goodnessfit: Goodness of Fit results for Latent Budget Analysis

Description

The goodness of fit results assesses how well the model fits the data. It consists of measures of the resemblance between the observed and the expected data, and the parsimony of the model.

Usage

goodnessfit(object)

Arguments

object

An object of one of following classes: lba.ls, lba.ls.fe, lba.ls.logit, lba.mle, lba.mle.fe, lba.mle.logit

Value

The goodnessfit function to the method lba.mle, lba.mle.fe and lba.mle.logit returns a list with the slots:
dfdbDegrees of freedom of the base model
dfdDegrees of freedom of the full model
G2bLikelihood ratio statistic of the base model
G2Likelihood ratio statistic of the full model
chi2bChi-square statistic of the base model
chi2Chi-square statistic of the full model
proG1P-value of likelihood ratio statistic of the base model
proGP-value of likelihood ratio statistic of the full model
prochi1P-value of chi-square statistic of the base model
prochiP-value of chi-square statistic of the full model
AICbAIC criteria of the base model
AICCAIC criteria of the full model
BICbBIC criteria of the base model
BICCBIC criteria of the full model
CAICbCAIC criteria of the base model
CAICCAIC criteria of the full model
delta1Normed fit index
delta2Normed fit index modified
rho1Bollen index
rho2Tucker-Lewis index
RSS1Residual sum of square of the base model
RSSResidual sum of square of the full model
impRSSImprovement of RSS
impPBImprovement per budget
impDFAverage improvement per degree of freedom
D1Index of dissimilarity of the base model
DIndex of dissimilarity of the full model
pccbProportion of correctly classified data of the base model
pccProportion of correctly classified data of the full model
impDImprovement of proportion of correctly classified data
impPCCBImprovement of Proportion of correctly classified data per budget
AimpPCCDFAverage improvement of Proportion of correctly classified data per degree of freedom
mad1Mean angular deviation of the base model
madkMean angular deviation of the full model
impMadImprovement mean angular deviation
impPBsatImprovement mean angular deviation per budget
impDFsatAverage improvement mean angular deviation per degree of freedom
The goodnessfit function to the method lba.ls, lba.ls.fe and lba.ls.logit returns a list with the slots:
dfdbDegrees of freedom of the base model
dfdDegrees of freedom of the full model
RSS1Residual sum of square of the base model
RSSResidual sum of square of the full model
impRSSImprovement of RSS
impPBImprovement per budget
impDFAverage improvement per degree of freedom
wRSS1Weighted residual sum of square of the base model
wRSSWeighted residual sum of square of the full model
impwRSSImprovement of wRSS
D1Index of dissimilarity of the base model
DIndex of dissimilarity of the full model
pccbProportion of correctly classified data of the base model
pccProportion of correctly classified data of the full model
impDImprovement of proportion of correctly classified data
impPCCBImprovement of Proportion of correctly classified data per budget
AimpPCCDFAverage improvement of Proportion of correctly classified data per degree of freedom
mad1Mean angular deviation of the base model
madkMean angular deviation of the full model
impMadImprovement mean angular deviation
impPBsatImprovement mean angular deviation per budget
impDFsatAverage improvement mean angular deviation per degree of freedom

References

Agresti, Alan. 2002. Categorical Data Analysis, second edition. Hoboken: John Wiley & Sons. van der Ark, A. L. 1999. Contributions to Latent Budget Analysis, a tool for the analysis of compositional data. Ph.D. Thesis University of Utrecht.

Examples

Run this code

data('votB')

# Using LS method (default) without constraint
# K = 2
ex1 <- lba(city ~ parties,
           votB,
           K = 2)

gx1 <- goodnessfit(ex1)
gx1

# Using MLE method without constraint
# K = 2
exm <- lba(city ~ parties,
           votB,
           K = 2,
           method='mle')

gxm <- goodnessfit(exm)
gxm