LogisticDx (version 0.1)

logiDx: Diagnostics for logistic regression

Description

Returns standard diagnostic measures for a logistic regression model by covariate pattern

Usage

logiDx(x, round = FALSE, roundTo = 3)

Arguments

x

A model of class glm

round

If round=TRUE, digits will be ronded to roundTo decimal places

roundTo

No. decimal places to which to round digits

Value

• A data.table. There is one row per covariate pattern with at least one observation. These are sorted by dBhat (see below). The initial columns give all combinations of the predictor variables with at least one observation. Subsequent columns are labelled as follows:
• obs
  Number of observations with this covariate pattern
• prob
  Probability of this covariate pattern
• yhat
  Number of observations of $y=1$, predicted by the model
• y
  Actual number of observations of $y=1$ from the data
• lev
  Leverage, the diagonal of the hat matrix used to generate the model; a measure of influence of this covariate pattern
• devR
  Deviance residual, calculated by covariate pattern; a measure of influence of this covariate pattern
• PeR
  Pearson residual, calculated by covariate pattern; a measure of influence of this covariate pattern. Given by: $$\sqrt{obs}\sqrt{\frac{prob}{(1-prob)}}$$
• sPeR
  Standardized Pearson residual calculated by covariate pattern; a measure of influence of this covariate pattern. Given by: $$\frac{PeR}{\sqrt{(1-lev)}}$$
• dBhat
  Change in Bhat, the standardized difference between the original maximum likelihood estimates B and that estimates with this covariate pattern excluded
• dXsq
  Change in chi-square, decrease in the value of Pearson chi-square statistic with this covariate pattern excluded. Given by: $$sPeR^2$$
• dDev
  Change in deviance D with this covariate pattern excluded. Given by: $$\frac{dev^2}{(1-lev)}$$

See Also

plotLogiDx

Examples

d1 <- genLogiDt(model=FALSE)
f1 <- stats::glm(y ~ I(x5^2)*x1 -1, family=binomial("logit"), data=d1)
logiDx(f1)