effect_size_multiplicative {multiplestressR}

Multiplicative Null Model

Description

Calculate the multiplicative null model for one, or more, experiments.

Usage

effect_size_multiplicative(
  Control_N,
  Control_SD,
  Control_Mean,
  StressorA_N,
  StressorA_SD,
  StressorA_Mean,
  StressorB_N,
  StressorB_SD,
  StressorB_Mean,
  StressorsAB_N,
  StressorsAB_SD,
  StressorsAB_Mean,
  Significance_Level
)

Arguments

Control_N

Sample size of the control treatment (numeric)

Control_SD

Standard deviation of the control treatment (numeric)

Control_Mean

Mean value of the control treatment (numeric)

StressorA_N

Sample size of stressor A treatment (numeric)

StressorA_SD

Standard deviation of stressor A treatment (numeric)

StressorA_Mean

Mean value of stressor A treatment (numeric)

StressorB_N

Sample size of stressor B treatment (numeric)

StressorB_SD

Standard deviation of stressor B treatment (numeric)

StressorB_Mean

Mean value of stressor B treatment (numeric)

StressorsAB_N

Sample size of stressors A and B treatment (numeric)

StressorsAB_SD

Standard deviation of stressors A and B treatment (numeric)

StressorsAB_Mean

Mean value of stressors A and B treatment (numeric)

Significance_Level

The value of alpha for which confidence intervals are calculated (numeric, between 0 and 1; default is 0.05)

Details

The form of the multiplicative null model used here is taken from Lajeunesse (2011).

Interaction effect sizes, variances, and confidence intervals are calculated.

Here, the factorial form of the response ratio is calculated.

Value

The function returns a dataframe containing

i. effect sizes

ii. effect size variances

iii. upper and lower confidence intervals

iv. user specified numeric parameters

The equations used to calculate effect sizes, effect size variances, and confidence intervals are described in Burgess et al. (2021).

References

Burgess, B. J., Jackson, M. C., & Murrell, D. J. (2021). Multiple stressor null models frequently fail to detect most interactions due to low statistical power. bioRxiv.

Lajeunesse, M. J. (2011). On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology, 92(11), 2049-2055.

Examples

effect_size_multiplicative(Control_N = 4,
                          Control_SD = 0.114,
                          Control_Mean = 0.90,
                          StressorA_N = 4,
                          StressorA_SD = 0.11,
                          StressorA_Mean = 0.77,
                          StressorB_N = 3,
                          StressorB_SD = 0.143,
                          StressorB_Mean = 0.72,
                          StressorsAB_N = 4,
                          StressorsAB_SD = 0.088,
                          StressorsAB_Mean = 0.55,
                          Significance_Level = 0.05)

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

#calculating effect sizes
df <- effect_size_multiplicative(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)