| Title: | Partitioning R2 in GLMMs |
| Version: | 0.9.2 |
| Depends: | R (≥ 3.5.0) |
| Description: | Partitioning the R2 of GLMMs into variation explained by each predictor and combination of predictors using semi-partial (part) R2 and inclusive R2. Methods are based on the R2 for GLMMs described in Nakagawa & Schielzeth (2013) and Nakagawa, Johnson & Schielzeth (2017). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/mastoffel/partR2 |
| BugReports: | https://github.com/mastoffel/partR2/issues |
| Imports: | methods, stats, lme4 (≥ 1.1-21), pbapply (≥ 1.4-2), dplyr (≥ 1.0.0), purrr (≥ 0.3.3), rlang (≥ 0.4.2), tibble (≥ 2.1.3), magrittr (≥ 1.5), ggplot2 (≥ 3.3.0), tidyr (≥ 1.1) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Suggests: | testthat, future, furrr, knitr, rmarkdown, patchwork, covr |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-03-04 13:03:24 UTC; mstoffel |
| Author: | Martin A. Stoffel [aut, cre], Shinichi Nakagawa [aut], Holger Schielzeth [aut] |
| Maintainer: | Martin A. Stoffel <martin.adam.stoffel@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-03-04 13:20:02 UTC |
partR2: Partitioning R2 in generalized linear mixed models
Description
The partR2 package provides a simple way to estimate R2 in mixed models fitted with lme4 as well as part (semi-partial) R2 for specific predictors and combinations of predictors, among other several other statistics. Here is an overview:
Details
Marginal and conditional R2 for LMMs and GLMMs.
Part (semi-partial) R2 which estimate the explained variance for specific predictors and combinations of predictors.
Structure coefficients (SC). SC are the correlation between a predictor and the predicted response (called the linear predictor), independent of the other predictors.
Inclusive R2 (IR2), which estimate the the total variance explained by a predictor independent of other predictors. IR2 is estimated with SC^2 * R2_full_model.
Beta weights, which are standardised regression coefficients. If beta is a model estimate for variable x, and y is the response,then the beta weight is beta * (sd(x)/sd(y).
Confidence intervals for all estimates using parametric bootstrapping.
The package has one main function partR2 which takes a fitted model
from lme4. At the moment, Gaussian, Poisson and binomial models are supported.
For Poisson and non-binary binomial models, partR2 adds an
observational level random effect to model additive overdispersion (if
an olre is not fitted already).
The summary.partR2 function provides an extended summary with R2s, semi-partial
R2s, model estimates and structure coefficients. The forestplot
function provides a means of plotting the results.
References
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.
Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
BeetlesBody dataset
Description
BeetlesBody dataset
Details
This is an simulated dataset which was used as a toy example for a different purpose
(Nakagawa & Schielzeth 2013).
It offers a balanced dataset with rather simple structure, sizable effects and decent sample size,
just right for demonstrating some features of partR2.
Sufficient sample size is required in particular for the non-Gaussian traits,
because those tend to be more computationally demanding and less rich in information per data
point than simple Gaussian traits.
In brief the imaginary sampling design of the simulated dataset is as follows. Beetle larvae were sampled from 12 populations ('Population') with samples taken from two discrete microhabitats at each location ('Habitat'). Samples were split in equal proportion and raised in two dietary treatments ('Treatment'). Beetles were sexed at the pupal stage ('Sex') and pupae were kept in sex-homogeneous containers ('Container'). The phenotype in this dataset is body length ('BodyL').
References
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: 133-142.
BeetlesFemale dataset
Description
BeetlesFemale dataset
Details
This is an simulated dataset which was used as a toy example for a different purpose
(Nakagawa & Schielzeth 2013).
It offers a balanced dataset with rather simple structure, sizable effects and decent sample size,
just right for demonstrating some features of partR2.
Sufficient sample size is required in particular for the non-Gaussian traits,
because those tend to be more computationally demanding and less rich in information per data
point than simple Gaussian traits.
In brief the imaginary sampling design of the simulated dataset is as follows. Beetle larvae were sampled from 12 populations ('Population') with samples taken from two discrete microhabitats at each location ('Habitat'). Samples were split in equal proportion and raised in two dietary treatments ('Treatment'). Beetles were sexed at the pupal stage ('Sex') and pupae were kept in sex-homogeneous containers ('Container'). The phenotype in this dataset is the number of eggs laid by female beetles ('Egg').
References
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: 133-142.
BeetlesMale dataset
Description
BeetlesMale dataset
Details
This is an simulated dataset which was used as a toy example for a different purpose
(Nakagawa & Schielzeth 2013).
It offers a balanced dataset with rather simple structure, sizable effects and decent sample size,
just right for demonstrating some features of rptR.
Sufficient sample size is required in particular for the non-Gaussian traits,
because those tend to be more computationally demanding and less rich in information per data
point than simple Gaussian traits.
In brief the imaginary sampling design of the simulated dataset is as follows. Beetle larvae were sampled from 12 populations ('Population') with samples taken from two discrete microhabitats at each location ('Habitat'). Samples were split in equal proportion and raised in two dietary treatments ('Treatment'). Beetles were sexed at the pupal stage ('Sex') and pupae were kept in sex-homogeneous containers ('Container'). The phenotype in this dataset is a binary variable containing the two distinct color morphs of males: dark and reddish-brown ('Colour').
References
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: 133-142.
Grasshoppers dataset
Description
Grasshoppers dataset
Details
This is a real dataset from grasshoppers.
This dataset contains data on spatial variation in color morph ratios in a color-polymorphic species of grasshopper (Dieker et al 2018). Individuals of this species occur either in green or a brown color variant and the dataset contains counts of brown and green individuals (seprarated for females and males) from 42 sites sampled in the field. All 'Bio' variables describe various aspects of ecologically relevant climatic conditions (see Karger et al. 2017).
References
Dieker, P., L. Beckmann, J. Teckentrup, and H. Schielzeth (2018) Spatial analyses of two colour polymorphisms in an alpine grasshopper reveal a role of small-scale heterogeneity. Ecology and Evolution, 8, 7273-7284.
Karger, D. N., O. Conrad, J. Bohner, T. Kawohl, H. Kreft, R. W. Soria-Auza, N. E. Zimmermann, H. P. Linder, and M. Kessler (2017) Data descriptor: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4, 170122.
GuineaPigs dataset
Description
GuineaPigs dataset
Details
This is a real dataset from guinea pigs.
The dataset contains testosterone measurements ('Testo') of 31 male guinea pigs, each measured at 5 time points. (age between 120 and 240 days at 30-day intervals). As covariates the dataset contains the time point of measurement ('Time') and a rank index derived from behavioral observations ('Rank') around the time of measurement (see Mutwill et al. in prep. for details).
Calculate R2
Description
Calculate R2
Usage
R2_pe(mod, expct, overdisp_name, R2_type)
Arguments
mod |
merMod object |
expct |
Expectation |
overdisp_name |
Name of overdispersion term |
R2_type |
"marginal" or "conditional" |
Value
data.frame with one element, R2
Structure coefficients
Description
Structure coefficients
Usage
SC_pe(mod)
Arguments
mod |
merMod object. |
Value
data.frame with structure coefficients
Adds columns for CI
Description
Adds columns for CI
Usage
add_CI_cols(df)
Arguments
df |
data.frame with point estimates |
Value
data.frame with two additional columns for lower and upper CI containing NAs
biomass dataset
Description
biomass dataset
Details
This is an simulated dataset about grassland biodiversity and biomass.
In brief the imaginary sampling design of the simulated dataset is as follows. Invertebrates were sampled once every year over 10 successive years ('Year') from 20 different populations ('Population'). For each sample, the temperature ('Temperature') and ('Precipitation') were measured and overall species diversity ('SpeciesDiversity') and biomass were recorded ('Biomass').
List to data.frame with bootstrap samples per row
Description
List to data.frame with bootstrap samples per row
Usage
boot_to_df(lcol)
Arguments
lcol |
A bootstrap list-column |
Value
data.frame with bootstrap estimates as list-column
Parametric bootstrapping
Description
Parametric bootstrapping
Usage
bootstrap_all(
nboot,
mod,
R2_type,
all_comb,
partition,
data_mod,
allow_neg_r2,
parallel,
expct,
overdisp_name
)
Arguments
nboot |
Number of parametric bootstrap iterations for confidence interval estimation
(defaults to NULL, i.e. no bootstrapping). Larger numbers of bootstraps give a better
asymptotic CI, but may be time-consuming. Bootstrapping can be switched on by setting
|
mod |
merMod object, lme4 fit |
R2_type |
"marginal" or "conditional" R2. With "marginal", the variance explained by fixed effects is calculated. With "conditional", the variance explained by both fixed and random effects is calculated. |
all_comb |
list of predictor combinations |
partition |
TRUE or FALSE |
data_mod |
Data for model |
allow_neg_r2 |
Calculating part R2 involves fitting two models, one with and one without the predictor of interest. In cases where the predictor has little association with the response, the resulting part R2 value can become negative. By default we set negative values to 0, but by setting this parameter to TRUE, R2 values can become negative. |
parallel |
If TRUE, computation uses |
expct |
A string specifying the method for estimating the expectation in Poisson models with log link and in Binomial models with logit link (in all other cases the argument is ignored). The only valid terms are 'meanobs', 'latent', 'none' (and 'liability for binary and proportion data). With the default 'meanobs', the expectation is estimated as the mean of the observations in the sample. With 'latent', the expectation is estimated from estimates of the intercept and variances on the link scale. While this is a preferred solution, it is susceptible to the distribution of fixed effect covariates and gives appropriate results typically only when all covariances are centered to zero. With 'liability' estimates follow formulae as presented in Nakagawa & Schielzeth (2010). With 'none', R2 is calculated without distribution specific variance in the denominator. |
overdisp_name |
Name of overdispersion term |
Value
Bootstrap samples for all statistics, plus associated warnings
Calculates CI from bootstrap replicates
Description
Calculates CI from bootstrap replicates
Usage
calc_CI(x, CI)
Arguments
x |
numeric vector |
CI |
CI level, e.g. 0.95 |
Value
data.frame with two columns for lower and upper CI
Get tidy summary of fixed effect estimates
Description
Get tidy summary of fixed effect estimates
Usage
fixef_simple(mod, intcp = FALSE)
Arguments
mod |
merMod object |
intcp |
include intercept in summary? Defaults to FALSE |
Value
tibble with two columns: term and estimate
Calculate fixed effect variance from a reduced model
Description
Calculate fixed effect variance from a reduced model
Usage
fixvar_of_red_mod(partvar, mod, dat, expct, overdisp_name, R2_type)
Arguments
partvar |
One or more fixed effect variables which are taken out of the model. |
mod |
merMod object. |
dat |
Data.frame to fit the model |
expct |
Expectation |
overdisp_name |
Name of overdispersion term |
R2_type |
"marginal" or "conditional" |
Value
R2 of reduced model.
Plot a partR2 object
Description
Forestplot of the partR2 results
Usage
forestplot(
x,
type = c("R2", "BW", "SC", "IR2", "Ests"),
line_size = 0.5,
text_size = 12,
point_size = 3
)
Arguments
x |
A partR2 object. |
type |
Plot either "R2" or "SC" or "Ests" for R2s, structure coefficients or model estimates. |
line_size |
Controls size of all lines in the forestplot. Defaults to 0.5 which usually looks good. |
text_size |
Base text size, default is 12. |
point_size |
Point size, default is 3. |
Get beta weights
Description
Get beta weights
Usage
get_bw(mod)
Arguments
mod |
merMod object. |
Value
data.frame with bw instead of raw estimates
Get numerator dfs for reduced models
Description
Get numerator dfs for reduced models
Usage
get_ndf(partvar, mod, dat)
Arguments
partvar |
One or more fixed effect variables which are taken out of the model. |
mod |
merMod object. |
dat |
Data.frame to fit the model |
Value
Numeric vector, giving numerator degrees of freedom
Extracts random effect variances
Description
This function computes the sum of random effect variances where one or more of the random effects are random slopes. It uses Paul Johnson' method to compute the average group variance across the levels of a covariate. This function extracts only grouping factors, no residual or overdispersion.
Usage
get_ran_var(mod, overdisp_name = NULL)
Arguments
mod |
An lme4 model object. |
overdisp_name |
name of overdispersion term |
Extract variance components from merMod.
Description
Extract variance components from merMod.
Usage
get_var_comps(mod, expct, overdisp_name)
Arguments
mod |
A merMod object. |
expct |
expectation. |
overdisp_name |
name of overdispersion term |
Value
Fixed, random and residual variance
Create list of combination of variables.
Description
Create list of combination of variables.
Usage
make_combs(partvars, partbatch, max_level)
Arguments
partvars |
Character vector specifying the predictors (fixed effects) for which to partition the R2. Can be main effects like c("Var1", "Var2") and interactions ("Var1:Var2"). Predictors specified in partvars have to be named precisely like the terms in the formula to fit the model. |
partbatch |
List of character vectors with predictors that should be fitted and removed together. For example, partbatch = list(batch1 = c("V1", "V2", "V3"), batch2 = c("V4", "V5", "V6")) would calculate part R2 only for combinations of predictors which contain the variables V1, V2, V3 together or/and V4,V5,V6 together. This is useful when the number of potential subsets gets too large to be computationally practical, for example when dummy coding is used. See our vignette for details. This feature is still experimental and should be used with caution. |
max_level |
Level up to which shared semi-partial R2s are calculated. The number of sets for which to calculate R2 increases exponentially, i.e. for 10 variables 2^10 - 1 R2s can be calculated. If you are only interested in the unique but not the shared effects, use max_level = 1. If interested in unique effects and combinations of two terms, use max_level = 2 etc. |
Value
list with all combinations of predictors specified in partvars/partbatch
Merge partR2 objects to combine R2s for main effects and interactions
Description
The function merges partR2 object based on a full model with interactions with a partR2 object based on a reduced model without interaction. The reduced model is used to infer main effect semi-partial R2s. This function essentially takes over the complete partR2 object for the full model and adds semi-partial R2s which have been calculated based on the reduced model and are not already present in the full model partR2 object (which can be main effects). The function also combines the bootstrap estimates, accessible with partR2_obj$R2_boot.
Usage
mergeR2(R2_full, ...)
Arguments
R2_full |
partR2 object for the full model, with the interaction (but not the main effects) in partvars. |
... |
other partR2 objects, which do not contain the interaction so that the semi-partial R2s for the main effects could be calculated. |
Details
This function is experimental and should be used with caution. See vignette or paper on how to use it to obtain semi-partial R2s for main effects which are also involved in interactions.
Value
Returns an object of class partR2, which takes most components from the
full model except for semi-partial R2s.
Examples
data(biomass)
library(lme4)
# scale data
biomass[] <- lapply(biomass, function(x) if (is.double(x)) scale(x) else x)
# Full model
mod_full <- lmer(Biomass ~ Year + Temperature * Precipitation + SpeciesDiversity + (1|Population),
data = biomass)
# Semi-partial R2 for interaction and all other predictors of interest
(R2_full <- partR2(mod_full, partvars = c("Temperature:Precipitation", "SpeciesDiversity", "Year"),
data = biomass))
# model without interaction to get main effect semi-partial R2s
mod_noIA <- lmer(Biomass ~ Year + Temperature + Precipitation + SpeciesDiversity + (1|Population),
data = biomass)
(R2_noIA <- partR2(mod_noIA, partvars = c("Temperature", "Precipitation"), data = biomass))
# combine both
(R2_comb <- mergeR2(R2_full, R2_noIA))
Modify term names if partbatch is a named list
Description
Modify term names if partbatch is a named list
Usage
mod_names_partbatch(partbatch, part_names)
Arguments
partbatch |
list with batches |
part_names |
character vector with names of terms (and their combinations) |
Value
Modified names for partvars, where partvar combinations are replaced with partbatch names.
Adds an observational level random effect to a model, if applicable.
Description
Adds an observational level random effect to a model, if applicable.
Usage
model_overdisp(mod, dat, olre)
Arguments
mod |
merMod object. |
dat |
The underlying data.frame |
Value
list with three elements: merMod object with added OLRE, data.frame with added OLRE variable, and name of OLRE.
Partitioning R2 (R-square) in mixed models
Description
R2, semi-partial (part) R2 for predictors and their combinations as well as inclusive R2, structure coefficients and beta weights for Gaussian, Poisson and binomial mixed models.
Usage
partR2(
mod,
partvars = NULL,
data = NULL,
R2_type = "marginal",
max_level = NULL,
nboot = NULL,
CI = 0.95,
parallel = FALSE,
expct = "meanobs",
olre = TRUE,
partbatch = NULL,
allow_neg_r2 = FALSE
)
Arguments
mod |
Fitted lme4 model (a merMod object). |
partvars |
Character vector specifying the predictors (fixed effects) for which to partition the R2. Can be main effects like c("Var1", "Var2") and interactions ("Var1:Var2"). Predictors specified in partvars have to be named precisely like the terms in the formula to fit the model. |
data |
The data.frame used to fit the lme4 model. If not provided, partR2 will try to fetch it. |
R2_type |
"marginal" or "conditional" R2. With "marginal", the variance explained by fixed effects is calculated. With "conditional", the variance explained by both fixed and random effects is calculated. |
max_level |
Level up to which shared semi-partial R2s are calculated. The number of sets for which to calculate R2 increases exponentially, i.e. for 10 variables 2^10 - 1 R2s can be calculated. If you are only interested in the unique but not the shared effects, use max_level = 1. If interested in unique effects and combinations of two terms, use max_level = 2 etc. |
nboot |
Number of parametric bootstrap iterations for confidence interval estimation
(defaults to NULL, i.e. no bootstrapping). Larger numbers of bootstraps give a better
asymptotic CI, but may be time-consuming. Bootstrapping can be switched on by setting
|
CI |
Width of the required confidence interval between 0 and 1 (defaults to 0.95). |
parallel |
If TRUE, computation uses |
expct |
A string specifying the method for estimating the expectation in Poisson models with log link and in Binomial models with logit link (in all other cases the argument is ignored). The only valid terms are 'meanobs', 'latent', 'none' (and 'liability for binary and proportion data). With the default 'meanobs', the expectation is estimated as the mean of the observations in the sample. With 'latent', the expectation is estimated from estimates of the intercept and variances on the link scale. While this is a preferred solution, it is susceptible to the distribution of fixed effect covariates and gives appropriate results typically only when all covariances are centered to zero. With 'liability' estimates follow formulae as presented in Nakagawa & Schielzeth (2010). With 'none', R2 is calculated without distribution specific variance in the denominator. |
olre |
Logical, defaults to TRUE. This argument allows the user to prevent the automatic fitting of an observation level random effect (by setting it to FALSE) in Poisson and binomial models. The OLRE is used to account for overdispersion. |
partbatch |
List of character vectors with predictors that should be fitted and removed together. For example, partbatch = list(batch1 = c("V1", "V2", "V3"), batch2 = c("V4", "V5", "V6")) would calculate part R2 only for combinations of predictors which contain the variables V1, V2, V3 together or/and V4,V5,V6 together. This is useful when the number of potential subsets gets too large to be computationally practical, for example when dummy coding is used. See our vignette for details. This feature is still experimental and should be used with caution. |
allow_neg_r2 |
Calculating part R2 involves fitting two models, one with and one without the predictor of interest. In cases where the predictor has little association with the response, the resulting part R2 value can become negative. By default we set negative values to 0, but by setting this parameter to TRUE, R2 values can become negative. |
Value
Returns an object of class partR2 that is a a list with the following elements:
call |
model call |
R2_type |
Marginal or conditional R2 |
R2 |
R2 and confidence intervals for full model and semi-partial R2 for predictors and their combinations |
SC |
Structure coefficients and confidence intervals. SC are the correlation between a predictor and the predicted response. |
IR2 |
Inclusive R2. This is SC^2 * R2_full. |
BW |
Standardised model estimates (beta weights) for fixed effects. Beta weights for Gaussian models are calculated as beta * sd(x)/sd(y), with beta being the estimated slope of a fixed effect for predictor x and response y. Beta weight for Non-Gaussian models are calculated as beta * sd(x). Beta weights for interactions or polynomial terms are not informative at the moment and we recommend users to standardise variables themselves before fitting the model and to look at the model estimates (Ests) instead of beta weights (BW) in the partR2 output. See vignette for details. |
Ests |
Model estimates and confidence intervals. |
R2_boot |
Parametric bootstrap samples for R2 for full model and partitions |
SC_boot |
Parametric bootstrap samples for structure coefficients |
IR2_boot |
Parametric bootstrap samples for inclusive R2 values |
BW_boot |
Parametric bootstrap samples for beta weights |
Ests_boot |
Parametric bootstrap samples for model estimates |
partvars |
Predictors to partition |
CI |
Coverage of the confidence interval as specified by the |
boot_warnings |
Potential warnings from estimating partial R2s during parametric bootstrapping |
boot_message |
Potential messages from estimating partial R2s during parametric bootstrapping. Common are for example singularity messages in lme4. |
References
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.
Nakagawa, S., Johnson, P. C., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface, 14(134), 20170213.
Examples
data(biomass)
library(lme4)
# scale data
biomass[] <- lapply(biomass, function(x) if (is.double(x)) scale(x) else x)
# Gaussian data
mod <- lmer(Biomass ~ Year + Temperature + Precipitation + SpeciesDiversity + (1 | Population),
data = biomass)
# R2
(R2_1 <- partR2(mod))
# R2 with CI
(R2_2 <- partR2(mod, R2_type = "marginal", nboot = 15, CI = 0.95))
# Part (semi-partial) R2s with CIs
(R2_3 <- partR2(mod,
partvars = c("SpeciesDiversity", "Temperature", "Precipitation"),
R2_type = "marginal", nboot = 10, CI = 0.95
))
Calculate part R2
Description
Calculate part R2
Usage
part_R2s(
mod,
expct,
overdisp_name,
R2_type,
all_comb,
partition,
data_mod,
allow_neg_r2
)
Arguments
mod |
merMod object |
expct |
Expectation |
overdisp_name |
Name of overdispersion term |
R2_type |
"marginal" or "conditional" |
all_comb |
variable combinations to remove to calculate R2 |
partition |
check if R2 partitioning is needed |
data_mod |
data.frame |
allow_neg_r2 |
TRUE or FALSE |
Value
R2, atm data.frame with one element
Print a partR2 object
Description
Displays the results a partR2object (i.e. the result of a partR2 function call) in a nice form.
Usage
## S3 method for class 'partR2'
print(x, round_to = 4, ...)
Arguments
x |
partR2 object returned from one of the partR2 functions |
round_to |
defaults to 4 (decimals) |
... |
Additional arguments; none are used in this method. |
Value
No return value, prints concise results of partR2 calculation.
sim_data dataset
Description
sim_data dataset
Details
Simulated dataset to test random slope models. Source: https://stats.stackexchange.com/questions/162553/how-to-simulate-a-random-slope-model
Complete summary of a partR2 object
Description
Displays extended results of partR2, including R2, part (semi-partial) R2, inclusive R2, structure coefficients and beta weights.
Usage
## S3 method for class 'partR2'
summary(object, round_to = 4, ests = FALSE, ...)
Arguments
object |
partR2 object returned from one of the partR2 functions |
round_to |
Defaults to 4 (decimals) |
ests |
Defaults to FALSE, if TRUE, also prints raw model estimates. |
... |
Additional arguments; not used at the moment |
Value
No return value, prints extended summary of partR2 calculation.
Get variance components for binomial model with binary response.
Description
Get variance components for binomial model with binary response.
Usage
var_comps_binary(mod, expct)
Arguments
mod |
merMod object with binomial family and binary response. |
expct |
"latent", "meanobs" of "liability" |
Value
Fixed, random and residual variance
Get variance components for gaussian model.
Description
Get variance components for gaussian model.
Usage
var_comps_gaussian(mod, ...)
Arguments
mod |
merMod object with gaussian family. |
Value
Fixed, random and residual variance
Get variance components for merMod with poisson response.
Description
Get variance components for merMod with poisson response.
Usage
var_comps_poisson(mod, expct, overdisp_name)
Arguments
mod |
merMod object with poisson family. |
expct |
"meanobs" or "latent". "latent" recommended. |
overdisp_name |
name of overdispersion term |
Value
Fixed, random and residual variance
Get variance components for binomial model with proportion response.
Description
Get variance components for binomial model with proportion response.
Usage
var_comps_proportion(mod, expct, overdisp_name)
Arguments
mod |
merMod object with binomial family and binary response. |
expct |
"latent", "meanobs" of "liability" |
Value
Fixed, random and residual variance
Captures and suppresses (still to find out why) warnings of an expression
Description
This function is used within partR2 to capture lme4 model fitting warnings in the bootstrap and permutation procedures.
Usage
with_warnings(expr)
Arguments
expr |
An expression, such as the sequence of code used by rptR to calculate bootstrap or permutation estimates |
Value
List of warnings.