influence.measures(model)
## S3 method for class 'infl':
print(x, digits = max(3, getOption("digits") - 4), ...)
## S3 method for class 'infl':
summary(object, digits = max(2, getOption("digits") - 5), ...)
hatvalues(model, ...)
## S3 method for class 'lm':
hatvalues(model, infl = lm.influence(model, do.coef = FALSE), ...)
rstandard(model, ...)
## S3 method for class 'lm':
rstandard(model, infl = lm.influence(model, do.coef = FALSE),
sd = sqrt(deviance(model)/df.residual(model)), ...)
## S3 method for class 'glm':
rstandard(model, infl = influence(model, do.coef = FALSE),
type = c("deviance", "pearson"), ...)
rstudent(model, ...)
## S3 method for class 'lm':
rstudent(model, infl = lm.influence(model, do.coef = FALSE),
res = infl$wt.res, ...)
## S3 method for class 'glm':
rstudent(model, infl = influence(model, do.coef = FALSE), ...)
dfbeta(model, ...)
## S3 method for class 'lm':
dfbeta(model, infl = lm.influence(model, do.coef = TRUE), ...)
dfbetas(model, ...)
## S3 method for class 'lm':
dfbetas(model, infl = lm.influence(model, do.coef = TRUE), ...)
dffits(model, infl = lm.influence(model, do.coef = FALSE),
res = weighted.residuals(model))
covratio(model, infl = lm.influence(model, do.coef = FALSE),
res = weighted.residuals(model))
cooks.distance(model, ...)
## S3 method for class 'lm':
cooks.distance(model, infl = lm.influence(model, do.coef = FALSE),
res = weighted.residuals(model),
sd = sqrt(deviance(model)/df.residual(model)), hat = infl$hat, ...)
## S3 method for class 'glm':
cooks.distance(model, infl = influence(model, do.coef = FALSE),
res = infl$pear.res, dispersion = summary(model)$dispersion,
hat = infl$hat, ...)
| model | a linear model or generalized linear model representing fit. Normally, it is an object of class (or inherited from) "lm" or "glm", returned by function lm or glm. |
| x, object | an object of class "infl", returned by the function influence.measures normally. |
| digits | the number of significant digits to print. |
| infl | an influence structure returned by lm.influence or influence. |
| sd | standard deviation to use in the calculations. |
| res | residual to use in the calculations. |
| hat | the hat values to use in the calculations. |
| dispersion | the dispersion values to use in the calculations. |
| ... | other arguments pass to the functions. |
| influence.measures | returns an object of
class "infl", which contains the following components:
| ||||||
| hatvalues | returns the diagonal values of the hat matrix for model. Its method hatvalues.lm extracts the hat value from an infl structure, which is generated by the lm.influence function. | ||||||
| rstandard | generic. Methods rstandard.lm and rstandard.glm return the standardized residuals for linear and generalized linear models, respectively. | ||||||
| rstudent | generic. Methods rstudent.lm and rstudent.glm return the studentized residuals for linear and generalized linear models, respectively. | ||||||
| dfbeta | returns a matrix containing the differences in regression coefficients. (The i-th row shows the change in coefficients from omitting the i-th observation from the model.) | ||||||
| dfbetas | returns a standardized version of dfbeta. | ||||||
| dffits | returns the standardized change in fitted values from omitting the i-th observation from the model. | ||||||
| covratio | returns a measure of the change in the covariance of the coefficients from omitting the i-th observation from the model. | ||||||
| cooks.distance | returns the cooks distance for the model. |
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
ldose <- rep(0:5, 2)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
bwormGlm <- glm(SF ~ ldose + sex + ldose:sex, family = binomial)
summary(bwormGlm)
infl <- influence.measures(bwormGlm)
print(infl)
summary(infl)
hatvalues(bwormGlm)
rstandard(bwormGlm)
rstudent(bwormGlm)
dfbeta(bwormGlm)
dfbetas(bwormGlm)
dffits(bwormGlm)
covratio(bwormGlm)
cooks.distance(bwormGlm)