Effect Sizes

Takes the output of a minc modelling function and computes the unbiased hedges g* and variance of hedges g*

Usage

vertexEffectSize(buffer, predictors = NULL)

mincEffectSize(buffer, predictors = NULL)

anatEffectSize(buffer, predictors = NULL)

Arguments

buffer

The results of a vertex/anat/mincLm run

predictors

A vector of factor predictor names. By default the effect size be computed for all treatment-coded factor columns.

Value

An object with columns of hedgesg-<factorlevel> and hedgesg_var-<factorlevel> for each factor predictor in the GLM. The class and attributes of the input are preserved:

• mincEffectSize/mincMultiDim for voxel-wise inputs

• vertexEffectSize/vertexMultiDim for vertex-wise inputs

• anatEffectSize/anatModel for anatomy-wise inputs

Attributes likeVolume, filenames, model, data, call, df, atlas, definitions, and stat-type are carried over from the input.

Details

This code implements the methods from Nakagawa, S., Cuthill, I.C., 2007. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 82, 591-05. https://doi.org/10.1111/j.1469-185X.2007.00027.x for computing effect size of group comparisons from a GLM.

For now, interactions are explicitly excluded from being predictors. To get effect size for interactions, use the interaction() function to create a new treatment coded factor to use as a predictor.

Functions

• mincEffectSize(): mincEffectSize

• anatEffectSize(): anatEffectSize

Examples

if (FALSE) { # \dontrun{
getRMINCTestData()
# read the text file describing the dataset
gf <- read.csv("/tmp/rminctestdata/test_data_set.csv")
# run a linear model relating the data in all voxels to Genotype
vs <- mincLm(jacobians_fixed_2 ~ Sex, gf)
effectsize <- mincEffectSize(vs)
} # }