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.
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.
______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
In case you want to estimate U and V as well, there's a segmented regression
package (called "segmented") on http://cran.r-project.org for this.
Reid Huntsinger
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Dimitris Rizopoulos
Sent: Monday, August 01, 2005 4:40 AM
To: Arie
Cc: r-help at stat.math.ethz.ch
Subject: Re: [R] Conditional piece-wise dependent regression
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.
______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
______________________________________________
R-help at stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html