Print model parameters
A print()-method for objects from model_parameters().
## S3 method for class '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, ... )
x |
An object returned by |
pretty_names |
Return "pretty" (i.e. more human readable) parameter names. |
split_components |
Logical, if |
select |
Character vector (or numeric index) of column names that should
be printed. If |
caption |
Table caption as string. If |
digits |
Number of decimal places for numeric values (except confidence intervals and p-values). |
ci_digits |
Number of decimal places for confidence intervals. |
p_digits |
Number of decimal places for p-values. May also be |
footer_digits |
Number of decimal places for values in the footer summary. |
show_sigma |
Logical, if |
show_formula |
Logical, if |
zap_small |
Logical, if |
... |
Arguments passed to or from other methods. |
Invisibly returns the original input object.
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().
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().
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")
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.