Description Usage Arguments Author(s) References See Also Examples
Plots age profiles estimated through command ageprofile.
1 2 |
x |
the resulting list given by the ageprofile command. |
lev.labels |
a vector of strings specifying the factor levels to consider. For example: |
baseline |
if TRUE the baseline will be drawn. |
unsmoothed |
if TRUE the unsmoothed transition rates, i.e. the ratio between occurences and exposures for each age and factor level, are plotted as points in the graph. |
xlim |
a vector of two values defining the limits of X axis measured in years of age (default value: c(min(age), max(age)) |
ylim |
a value defining the upper limit of the Y axis (transition rates). Given that transition rates a re strictly positive, the lower limit is always 0 (default value: 2*max(baseline rates)). |
title |
title of the graph (default: transition name as stored in the ageprofile argument. |
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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # 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$d1marr,tvar.lev=c("not_marr","marr"),
tvar.name="mar")
# Estimates age profiles for the transition to the first birth
# according to the following factors:
# sex (respondent'sex w/2 levels: 'Male', 'Female');
# edu ('Level of education w/3 levels: 'low_sec','upp_sec', 'tert');
# mar (ever married w/2 levels: 'not_marr', 'marr')
ch1.ap<-ageprofile(formula=~sex+edu+mar, epdata=ep2,
tr.name="First child", agelimits=c(15,50))
# Plot age profiles in three different graphs
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("Male","Female"),title='first child by sex')
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("low_sec","upp_sec","tert"),title='first child by education')
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("not_marr","marr"),title='first child by marital status',
ylim=0.4)
# Plot age profiles for the combined effect of sex and level of education
# under the independence hypothesis
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("Female*low_sec","Female*upp_sec","Female*tert"),
title='first child by education - women (indep hp)')
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("Male*low_sec","Male*upp_sec","Male*tert"),
title='first child by education - men (indep hp)')
# The estimates are obtained under the hypothesis of independence among
# factors. We can relax this hp by considering the interaction between
# factors. The following commands add the interaction between sex and edu.
ep2$inter<-ep2$sex:ep2$edu
ch1.ap<-ageprofile(formula=~sex+edu+mar+inter, epdata=ep2,
tr.name="First child", agelimits=c(15,50))
# Plot age profile for the interaction between sex and level of education
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("Female:low_sec","Female:upp_sec","Female:tert"),
title='first child by education - women')
plotap(ch1.ap,base=TRUE, unsmoo=TRUE,
lev=c("Male:low_sec","Male:upp_sec","Male:tert"),
title='first child by education - men')
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