model_parameters() and compare_parameters()
are functions that return a data frame of model summaries in a
consistent way. The printed table of those summaries is formatted to
make the output more readable and removes or collapses redundant
columns, to get a compact and yet comprehensive summary table. (N.B.
for developers: the function standardize_names()
standardizes the column names, so column names are consistent and the
same for any model object, also in broom style, which
makes it easy to build your packages on top of the
parameters package.)
The default print-methods
for model_parameters() and
compare_parameters() allows the user to modify the layout
and style of the output.
Summaries for a single model
In the following examples for model_parameters(), which
returns tabular output for single models, are shown.
Pretty parameter names formatting
By default, the argument pretty_names is
TRUE, meaning that parameter names are formatted to make
them more “human readable”, i.e. factor levels are separated from the
variable names, interactions are denoted by * etc.
library(parameters)
data(iris)
model <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
model_parameters(model)
#> Parameter | Coefficient | SE | 95% CI | t(144) | p
#> -------------------------------------------------------------------------------------------
#> (Intercept) | 4.21 | 0.41 | [ 3.41, 5.02] | 10.34 | < .001
#> Species [versicolor] | -1.81 | 0.60 | [-2.99, -0.62] | -3.02 | 0.003
#> Species [virginica] | -3.15 | 0.63 | [-4.41, -1.90] | -4.97 | < .001
#> Petal Length | 0.54 | 0.28 | [ 0.00, 1.09] | 1.96 | 0.052
#> Species [versicolor] × Petal Length | 0.29 | 0.30 | [-0.30, 0.87] | 0.97 | 0.334
#> Species [virginica] × Petal Length | 0.45 | 0.29 | [-0.12, 1.03] | 1.56 | 0.120
mp <- model_parameters(model)
print(mp, pretty_names = FALSE)
#> Parameter | Coefficient | SE | 95% CI | t(144) | p
#> --------------------------------------------------------------------------------------
#> (Intercept) | 4.21 | 0.41 | [ 3.41, 5.02] | 10.34 | < .001
#> Speciesversicolor | -1.81 | 0.60 | [-2.99, -0.62] | -3.02 | 0.003
#> Speciesvirginica | -3.15 | 0.63 | [-4.41, -1.90] | -4.97 | < .001
#> Petal.Length | 0.54 | 0.28 | [ 0.00, 1.09] | 1.96 | 0.052
#> Speciesversicolor:Petal.Length | 0.29 | 0.30 | [-0.30, 0.87] | 0.97 | 0.334
#> Speciesvirginica:Petal.Length | 0.45 | 0.29 | [-0.12, 1.03] | 1.56 | 0.120If data is labelled,
pretty_names = "labels" will use variable and value labels
as pretty names. If data is not labelled, default pretty names will be
used.
data(efc, package = "datawizard")
model <- lm(neg_c_7 ~ e42dep + c172code, data = efc)
# default printing
model_parameters(model)
#> Parameter | Coefficient | SE | 95% CI | t(80) | p
#> -----------------------------------------------------------------
#> (Intercept) | 8.72 | 3.56 | [ 1.63, 15.80] | 2.45 | 0.017
#> e42dep [2] | -1.00 | 3.72 | [-8.41, 6.41] | -0.27 | 0.789
#> e42dep [3] | 2.68 | 3.16 | [-3.60, 8.96] | 0.85 | 0.398
#> e42dep [4] | 3.88 | 3.10 | [-2.29, 10.04] | 1.25 | 0.214
#> c172code | 1.14 | 0.93 | [-0.70, 2.99] | 1.23 | 0.221
# using value and variable labels
mp <- model_parameters(model)
print(mp, pretty_names = "labels")
#> Parameter | Coefficient | SE | 95% CI | t(80) | p
#> -----------------------------------------------------------------------------------------------
#> (Intercept) | 8.72 | 3.56 | [ 1.63, 15.80] | 2.45 | 0.017
#> elder's dependency [slightly dependent] | -1.00 | 3.72 | [-8.41, 6.41] | -0.27 | 0.789
#> elder's dependency [moderately dependent] | 2.68 | 3.16 | [-3.60, 8.96] | 0.85 | 0.398
#> elder's dependency [severely dependent] | 3.88 | 3.10 | [-2.29, 10.04] | 1.25 | 0.214
#> carer's level of education | 1.14 | 0.93 | [-0.70, 2.99] | 1.23 | 0.221More compact output
Using summary, or the select argument via
the print() method allows for a more compact table, in case
not all information is required. summary() will return the
coefficient, confidence intervals and p-values. select
allows to select specific columns only.
data(iris)
model <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- model_parameters(model)
# Coefficients, CI and p
summary(result)
#> Parameter | Coefficient | 95% CI | p
#> ---------------------------------------------------------------------------
#> (Intercept) | 4.21 | [ 3.41, 5.02] | < .001
#> Species [versicolor] | -1.81 | [-2.99, -0.62] | 0.003
#> Species [virginica] | -3.15 | [-4.41, -1.90] | < .001
#> Petal Length | 0.54 | [ 0.00, 1.09] | 0.052
#> Species [versicolor] × Petal Length | 0.29 | [-0.30, 0.87] | 0.334
#> Species [virginica] × Petal Length | 0.45 | [-0.12, 1.03] | 0.120
#>
#> Model: Sepal.Length ~ Species * Petal.Length (150 Observations)
#> Residual standard deviation: 0.336 (df = 144)
# Parameter name, SE and p
print(result, select = c("Parameter", "SE", "p"))
#> Parameter | SE | p
#> ---------------------------------------------------
#> (Intercept) | 0.41 | < .001
#> Species [versicolor] | 0.60 | 0.003
#> Species [virginica] | 0.63 | < .001
#> Petal Length | 0.28 | 0.052
#> Species [versicolor] × Petal Length | 0.30 | 0.334
#> Species [virginica] × Petal Length | 0.29 | 0.120The select argument can also be used for a more
customized output. See examples below for
compare_parameters().
Splitting model components
Again by default, the argument split_components is
TRUE, which means that models with multiple components like
fixed and random effects, count and zero-inflated part etc. are split
into separate tables in the output.
library(glmmTMB)
data("Salamanders")
model <- glmmTMB(count ~ spp + mined + (1 | site),
ziformula = ~ spp + mined,
family = nbinom2(),
data = Salamanders
)
model_parameters(model)
#> # Fixed Effects (Count Model)
#>
#> Parameter | Log-Mean | SE | 95% CI | z | p
#> --------------------------------------------------------------
#> (Intercept) | -0.61 | 0.41 | [-1.40, 0.18] | -1.51 | 0.132
#> spp [PR] | -0.96 | 0.64 | [-2.23, 0.30] | -1.50 | 0.134
#> spp [DM] | 0.17 | 0.24 | [-0.29, 0.63] | 0.73 | 0.468
#> spp [EC-A] | -0.39 | 0.34 | [-1.06, 0.28] | -1.13 | 0.258
#> spp [EC-L] | 0.49 | 0.24 | [ 0.02, 0.96] | 2.05 | 0.041
#> spp [DES-L] | 0.59 | 0.23 | [ 0.14, 1.04] | 2.59 | 0.010
#> spp [DF] | -0.11 | 0.24 | [-0.59, 0.36] | -0.46 | 0.642
#> mined [no] | 1.43 | 0.37 | [ 0.71, 2.15] | 3.90 | < .001
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ---------------------------------------------------------------
#> (Intercept) | 0.91 | 0.63 | [-0.32, 2.14] | 1.45 | 0.147
#> spp [PR] | 1.16 | 1.33 | [-1.45, 3.78] | 0.87 | 0.384
#> spp [DM] | -0.94 | 0.80 | [-2.51, 0.63] | -1.17 | 0.241
#> spp [EC-A] | 1.04 | 0.71 | [-0.36, 2.44] | 1.46 | 0.144
#> spp [EC-L] | -0.56 | 0.73 | [-1.99, 0.86] | -0.77 | 0.439
#> spp [DES-L] | -0.89 | 0.75 | [-2.37, 0.58] | -1.19 | 0.236
#> spp [DF] | -2.54 | 2.18 | [-6.82, 1.74] | -1.16 | 0.244
#> mined [no] | -2.56 | 0.60 | [-3.75, -1.38] | -4.24 | < .001
#>
#> # Dispersion
#>
#> Parameter | Coefficient | 95% CI
#> ----------------------------------------
#> (Intercept) | 1.51 | [0.93, 2.46]
#>
#> # Random Effects Variances
#>
#> Parameter | Coefficient | 95% CI
#> -------------------------------------------------
#> SD (Intercept: site) | 0.38 | [0.17, 0.87]Redundant columns are removed. The related model component is shown as table header. However, you can also return a single table:
mp <- model_parameters(model)
print(mp, split_component = FALSE)
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | z | p | Effects | Component
#> -----------------------------------------------------------------------------------------------------
#> (Intercept) | -0.61 | 0.41 | [-1.40, 0.18] | -1.51 | 0.132 | fixed | conditional
#> spp [PR] | -0.96 | 0.64 | [-2.23, 0.30] | -1.50 | 0.134 | fixed | conditional
#> spp [DM] | 0.17 | 0.24 | [-0.29, 0.63] | 0.73 | 0.468 | fixed | conditional
#> spp [EC-A] | -0.39 | 0.34 | [-1.06, 0.28] | -1.13 | 0.258 | fixed | conditional
#> spp [EC-L] | 0.49 | 0.24 | [ 0.02, 0.96] | 2.05 | 0.041 | fixed | conditional
#> spp [DES-L] | 0.59 | 0.23 | [ 0.14, 1.04] | 2.59 | 0.010 | fixed | conditional
#> spp [DF] | -0.11 | 0.24 | [-0.59, 0.36] | -0.46 | 0.642 | fixed | conditional
#> mined [no] | 1.43 | 0.37 | [ 0.71, 2.15] | 3.90 | < .001 | fixed | conditional
#> (Intercept) | 0.91 | 0.63 | [-0.32, 2.14] | 1.45 | 0.147 | fixed | zero_inflated
#> sppPR | 1.16 | 1.33 | [-1.45, 3.78] | 0.87 | 0.384 | fixed | zero_inflated
#> sppDM | -0.94 | 0.80 | [-2.51, 0.63] | -1.17 | 0.241 | fixed | zero_inflated
#> sppEC-A | 1.04 | 0.71 | [-0.36, 2.44] | 1.46 | 0.144 | fixed | zero_inflated
#> sppEC-L | -0.56 | 0.73 | [-1.99, 0.86] | -0.77 | 0.439 | fixed | zero_inflated
#> sppDES-L | -0.89 | 0.75 | [-2.37, 0.58] | -1.19 | 0.236 | fixed | zero_inflated
#> sppDF | -2.54 | 2.18 | [-6.82, 1.74] | -1.16 | 0.244 | fixed | zero_inflated
#> minedno | -2.56 | 0.60 | [-3.75, -1.38] | -4.24 | < .001 | fixed | zero_inflated
#> (Intercept) | 1.51 | | [ 0.93, 2.46] | | | fixed | dispersion
#> SD (Intercept: site) | 0.38 | | [ 0.17, 0.87] | | | random | conditionalAdding model summaries
A model summary can be added to the table when
summary = TRUE in the call to
model_parameters():
model <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
model_parameters(model, summary = TRUE)
#> Parameter | Coefficient | SE | 95% CI | t(144) | p
#> -------------------------------------------------------------------------------------------
#> (Intercept) | 4.21 | 0.41 | [ 3.41, 5.02] | 10.34 | < .001
#> Species [versicolor] | -1.81 | 0.60 | [-2.99, -0.62] | -3.02 | 0.003
#> Species [virginica] | -3.15 | 0.63 | [-4.41, -1.90] | -4.97 | < .001
#> Petal Length | 0.54 | 0.28 | [ 0.00, 1.09] | 1.96 | 0.052
#> Species [versicolor] × Petal Length | 0.29 | 0.30 | [-0.30, 0.87] | 0.97 | 0.334
#> Species [virginica] × Petal Length | 0.45 | 0.29 | [-0.12, 1.03] | 1.56 | 0.120
#>
#> Model: Sepal.Length ~ Species * Petal.Length (150 Observations)
#> Residual standard deviation: 0.336 (df = 144)
#> R2: 0.840; adjusted R2: 0.835Including the reference level of categorical variables
Sometimes, it can be helpful to include the reference level of
categorical predictors in the table. This can be done by setting
include_reference = TRUE (either directly in
model_parameters() or in the print() method).
Since the reference level is not a parameter, it is shown in a separate
row, with 0 for the coefficient and blank cells for the
remaining columns.
model <- lm(Sepal.Length ~ Petal.Length + Species, data = iris)
model_parameters(model, include_reference = TRUE)
#> Parameter | Coefficient | SE | 95% CI | t(146) | p
#> ----------------------------------------------------------------------------
#> (Intercept) | 3.68 | 0.11 | [ 3.47, 3.89] | 34.72 | < .001
#> Petal Length | 0.90 | 0.06 | [ 0.78, 1.03] | 13.96 | < .001
#> Species [setosa] | 0.00 | | | |
#> Species [versicolor] | -1.60 | 0.19 | [-1.98, -1.22] | -8.28 | < .001
#> Species [virginica] | -2.12 | 0.27 | [-2.66, -1.58] | -7.74 | < .001Changing number of digits
digits changes the digits for coefficients, standard
errors and statistics. ci_digits and p_digits
are especially for the confidence intervals and p-values.
model <- lm(Sepal.Length ~ Species, data = iris)
model_parameters(model, digits = 4)
#> Parameter | Coefficient | SE | 95% CI | t(147) | p
#> ---------------------------------------------------------------------------------
#> (Intercept) | 5.0060 | 0.0728 | [4.8621, 5.1499] | 68.7616 | < .001
#> Species [versicolor] | 0.9300 | 0.1030 | [0.7265, 1.1335] | 9.0328 | < .001
#> Species [virginica] | 1.5820 | 0.1030 | [1.3785, 1.7855] | 15.3655 | < .001p-values can be displayed in exact, scientific notation if required.
model_parameters(model, p_digits = "scientific")
#> Parameter | Coefficient | SE | 95% CI | t(147) | p
#> --------------------------------------------------------------------------------
#> (Intercept) | 5.01 | 0.07 | [4.86, 5.15] | 68.76 | 1.13429e-113
#> Species [versicolor] | 0.93 | 0.10 | [0.73, 1.13] | 9.03 | 8.77019e-16
#> Species [virginica] | 1.58 | 0.10 | [1.38, 1.79] | 15.37 | 2.21482e-32Fixing column widths
By default, the width of table columns is set to the minimum required width. This works well for models that produce just one table. However, for models with multiple components, where each component is shown as separate table, columns are possibly no longer aligned across tables. See the following example from a zero-inflated mixed model that has three components (fixed count, fixed zero-inflated, random effects):
data("Salamanders")
# we create very long parameter names for this predictor here
levels(Salamanders$spp) <- paste("long", levels(Salamanders$spp))
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
# default printing
model_parameters(model)
#> # Fixed Effects (Count Model)
#>
#> Parameter | Log-Mean | SE | 95% CI | z | p
#> --------------------------------------------------------------------
#> (Intercept) | -0.36 | 0.28 | [-0.90, 0.18] | -1.30 | 0.194
#> spp [long PR] | -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001
#> spp [long DM] | 0.27 | 0.14 | [ 0.00, 0.54] | 1.95 | 0.051
#> spp [long EC-A] | -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006
#> spp [long EC-L] | 0.67 | 0.13 | [ 0.41, 0.92] | 5.20 | < .001
#> spp [long DES-L] | 0.63 | 0.13 | [ 0.38, 0.87] | 4.96 | < .001
#> spp [long DF] | 0.12 | 0.15 | [-0.17, 0.40] | 0.78 | 0.435
#> mined [no] | 1.27 | 0.27 | [ 0.74, 1.80] | 4.72 | < .001
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ---------------------------------------------------------------
#> (Intercept) | 0.79 | 0.27 | [ 0.26, 1.32] | 2.90 | 0.004
#> mined [no] | -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001
#>
#> # Random Effects Variances
#>
#> Parameter | Coefficient | 95% CI
#> -------------------------------------------------
#> SD (Intercept: site) | 0.33 | [0.18, 0.63]The column_width argument can be used to either define
the width of specific columns, or to fix column widths of the same
columns across tables to have the same width. In the latter case, use
column_width = "fixed" in the print()
method.
mp <- model_parameters(model)
print(mp, column_width = "fixed")
#> # Fixed Effects (Count Model)
#>
#> Parameter | Log-Mean | SE | 95% CI | z | p
#> ------------------------------------------------------------------------
#> (Intercept) | -0.36 | 0.28 | [-0.90, 0.18] | -1.30 | 0.194
#> spp [long PR] | -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001
#> spp [long DM] | 0.27 | 0.14 | [ 0.00, 0.54] | 1.95 | 0.051
#> spp [long EC-A] | -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006
#> spp [long EC-L] | 0.67 | 0.13 | [ 0.41, 0.92] | 5.20 | < .001
#> spp [long DES-L] | 0.63 | 0.13 | [ 0.38, 0.87] | 4.96 | < .001
#> spp [long DF] | 0.12 | 0.15 | [-0.17, 0.40] | 0.78 | 0.435
#> mined [no] | 1.27 | 0.27 | [ 0.74, 1.80] | 4.72 | < .001
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ------------------------------------------------------------------------
#> (Intercept) | 0.79 | 0.27 | [ 0.26, 1.32] | 2.90 | 0.004
#> mined [no] | -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001
#>
#> # Random Effects Variances
#>
#> Parameter | Coefficient | 95% CI
#> ---------------------------------------------------
#> SD (Intercept: site) | 0.33 | [0.18, 0.63]If column_width is a named vector, names are matched
against column names, and those columns gain the specified minimum
width.
print(mp, column_width = c(SE = 8, `95% CI` = 12, p = 7))
#> # Fixed Effects (Count Model)
#>
#> Parameter | Log-Mean | SE | 95% CI | z | p
#> -------------------------------------------------------------------------
#> (Intercept) | -0.36 | 0.28 | [-0.90, 0.18] | -1.30 | 0.194
#> spp [long PR] | -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001
#> spp [long DM] | 0.27 | 0.14 | [ 0.00, 0.54] | 1.95 | 0.051
#> spp [long EC-A] | -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006
#> spp [long EC-L] | 0.67 | 0.13 | [ 0.41, 0.92] | 5.20 | < .001
#> spp [long DES-L] | 0.63 | 0.13 | [ 0.38, 0.87] | 4.96 | < .001
#> spp [long DF] | 0.12 | 0.15 | [-0.17, 0.40] | 0.78 | 0.435
#> mined [no] | 1.27 | 0.27 | [ 0.74, 1.80] | 4.72 | < .001
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> --------------------------------------------------------------------
#> (Intercept) | 0.79 | 0.27 | [ 0.26, 1.32] | 2.90 | 0.004
#> mined [no] | -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001
#>
#> # Random Effects Variances
#>
#> Parameter | Coefficient | 95% CI
#> -------------------------------------------------
#> SD (Intercept: site) | 0.33 | [0.18, 0.63]Group parameters
The groups argument can be used to group parameters in
the table. groups must be a named list, where the names of
the list elements equal the header of each group, while the values of
the list elements equal the parameter names, or the position of the
parameters in the table (data frame).
In the following example, we see the names of the parameters in the
Parameter column, while the rownumbers indicate their
position.
data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$gear <- as.factor(mtcars$gear)
model <- lm(mpg ~ hp + gear * vs + cyl + drat, data = mtcars)
# don't select "Intercept" parameter
mp <- model_parameters(model, drop = "^\\(Intercept")
# inspect data frame
as.data.frame(mp)
#> Parameter Coefficient SE CI CI_low CI_high t df_error p
#> 1 hp -0.062 0.021 0.95 -0.11 -0.018 -2.91 22 0.0081
#> 2 gear4 3.100 4.339 0.95 -5.90 12.098 0.71 22 0.4825
#> 3 gear5 4.798 3.478 0.95 -2.42 12.011 1.38 22 0.1816
#> 4 vs 3.183 3.790 0.95 -4.68 11.042 0.84 22 0.4100
#> 5 cyl6 -2.466 2.210 0.95 -7.05 2.116 -1.12 22 0.2764
#> 6 cyl8 1.975 5.111 0.95 -8.63 12.575 0.39 22 0.7029
#> 7 drat 2.697 2.033 0.95 -1.52 6.913 1.33 22 0.1983
#> 8 gear4:vs -2.897 4.665 0.95 -12.57 6.778 -0.62 22 0.5410
#> 9 gear5:vs 2.588 4.537 0.95 -6.82 11.998 0.57 22 0.5741Now we create a group named "Engine", which encompasses
the parameters "cyl6", "cyl8",
"vs" and "hp". The "Interactions"
group includes "gear4:vs" and "gear5:vs". The
group "controls" has the parameters from rows 2, 3 and
7.
Note that the parameters in the table summary are re-ordered
according to the order specified in groups.
# group parameters, either by parameter name or position
print(mp, groups = list(
"Engine" = c("cyl6", "cyl8", "vs", "hp"),
"Interactions" = c("gear4:vs", "gear5:vs"),
"Controls" = c(2, 3, 7)
)) # gear 4 and 5, drat
#> Parameter | Coefficient | SE | 95% CI | t(22) | p
#> -----------------------------------------------------------------------
#> Engine | | | | |
#> cyl [6] | -2.47 | 2.21 | [ -7.05, 2.12] | -1.12 | 0.276
#> cyl [8] | 1.97 | 5.11 | [ -8.63, 12.58] | 0.39 | 0.703
#> vs | 3.18 | 3.79 | [ -4.68, 11.04] | 0.84 | 0.410
#> hp | -0.06 | 0.02 | [ -0.11, -0.02] | -2.91 | 0.008
#> Interactions | | | | |
#> gear [4] × vs | -2.90 | 4.67 | [-12.57, 6.78] | -0.62 | 0.541
#> gear [5] × vs | 2.59 | 4.54 | [ -6.82, 12.00] | 0.57 | 0.574
#> Controls | | | | |
#> gear [4] | 3.10 | 4.34 | [ -5.90, 12.10] | 0.71 | 0.482
#> gear [5] | 4.80 | 3.48 | [ -2.42, 12.01] | 1.38 | 0.182
#> drat | 2.70 | 2.03 | [ -1.52, 6.91] | 1.33 | 0.198If you prefer tables without vertical borders, use the
sep argument to define the string that is used as
border-separator. This argument is passed down to
insight::export_table().
# group parameters, either by parameter name or position
print(mp,
sep = " ",
groups = list(
"Engine" = c("cyl6", "cyl8", "vs", "hp"),
"Interactions" = c("gear4:vs", "gear5:vs"),
"Controls" = c(2, 3, 7)
)
)
#> Parameter Coefficient SE 95% CI t(22) p
#> ------------------------------------------------------------------
#> Engine
#> cyl [6] -2.47 2.21 [ -7.05, 2.12] -1.12 0.276
#> cyl [8] 1.97 5.11 [ -8.63, 12.58] 0.39 0.703
#> vs 3.18 3.79 [ -4.68, 11.04] 0.84 0.410
#> hp -0.06 0.02 [ -0.11, -0.02] -2.91 0.008
#> Interactions
#> gear [4] × vs -2.90 4.67 [-12.57, 6.78] -0.62 0.541
#> gear [5] × vs 2.59 4.54 [ -6.82, 12.00] 0.57 0.574
#> Controls
#> gear [4] 3.10 4.34 [ -5.90, 12.10] 0.71 0.482
#> gear [5] 4.80 3.48 [ -2.42, 12.01] 1.38 0.182
#> drat 2.70 2.03 [ -1.52, 6.91] 1.33 0.198Summaries for multiple models
compare_parameters() (or its alias
compare_models()) allows to create tables for multiple
models, aligned side by side.
By default, estimates and confidence intervals are shown.
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
compare_parameters(lm1, lm2, lm3)
#> Parameter | lm1 | lm2 | lm3
#> -----------------------------------------------------------------------------------------------------
#> (Intercept) | 5.01 (4.86, 5.15) | 3.68 ( 3.47, 3.89) | 4.21 ( 3.41, 5.02)
#> Species (versicolor) | 0.93 (0.73, 1.13) | -1.60 (-1.98, -1.22) | -1.81 (-2.99, -0.62)
#> Species (virginica) | 1.58 (1.38, 1.79) | -2.12 (-2.66, -1.58) | -3.15 (-4.41, -1.90)
#> Petal Length | | 0.90 ( 0.78, 1.03) | 0.54 ( 0.00, 1.09)
#> Species (versicolor) × Petal Length | | | 0.29 (-0.30, 0.87)
#> Species (virginica) × Petal Length | | | 0.45 (-0.12, 1.03)
#> -----------------------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150Changing style of column output
By default, estimates and confidence intervals are shown. Using
select allows us to create different output, e.g. standard
errors instead of confidence intervals, or including p-values.
compare_parameters(lm1, lm2, lm3, select = "se_p")
#> Parameter | lm1 | lm2 | lm3
#> ----------------------------------------------------------------------------------------
#> (Intercept) | 5.01*** (0.07) | 3.68*** (0.11) | 4.21*** (0.41)
#> Species (versicolor) | 0.93*** (0.10) | -1.60*** (0.19) | -1.81 ** (0.60)
#> Species (virginica) | 1.58*** (0.10) | -2.12*** (0.27) | -3.15*** (0.63)
#> Petal Length | | 0.90*** (0.06) | 0.54 (0.28)
#> Species (versicolor) × Petal Length | | | 0.29 (0.30)
#> Species (virginica) × Petal Length | | | 0.45 (0.29)
#> ----------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150The select argument also has a basic support for
glue-like syntax, which lets you layout model elements in a very
flexible way. Following tokens are replaced by the related summary
coefficients or statistics:
-
{estimate}(or{coefficient}or{coef}): coefficient -
{se}(or{std.error}or{standard error}): standard error -
{ci_low}and{ci_high}: lower/upper confidence interval limits -
{p}(or{pval}or{p.value}): p-values - stars: significant stars for p-values
Note that you have to add parentheses manually, e.g. around confidence intervals.
# estimates, p-stars and standard error in parentheses
compare_parameters(lm1, lm2, lm3, select = "{estimate}{stars} ({se})")
#> Parameter | lm1 | lm2 | lm3
#> ----------------------------------------------------------------------------------------
#> (Intercept) | 5.01*** (0.07) | 3.68*** (0.11) | 4.21*** (0.41)
#> Species (versicolor) | 0.93*** (0.10) | -1.60*** (0.19) | -1.81 ** (0.60)
#> Species (virginica) | 1.58*** (0.10) | -2.12*** (0.27) | -3.15*** (0.63)
#> Petal Length | | 0.90*** (0.06) | 0.54 (0.28)
#> Species (versicolor) × Petal Length | | | 0.29 (0.30)
#> Species (virginica) × Petal Length | | | 0.45 (0.29)
#> ----------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150
# estimates, CI, p and stars
compare_parameters(lm1, lm2, lm3, select = "{estimate} ({ci_low}, {ci_high}), p={p}{stars}")
#> Parameter | lm1 | lm2 | lm3
#> -----------------------------------------------------------------------------------------------------------------------------------------
#> (Intercept) | 5.01 (4.86, 5.15), p<0.001*** | 3.68 ( 3.47, 3.89), p<0.001*** | 4.21 ( 3.41, 5.02), p<0.001***
#> Species (versicolor) | 0.93 (0.73, 1.13), p<0.001*** | -1.60 (-1.98, -1.22), p<0.001*** | -1.81 (-2.99, -0.62), p=0.003 **
#> Species (virginica) | 1.58 (1.38, 1.79), p<0.001*** | -2.12 (-2.66, -1.58), p<0.001*** | -3.15 (-4.41, -1.90), p<0.001***
#> Petal Length | | 0.90 ( 0.78, 1.03), p<0.001*** | 0.54 ( 0.00, 1.09), p=0.052
#> Species (versicolor) × Petal Length | | | 0.29 (-0.30, 0.87), p=0.334
#> Species (virginica) × Petal Length | | | 0.45 (-0.12, 1.03), p=0.120
#> -----------------------------------------------------------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150select also works for model_parameters(),
however, it’s necessary to call this argument via the
print() method:
# estimates, p-stars and CI in parentheses
result <- model_parameters(lm3)
print(result, select = "{estimate}{stars} ({ci})")
#> Parameter | Estimate (ci)
#> -------------------------------------------------------------
#> (Intercept) | 4.21*** ( 3.41, 5.02)
#> Species [versicolor] | -1.81 ** (-2.99, -0.62)
#> Species [virginica] | -3.15*** (-4.41, -1.90)
#> Petal Length | 0.54 ( 0.00, 1.09)
#> Species [versicolor] × Petal Length | 0.29 (-0.30, 0.87)
#> Species [virginica] × Petal Length | 0.45 (-0.12, 1.03)Defining column names
The column names for the models are by default the objects’ names.
You can define own names using the column_names
argument.
compare_parameters(
lm1, lm2, lm3,
column_names = c("First Model", "Second Model", "Third Model")
)
#> Parameter | First Model | Second Model | Third Model
#> -----------------------------------------------------------------------------------------------------
#> (Intercept) | 5.01 (4.86, 5.15) | 3.68 ( 3.47, 3.89) | 4.21 ( 3.41, 5.02)
#> Species (versicolor) | 0.93 (0.73, 1.13) | -1.60 (-1.98, -1.22) | -1.81 (-2.99, -0.62)
#> Species (virginica) | 1.58 (1.38, 1.79) | -2.12 (-2.66, -1.58) | -3.15 (-4.41, -1.90)
#> Petal Length | | 0.90 ( 0.78, 1.03) | 0.54 ( 0.00, 1.09)
#> Species (versicolor) × Petal Length | | | 0.29 (-0.30, 0.87)
#> Species (virginica) × Petal Length | | | 0.45 (-0.12, 1.03)
#> -----------------------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150Models with multiple components
For models with multiple components, like mixed models with fixed and
random effects, or models with count- and zero-inflation parts, using
arguments effects = "all" and/or
component = "all" prints separate tables for each model
component.
library(glmmTMB)
data("fish")
m0 <- glm(count ~ child + camper, data = fish, family = poisson())
m1 <- glmmTMB(
count ~ child + camper + (1 | persons) + (1 | ID),
data = fish,
family = poisson()
)
m2 <- glmmTMB(
count ~ child + camper + zg + (1 | ID),
ziformula = ~ child + (1 | persons),
data = fish,
family = truncated_poisson()
)
compare_parameters(m0, m1, m2, effects = "all", component = "all")
#> # Fixed Effects
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------------
#> (Intercept) | 0.91 ( 0.75, 1.07) | 0.68 (-0.54, 1.91) | 1.41 ( 1.06, 1.75)
#> child | -1.23 (-1.39, -1.08) | -1.67 (-1.84, -1.51) | -0.53 (-0.77, -0.29)
#> camper (1) | 1.05 ( 0.88, 1.23) | 0.94 ( 0.77, 1.12) | 0.58 ( 0.39, 0.78)
#> zg | | | 0.13 ( 0.05, 0.21)
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------
#> (Intercept) | | | -0.92 (-2.07, 0.22)
#> child | | | 1.96 ( 1.38, 2.54)
#>
#> # Random Effects
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------
#> SD (Intercept: ID) | | 0.27 ( 0.11, 0.63) | 0.28 ( 0.13, 0.60)
#> SD (Intercept: persons) | | 1.21 ( 0.60, 2.43) |
#>
#> # Random Effects (Zero-Inflation Component)
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------
#> SD (Intercept: persons) | | | 1.08 ( 0.49, 2.37)For such tables, they usually become clearer when the columns per
model are aligned to a fixed width. You can do this using
column_width = "fixed".
cp <- compare_parameters(m0, m1, m2, effects = "all", component = "all")
print(cp, column_width = "fixed")
#> # Fixed Effects
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------------------------
#> (Intercept) | 0.91 ( 0.75, 1.07) | 0.68 (-0.54, 1.91) | 1.41 ( 1.06, 1.75)
#> child | -1.23 (-1.39, -1.08) | -1.67 (-1.84, -1.51) | -0.53 (-0.77, -0.29)
#> camper (1) | 1.05 ( 0.88, 1.23) | 0.94 ( 0.77, 1.12) | 0.58 ( 0.39, 0.78)
#> zg | | | 0.13 ( 0.05, 0.21)
#>
#> # Fixed Effects (Zero-Inflation Component)
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------------------------
#> (Intercept) | | | -0.92 (-2.07, 0.22)
#> child | | | 1.96 ( 1.38, 2.54)
#>
#> # Random Effects
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------------------------
#> SD (Intercept: ID) | | 0.27 ( 0.11, 0.63) | 0.28 ( 0.13, 0.60)
#> SD (Intercept: persons) | | 1.21 ( 0.60, 2.43) |
#>
#> # Random Effects (Zero-Inflation Component)
#>
#> Parameter | m0 | m1 | m2
#> --------------------------------------------------------------------------------------------
#> SD (Intercept: persons) | | | 1.08 ( 0.49, 2.37)Group parameters of multiple model tables
Grouping parameters works for compare_parameters() in
the same way as shown above for model_parameters().
Note: By default, the interaction mark is
×, not * (see also section on global options
in
this vignette). Since parameter names in
compare_parameters() are already formatted before printing,
as.data.frame(cp)$Parameter will probably return special
unicode characters that you need to take care of in the
groups argument (unless you use numeric indices).
lm1 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm2 <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)
# remove intercept
cp <- compare_parameters(lm1, lm2, drop = "^\\(Intercept")
# look at parameters names, to know their names for "groups" argument
as.data.frame(cp)$Parameter # note the unicode char as interaction mark
#> [1] "Species (versicolor)" "Species (virginica)"
#> [3] "Petal Length" "Species (versicolor) × Petal Length"
#> [5] "Species (virginica) × Petal Length"
# create groups. Interactions only present in 2nd model
print(cp, groups = list(
Species = c(
"Species (versicolor)",
"Species (virginica)"
),
Interactions = c(
"Species (versicolor) × Petal Length", # note the unicode char!
"Species (virginica) × Petal Length"
),
Controls = "Petal Length"
))
#> Parameter | lm1 | lm2
#> -----------------------------------------------------------------------------------
#> Species | |
#> Species (versicolor) | -1.60 (-1.98, -1.22) | -1.69 (-2.80, -0.57)
#> Species (virginica) | -2.12 (-2.66, -1.58) | -1.19 (-2.37, -0.01)
#> Interactions | |
#> Species (versicolor) × Petal Length | | -0.01 (-0.56, 0.53)
#> Species (virginica) × Petal Length | | -0.15 (-0.69, 0.39)
#> Controls | |
#> Petal Length | 0.90 ( 0.78, 1.03) | 0.39 (-0.13, 0.90)
#> -----------------------------------------------------------------------------------
#> Observations | 150 | 150Splitting wide tables into multiple table parts
For very wide tables that cannot be displayed properly, you can use
the table_width argument in the print() method
to split tables into multiple parts. table_width can be a
numeric value, or "auto", indicating the width of the
complete table. If table_width = "auto" and the table is
wider than the current available width (i.e. line length) of the console
(or any other source for textual output, like markdown files), the table
is split into multiple parts. Else, if table_width is
numeric and table rows are wider than table_width, the
table is split into multiple parts.
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
lm4 <- lm(Sepal.Length ~ Species * Petal.Length + Petal.Width, data = iris)
# very wide table
compare_parameters(lm1, lm2, lm3, lm4)
#> Parameter | lm1 | lm2 | lm3 | lm4
#> ----------------------------------------------------------------------------------------------------------------------------
#> (Intercept) | 5.01 (4.86, 5.15) | 3.68 ( 3.47, 3.89) | 4.21 ( 3.41, 5.02) | 4.21 ( 3.41, 5.02)
#> Species (versicolor) | 0.93 (0.73, 1.13) | -1.60 (-1.98, -1.22) | -1.81 (-2.99, -0.62) | -1.80 (-2.99, -0.62)
#> Species (virginica) | 1.58 (1.38, 1.79) | -2.12 (-2.66, -1.58) | -3.15 (-4.41, -1.90) | -3.19 (-4.50, -1.88)
#> Petal Length | | 0.90 ( 0.78, 1.03) | 0.54 ( 0.00, 1.09) | 0.54 (-0.02, 1.09)
#> Species (versicolor) × Petal Length | | | 0.29 (-0.30, 0.87) | 0.28 (-0.30, 0.87)
#> Species (virginica) × Petal Length | | | 0.45 (-0.12, 1.03) | 0.45 (-0.12, 1.03)
#> Petal Width | | | | 0.03 (-0.28, 0.34)
#> ----------------------------------------------------------------------------------------------------------------------------
#> Observations | 150 | 150 | 150 | 150
# table split into two parts
tab <- compare_parameters(lm1, lm2, lm3, lm4)
print(tab, table_width = 80)
#> 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)
#> Species (versicolor) × Petal Length | |
#> Species (virginica) × Petal Length | |
#> Petal Width | |
#> ------------------------------------------------------------------------------
#> Observations | 150 | 150
#>
#> Parameter | lm3 | lm4
#> ---------------------------------------------------------------------------------
#> (Intercept) | 4.21 ( 3.41, 5.02) | 4.21 ( 3.41, 5.02)
#> Species (versicolor) | -1.81 (-2.99, -0.62) | -1.80 (-2.99, -0.62)
#> Species (virginica) | -3.15 (-4.41, -1.90) | -3.19 (-4.50, -1.88)
#> Petal Length | 0.54 ( 0.00, 1.09) | 0.54 (-0.02, 1.09)
#> Species (versicolor) × Petal Length | 0.29 (-0.30, 0.87) | 0.28 (-0.30, 0.87)
#> Species (virginica) × Petal Length | 0.45 (-0.12, 1.03) | 0.45 (-0.12, 1.03)
#> Petal Width | | 0.03 (-0.28, 0.34)
#> ---------------------------------------------------------------------------------
#> Observations | 150 | 150More advances tables and markdown / HTML formatting
The print_md() as well as print_html()
functions can be used to create markdown (for knitting to PDF or Word)
and HTML tables.
Meanwhile, there are a lot of additional packages that allow users to have even more flexibility regarding table layouts. One package we can recommend is the modelsummary package.