The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
minter is a collection of functions for calculating effect sizes for meta-analysis of interactions.
The interaction can be between two factors (2-by-2 factorials) or between a factor and time (repeated measures experiments). For both design types, four effect size measures are available:
lnRR: Log Response Ratio
SMD: Standardized Mean Difference
lnVR: Log Variability Ratio
lnCVR: Log Coefficient of Variation Ratio
Typical effect sizes compare two values, the treatment vs. the control. However, factorial experiments allow computing different contrasts (and minter has functions for each):
Individual effects: Direct comparisons between single treatments, the classic effect size.
Main effects: Average effect of one factor across levels of another factor, analogous to the main effect in ANOVA.
Interaction effects: How the effect of one factor depends on the level of another factor.
For repeated measures experiments, the effect size quantifies the interaction between a factor and time.
See References for more.
From CRAN:
install.packages("minter")For a real example, see the article recreating the results from Macartney et al. 2022
For a quick example, suppose you need to estimate the effect size of the interaction between fertilization × drought on plant biomass:
library(minter)
# Dummy data from 8 studies examining fertilization × drought on plant biomass
studies <- data.frame(
study_id = 1:8,
# Control: No fertilization, well-watered
Ctrl_mean = c(12.3, 15.7, 10.8, 14.2, 11.9, 13.5, 16.1, 12.7),
Ctrl_sd = c(2.1, 3.2, 1.8, 2.7, 2.3, 2.9, 3.5, 2.4),
Ctrl_n = c(20, 24, 18, 22, 19, 25, 23, 21),
# Fertilization only
Fert_mean = c(18.5, 21.3, 16.2, 19.8, 17.1, 20.4, 22.7, 18.9),
Fert_sd = c(3.1, 4.1, 2.7, 3.6, 3.2, 3.8, 4.2, 3.4),
Fert_n = c(22, 25, 20, 24, 21, 26, 25, 23),
# Drought only
Drought_mean = c(8.7, 11.2, 7.9, 10.1, 8.3, 9.8, 11.7, 9.4),
Drought_sd = c(1.8, 2.4, 1.6, 2.1, 1.9, 2.3, 2.6, 2.0),
Drought_n = c(19, 23, 17, 21, 18, 24, 22, 20),
# Both treatments
Both_mean = c(14.2, 17.8, 12.9, 16.3, 13.7, 16.1, 18.4, 15.2),
Both_sd = c(2.9, 3.7, 2.5, 3.3, 2.8, 3.4, 3.9, 3.1),
Both_n = c(21, 26, 19, 23, 20, 27, 24, 22)
)
# Calculate interaction effect: Does fertilization work differently under drought?
interaction_results <- lnRR_inter(
data = studies,
Ctrl_mean = "Ctrl_mean", Ctrl_sd = "Ctrl_sd", Ctrl_n = "Ctrl_n",
A_mean = "Fert_mean", A_sd = "Fert_sd", A_n = "Fert_n",
B_mean = "Drought_mean", B_sd = "Drought_sd", B_n = "Drought_n",
AB_mean = "Both_mean", AB_sd = "Both_sd", AB_n = "Both_n"
)
head(interaction_results)
#> study_id Ctrl_mean Ctrl_sd ... yi vi
#> 1 1 12.3 2.1 0.081 0.0069
#> 2 2 15.7 3.2 0.158 0.0068
#> 3 3 10.8 1.8 0.084 0.0073
# Meta-analysis
library(metafor)
res <- rma(yi, vi, ..., data = interaction_results)Thanks to Shinichi Nakagawa and Daniel Noble for generously sharing their formulas.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.