Description Details Author(s) References Examples
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.
Package: | MAPLES |
Type: | Package |
Version: | 1.0 |
Date: | 2011-04-08 |
License: | GPL-2 |
LazyLoad: | yes |
LazyData: | yes |
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.
Utilities:
- 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 roberto.impicciatore@unimi.it
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),
DEAS WP,
http://ideas.repec.org/p/mil/wpdepa/2010-40.html
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # 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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