summary_plots: Generate Summary Figures

In multiplestressR: Additive and Multiplicative Null Models for Multiple Stressor Data

Description

Using the output from classify_interactions function, summary figures can be created using this function.

Usage

summary_plots(
  effect_size_dataframe,
  Small_Sample_Correction,
  Significance_Level
)

Arguments

effect_size_dataframe	Output from the classify_interactions function.
Small_Sample_Correction	Whether the correction for small sample sizes should be enacted (TRUE or FALSE; default is TRUE) Note that if the multiplicative null model (see effect_size_multiplicative) was implemented, this parameter is not used and can be ignored. If the additive null model (see effect_size_additive) was implemented, then this parameter should be assigned the same value as in that analysis.
Significance_Level	The value of alpha for which confidence intervals are calculated (numeric, between 0 and 1; default is 0.05) Note that if the multiplicative null model (see effect_size_multiplicative) was implemented, this parameter is not used and can be ignored. If the additive null model (see effect_size_additive) was implemented, then this parameter should be assigned the same value as in that analysis.

Details

The figures include:

a) The proportions of the different interaction classifications from the dataset

b) Median sample sizes plotted against effect size (different interaction classifications are highlighted). Where the additive null model was used in the analysis, lines for critical effect sizes are plotted (see critical_effect_size_additive function).

c) Density of different median sample sizes.

d) Inverse of effect size variance plotted against effect size (i.e., one iteration of a funnel plot).

e) Effect size standard error (i.e., the square root of the effect size variance) plotted against effect size (i.e., one iteration of a funnel plot)).

Note that c - e) are most useful for researchers conducting a meta-analysis.

Value

The function returns a series of figures each of which is outlined above.

Examples

#loading up an example dataset from the multiplestressR package
df <- multiplestressR::survival

#calculating effect sizes
df <- 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,
                           Significance_Level = 0.05);

#classifying interactions
df <- classify_interactions(effect_size_dataframe = df,
                   assign_reversals = TRUE,
                   remove_directionality = TRUE);

#generate summary plots
df_plots <- summary_plots(effect_size_dataframe = df,
                   Significance_Level = 0.05)

multiplestressR documentation built on Oct. 26, 2021, 9:06 a.m.