Re: [R] R-Square in WLS
Hi Peter, why you are involving -1 with this concept? Can you explain more please Cheers Date: Sun, 18 Nov 2012 23:28:26 -0800 From: ml-node+s789695n4650012...@n4.nabble.com To: frespi...@hotmail.com Subject: Re: R-Square in WLS On Nov 18, 2012, at 21:32 , Thomas Lumley wrote: > On Fri, Nov 16, 2012 at 4:48 PM, frespider <[hidden email]> wrote: > >> Hi, >> >> I am fitting a weighted least square regression and trying to compute >> SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know >> what I am coding wrong. Can you help please? >> > > > For a start, you need to replace your mu and muZ by weighted means. The -1 in the model formulas also suggests that there will be problems even in the non-weighted case. The addition formula for SSDs works for successive model reductions, so it is required that the span of the design matrix X contains the vector of all ones. > >-thomas > > [snip] > >> ## Y = Log(Z) Scale >> >> Yhat <- X%*%bhat # predicted values >> mu <- mean(Y) >> To <- Y - mu >> Er <- Y - Yhat >> Re <- Yhat - mu >> lgSST <- sum(Weights*(To)^2)# log SST >> lgSSE <- sum(Weights*(Er)^2)# log SSE >> lgSSR <- sum(Weights*(Re)^2)# log SSR >> lgR-sq <- lgSSR/lgSST >> ### Z Scale >> ## >> Z <- exp(Y) >> muZ <- mean(Z) >> Zhat <- exp(Yhat+0.5*Sigma2) >> ToZ <- Z-muZ >> ErZ <- Z - Zhat >> ReZ <- Zhat - muZ >> SST <- sum(Weights*(ToZ)^2) # SST >> SSE <- sum(Weights*(ErZ)^2) # SSE >> SSR <- sum(Weights*(ReZ)^2) # SSR >> Rsq <- SSR/SST >> >> I don't understand what is wrong with the code. The sum square regression >> plus the sum square error do not add up to the sum square total in both the >> Y scale and Z scale. Y is a normal distribution and Z is log normally >> distributed. Where is the error? >> Also, is there a way to calculate the weighted sum square? >> >> > > -thomas > > -- > Thomas Lumley > Professor of Biostatistics > University of Auckland > > [[alternative HTML version deleted]] > > __ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: [hidden email] Priv: [hidden email] __ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/R-Square-in-WLS-tp4649693p4650012.html To unsubscribe from R-Square in WLS, click here. NAML -- View this message in context: http://r.789695.n4.nabble.com/R-Square-in-WLS-tp4649693p4650032.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R-Square in WLS
On Nov 18, 2012, at 21:32 , Thomas Lumley wrote: > On Fri, Nov 16, 2012 at 4:48 PM, frespider wrote: > >> Hi, >> >> I am fitting a weighted least square regression and trying to compute >> SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know >> what I am coding wrong. Can you help please? >> > > > For a start, you need to replace your mu and muZ by weighted means. The -1 in the model formulas also suggests that there will be problems even in the non-weighted case. The addition formula for SSDs works for successive model reductions, so it is required that the span of the design matrix X contains the vector of all ones. > >-thomas > > [snip] > >> ## Y = Log(Z) Scale >> >> Yhat <- X%*%bhat # predicted values >> mu <- mean(Y) >> To <- Y - mu >> Er <- Y - Yhat >> Re <- Yhat - mu >> lgSST <- sum(Weights*(To)^2)# log SST >> lgSSE <- sum(Weights*(Er)^2)# log SSE >> lgSSR <- sum(Weights*(Re)^2)# log SSR >> lgR-sq <- lgSSR/lgSST >> ### Z Scale >> ## >> Z <- exp(Y) >> muZ <- mean(Z) >> Zhat <- exp(Yhat+0.5*Sigma2) >> ToZ <- Z-muZ >> ErZ <- Z - Zhat >> ReZ <- Zhat - muZ >> SST <- sum(Weights*(ToZ)^2) # SST >> SSE <- sum(Weights*(ErZ)^2) # SSE >> SSR <- sum(Weights*(ReZ)^2) # SSR >> Rsq <- SSR/SST >> >> I don't understand what is wrong with the code. The sum square regression >> plus the sum square error do not add up to the sum square total in both the >> Y scale and Z scale. Y is a normal distribution and Z is log normally >> distributed. Where is the error? >> Also, is there a way to calculate the weighted sum square? >> >> > > -thomas > > -- > Thomas Lumley > Professor of Biostatistics > University of Auckland > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd@cbs.dk Priv: pda...@gmail.com __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R-Square in WLS
Hi Thomas, Can you please edit my code in way it works. I appericated your help. I need to calculate the R2 in the Z scale and it is not make sense to me. or Can I have some documentation to read? Thanks Date: Sun, 18 Nov 2012 12:35:03 -0800 From: ml-node+s789695n4649969...@n4.nabble.com To: frespi...@hotmail.com Subject: Re: R-Square in WLS On Fri, Nov 16, 2012 at 4:48 PM, frespider <[hidden email]> wrote: > Hi, > > I am fitting a weighted least square regression and trying to compute > SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know > what I am coding wrong. Can you help please? > For a start, you need to replace your mu and muZ by weighted means. -thomas [snip] > ## Y = Log(Z) Scale > > Yhat <- X%*%bhat # predicted values > mu <- mean(Y) > To <- Y - mu > Er <- Y - Yhat > Re <- Yhat - mu > lgSST <- sum(Weights*(To)^2)# log SST > lgSSE <- sum(Weights*(Er)^2)# log SSE > lgSSR <- sum(Weights*(Re)^2)# log SSR > lgR-sq <- lgSSR/lgSST > ### Z Scale > ## > Z <- exp(Y) > muZ <- mean(Z) > Zhat <- exp(Yhat+0.5*Sigma2) > ToZ <- Z-muZ > ErZ <- Z - Zhat > ReZ <- Zhat - muZ > SST <- sum(Weights*(ToZ)^2) # SST > SSE <- sum(Weights*(ErZ)^2) # SSE > SSR <- sum(Weights*(ReZ)^2) # SSR > Rsq <- SSR/SST > > I don't understand what is wrong with the code. The sum square regression > plus the sum square error do not add up to the sum square total in both the > Y scale and Z scale. Y is a normal distribution and Z is log normally > distributed. Where is the error? > Also, is there a way to calculate the weighted sum square? > > -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland [[alternative HTML version deleted]] __ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. If you reply to this email, your message will be added to the discussion below: http://r.789695.n4.nabble.com/R-Square-in-WLS-tp4649693p4649969.html To unsubscribe from R-Square in WLS, click here. NAML -- View this message in context: http://r.789695.n4.nabble.com/R-Square-in-WLS-tp4649693p4649983.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R-Square in WLS
On Fri, Nov 16, 2012 at 4:48 PM, frespider wrote: > Hi, > > I am fitting a weighted least square regression and trying to compute > SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know > what I am coding wrong. Can you help please? > For a start, you need to replace your mu and muZ by weighted means. -thomas [snip] > ## Y = Log(Z) Scale > > Yhat <- X%*%bhat # predicted values > mu <- mean(Y) > To <- Y - mu > Er <- Y - Yhat > Re <- Yhat - mu > lgSST <- sum(Weights*(To)^2)# log SST > lgSSE <- sum(Weights*(Er)^2)# log SSE > lgSSR <- sum(Weights*(Re)^2)# log SSR > lgR-sq <- lgSSR/lgSST > ### Z Scale > ## > Z <- exp(Y) > muZ <- mean(Z) > Zhat <- exp(Yhat+0.5*Sigma2) > ToZ <- Z-muZ > ErZ <- Z - Zhat > ReZ <- Zhat - muZ > SST <- sum(Weights*(ToZ)^2) # SST > SSE <- sum(Weights*(ErZ)^2) # SSE > SSR <- sum(Weights*(ReZ)^2) # SSR > Rsq <- SSR/SST > > I don't understand what is wrong with the code. The sum square regression > plus the sum square error do not add up to the sum square total in both the > Y scale and Z scale. Y is a normal distribution and Z is log normally > distributed. Where is the error? > Also, is there a way to calculate the weighted sum square? > > -thomas -- Thomas Lumley Professor of Biostatistics University of Auckland [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
