apc.indiv.compare.direct {apc}R Documentation

Implements direct tests between APC models

Description

This function allows the user to directly compare any of the APC model, its submodels, or the TS model to any smaller model. For example, the function can be used to compare the TS to the Ad model or the Ad model to the A model. Comparisons are by likelihood ratio or Wald tests.

Usage

	apc.indiv.compare.direct(data, big.model, small.model, unit=1,
        			dep.var, covariates=NULL, model.family,
                    n.coh.excl.start=0, n.coh.excl.end=0,
                    n.age.excl.start=0, n.age.excl.end=0,
                    n.per.excl.start=0, n.per.excl.end=0,
                    NR.controls=NULL, test, dist,
                    wt.var=NULL, plmmodel="notplm", id.var=NULL)
        apc.indiv.waldtest.fullapc(data, dist="F", big.model="APC", 
                    small.model, dep.var, covariates=NULL,
                    model.family="gaussian", unit=1, 
                    n.coh.excl.start=0, n.coh.excl.end=0,
                    n.age.excl.start=0, n.age.excl.end=0,
                    n.per.excl.start=0, n.per.excl.end=0,
                    existing.big.model.fit=NULL, 
                    existing.small.model.fit=NULL,
                    existing.collinear=NULL,
					plmmodel = "notplm", id.var=NULL, wt.var=NULL)
        apc.indiv.waldtest.TS(data, dist="F", small.model="APC",
                    dep.var, covariates=NULL,
                    model.family="gaussian", unit=1, 
                    n.coh.excl.start=0, n.coh.excl.end = 0,
                    n.age.excl.start=0, n.age.excl.end = 0,
                    n.per.excl.start=0, n.per.excl.end = 0,
                    existing.small.model.fit=NULL, 
                    existing.big.model.fit=NULL, 
                    existing.collinear=NULL)
        apc.indiv.LRtest.fullapc(data,  big.model="APC", 
                    small.model,
                    dep.var, covariates=NULL, 
                    model.family="binomial", unit=1,
                    n.coh.excl.start=0, n.coh.excl.end=0,
                    n.age.excl.start=0, n.age.excl.end=0,
                    n.per.excl.start=0, n.per.excl.end=0,
                    existing.big.model.fit=NULL,
                    existing.small.model.fit=NULL,
                    existing.collinear=NULL)
       apc.indiv.LRtest.TS(data, small.model="APC", dep.var, covariates=NULL,
                    model.family="binomial", unit=1, 
                    n.coh.excl.start=0, n.coh.excl.end=0,
                    n.age.excl.start=0, n.age.excl.end=0,
                    n.per.excl.start=0, n.per.excl.end=0,
                    existing.small.model.fit=NULL, 
                    existing.big.model.fit=NULL, 
                    existing.collinear=NULL,
                    NR.controls=NULL)                                      

Arguments

data

The data.frame in use.

big.model

The name of the larger of the two models to be tested.

small.model

The name of the smaller of the two models to be tested.

unit

The interval at which age, period, and cohort are recorded (must be the same for each). Default 1.

dep.var

The name of the dependent variable as it appears in the data

covariates

A vector of the names of covariates as they appear in the data. Default NULL.

model.family

Either "gaussian" or "binomial"

n.coh.excl.start

If any cohorts have been censored (AP data only). Default 0.

n.coh.excl.end

If any cohorts have been censored (AP data only). Default 0.

n.per.excl.start

If any periods have been censored (AC data only). Default 0.

n.per.excl.end

If any periods have been censored (AC data only). Default 0.

n.age.excl.start

If any ages have been censored (PC data only). Default 0.

n.age.excl.end

If any ages have been censored (PC data only). Default 0.

NR.controls

Optional list to modify aspects of the Newton-Rhapson iteration for binomial TS model. See details in apc.indiv.est.model.

test

The type of test. One of "LR", "Wald".

dist

The distribution against which the test statistic is compared. One of "F", "Chisq".

wt.var

Only if using survey weights. The name of the weights variable.

plmmodel

Used to indicate whether a panel data model is to be estimated and if so what type. Default is "notplm", for not panel data. Other values are "pooling", "within", "random". Further details in plm.

id.var

Only if using panel data. The name of the individual ID variable.

existing.big.model.fit

Optional specify the output of apc.indiv.fit.model, if already run for the big model.

existing.small.model.fit

Optional specify the output of apc.indiv.fit.model, if already run for the small model.

existing.collinear

Optional specify the output of apc.indiv.design.collinear, if already run.

Details

These functions are designed to facilitate direct comparison between sub-models. The functions are used to construct the rows of tables in apc.indiv.model.table but can also more helpfully be used to compare nested sub-models that gain similar levels of suport from such a table, e.g. PC to P.

Value

test.type

The type of test, one of "LR", "Wald".

dist.type

The distribution against which the test statistic is compared. One of "F", "Chisq".

test.stat

The value of the test statistic.

df

Degrees of freedom.

df.num

Gaussian models only. Degrees of freedom used in the numerator of the F-statistic.

df.denom

Gaussian models only. Degrees of freedom used in the denominator of the F-statistic.

p.value

P-value from testing against a chi-square or F distribution.

aic.big

AIC of the big model.

aic.small

AIC of the small model.

lik.big

Log-likelihood of the big model.

lik.small

Log-likelihood of the small model.

NR.report

Binomial TS model only. Report on the Newton-Rhapson algorithm.

Author(s)

Zoe Fannon <zoe.fannon@economics.ox.ac.uk> 26 Jun 2020

References

Fannon, Z. (2018) apc.indiv: R tools to estimate age-period-cohort models with repeated cross section data. Mimeo. University of Oxford.

Fannon, Z., Monden, C. and Nielsen, B. (2018) Age-period-cohort modelling and covariates, with an application to obesity in England 2001-2014. Mimeo. University of Oxford.

See Also

For model estimation: apc.indiv.est.model. The data in these examples are the Wage data from the package ISLR and the PSID7682 data from the package AER.

For examples, see the vignette IntroductionIndividualData.pdf, IntroductionIndividualData.R on Vignettes. Further examples in the vignette IntroductionIndividualDataFurtherExamples.pdf, IntroductionIndividualDataFurtherExamples.R.

Examples

#### see vignettes

[Package apc version 2.0.0 Index]