A print()-method for objects from model_parameters().

# S3 method for parameters_model
print(
x,
pretty_names = TRUE,
split_components = TRUE,
select = NULL,
caption = NULL,
digits = 2,
ci_digits = 2,
p_digits = 3,
footer_digits = 3,
show_sigma = FALSE,
show_formula = FALSE,
zap_small = FALSE,
groups = NULL,
...
)

# S3 method for parameters_model
summary(object, ...)

## Arguments

x, object An object returned by model_parameters(). Return "pretty" (i.e. more human readable) parameter names. Logical, if TRUE (default), For models with multiple components (zero-inflation, smooth terms, ...), each component is printed in a separate table. If FALSE, model parameters are printed in a single table and a Component column is added to the output. Character vector (or numeric index) of column names that should be printed. If NULL (default), all columns are printed. The shortcut select = "minimal" prints coefficient, confidence intervals and p-values, while select = "short" prints coefficient, standard errors and p-values. Table caption as string. If NULL, no table caption is printed. Number of decimal places for numeric values (except confidence intervals and p-values). Number of decimal places for confidence intervals. Number of decimal places for p-values. May also be "scientific" for scientific notation of p-values. Number of decimal places for values in the footer summary. Logical, if TRUE, adds information about the residual standard deviation. Logical, if TRUE, adds the model formula to the output. Logical, if TRUE, small values are rounded after digits decimal places. If FALSE, values with more decimal places than digits are printed in scientific notation. 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. Arguments passed to or from other methods.

## Value

Invisibly returns the original input object.

## Details

summary() is a convenient shortcut for print(object, select = "minimal", show_sigma = TRUE, show_formula = TRUE).

## Interpretation of Interaction Terms

Note that the interpretation of interaction terms depends on many characteristics of the model. The number of parameters, and overall performance of the model, can differ or not between a * b a : b, and a / b, suggesting that sometimes interaction terms give different parameterizations of the same model, but other times it gives completely different models (depending on a or b being factors of covariates, included as main effects or not, etc.). Their interpretation depends of the full context of the model, which should not be inferred from the parameters table alone - rather, we recommend to use packages that calculate estimated marginal means or marginal effects, such as modelbased, emmeans or ggeffects. To raise awareness for this issue, you may use print(...,show_formula=TRUE) to add the model-specification to the output of the print() method for model_parameters().

## Labeling the Degrees of Freedom

Throughout the parameters package, we decided to label the residual degrees of freedom df_error. The reason for this is that these degrees of freedom not always refer to the residuals. For certain models, they refer to the estimate error - in a linear model these are the same, but in - for instance - any mixed effects model, this isn't strictly true. Hence, we think that df_error is the most generic label for these degrees of freedom.

There is a dedicated method to use inside rmarkdown files, print_md().

## Examples

# \donttest{
library(parameters)
if (require("glmmTMB", quietly = TRUE)) {
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
mp <- model_parameters(model)

print(mp, pretty_names = FALSE)

print(mp, split_components = FALSE)

print(mp, select = c("Parameter", "Coefficient", "SE"))

print(mp, select = "minimal")
}
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> # 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
#> sppPR       |    -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001
#> sppDM       |     0.27 | 0.14 | [ 0.00,  0.54] |  1.95 | 0.051
#> sppEC-A     |    -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006
#> sppEC-L     |     0.67 | 0.13 | [ 0.41,  0.92] |  5.20 | < .001
#> sppDES-L    |     0.63 | 0.13 | [ 0.38,  0.87] |  4.96 | < .001
#> sppDF       |     0.12 | 0.15 | [-0.17,  0.40] |  0.78 | 0.435
#> minedno     |     1.27 | 0.27 | [ 0.74,  1.80] |  4.72 | < .001
#>
#> # Fixed Effects (Zero-Inflated Model)
#>
#> Parameter   | Log-Odds |   SE |         95% CI |     z |      p
#> ---------------------------------------------------------------
#> (Intercept) |     0.79 | 0.27 | [ 0.26,  1.32] |  2.90 | 0.004
#> minedno     |    -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001
#>
#> # Random Effects Variances
#>
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: site) |        0.33
#> SD (Residual)        |        1.00
#>
#> # Random Effects (Zero-Inflated Model)
#>
#> Parameter     | Coefficient
#> ---------------------------
#> SD (Residual) |        1.00
#> # Fixed Effects
#>
#> Parameter            | Coefficient |   SE |         95% CI |     z |      p | Effects |     Component
#> -----------------------------------------------------------------------------------------------------
#> (Intercept)          |       -0.36 | 0.28 | [-0.90,  0.18] | -1.30 | 0.194  |   fixed |   conditional
#> spp [PR]             |       -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001 |   fixed |   conditional
#> spp [DM]             |        0.27 | 0.14 | [ 0.00,  0.54] |  1.95 | 0.051  |   fixed |   conditional
#> spp [EC-A]           |       -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006  |   fixed |   conditional
#> spp [EC-L]           |        0.67 | 0.13 | [ 0.41,  0.92] |  5.20 | < .001 |   fixed |   conditional
#> spp [DES-L]          |        0.63 | 0.13 | [ 0.38,  0.87] |  4.96 | < .001 |   fixed |   conditional
#> spp [DF]             |        0.12 | 0.15 | [-0.17,  0.40] |  0.78 | 0.435  |   fixed |   conditional
#> mined [no]           |        1.27 | 0.27 | [ 0.74,  1.80] |  4.72 | < .001 |   fixed |   conditional
#> (Intercept)          |        0.79 | 0.27 | [ 0.26,  1.32] |  2.90 | 0.004  |   fixed | zero_inflated
#> minedno              |       -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001 |   fixed | zero_inflated
#> SD (Intercept: site) |        0.33 |      |                |       |        |  random |   conditional
#> SD (Residual)        |        1.00 |      |                |       |        |  random |   conditional
#> SD (Residual)        |        1.00 |      |                |       |        |  random | zero_inflated
#> # Fixed Effects (Count Model)
#>
#> Parameter   | Log-Mean |   SE
#> -----------------------------
#> (Intercept) |    -0.36 | 0.28
#> spp [PR]    |    -1.27 | 0.24
#> spp [DM]    |     0.27 | 0.14
#> spp [EC-A]  |    -0.57 | 0.21
#> spp [EC-L]  |     0.67 | 0.13
#> spp [DES-L] |     0.63 | 0.13
#> spp [DF]    |     0.12 | 0.15
#> mined [no]  |     1.27 | 0.27
#>
#> # Fixed Effects (Zero-Inflated Model)
#>
#> Parameter   | Log-Odds |   SE
#> -----------------------------
#> (Intercept) |     0.79 | 0.27
#> mined [no]  |    -1.84 | 0.31
#>
#> # Random Effects Variances
#>
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: site) |        0.33
#> SD (Residual)        |        1.00
#>
#> # Random Effects (Zero-Inflated Model)
#>
#> Parameter     | Coefficient
#> ---------------------------
#> SD (Residual) |        1.00
#> # Fixed Effects (Count Model)
#>
#> Parameter   | Log-Mean |         95% CI |      p
#> ------------------------------------------------
#> (Intercept) |    -0.36 | [-0.90,  0.18] | 0.194
#> spp [PR]    |    -1.27 | [-1.74, -0.80] | < .001
#> spp [DM]    |     0.27 | [ 0.00,  0.54] | 0.051
#> spp [EC-A]  |    -0.57 | [-0.97, -0.16] | 0.006
#> spp [EC-L]  |     0.67 | [ 0.41,  0.92] | < .001
#> spp [DES-L] |     0.63 | [ 0.38,  0.87] | < .001
#> spp [DF]    |     0.12 | [-0.17,  0.40] | 0.435
#> mined [no]  |     1.27 | [ 0.74,  1.80] | < .001
#>
#> # Fixed Effects (Zero-Inflated Model)
#>
#> Parameter   | Log-Odds |         95% CI |      p
#> ------------------------------------------------
#> (Intercept) |     0.79 | [ 0.26,  1.32] | 0.004
#> mined [no]  |    -1.84 | [-2.46, -1.23] | < .001
#>
#> # Random Effects Variances
#>
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: site) |        0.33
#> SD (Residual)        |        1.00
#>
#> # Random Effects (Zero-Inflated Model)
#>
#> Parameter     | Coefficient
#> ---------------------------
#> SD (Residual) |        1.00

# group parameters ------

data(iris)
model <- lm(
Sepal.Width ~ Sepal.Length + Species + Petal.Length,
data = iris
)
# don't select "Intercept" parameter
mp <- model_parameters(model, parameters = "^(?!\\(Intercept)")
groups <- list(
"Focal Predictors" = c("Speciesversicolor", "Speciesvirginica"),
"Controls" = c("Sepal.Length", "Petal.Length")
)
print(mp, groups = groups)
#> Parameter              | Coefficient |   SE |         95% CI | t(145) |      p
#> ------------------------------------------------------------------------------
#> Focal Predictors       |             |      |                |        |
#>   Species [versicolor] |       -0.89 | 0.20 | [-1.29, -0.49] |  -4.43 | < .001
#>   Species [virginica]  |       -0.88 | 0.28 | [-1.43, -0.33] |  -3.15 | 0.002
#> Controls               |             |      |                |        |
#>   Sepal Length         |        0.38 | 0.07 | [ 0.24,  0.52] |   5.31 | < .001
#>   Petal Length         |       -0.04 | 0.08 | [-0.21,  0.13] |  -0.50 | 0.618
#> (Intercept)            |        1.60 | 0.28 | [ 1.06,  2.15] |   5.80 | < .001

# or use row indices
print(mp, groups = list(
"Focal Predictors" = c(1, 4),
"Controls" = c(2, 3)
))
#> Parameter              | Coefficient |   SE |         95% CI | t(145) |      p
#> ------------------------------------------------------------------------------
#> Focal Predictors       |             |      |                |        |
#>   (Intercept)          |        1.60 | 0.28 | [ 1.06,  2.15] |   5.80 | < .001
#>   Species [virginica]  |       -0.88 | 0.28 | [-1.43, -0.33] |  -3.15 | 0.002
#> Controls               |             |      |                |        |
#>   Sepal Length         |        0.38 | 0.07 | [ 0.24,  0.52] |   5.31 | < .001
#>   Species [versicolor] |       -0.89 | 0.20 | [-1.29, -0.49] |  -4.43 | < .001
#> Petal Length           |       -0.04 | 0.08 | [-0.21,  0.13] |  -0.50 | 0.618

# only show coefficients, CI and p,
# put non-matched parameters to the end

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, parameters = "^(?!\\(Intercept)")
print(mp, groups = list(
"Engine" = c("cyl6", "cyl8", "vs", "hp"),
"Interactions" = c("gear4:vs", "gear5:vs")
))
#> 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
#> (Intercept)      |       16.63 | 7.77 | [  0.53, 32.74] |  2.14 | 0.044
#> 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 | 0.198
# }