Compute and extract model parameters of multiple regression
models. See `model_parameters()`

for further details.

## Usage

```
compare_parameters(
...,
ci = 0.95,
effects = "fixed",
component = "conditional",
standardize = NULL,
exponentiate = FALSE,
ci_method = "wald",
p_adjust = NULL,
select = NULL,
column_names = NULL,
pretty_names = TRUE,
coefficient_names = NULL,
keep = NULL,
drop = NULL,
include_reference = FALSE,
groups = NULL,
verbose = TRUE
)
compare_models(
...,
ci = 0.95,
effects = "fixed",
component = "conditional",
standardize = NULL,
exponentiate = FALSE,
ci_method = "wald",
p_adjust = NULL,
select = NULL,
column_names = NULL,
pretty_names = TRUE,
coefficient_names = NULL,
keep = NULL,
drop = NULL,
include_reference = FALSE,
groups = NULL,
verbose = TRUE
)
```

## Arguments

- ...
One or more regression model objects, or objects returned by

`model_parameters()`

. Regression models may be of different model types. Model objects may be passed comma separated, or as a list. If model objects are passed with names or the list has named elements, these names will be used as column names.- ci
Confidence Interval (CI) level. Default to

`0.95`

(`95%`

).- effects
Should parameters for fixed effects (

`"fixed"`

), random effects (`"random"`

), or both (`"all"`

) be returned? Only applies to mixed models. May be abbreviated. If the calculation of random effects parameters takes too long, you may use`effects = "fixed"`

.- component
Model component for which parameters should be shown. See documentation for related model class in

`model_parameters()`

.- standardize
The method used for standardizing the parameters. Can be

`NULL`

(default; no standardization),`"refit"`

(for re-fitting the model on standardized data) or one of`"basic"`

,`"posthoc"`

,`"smart"`

,`"pseudo"`

. See 'Details' in`standardize_parameters()`

.**Importantly**:The

`"refit"`

method does*not*standardize categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as**lm.beta**) or other software packages (like SPSS). to mimic such behaviours, either use`standardize="basic"`

or standardize the data with`datawizard::standardize(force=TRUE)`

*before*fitting the model.For mixed models, when using methods other than

`"refit"`

, only the fixed effects will be standardized.Robust estimation (i.e.,

`vcov`

set to a value other than`NULL`

) of standardized parameters only works when`standardize="refit"`

.

- exponentiate
Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use

`exponentiate = TRUE`

for models with log-transformed response values.**Note:**Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For`compare_parameters()`

,`exponentiate = "nongaussian"`

will only exponentiate coefficients from non-Gaussian families.- ci_method
Method for computing degrees of freedom for p-values and confidence intervals (CI). See documentation for related model class in

`model_parameters()`

.- p_adjust
Character vector, if not

`NULL`

, indicates the method to adjust p-values. See`stats::p.adjust()`

for details. Further possible adjustment methods are`"tukey"`

,`"scheffe"`

,`"sidak"`

and`"none"`

to explicitly disable adjustment for`emmGrid`

objects (from**emmeans**).- select
Determines which columns and and which layout columns are printed. There are three options for this argument:

Selecting columns by name or index

`select`

can be a character vector (or numeric index) of column names that should be printed. There are two pre-defined options for selecting columns:`select = "minimal"`

prints coefficients, confidence intervals and p-values, while`select = "short"`

prints coefficients, standard errors and p-values.A string expression with layout pattern

`select`

is a string with "tokens" enclosed in braces. These tokens will be replaced by their associated columns, where the selected columns will be collapsed into one column. However, it is possible to create multiple columns as well. Following tokens are replaced by the related coefficients or statistics:`{estimate}`

,`{se}`

,`{ci}`

(or`{ci_low}`

and`{ci_high}`

),`{p}`

and`{stars}`

. The token`{ci}`

will be replaced by`{ci_low}, {ci_high}`

. Furthermore, a`|`

separates values into new cells/columns. If`format = "html"`

, a`<br>`

inserts a line break inside a cell. See 'Examples'.A string indicating a pre-defined layout

`select`

can be one of the following string values, to create one of the following pre-defined column layouts:`"ci"`

: Estimates and confidence intervals, no asterisks for p-values. This is equivalent to`select = "{estimate} ({ci})"`

.`"se"`

: Estimates and standard errors, no asterisks for p-values. This is equivalent to`select = "{estimate} ({se})"`

.`"ci_p"`

: Estimates, confidence intervals and asterisks for p-values. This is equivalent to`select = "{estimate}{stars} ({ci})"`

.`"se_p"`

: Estimates, standard errors and asterisks for p-values. This is equivalent to`select = "{estimate}{stars} ({se})"`

..`"ci_p2"`

: Estimates, confidence intervals and numeric p-values, in two columns. This is equivalent to`select = "{estimate} ({ci})|{p}"`

.`"se_p2"`

: Estimate, standard errors and numeric p-values, in two columns. This is equivalent to`select = "{estimate} ({se})|{p}"`

.

For

`model_parameters()`

, glue-like syntax is still experimental in the case of more complex models (like mixed models) and may not return expected results.- column_names
Character vector with strings that should be used as column headers. Must be of same length as number of models in

`...`

.- pretty_names
Can be

`TRUE`

, which will return "pretty" (i.e. more human readable) parameter names. Or`"labels"`

, in which case value and variable labels will be used as parameters names. The latter only works for "labelled" data, i.e. if the data used to fit the model had`"label"`

and`"labels"`

attributes. See also section*Global Options to Customize Messages when Printing*.- coefficient_names
Character vector with strings that should be used as column headers for the coefficient column. Must be of same length as number of models in

`...`

, or length 1. If length 1, this name will be used for all coefficient columns. If`NULL`

, the name for the coefficient column will detected automatically (as in`model_parameters()`

).- keep
Character containing a regular expression pattern that describes the parameters that should be included (for

`keep`

) or excluded (for`drop`

) in the returned data frame.`keep`

may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If`keep`

is a character vector, every parameter name in the*"Parameter"*column that matches the regular expression in`keep`

will be selected from the returned data frame (and vice versa, all parameter names matching`drop`

will be excluded). Furthermore, if`keep`

has more than one element, these will be merged with an`OR`

operator into a regular expression pattern like this:`"(one|two|three)"`

. If`keep`

is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where`model_parameters()`

returns multiple columns with parameter components, like in`model_parameters.lavaan()`

. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the`$Parameter`

column of the parameters table to get the exact parameter names.- drop
See

`keep`

.- include_reference
Logical, if

`TRUE`

, the reference level of factors will be added to the parameters table. This is only relevant for models with categorical predictors. The coefficient for the reference level is always`0`

(except when`exponentiate = TRUE`

, then the coefficient will be`1`

), so this is just for completeness.- groups
Named list, can be used to group parameters in the printed output. List elements may either be character vectors that match the name of those parameters that belong to one group, or list elements can be row numbers of those parameter rows that should belong to one group. The names of the list elements will be used as group names, which will be inserted as "header row". A possible use case might be to emphasize focal predictors and control variables, see 'Examples'. Parameters will be re-ordered according to the order used in

`groups`

, while all non-matching parameters will be added to the end.- verbose
Toggle warnings and messages.

## Details

This function is in an early stage and does not yet cope with more complex models, and probably does not yet properly render all model components. It should also be noted that when including models with interaction terms, not only do the values of the parameters change, but so does their meaning (from main effects, to simple slopes), thereby making such comparisons hard. Therefore, you should not use this function to compare models with interaction terms with models without interaction terms.

## Examples

```
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
compare_parameters(lm1, lm2)
#> Parameter | lm1 | lm2
#> ---------------------------------------------------------------
#> (Intercept) | 5.01 (4.86, 5.15) | 3.68 ( 3.47, 3.89)
#> Species (versicolor) | 0.93 (0.73, 1.13) | -1.60 (-1.98, -1.22)
#> Species (virginica) | 1.58 (1.38, 1.79) | -2.12 (-2.66, -1.58)
#> Petal Length | | 0.90 ( 0.78, 1.03)
#> ---------------------------------------------------------------
#> Observations | 150 | 150
# custom style
compare_parameters(lm1, lm2, select = "{estimate}{stars} ({se})")
#> Parameter | lm1 | lm2
#> -------------------------------------------------------
#> (Intercept) | 5.01*** (0.07) | 3.68*** (0.11)
#> Species (versicolor) | 0.93*** (0.10) | -1.60*** (0.19)
#> Species (virginica) | 1.58*** (0.10) | -2.12*** (0.27)
#> Petal Length | | 0.90*** (0.06)
#> -------------------------------------------------------
#> Observations | 150 | 150
# \donttest{
# custom style, in HTML
result <- compare_parameters(lm1, lm2, select = "{estimate}<br>({se})|{p}")
print_html(result)
```Estimate(SE)
p
Estimate(SE)
p

# }
data(mtcars)
m1 <- lm(mpg ~ wt, data = mtcars)
m2 <- glm(vs ~ wt + cyl, data = mtcars, family = "binomial")
compare_parameters(m1, m2)
#> Parameter | m1 | m2
#> ----------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 10.62 ( 2.45, 18.79)
#> wt | -5.34 (-6.49, -4.20) | 2.10 (-0.93, 5.13)
#> cyl | | -2.93 (-5.63, -0.23)
#> ----------------------------------------------------------
#> Observations | 32 | 32
# \donttest{
# exponentiate coefficients, but not for lm
compare_parameters(m1, m2, exponentiate = "nongaussian")
#> Parameter | m1 | m2
#> ----------------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 40911.34 (11.59, 1.44e+08)
#> wt | -5.34 (-6.49, -4.20) | 8.17 ( 0.39, 169.06)
#> cyl | | 0.05 ( 0.00, 0.80)
#> ----------------------------------------------------------------
#> Observations | 32 | 32
# change column names
compare_parameters("linear model" = m1, "logistic reg." = m2)
#> Parameter | linear model | logistic reg.
#> ----------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 10.62 ( 2.45, 18.79)
#> wt | -5.34 (-6.49, -4.20) | 2.10 (-0.93, 5.13)
#> cyl | | -2.93 (-5.63, -0.23)
#> ----------------------------------------------------------
#> Observations | 32 | 32
compare_parameters(m1, m2, column_names = c("linear model", "logistic reg."))
#> Parameter | linear model | logistic reg.
#> ----------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 10.62 ( 2.45, 18.79)
#> wt | -5.34 (-6.49, -4.20) | 2.10 (-0.93, 5.13)
#> cyl | | -2.93 (-5.63, -0.23)
#> ----------------------------------------------------------
#> Observations | 32 | 32
# or as list
compare_parameters(list(m1, m2))
#> Parameter | Model 1 | Model 2
#> ----------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 10.62 ( 2.45, 18.79)
#> wt | -5.34 (-6.49, -4.20) | 2.10 (-0.93, 5.13)
#> cyl | | -2.93 (-5.63, -0.23)
#> ----------------------------------------------------------
#> Observations | 32 | 32
compare_parameters(list("linear model" = m1, "logistic reg." = m2))
#> Parameter | linear model | logistic reg.
#> ----------------------------------------------------------
#> (Intercept) | 37.29 (33.45, 41.12) | 10.62 ( 2.45, 18.79)
#> wt | -5.34 (-6.49, -4.20) | 2.10 (-0.93, 5.13)
#> cyl | | -2.93 (-5.63, -0.23)
#> ----------------------------------------------------------
#> Observations | 32 | 32
# }