Compute and extract model parameters. See the documentation for your object's class:
Correlations, t-tests, ... (htest, pairwise.htest)
ANOVAs (aov, anova, afex, ...)
Regression models (lm, glm, survey, ...)
Additive models (gam, gamm, ...)
Zero-inflated models (hurdle, zeroinfl, zerocount)
Multinomial, ordinal and cumulative link models (bracl, multinom, mlm, ...)
Other special models (model.avg, betareg, glmx, ...)
Mixed models (lme4, nlme, glmmTMB, afex, ...)
Bayesian tests (BayesFactor)
Bayesian models (rstanarm, brms, MCMCglmm, blavaan, ...)
PCA and FA (psych)
CFA and SEM (lavaan)
Cluster models (k-means, ...)
Meta-Analysis via linear (mixed) models (rma, metaplus, metaBMA, ...)
Hypothesis testing (glht, PMCMRplus)
Robust statistical tests (WRS2)
model_parameters(model, ...)parameters(model, ...)
Statistical Model.
Arguments passed to or from other methods. Non-documented
arguments are digits, p_digits, ci_digits and
footer_digits to set the number of digits for the output.
group can also be passed to the print() method. See details
in print.parameters_model and 'Examples' in
model_parameters.default.
A data frame of indices related to the model's 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.
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().
Standardization is based on standardize_parameters().
In case of standardize = "refit", the data used to fit the model
will be standardized and the model is completely refitted. In such cases,
standard errors and confidence intervals refer to the standardized
coefficient. The default, standardize = "refit", never standardizes
categorical predictors (i.e. factors), which may be a different behaviour
compared to other R packages or other software packages (like SPSS).
To mimic behaviour of SPSS or packages such as lm.beta, use
standardize = "basic".
For full details, please refer to standardize_parameters().
This method is based on a complete model re-fit with a standardized version
of the data. Hence, this method is equal to standardizing the variables
before fitting the model. It is the "purest" and the most accurate
(Neter et al., 1989), but it is also the most computationally costly and
long (especially for heavy models such as Bayesian models).The
robust argument (default to FALSE) enables a robust standardization
of data, i.e., based on the median and MAD instead of the
mean and SD.
Post-hoc standardization of the parameters, aiming at emulating the
results obtained by "refit" without refitting the model. The coefficients
are divided by the standard deviation (or MAD if robust=TRUE) of
the outcome (which becomes their expression 'unit'). Then, the coefficients
related to numeric variables are additionally multiplied by the standard
deviation (or MAD) of the related terms, so that they correspond to
changes of 1 SD of the predictor. This does not apply to binary
variables or factors, so the coefficients are still related to changes in
levels. This method is not accurate and tend to give aberrant results when
interactions are specified.
(Standardization of Model's parameters with Adjustment, Reconnaissance
and Transformation - experimental): Similar to method="posthoc"
in that it does not involve model refitting. The difference is that the
SD (or MAD) of the response is computed on the relevant section of the
data. For instance, if a factor with 3 levels A (the intercept), B and C
is entered as a predictor, the effect corresponding to B vs. A will be
scaled by the variance of the response at the intercept only. As a results,
the coefficients for effects of factors are similar to a Glass' delta.
This method is similar to method="posthoc", but treats all
variables as continuous: it also scales the coefficient by the standard
deviation of model's matrix' parameter of factors levels (transformed to
integers) or binary predictors. Although being inappropriate for these cases,
this method is the one implemented by default in other software packages,
such as lm.beta::lm.beta().
In this (post-hoc) method, the response and the predictor are standardized
based on the level of prediction (levels are detected with check_heterogeneity:
Predictors are standardized based on their SD at level of prediction
(see also demean). The outcome (in linear LMMs) is
standardized based on a fitted random-intercept-model, where
sqrt(random-intercept-variance) is used for level 2 predictors,
and sqrt(residual-variance) is used for level 1 predictors
(Hoffman 2015, page 342). A warning is given when a within-group variable
is found to have access between-group variance.
Hoffman, L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. Routledge.
Neter, J., Wasserman, W., & Kutner, M. H. (1989). Applied linear regression models.
standardize_names() to
rename columns into a consistent, standardized naming scheme.