glm_influence: Influence measures for a bg_GLM object
In brainGraph: Graph Theory Analysis of Brain MRI Data

Description Usage Arguments Details Value Outlier values Author(s) See Also

These functions compute common (leave-one-out) diagnostics for the models in a bg_GLM object.

## S3 method for class 'bg_GLM'
rstandard(model, type = c("sd.1", "predictive"), ...)

## S3 method for class 'bg_GLM'
rstudent(model, ...)

## S3 method for class 'bg_GLM'
hatvalues(model, ...)

## S3 method for class 'bg_GLM'
cooks.distance(model, ...)

dffits.bg_GLM(model)

## S3 method for class 'bg_GLM'
dfbeta(model, ...)

## S3 method for class 'bg_GLM'
dfbetas(model, ...)

covratio.bg_GLM(model)

## S3 method for class 'bg_GLM'
influence(model, do.coef = TRUE, region = NULL, ...)

`model`	A `bg_GLM` object
`type`	The type of standardized residuals. Default: `'sd.1'`
`...`	Unused
`do.coef`	Logical indicating whether to calculate `dfbeta`
`region`	Character string of the region(s) to return results for. Default is to calculate for all regions

The influence method calculates all diagnostics present in lm.influence and influence.measures, consisting of the following functions:

rstandard: Standardized residuals. Choosing type='predictive' returns leave-one-out cross validation residuals. The “PRESS” statistic can be calculated as colSums(resids.p^2)
rstudent: Studentized residuals
hatvalues: The leverage, or the diagonal of the hat/projection matrix
cooks.distance: Cook's distance
dffits.bg_GLM: The change in fitted values when deleting observations
dfbeta: The change in parameter estimates (coefficients) when deleting observations
dfbetas: The scaled change in parameter estimates
covratio.bg_GLM: The covariance ratios, or the change in the determinant of the covariance matrix of parameter estimates when deleting observations

Most influence functions return a numeric matrix in which rownames are Study ID's and column names are regions. dfbeta and dfbetas return a numeric array in which each column is a parameter estimate and the 3rd dimension is for each region. influence returns a list with class infl.bg_GLM and elements:

`infmat`	Numeric array (like `dfbeta`) with DFBETAs, DFFITs, covratios, Cook's distance, and hat values
`is.inf`	Logical array of the same data as `infmat`; values of `TRUE` indicate the subject-variable-region combination is an outlier value
`f`	The model formula
`sigma`	The leave-one-out residual standard deviation
`wt.res`	Model residuals

Each variable has a different criterion for determining outliers. In the following: x is the influence variable (for DFBETA, the criterion applies to all DFBETAs); k is the number of columns of the design matrix; dfR is the residual degrees of freedom; and n is the number of observations.

DFBETAs: If |x| > 1
DFFITs: If |x| > 3 √{k / dfR}
covratio: If |1 - x| > (3k / dfR)
cook: If F_{k, dfR}(x) > 0.5
hat: If x > 3k / n