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minor tweaks
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@IndrajeetPatil
IndrajeetPatil committed Mar 17, 2020
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3 changes: 2 additions & 1 deletion .github/workflows/check-full.yaml
View file
Expand Up @@ -19,6 +19,7 @@ jobs:
matrix:
config:
- {os: windows-latest, r: '3.6', args: "--no-manual"}
- {os: windows-latest, r: 'devel', args: "--no-manual"}
- {os: macOS-latest, r: '3.6', args: "--no-manual"}
- {os: macOS-latest, r: 'devel', args: "--no-manual"}
- {os: ubuntu-16.04, r: '3.6', cran: "https://demo.rstudiopm.com/all/__linux__/xenial/latest", args: "--no-manual"}
Expand Down Expand Up @@ -77,6 +78,6 @@ jobs:
path: check

- name: Test coverage
if: matrix.config.os == 'macOS-latest' && matrix.config.r == '3.6'
if: matrix.config.os == 'macOS-latest'
run: covr::codecov(token = "${{secrets.CODECOV_TOKEN}}")
shell: Rscript {0}
63 changes: 36 additions & 27 deletions README.Rmd
View file
Expand Up @@ -106,19 +106,23 @@ packages also provide similar functions, more specifically
[parameters](https://easystats.github.io/parameters/) and
[performance](https://easystats.github.io/performance/). Sometimes the `broom`
packages might not contain a `tidy`/`glance` method for a given regression
object, while `easystats` packages would and vice versa. The hybrid functions in
`broomExtra` make it easy to retrieve these summaries with the appropriate
method and do so robustly:
object, while `easystats` packages would and *vice versa*.

The hybrid functions in `broomExtra` make it easy to retrieve these summaries
with the appropriate method and do so robustly:

- `broom::tidy` + `parameters::model_parameters` =
`broomExtra::tidy_parameters`

- `broom::glance` + `performance::model_performance` =
`broomExtra::glance_performance`

## `tidy_parameters`
## Benefits of using `tidy_parameters`

The `tidy_parameters` will return a model summary either from `broom::tidy` or
`parameters::model_parameters`.
<font color="green">
The `tidy_parameters` will return a model summary either from `broomExtra::tidy`
or `parameters::model_parameters`.
</font>

Sometimes the method will not be available in `broom`, while it will be in
`parameters`:
Expand Down Expand Up @@ -163,10 +167,13 @@ parameters::model_parameters(co)
broomExtra::tidy_parameters(co)
```

These functions are also pretty robust such that they won't fail if the `...`
contains misspecified arguments. This makes these functions much easier to work
with while writing wrapper functions around `broom::tidy` or
`parameters::model_parameters`.
<font color="green">
These functions are robust such that they won't fail if the `...` contains
misspecified arguments.
</font>

This makes these functions much easier to work with while writing wrapper
functions around `broomExtra::tidy` or `parameters::model_parameters`.

```{r tidy_parameters3}
# setup
Expand Down Expand Up @@ -198,9 +205,10 @@ parameters::model_parameters(mod_lavaan, exponentiate = TRUE)
broomExtra::tidy_parameters(mod_lavaan, exponentiate = TRUE)
```

Additional benefit of using this function is that it will also make sure that
*p*-values and confidence intervals for regression estimates are consistently
included.
<font color="green">
Additionally, the *p*-values and confidence intervals for regression estimates
are consistently included.
</font>

```{r tidy_parameters4}
# setup
Expand All @@ -215,10 +223,12 @@ broomExtra::tidy(mod, conf.int = TRUE)
broomExtra::tidy_parameters(mod)
```

## `glance_performance`
## Benefits of using `glance_performance`

<font color="green">
The `glance_performance` will return a model summary either from `broom::glance`
or `performance::model_performance`:
or `performance::model_performance`.
</font>

```{r glance_performanc1}
# mixor object
Expand Down Expand Up @@ -262,11 +272,14 @@ performance::model_performance(co)
broomExtra::glance_performance(co)
```

Another benefit of using this function is that it will return model summary that
contains combined metrics from these two packages. For example, some unique
performance measures are present only in the `performance` package contains
(e.g., Nagelkerke's R-squared, Tjur's R-squared, etc.), but not `broom` package
and vice versa.
<font color="green">
Additionally, it will return model summary that contains combined metrics from
these two packages.
</font>

For example, some unique performance measures are present only in the
`performance` package contains (e.g., Nagelkerke's *R*-squared, Tjur's
*R*-squared, etc.), but not `broom` package and *vice versa*.

```{r glance_performanc3}
# setup
Expand Down Expand Up @@ -295,6 +308,7 @@ Currently, `S3` methods for mixed-effects model objects are included in the
of the object (e.g., "if it is `merMod` object, use
`broom.mixed::tidy()`/`broom.mixed::glance()`/`broom.mixed::augment()`, but if
it is `polr` object, use `broom::tidy()`/`broom::glance()`/`broom::augment()`").

Using generics from `broomExtra` means you no longer have to worry about this,
as calling `broomExtra::tidy()`/`broomExtra::glance()`/`broomExtra::augment()`
will search the appropriate method from these two packages and return the
Expand All @@ -315,17 +329,12 @@ broomExtra::tidy(x = lmm.mod, effects = "fixed")
# linear model (`broom` will be used)
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::tidy(x = lm.mod, conf.int = TRUE)
broomExtra::tidy(lm.mod, conf.int = TRUE)
# another example with `broom`
# cumulative Link Models
clm.mod <- clm(rating ~ temp * contact, data = wine)
broomExtra::tidy(
x = clm.mod,
exponentiate = TRUE,
conf.int = TRUE,
conf.type = "Wald"
)
broomExtra::tidy(x = clm.mod, exponentiate = TRUE)
# unsupported object (the function will return `NULL` in such cases)
broomExtra::tidy(list(1, c("x", "y")))
Expand Down
91 changes: 42 additions & 49 deletions README.md
View file
Expand Up @@ -92,19 +92,22 @@ tidy summaries for model parameters and model performance. The
specifically [parameters](https://easystats.github.io/parameters/) and
[performance](https://easystats.github.io/performance/). Sometimes the
`broom` packages might not contain a `tidy`/`glance` method for a given
regression object, while `easystats` packages would and vice versa. The
hybrid functions in `broomExtra` make it easy to retrieve these
regression object, while `easystats` packages would and *vice versa*.

The hybrid functions in `broomExtra` make it easy to retrieve these
summaries with the appropriate method and do so robustly:

- `broom::tidy` + `parameters::model_parameters` =
`broomExtra::tidy_parameters`

- `broom::glance` + `performance::model_performance` =
`broomExtra::glance_performance`

## `tidy_parameters`
## Benefits of using `tidy_parameters`

The `tidy_parameters` will return a model summary either from
`broom::tidy` or `parameters::model_parameters`.
<font color="green"> The `tidy_parameters` will return a model summary
either from `broomExtra::tidy` or `parameters::model_parameters`.
</font>

Sometimes the method will not be available in `broom`, while it will be
in `parameters`:
Expand Down Expand Up @@ -179,10 +182,12 @@ broomExtra::tidy_parameters(co)
#> 4 disp -0.00136 0.0110 -0.123 0.903
```

These functions are also pretty robust such that they won’t fail if the
`...` contains misspecified arguments. This makes these functions much
easier to work with while writing wrapper functions around `broom::tidy`
or `parameters::model_parameters`.
<font color="green"> These functions are robust such that they won’t
fail if the `...` contains misspecified arguments. </font>

This makes these functions much easier to work with while writing
wrapper functions around `broomExtra::tidy` or
`parameters::model_parameters`.

``` r
# setup
Expand Down Expand Up @@ -242,9 +247,8 @@ broomExtra::tidy_parameters(mod_lavaan, exponentiate = TRUE)
#> # ... with 14 more rows
```

Additional benefit of using this function is that it will also make sure
that *p*-values and confidence intervals for regression estimates are
consistently included.
<font color="green"> Additionally, the *p*-values and confidence
intervals for regression estimates are consistently included. </font>

``` r
# setup
Expand Down Expand Up @@ -273,10 +277,11 @@ broomExtra::tidy_parameters(mod)
#> 4 Acid.Conc. -0.128 0.129 -0.992 -0.380 0.125 0.335
```

## `glance_performance`
## Benefits of using `glance_performance`

The `glance_performance` will return a model summary either from
`broom::glance` or `performance::model_performance`:
<font color="green"> The `glance_performance` will return a model
summary either from `broom::glance` or `performance::model_performance`.
</font>

``` r
# mixor object
Expand Down Expand Up @@ -333,11 +338,12 @@ broomExtra::glance_performance(co)
#> 1 2.06 0.521 32
```

Another benefit of using this function is that it will return model
summary that contains combined metrics from these two packages. For
example, some unique performance measures are present only in the
`performance` package contains (e.g., Nagelkerke’s R-squared, Tjur’s
R-squared, etc.), but not `broom` package and vice versa.
<font color="green"> Additionally, it will return model summary that
contains combined metrics from these two packages. </font>

For example, some unique performance measures are present only in the
`performance` package contains (e.g., Nagelkerke’s *R*-squared, Tjur’s
*R*-squared, etc.), but not `broom` package and *vice versa*.

``` r
# setup
Expand Down Expand Up @@ -378,9 +384,10 @@ keep track of the class of the object (e.g., “if it is `merMod` object,
use
`broom.mixed::tidy()`/`broom.mixed::glance()`/`broom.mixed::augment()`,
but if it is `polr` object, use
`broom::tidy()`/`broom::glance()`/`broom::augment()`”). Using generics
from `broomExtra` means you no longer have to worry about this, as
calling
`broom::tidy()`/`broom::glance()`/`broom::augment()`”).

Using generics from `broomExtra` means you no longer have to worry about
this, as calling
`broomExtra::tidy()`/`broomExtra::glance()`/`broomExtra::augment()` will
search the appropriate method from these two packages and return the
results.
Expand All @@ -406,7 +413,7 @@ broomExtra::tidy(x = lmm.mod, effects = "fixed")

# linear model (`broom` will be used)
lm.mod <- lm(Reaction ~ Days, sleepstudy)
broomExtra::tidy(x = lm.mod, conf.int = TRUE)
broomExtra::tidy(lm.mod, conf.int = TRUE)
#> # A tibble: 2 x 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
Expand All @@ -416,31 +423,17 @@ broomExtra::tidy(x = lm.mod, conf.int = TRUE)
# another example with `broom`
# cumulative Link Models
clm.mod <- clm(rating ~ temp * contact, data = wine)
broomExtra::tidy(
x = clm.mod,
exponentiate = TRUE,
conf.int = TRUE,
conf.type = "Wald"
)
#> # A tibble: 7 x 8
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1|2 0.244 0.545 -2.59 9.66e- 3 0.0837 0.710
#> 2 2|3 3.14 0.510 2.24 2.48e- 2 1.16 8.52
#> 3 3|4 29.3 0.638 5.29 1.21e- 7 8.38 102.
#> 4 4|5 140. 0.751 6.58 4.66e-11 32.1 610.
#> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 2.58 40.2
#> 6 contactyes 3.85 0.660 2.04 4.13e- 2 1.05 14.0
#> 7 tempwarm:contactyes 1.43 0.924 0.389 6.97e- 1 0.234 8.76
#> coef.type
#> <chr>
#> 1 intercept
#> 2 intercept
#> 3 intercept
#> 4 intercept
#> 5 location
#> 6 location
#> 7 location
broomExtra::tidy(x = clm.mod, exponentiate = TRUE)
#> # A tibble: 7 x 6
#> term estimate std.error statistic p.value coef.type
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 1|2 0.244 0.545 -2.59 9.66e- 3 intercept
#> 2 2|3 3.14 0.510 2.24 2.48e- 2 intercept
#> 3 3|4 29.3 0.638 5.29 1.21e- 7 intercept
#> 4 4|5 140. 0.751 6.58 4.66e-11 intercept
#> 5 tempwarm 10.2 0.701 3.31 9.28e- 4 location
#> 6 contactyes 3.85 0.660 2.04 4.13e- 2 location
#> 7 tempwarm:contactyes 1.43 0.924 0.389 6.97e- 1 location

# unsupported object (the function will return `NULL` in such cases)
broomExtra::tidy(list(1, c("x", "y")))
Expand Down
2 changes: 1 addition & 1 deletion docs/articles/tests_and_coverage.html
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tests/testthat/test-hybrid_generics.R:98:7: style: Commented code should be removed.

​    # testthat::expect_equal(dim(df_orcutt), c(1L, 9L))
      ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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tests/testthat/test-hybrid_generics.R:98:7: style: Commented code should be removed.

​    # testthat::expect_equal(dim(df_orcutt), c(1L, 9L))
      ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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tests/testthat/test-hybrid_generics.R:98:7: style: Commented code should be removed.

​    # testthat::expect_equal(dim(df_orcutt), c(1L, 9L))
      ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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tests/testthat/test-hybrid_generics.R:98:7: style: Commented code should be removed.

​    # testthat::expect_equal(dim(df_orcutt), c(1L, 9L))
      ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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