The `reghelper`

R package includes a set of functions used
to automate commonly used methods in regression analysis. This includes
plotting interactions, calculating simple slopes, calculating
standardized coefficients, etc.

Version 1.1.2 has been released. The most recent stable release is available on CRAN, and can be installed like so:

`install.packages("reghelper")`

You can also install the stable release from Github:

```
install.packages("devtools")
::install_github("jeff-hughes/reghelper@v1.1.2") devtools
```

If you would like to install the latest development version, you can do so with the following code:

```
install.packages("devtools")
::install_github("jeff-hughes/reghelper") devtools
```

Networked computers can sometimes result in installation issues, as
the `install_github`

function sometimes has difficulty with
networked directories. If this happens to you, use the
`.libPaths()`

function to find the path to your R libraries.
That will likely give you a path starting with two backslashes, but you
will need to convert that to a path starting with a drive letter (e.g.,
‘C:’, ‘D:’). From there, use the following code:

```
install.packages("devtools")
::install_github("jeff-hughes/reghelper", args=c('--library="N:/path/to/libraries/"')) devtools
```

Obviously, change the path to the path where your R libraries are stored.

`beta`

Calculates standardized beta coefficients.`build_model`

Allows variables to be added to a series of regression models sequentially (similar to SPSS).`ICC`

Calculates the intra-class correlation for a multi-level model.`cell_means`

Calculates the estimated means for a fitted model.`graph_model`

Easily graph interactions at +/- 1 SD (uses ggplot2 package).`sig_regions`

Calculate the Johnson-Neyman regions of significance for an interaction.`simple_slopes`

Easily calculate the simple effects of an interaction.

The table below shows the current types of models for which each function has been implemented:

Function | lm | glm | aov | lme | lmer |
---|---|---|---|---|---|

beta | ✓ | ✓ | ✓ | – | – |

build_model | ✓ | ✓ | ✓ | ||

ICC | – | – | – | ✓ | ✓ |

cell_means | ✓ | ✓ | ✓ | ||

graph_model | ✓ | ✓ | ✓ | ✓ | ✓ |

sig_regions | ✓ | ✓ | – | ||

simple_slopes | ✓ | ✓ | ✓ | ✓ | ✓ |

Strategies for how to center Level 1 variables in multilevel models has been a topic of discussion among methodologists, with a variety of approaches suggested. Two of the most common approaches are grand-mean centering vs. group-mean (or within-cluster) centering (Bryk & Raudenbush, 1992; Enders & Tofighi, 2007). This latter approach could actively be impaired by standardizing the variable after applying the group-mean centering strategy, potentially leading to misinterpretation of the model effects. Some researchers have suggested that Level 1 effects should be standardized within groups (i.e., using the group mean and group standard deviation; Schuurman et al., 2016), but this is not a universally accepted practice and strategies vary. Thus, there is no agreed-upon method of standardizing the random effects of a model, and applying one approach in an R function could have deleterious impacts on interpretation.

Standardizing the fixed effects is less contentious, but there is no restriction on what level variables can be found in the model, and in some cases, variables may be included as both fixed and random effects. Other complications in interpretation could arise if a model includes cross-level effects (cf. Aguinis, Gottfredson, & Culpepper, 2013), custom covariance structures, etc. Because of this, there is no real “default” approach for which variables to standardize in a multilevel model that will be suitable for all model structures, and the researcher should give careful consideration to this based on the structure of their particular model, and relevant theoretical considerations (Enders & Tofighi, 2007; Nezlek, 2012).

Because of this, defining a `beta()`

function in a
consistent way for multilevel models (nlme and lme4), in a way that
would not lead to potential misinterpretation and/or unexpected
behaviour, proves difficult. While the initial implementation of this
function aimed to standardize just the fixed effects and the outcome
variable, this proved insufficient—in cases where the same variable
appears as a fixed effect and random effect, what should happen to this
variable? Surprising users with models where some variables end up
standardized and others are not is not a suitable result.

As a consequence, the `beta()`

function was deprecated in
v0.3.6 and later removed. It will still remain implemented for
`lm`

, `glm`

, and `aov`

models.
Researchers who wish to obtain standardized effects for their multilevel
models should manually standardize the variables that are appropriate
given their model structure and theory. However, consideration should be
given to the centering strategy used in their model as well. For further
discussion on these issues, please see the references below.

Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. (2013).
Best-practice recommendations for estimating cross-level interaction
effects using multilevel modeling. *Journal of Management,
39*(6), 1490-1528. http://dx.doi.org/10.1177/0149206313478188

Bryk, A. S., & Raudenbush, S. W. (1992). *Hierarchical linear
models: Applications and data analysis methods*. Sage
Publications.

Enders, C. K., & Tofighi, D. (2007). Centering predictor
variables in cross-sectional multilevel models: A new look at an old
issue. *Psychological Methods, 12*(2), 121-138. http://dx.doi.org/10.1037/1082-989X.12.2.121

Nezlek, J. B. (2012). Multilevel modeling for psychologists. In H.
Cooper (Ed.), *APA handbook of research methods in psychology*
(Vol. 3). (pp. 219-241). American Psychological Association. http://dx.doi.org/10.1037/13621-011

Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., &
Hamaker, E. L. (2016). How to compare cross-lagged associations in a
multilevel autoregressive model. *Psychological Methods, 21*(2),
206–221. https://doi.org/10.1037/met0000062