The package is simple to use. First, lets load the basic packages.
library(rmdl)
#> Loading required package: vctrs
#> Loading required package: tibble
#>
#> Attaching package: 'tibble'
#> The following object is masked from 'package:vctrs':
#>
#> data_frameThe mtcars dataset will serve as the example, and we
will use linear regressions as the primary test. Next, we will evaluate
a toy dataset and evaluate how a fmls object is
generated.
# Look at potential data from the `mtcars` dataset
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
baseFormula <- mpg ~ wt + hp
rFormula <- fmls(mpg ~ wt + hp)
# Similar to the base formula
rFormula
#> mpg ~ wt + hpNow we can fit the hypothesis to its data - in this case, a simple
linear regression. The option to return the model as raw or not is
given. If TRUE, the default, then the expected result from
the modeling fit will be returned in the form of a list of models, based
on the fitting function provided.
# Uses a custom fit function to return linear models
listModels <-
rFormula |>
fit(.fn = lm, data = mtcars, raw = TRUE)For our purposes though, we want to use the custom fit method, which
retains more key information. This creates a mdl object,
which is simply a wrapper around base or package-specific models.
# Uses a custom fit function
rModel <-
rFormula |>
fit(.fn = lm, data = mtcars, raw = FALSE)
rModel
#> <model[1]>
#> lm(mpg ~ wt + hp)The model wrapper is helpful in that it can be unpacked into a table
of elements, which then stores our model for later usage in a research
workflow. For this purpose, we introduce the mdl_tbl class,
which another core class with specific and generic dispatch methods.
# An additional model to work with
r2Model <-
fmls(am ~ cyl + hp, pattern = "sequential") |>
fit(.fn = glm, family = "binomial", data = mtcars, raw = FALSE)
# Displays the two additional logistic regressions performed
r2Model
#> <model[2]>
#> glm(am ~ cyl)
#> glm(am ~ cyl + hp)
# Creation of a table of models
rTable <- model_table(mileage = rModel, automatic = r2Model)
rTable
#> <mdl_tbl>
#> id formula_index data_id name model_call formula_call outcome exposure
#> <chr> <list> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 9f27b807… <int [3]> mtcars mile… lm mpg ~ wt + … mpg NA
#> 2 4ca7d927… <dbl [3]> mtcars auto… glm am ~ cyl am NA
#> 3 f577d80f… <dbl [3]> mtcars auto… glm am ~ cyl + … am NA
#> # ℹ 7 more variables: mediator <chr>, interaction <chr>, strata <lgl>,
#> # level <lgl>, model_parameters <list>, model_summary <list>,
#> # fit_status <lgl>The mdl_tbl class is a useful way to store and manage
multiple models, and can be used to generate tables for publication or
for internal use. To quickly access the content (e.g. estimates,
standard errors, etc.), there is an experimental function called
flatten_models() that can be used. Note that we are also
exponentiating the coefficients for the logistic regression models
(called by name).
fTable <-
rTable |>
flatten_models(exponentiate = TRUE, which = "automatic")
# Display contents
fTable
#> # A tibble: 8 × 34
#> formula_call model_call data_id name number outcome exposure mediator
#> <chr> <chr> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 mpg ~ wt + hp lm mtcars mileage 2 mpg NA NA
#> 2 mpg ~ wt + hp lm mtcars mileage 2 mpg NA NA
#> 3 mpg ~ wt + hp lm mtcars mileage 2 mpg NA NA
#> 4 am ~ cyl glm mtcars automatic 1 am NA NA
#> 5 am ~ cyl glm mtcars automatic 1 am NA NA
#> 6 am ~ cyl + hp glm mtcars automatic 2 am NA NA
#> 7 am ~ cyl + hp glm mtcars automatic 2 am NA NA
#> 8 am ~ cyl + hp glm mtcars automatic 2 am NA NA
#> # ℹ 26 more variables: interaction <chr>, strata <lgl>, level <lgl>,
#> # term <chr>, estimate <dbl>, std_error <dbl>, statistic <dbl>,
#> # p_value <dbl>, conf_low <dbl>, conf_high <dbl>, r_squared <dbl>,
#> # adj_r_squared <dbl>, sigma <dbl>, model_statistic <dbl>,
#> # model_p_value <dbl>, df <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>,
#> # deviance <dbl>, df_residual <int>, nobs <int>, degrees_freedom <dbl>,
#> # var_cov <list>, null_deviance <dbl>, df_null <int>
# Filter down to relevant models
fTable |>
dplyr::select(name, number, outcome, term, estimate, conf_low, conf_high, p_value, nobs)
#> # A tibble: 8 × 9
#> name number outcome term estimate conf_low conf_high p_value nobs
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 mileage 2 mpg (Intercep… 37.2 34.0 4.05e+1 2.57e-20 32
#> 2 mileage 2 mpg wt -3.88 -5.17 -2.58e+0 1.12e- 6 32
#> 3 mileage 2 mpg hp -0.0318 -0.0502 -1.33e-2 1.45e- 3 32
#> 4 automatic 1 am (Intercep… 43.7 2.58 1.28e+3 1.45e- 2 32
#> 5 automatic 1 am cyl 0.501 0.286 7.92e-1 6.42e- 3 32
#> 6 automatic 2 am (Intercep… 341. 9.44 3.91e+4 4.76e- 3 32
#> 7 automatic 2 am cyl 0.182 0.0436 5.05e-1 4.73e- 3 32
#> 8 automatic 2 am hp 1.03 1.00 1.06e+0 4.23e- 2 32