For a detailed description of the multiplestressR package, see this website.
The multiplestressR package, is aimed at researchers primarily working in multiple stressor ecology, to easily implement either the additive or multiplicative null models. These null model can be applied to the researcher’s own primary data, or for data collated from multiple sources as part of a larger project (e.g., a meta-analyses).
The package can be used to calculate either the additive or multiplicative null models, which correspond to the factorial forms of Hedges’ d or the response ratio respectively. Furthermore, the multiplestressR package is capable of classifying interactions base upon a commonly implemented framework.
The multiplestressR package does not require researchers to have a firm understanding of the statistics of these null models. Instead, the user simply needs to specify the input data and the entire analysis will be conducted. As such, the multiplestressR package provides an outlet which allows researchers to implement a consistent, standardised, and statistically rigorous method for classifying stressor interactions across different analyses.
At present the package can be accessed from github using the following code:
library(devtools)
install_github("benjburgess/multiplestressR")
library(multiplestressR)
This package is being updated in preparation to being submitted to CRAN. Once it is uploaded, these instructions for installing the package will be updated.
Outlined below is an example for how the multiplestressR package can be conducted on a dataset. Assuming the package has been installed (see above section) the following code allows researchers to implement the additive null model on an example dataset provided within the multiplestressR package.
library(multiplestressR)
The multiplestressR package comes with an example dataset called
survival, this dataset will be used throughout this
example.
df <- multiplestressR::survival
For simplicity survival has been renamed
df.
head(df)
By using, head(df) we can see the different columns
within our dataset. It is evident that our dataset contains the
variables of sample size, mean, and standard deviation for four
different treatments. Here, the treatments correspond to control
conditions, a temperature stressor, a pH stressor, and both a
temperature and pH stressor.
For the analyses, control conditions corresponds to the Control treatment, temperature to the Stressor A treatment, pH to Stressor B treatment, and both temperature and pH to the Stressors A and B treatment. (Note that temperature and pH could have been assigned to the Stressors B and A treatments - the ordering for these two treatments is not important).
The additive null model can be calculated using the following code:
df2 <- effect_size_additive(Control_N = df$Sample_Size_Control,
Control_SD = df$Standard_Deviation_Control,
Control_Mean = df$Mean_Control,
StressorA_N = df$Sample_Size_Temperature,
StressorA_SD = df$Standard_Deviation_Temperature,
StressorA_Mean = df$Mean_Temperature,
StressorB_N = df$Sample_Size_pH,
StressorB_SD = df$Standard_Deviation_pH,
StressorB_Mean = df$Mean_pH,
StressorsAB_N = df$Sample_Size_Temperature_pH,
StressorsAB_SD = df$Standard_Deviation_Temperature_pH,
StressorsAB_Mean = df$Mean_Temperature_pH)
Here, the effect_size_additive function is used to
calculate the additive null model for the example dataset. Note that the
different parameter values (i.e., treatments, sample sizes, means and
SDs) have to be specified by the user.
This function returns a dataframe containing the specified values
alongside the calculated values of Interaction_Effect_Size,
Interaction_Variance, and confidence intervals. It is these
values which are subsequently used to classify interactions, or should
be used in meta-analyses.
head(df2)
The next step is to classify interactions, this can be easily
conducted using the classify_interactions function. The
function can be run in a single line of code. However, other options
(e.g., distinguishing antagonistic and reversal interactions) can be
specified.
Interactions can be classified using the following code:
df2 <- classify_interactions(effect_size_dataframe = df2)
At this stage, the calculation of the additive null model and
subsequent classification of interactions has been conducted. However,
one final option is to plot some summary figures to better understand
these results. This can be done using the summary_plots
function.
The summary plots can be generated using the following code:
df_plots <- summary_plots(effect_size_dataframe = df2)
This is a very simple outline of a potential workflow for the multiplestressR package. For a more complex example of the multiplestressR package see this tutorial. Likewise, the documentation for each of the functions details the various options and parameters which can be specified.