MAPLES is a general method for the estimation of age profiles that uses standard micro-level demographic survey data. The aim is to estimate smoothed age profiles and relative risks for time-fixed and time-varying covariates.
Main functions in the package:
- epdata: prepare episode data for event history analysis
- splitter: Creates a time-varying factor variables within a episode-data.
- ageprofile: Computes smoothed transition rates by respondent's age (age profiles)
- plotap: plots age profiles.
- tabx: Prints univariate or a bivariate frequency distribution table including marginal distribution and total number of cases.
- tabm: Print regression estimates for previously fitted linear and logit regression models.
- mkdate: computes dates in continuous years or CMC.
- listvar: shows variables in a dataframe.
Roberto Impicciatore firstname.lastname@example.org
Impicciatore R. and Billari F.C., (2010), MAPLES: a general method for the estimation
of age profiles from standard demographic surveys (with an application to fertility),
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# creates an episode-data structure relating to the transition # childless-->first child ep1<-with(demogr,epdata(start=dbirth, event=dch1, rcensor=dint, birth=dbirth,id=id, addvar=subset(demogr,select=c(-id,-dbirth)))) # creates a new episode-data structure with a time-varying factor # variable relating to the status "never married" (not_marr) or # "ever married" (marr) ep2<-splitter(ep1,split=ep1$d1marr,tvar.lev=c("not_marr","marr"), tvar.name="mar") # Estimates age profiles for the transition to the first birth # according to the following factors: # sex (respondent'sex w/2 levels: 'Male', 'Female'); # edu ('Level of education w/3 levels: 'low_sec','upp_sec', 'tert'); # mar (ever married w/2 levels: 'not_marr', 'marr') ch1.ap<-ageprofile(formula=~sex+edu+mar, epdata=ep2, tr.name="First child", agelimits=c(15,50)) # Plot age profiles in three different graphs plotap(ch1.ap,base=TRUE, unsmoo=TRUE, lev=c("Male","Female"),title='first child by sex') plotap(ch1.ap,base=TRUE, unsmoo=TRUE, lev=c("low_sec","upp_sec","tert"),title='first child by education') plotap(ch1.ap,base=TRUE, unsmoo=TRUE, lev=c("not_marr","marr"),title='first child by marital status', ylim=0.4)
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