- Minor updates to code and documentation (e.g. see #41).

`getX()`

threw an error when trying to construct model design matrices using factors with less than two levels (where contrasts can’t be applied). Such an error might occur where e.g. the dataset called by a model contained at least one character/factor variable with only one unique value, which was incorrectly coerced to a factor by`getX()`

(typically in order to gain access to individual dummy variables not in the original design matrix).`bootEff()`

failed to name unnamed model lists where the names attribute was a character vector of empty strings (rather than`NULL`

). Such names are apparently generated by`piecewiseSEM::psem()`

.

12/10/2021

- New formatted table output for effect summaries for
`semEff()`

and`bootCI()`

, accessed via new`print()`

and`summary()`

methods. - Bootstrap standard errors and bias (mean minus original estimate)
now included with effect summaries and fitted values from
`predEff()`

. - Support for
`"psem"`

objects (`piecewiseSEM::psem()`

) in`bootEff()`

/`semEff()`

. - New package vignettes demonstrating some examples.

- All calculated indirect effects (individual, not summed) now also
returned as part of
`"semEff"`

object (for reference; does not include bootstrapped effects). Extract using`getAllInd()`

. - Unnamed model lists can now be supplied to
`bootEff()`

/`semEff()`

(names are generated automatically). - Updates to
`R2()`

(control of negative values, new improved default method for adjusted R-squared – Olkin-Pratt exact estimator). - New function
`getX()`

, for more flexible construction of model design matrices (mostly for internal use). - Removed
`…`

argument of`stdEff()`

– arguments to`R2()`

are now passed as named list to`R2.arg`

. - Renamed extractor functions for effects (e.g.
`dirEff()`

->`getDirEff()`

) and added some new ones. - Various other code and documentation updates.

- Amendment to previous incomplete fix for issue where
`xNam()`

was not evaluating factor/character terms correctly. The function now explicitly treats all non-numeric predictor variables as factors and coerces where necessary. It also has improved handling of factor contrasts when evaluating names. `pSapply()`

did not work with`parallel = "multicore"`

, due to relying completely on`parallel::parSapply()`

for parallel processing (which is`"snow"`

only). The function now wraps`parallel::mcmapply()`

for`"multicore"`

(not available on Windows systems).

09/04/2021

`R2()`

can now calculate R-squared based on Spearman’s Rho.- New function
`RVIF()`

, to calculate ‘root variance inflation factors’ (square root of VIFs).

- Renamed
`unique.x`

argument of`stdEff()`

to`unique.eff`

(old name temporarily allowed). - Removed default value of
`R`

argument of`bootEff()`

. The number of bootstrap resamples must now be explicitly specified, which is probably better practice (10,000 is often recommended for confidence intervals). - Added
`type`

argument to`bootEff()`

, to specify the type of bootstrapping to perform (for mixed models). This replaces`ran.eff = "crossed"`

, previously used to indicate parametric bootstrapping (although it’s temporarily allowed). `bootEff()`

will now treat a list containing both mixed and non-mixed models as all mixed (with a warning). Previously all such models were treated as non-mixed (unintentionally). This is presumably a relatively rare scenario.

`R2()`

did not calculate adjusted R-squared correctly for beta regression models (i.e. did not incorporate the ‘phi’ parameter in degrees of freedom calculations).`xNam()`

produced an error when attempting to evaluate factor contrasts in data, expecting that character vectors were factors (related to the change to`stringsAsFactors = FALSE`

as default in`R 4.0.0`

, but would have occurred in some cases regardless).

01/10/2020

- Standardised vs raw effects: added
`incl.raw`

argument to`stdEff()`

(`stdCoeff()`

), to append raw effects (unstandardised coefficients) to the output. This facilitates simultaneous bootstrapping of both sets of effects, allowing raw effects to be used alternatively for calculating (`semEff(..., use.raw = TRUE)`

) or predicting (`predEff(..., use.raw = TRUE)`

) effects/CIs.

- Renamed function
`stdCoeff()`

to`stdEff()`

, to better reflect the concept of standardised model coefficients as ‘effects’ (calling`stdCoeff()`

will still work – with a warning – until the next version at least). - Added
`offset`

argument to`getY()`

and`R2()`

, to explicitly retain/remove an offset (where present) in/from the response variable or fitted values. Offsets are removed by default, which ensures, for example, that standardised effects are scaled appropriately. - Added
`env`

argument to multiple functions, for explicitly specifying the location of data used to fit models (not necessary in most circumstances). This replaces the`...`

argument in many instances, which was previously used to pass an environment to`eval()`

(via`getData()`

).`env`

(and`data`

) can also now be passed (`...`

) to`bootEff()`

and`predEff()`

. - Added confidence interval attributes to
`bootCI()`

/`semEff()`

output (i.e. confidence level, type). `R2()`

no longer calculates predictive R-squared for GLMMs, as the interpretation of the hat matrix used in calculations is not reliable (see https://rdrr.io/cran/lme4/man/hatvalues.merMod.html).- Removed ability to pass arguments from
`getY()`

to`glt()`

, allowing more controlled output of`getY(..., link = TRUE)`

. - Various minor updates to function code and documentation, improvement and addition of some new internal helper functions.

`bootEff()`

specified with correlated errors failed for mixed models of class`"lmerModLmerTest"`

(issue with re-fitting models using`update()`

).`predEff()`

failed to evaluate some complex model terms (e.g. polynomials).`stdEff()`

(`stdCoeff()`

) did not re-fit model properly to calculate correct VIFs for a fully ‘centred’ model (i.e. did not account sufficiently for complex terms such as polynomials or transformations, where mean-centring should occur as the final step).`xNam()`

generated incorrect term names for categorical predictors under certain circumstances (different contrast types, interactive effects with no ‘main’ effects).`stdEff()`

(`stdCoeff()`

) incorrectly calculated ‘centred’ intercept for models with an offset specified.`predEff()`

failed when a nested list of models and list of numeric weights were supplied (i.e. a model averaging scenario).`stdEff()`

(`stdCoeff()`

) did not return the ‘phi’ parameter(s) for beta regression models.

25/03/2020

- Support for mixed models of class
`"lmerModLmerTest"`

. - New function
`glt()`

, for calculating ‘generalised’ link transformations for non-gaussian variables.

- Transfer of some functionality from
`getY()`

to`glt()`

. - Minor changes to arguments in
`bootEff()`

and`getY()`

. - Added ability in
`stdCoeff()`

to use variables not present in the model design matrix (e.g. a ‘missing’ main effect for an interaction). - Added ability to pass a boot object (from
`bootEff()`

) to the`effects`

argument of`predEff()`

. - Added a
`refit.x`

argument to`stdCoeff()`

, allowing control over whether to refit the model with centred predictors (for correct VIFs). - Various updates to documentation.

`xNam()`

did not generate correct term names for categorical variables with contrast types other than`contr.treatment()`

.`stdCoeff()`

did not correctly adjust for multicollinearity for a model containing categorical variables when centring was specified (`cen.x = TRUE`

).`getY()`

failed to generate an estimated working response when a variable with missing values (`NA`

) was supplied (this functionality now in`glt()`

).`predEff()`

failed for models with categorical variables (did not access dummy variables in model matrix).

15/01/2020

- Function
`semEff()`

did not output effects properly.

08/01/2020

- Added support for generalised least squares models (class
`"gls"`

). - Added support for beta regression models (class
`"betareg"`

).

- Function
`xNam()`

did not generate correct term names for interactions involving multi-coefficient terms (e.g. factors). - Function
`xNam()`

did not generate correct term names for factors when the model intercept is suppressed. - Function
`R2()`

with argument`pred = TRUE`

threw an error for models where any weights = 0.

04/11/2019

New package `semEff`

, allowing the automatic calculation
of effects for ‘piecewise’ structural equation models.