Dear Statalisters,

I want to estimate centiles for a single observation after using a finite mixture model (Mixture of 4 Gaussians/normal distributions).
The aim is to graph those values later on.

Below I do the same for an OLS regression and a Quantile Regression.
First I expand the data set by 99 to calculate 99 centiles for each observation
Id2 is the identifier for an observation and idq the quantile.

For the OLS regression I simply take the point estimate and add the centile of the residual.
For the Quantile regression I use the predicted value for the given quantile directly.

My question is: How do I proceed the same way if I have estimated classprobabilities, classpostprobabilities and point estimates given a latent class for each observation. As for OLS the assumption of normality within each group holds.

I'm stuck but somehow I should be able to
1. estimate the parameters of the normal distributions
2. calculate the centiles for a given observation by knowing the classprobabilities for the given observation and the parameters of the normal distributions.

Code:
regress $y3 $x1 $x2 $x3 $x4
predict OLS
predict OLSres, res

gen id2 = _n
expand 99
gen QRden = .
gen OLSden = .
gen FRMden = .
bysort id2: gen idq = _n

forvalues q=1/99{
local fq = `q'/100
qui qreg $y3 $x1 $x2 $x3 $x4 if idq==1, q(`q')
capture drop aux
predict aux
replace QRden = aux if idq==`q'

qui centile OLSres, centile(`q')
replace OLSden = OLS + r(c_1) if idq==`q'
}
E.g. twoway kdensity OLSden if id2==1 || kdensity QRden if id2==1 would be the predicted density of observation 1 by an OLS regression and by a Quantile regression.

Any help is appreciated.

Kind regards

Steffen Pl├╝tzke