Re: [R] Conditional piece-wise dependent regression
Thank you Dimitris Reid, you were very helpful. Best wishes, Arie. __ R-help@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] Conditional piece-wise dependent regression
Hi, after reading some R docs, I couldnt figure out how can I find the solution for the following problem, therefore I would ask this friendly list for an advice. Were 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@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
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 [EMAIL PROTECTED] To: r-help@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@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@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
Re: [R] Conditional piece-wise dependent regression
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: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Dimitris Rizopoulos Sent: Monday, August 01, 2005 4:40 AM To: Arie Cc: r-help@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 [EMAIL PROTECTED] To: r-help@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@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@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@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
