# mlm.influence: Calculate Regression Deletion Diagnostics for Multivariate... In mvinfluence: Influence Measures and Diagnostic Plots for Multivariate Linear Models

 mlm.influence R Documentation

## Calculate Regression Deletion Diagnostics for Multivariate Linear Models

### Description

mlm.influence is the main computational function in this package. It is usually not called directly, but rather via its alias, influence.mlm, the S3 method for a mlm object.

### Usage

mlm.influence(model, do.coef = TRUE, m = 1, ...)


### Arguments

 model An mlm object, as returned by lm with a multivariate response. do.coef logical. Should the coefficients be returned in the inflmlm object? m Size of the subsets for deletion diagnostics ... Further arguments passed to other methods

### Details

The computations and methods for the m=1 case are straight-forward, as are the computations for the m>1 case. Associated methods for m>1 are still under development.

### Value

mlm.influence returns an S3 object of class inflmlm, a list with the following components:

 m Deletion subset size H Hat values, H_I. If m=1, a vector of diagonal entries of the ‘hat’ matrix. Otherwise, a list of m\times m matrices corresponding to the subsets. Q Residuals, Q_I. CookD Cook's distance values L Leverage components R Residual components subsets Indices of the subsets CookD Cook's distance values L Leverage components R Residual components subsets Indices of the observations in the subsets of size m labels Observation labels call Model call for the mlm object Beta Deletion regression coefficients– included ifdo.coef=TRUE

Michael Friendly

### References

Barrett, B. E. and Ling, R. F. (1992). General Classes of Influence Measures for Multivariate Regression. Journal of the American Statistical Association, 87(417), 184-191.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression. Communications in Statistics – Theory and Methods, 32, 3, 667-680.

influencePlot.mlm

### Examples


Rohwer2 <- subset(Rohwer, subset=group==2)
rownames(Rohwer2)<- 1:nrow(Rohwer2)
Rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ n+s+ns+na+ss, data=Rohwer2)
Rohwer.mod
influence(Rohwer.mod)

# extract the most influential cases
influence(Rohwer.mod) |>
as.data.frame() |>
dplyr::arrange(dplyr::desc(CookD)) |>