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Print model parameters

Source: R/print.parameters_model.R
print.parameters_model.Rd

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

Usage

# 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,
  column_width = NULL,
  ci_brackets = c("[", "]"),
  ...
)

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

Arguments

x, object

An object returned by model_parameters().

pretty_names

Return "pretty" (i.e. more human readable) parameter names.

split_components

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.

select

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.

caption

Table caption as string. If NULL, no table caption is printed.

digits, ci_digits, p_digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

footer_digits

Number of decimal places for values in the footer summary.

show_sigma

Logical, if TRUE, adds information about the residual standard deviation.

show_formula

Logical, if TRUE, adds the model formula to the output.

zap_small

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.

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.

column_width

Width of table columns. Can be either NULL, a named numeric vector, or "fixed". If NULL, the width for each table column is adjusted to the minimum required width. If a named numeric vector, value names are matched against column names, and for each match, the specified width is used. If "fixed", and table is split into multiple components, columns across all table components are adjusted to have the same width.

ci_brackets

Logical, if TRUE (default), CI-values are encompassed in square brackets (else in parentheses).

...

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, ggeffects, or marginaleffects. 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.

See also

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")
}
#> # 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 |       95% CI
#> -------------------------------------------------
#> SD (Intercept: site) |        0.33 | [0.18, 0.63]
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.
#> # 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 |      | [ 0.18,  0.63] |       |        |  random |   conditional
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.
#> # 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
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.
#> # 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 |       95% CI
#> -------------------------------------------------
#> SD (Intercept: site) |        0.33 | [0.18, 0.63]
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald z-distribution approximation.


# 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
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald t-distribution approximation.

# 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 
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald t-distribution approximation.

# 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
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#>   using a Wald t-distribution approximation.
# }