R: The global deviance increment

devianceIncr {gamlss} | R Documentation

## The global deviance increment

### Description

The global deviance increment is the contribution of each individual observation to the global deviance. The function `devianceIncr()` can be used to extract the global deviance increment for a fitted gamlss model or for a new (test/validation) data set. Large values for global deviance increment indicate a bad fit and for new data a bad prediction.

### Usage

    devianceIncr(obj, newdata = NULL)

### Arguments

`obj` | a gamlss object
`newdata` | test data set to check the global deviance increment.

### Value

Returns a vector of the global deviance increments for each observation.

### Author(s)

Mikis Stasinopoulos

### References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, 1-38.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

### See Also

`deviance`

### Examples

    #-----------------------------------------------------------------
    # Count data set
    # fit Poisson model
    h1 <- gamlss(Claims~L_Population+L_Accidents+L_KI+L_Popdensity,
                 data=LGAclaims, family=PO)
    p1<-devianceIncr(h1)
    # fit negative binomial model
    h2 <- gamlss(Claims~L_Population+L_Accidents+L_KI+L_Popdensity,
                 data=LGAclaims, family=NBI)
    p2<-devianceIncr(h2)
    # comparing using boxplots
    boxplot(cbind(p1,p2))
    # comparing using emphirical cdf
    plot(ecdf(p1))
    lines(ecdf(p2), col=2)
    # comparing agaist the y-values
    plot(p1~LGAclaims$Claims, pch=20, col="gray")
    points(p2~LGAclaims$Claims, pch="-", col="orange")
    #----------------------------------------------------------------
    # Continuous data sets
    ## Not run:
    m1 <- gamlss(head~pb(age), data=db[1:6000,])
    p1<-devianceIncr(m1)
    m2 <- gamlss(head~pb(age), sigma.fo=~pb(age), nu.fo=~pb(age),
          tau.fo=~pb(age), data=db[1:6000,], family=BCT)
    p2<-d.evianceIncr(m2)
    # comparing using summaries
    summary(p1); summary(p2)
    # comparing using boxplots
    boxplot(cbind(p1,p2))
    # comparing using histograms
    hist(p1, col=rgb(1,0,0,0.5), xlim=c(0,50), breaks=seq(0,50,2))
    hist(p2, col=rgb(0,0,1,0.5), add=T)
    # comparing using emphirical cdf
    plot(ecdf(p1))
    lines(ecdf(p2), col=2)
    ## End(Not run)
    #----------------------------------------------------------------

[Package gamlss version 5.3-2 Index]