Description Usage Arguments Details Value Note References Examples

Compute Goodness-of-fit measures for various regression models, including mixed and Bayesian regression models.

1 2 3 4 5 6 7 8 9 10 11 12 |

`x` |
Fitted model of class |

`...` |
Currently not used. |

`n` |
Optional, an |

`loo` |
Logical, if |

For linear models, the r-squared and adjusted r-squared value is returned,
as provided by the `summary`

-function.

For mixed models (from lme4 or glmmTMB) marginal and
conditional r-squared values are calculated, based on
Nakagawa et al. 2017.

For `lme`

-models, an r-squared approximation by computing the
correlation between the fitted and observed values, as suggested by
Byrnes (2008), is returned as well as a simplified version of
the Omega-squared value (1 - (residual variance / response variance),
Xu (2003), Nakagawa, Schielzeth 2013), unless `n`

is specified.

If `n`

is given, for `lme`

-models pseudo r-squared measures based
on the variances of random intercept (tau 00, between-group-variance)
and random slope (tau 11, random-slope-variance), as well as the
r-squared statistics as proposed by Snijders and Bosker 2012 and
the Omega-squared value (1 - (residual variance full model / residual
variance null model)) as suggested by Xu (2003) are returned.

For generalized linear models, Cox & Snell's and Nagelkerke's
pseudo r-squared values are returned.

The ("unadjusted") r-squared value and its standard error for
`brmsfit`

or `stanreg`

objects are robust measures, i.e.
the median is used to compute r-squared, and the median absolute
deviation as the measure of variability. If `loo = TRUE`

,
a LOO-adjusted r-squared is calculated, which comes conceptionally
closer to an adjusted r-squared measure.

For `r2()`

, depending on the model, returns:

For linear models, the r-squared and adjusted r-squared values.

For mixed models, the marginal and conditional r-squared values.

For

`glm`

objects, Cox & Snell's and Nagelkerke's pseudo r-squared values.For

`brmsfit`

or`stanreg`

objects, the Bayesian version of r-squared is computed, calling`rstantools::bayes_R2()`

.If

`loo = TRUE`

, for`brmsfit`

or`stanreg`

objects a LOO-adjusted version of r-squared is returned.

For `cod()`

, returns the `D`

Coefficient of Discrimination,
also known as Tjur's R-squared value.

**cod()**-
This method calculates the Coefficient of Discrimination

`D`

for generalized linear (mixed) models for binary data. It is an alternative to other Pseudo-R-squared values like Nakelkerke's R2 or Cox-Snell R2. The Coefficient of Discrimination`D`

can be read like any other (Pseudo-)R-squared value. **r2()**-
For mixed models, the marginal r-squared considers only the variance of the fixed effects, while the conditional r-squared takes both the fixed and random effects into account.

For`lme`

-objects, if`n`

is given, the Pseudo-R2 statistic is the proportion of explained variance in the random effect after adding co-variates or predictors to the model, or in short: the proportion of the explained variance in the random effect of the full (conditional) model`x`

compared to the null (unconditional) model`n`

.

The Omega-squared statistics, if`n`

is given, is 1 - the proportion of the residual variance of the full model compared to the null model's residual variance, or in short: the the proportion of the residual variation explained by the covariates.

Alternative ways to assess the "goodness-of-fit" is to compare the ICC of the null model with the ICC of the full model (see`icc`

).

Bolker B et al. (2017): GLMM FAQ.

Byrnes, J. 2008. Re: Coefficient of determination (R^2) when using lme() (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2008q2/000713.html)

Kwok OM, Underhill AT, Berry JW, Luo W, Elliott TR, Yoon M. 2008. Analyzing Longitudinal Data with Multilevel Models: An Example with Individuals Living with Lower Extremity Intra-Articular Fractures. Rehabilitation Psychology 53(3): 370–86. doi: 10.1037/a0012765

Nakagawa S, Schielzeth H. 2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2):133–142. doi: 10.1111/j.2041-210x.2012.00261.x

Nakagawa S, Johnson P, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisted and expanded. J. R. Soc. Interface 14. doi: 10.1098/rsif.2017.0213

Rabe-Hesketh S, Skrondal A. 2012. Multilevel and longitudinal modeling using Stata. 3rd ed. College Station, Tex: Stata Press Publication

Raudenbush SW, Bryk AS. 2002. Hierarchical linear models: applications and data analysis methods. 2nd ed. Thousand Oaks: Sage Publications

Snijders TAB, Bosker RJ. 2012. Multilevel analysis: an introduction to basic and advanced multilevel modeling. 2nd ed. Los Angeles: Sage

Xu, R. 2003. Measuring explained variation in linear mixed effects models. Statist. Med. 22:3527-3541. doi: 10.1002/sim.1572

Tjur T. 2009. Coefficients of determination in logistic regression models - a new proposal: The coefficient of discrimination. The American Statistician, 63(4): 366-372

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
data(efc)
# Tjur's R-squared value
efc$services <- ifelse(efc$tot_sc_e > 0, 1, 0)
fit <- glm(services ~ neg_c_7 + c161sex + e42dep,
data = efc, family = binomial(link = "logit"))
cod(fit)
library(lme4)
fit <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
r2(fit)
fit <- lm(barthtot ~ c160age + c12hour, data = efc)
r2(fit)
# Pseudo-R-squared values
fit <- glm(services ~ neg_c_7 + c161sex + e42dep,
data = efc, family = binomial(link = "logit"))
r2(fit)
``` |

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