Re: [R] Conditional piece-wise dependent regression

From: Dimitris Rizopoulos <>
Date: Mon 01 Aug 2005 - 18:39:48 EST

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

I hope it helps.


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

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.

Arie. mailing list PLEASE do read the posting guide! mailing list PLEASE do read the posting guide! Received on Mon Aug 01 18:45:41 2005

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