | 80.635 1 80.635 11.130 0.2844 .0024
#> Error (from model) | 202.858 28 7.245
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324These are equivalent to the above:
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "I")
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "sequential")supernova(lm(mpg ~ hp * disp, data = mtcars), type = 2)
#> Analysis of Variance Table (Type II SS)
#> Model: mpg ~ hp * disp
#>
#> SS df MS F PRE p
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Model (error reduced) | 923.189 3 307.730 42.475 0.8198 .0000
#> hp | 33.665 1 33.665 4.647 0.1423 .0399
#> disp | 164.181 1 164.181 22.661 0.4473 .0001
#> hp:disp | 80.635 1 80.635 11.130 0.2844 .0024
#> Error (from model) | 202.858 28 7.245
#> ------- --------------- | -------- -- ------- ------ ------ -----
#> Total (empty model) | 1126.047 31 36.324These are equivalent to the above:
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "II")
supernova(lm(mpg ~ hp * disp, data = mtcars), type = "hierarchical")This package is based on a model comparison approach to understanding
regression and ANOVA. As such it is useful to know which models are
being compared for any given term in an ANOVA table. The
generate_models() function accepts a linear model and the
desired type of SS and returns a list of the models that should be
compared to appropriately evaluate each term in the full model.
generate_models(lm(mpg ~ hp * disp, data = mtcars), type = 2)
#>
#> ── Comparison Models for Type III SS ───────────────────────────────────────────
#>
#> ── Full Model
#> complex: mpg ~ hp + disp + hp:disp
#> simple: mpg ~ NULL
#>
#> ── hp
#> complex: mpg ~ hp + disp + hp:disp
#> simple: mpg ~ disp + hp:disp
#>
#> ── disp
#> complex: mpg ~ hp + disp + hp:disp
#> simple: mpg ~ hp + hp:disp
#>
#> ── hp:disp
#> complex: mpg ~ hp + disp + hp:disp
#> simple: mpg ~ hp + dispThe pairwise() function takes a linear model and
performs the requested pairwise comparisons on the categorical terms in
the model. For simple one-way models where a single categorical variable
predicts and outcome, you will get output similar to other methods of
computing pairwise comparisons (e.g. TukeyHSD() or
t.test()). Essentially, the differences on the outcome
between each of the groups defined by the categorical variable are
compared with the requested test, and their confidence intervals and
p-values are adjusted by the requested
correction.
However, when more than two variables are entered into the model, the outcome will diverge somewhat from other methods of computing pairwise comparisons. For traditional pairwise tests you need to estimate an error term, usually by pooling the standard deviation of the groups being compared. This means that when you have other predictors in the model, their presence is ignored when running these tests. For the functions in this package, we instead compute the pooled standard error by using the mean squared error (MSE) from the full model fit.
Let’s take a concrete example to explain that. If we are predicting a
car’s miles-per-gallon (mpg) based on whether it has an
automatic or manual transmission (am), we can create that
linear model and get the pairwise comparisons like this:
pairwise(lm(mpg ~ factor(am), data = mtcars))
#>
#> ── Tukey's Honestly Significant Differences ────────────────────────────────────
#> Model: mpg ~ factor(am)
#> factor(am)
#> Levels: 2
#> Family-wise error-rate: 0.05
#>
#> group_1 group_2 diff pooled_se q df lower upper p_adj
#> <chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 0 7.245 1.248 5.807 30 3.642 10.848 .0003The output of this code will is one table showing the comparison of manual and automatic transmissions with regard to miles-per-gallon. The pooled standard error is the same as the square root of the MSE from the full model.
In these data the am variable did not have any other
values than automatic and manual, but we can imagine
situations where the predictor has more than two levels. In these cases,
the pooled SD would be calculated by taking the MSE of the full model
(not of each group) and then weighting it based on the size of the
groups in question (divide by n).
To improve our model, we might add the car’s displacement
(disp) as a quantitative predictor:
pairwise(lm(mpg ~ factor(am) + disp, data = mtcars))
#>
#> ── Tukey's Honestly Significant Differences ────────────────────────────────────
#> Model: mpg ~ factor(am) + disp
#> factor(am)
#> Levels: 2
#> Family-wise error-rate: 0.05
#>
#> group_1 group_2 diff pooled_se q df lower upper p_adj
#> <chr> <chr> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 1 0 7.245 0.819 8.846 29 4.876 9.614 .0000Note that the output still only has a table for am. This
is because we can’t do a pairwise comparison using disp
because there are no groups to compare. Most functions will drop or not
let you use this variable during pairwise comparisons. Instead,
pairwise() uses the same approach as in the 3+ groups
situation: we use the MSE for the full model and then weight it
by the size of the groups being compared. Because we are using the MSE
for the full model, the effect of disp is accounted for in
the error term even though we are not explicitly comparing different
displacements. Importantly, the interpretation of the
outcome is different than in other traditional t-tests. Instead of
saying, “there is a difference in miles-per-gallon based on the type of
transmission,” we must add that this difference is found “after
accounting for displacement.”
Finally, the output can be plotted either by using
plot() on the returned object, or specifying
plot = TRUE:
output <- pairwise(lm(mpg ~ factor(am) + disp, data = mtcars))
plot(output)
This would produce an identical plot:
pairwise(lm(mpg ~ factor(am) + disp, data = mtcars), plot = TRUE)If you see an issue, problem, or improvement that you think we should know about, or you think would fit with this package, please let us know on our issues page. Alternatively, if you are up for a little coding of your own, submit a pull request:
git checkout -b my-new-featuregit commit -am 'Add some feature'git push origin my-new-feature