[R] Conditional piece-wise dependent regression

[Dimitris Rizopoulos]

Since you want least squares, I think you could use lm() here, i.e.,

U <- 50
V <- 100
Time <- 1:150
dat <- data.frame(y = rnorm(150), Time, f1 = as.numeric(Time > U), f2 
= as.numeric(Time > V))
###############
m <- lm(y ~ Time + I(Time - U):f1 + I(Time - V):f2, data = dat)

# check also the design matrix
model.matrix(m)


I hope it helps.

Best,
Dimitris

----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.be/biostat/
     http://www.student.kuleuven.be/~m0390867/dimitris.htm



----- Original Message ----- 
From: "Arie" <poohdov at yahoo.com>
To: <r-help at stat.math.ethz.ch>
Sent: Monday, August 01, 2005 10:17 AM
Subject: [R] Conditional piece-wise dependent regression


Hi, after reading some R docs, I couldn't figure out how can I find 
the solution for the following
problem, therefore I would ask this friendly list for an advice.

We're making a least square approximation for an experiment described 
by the following model:

T is the time,
Y is some measured value.

>From time=0 till time=U:
Y = b + p*T
>From time=U and on (some effect added):
Y = b + p*T + q*(T-U)
>From time=V and on (some additional effect added):
Y = b + p*T + q*(T-U) + r*(T-V)

Measured: Yi, Ti pairs.
Wanted: b, p, q, r.
b and p are the same for all time ranges;
q is the same for time=U and on.

Thanks,
Arie.

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