Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y is created by y = 1 - 2*x1 + 3*x3 + 4*x4 + error and the error term is standard normal distributed. Hope this helps. Regards, Christien In this section, we provide a simulation study to illustrate the performance of four estimators, the (GLS), S, MM and MM ridge estimator for SUR model. This simulation process is executed to generate data for the following equation Where In this simulation, we set the initial value for ?= [1,2,3] for k=3. The explanatory variables are generated by multivariate normal distribution MNNk=3 (0,?x) where diag(?x)=1, off-diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high interdependency. Where ?x is correlation between explanatory variables. We chose two sample size 25 for small sample and 100 for large sample. The specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0, ??), ?? the variance covariance matrix of errors, diag(??)= 1, off-diag(??)= ??= 0.15. To investigate the robustness of the estimators against outliers, we chosen different percentages of outliers ( 20%, 45%). We choose shrink parameter in (12) by minimize the new robust Cross Validation (CVMM) criterion which avoided 2018-03-04 0:52 GMT+01:00 David Winsemius <dwinsemius at comcast.net>:> > > On Mar 3, 2018, at 3:04 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > > > > Dear list members, > > > > I want to perform an MM-regression. This seems an easy task using the > > function lmrob(), however, this function provides me with NA > coefficients. > > My data generating process is as follows: > > > > rho <- 0.15 # low interdependency > > Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 > > x.clean <- mvrnorm(n, rep(0,d), Sigma) > > Which package are you using for mvrnorm? > > > beta <- c(1.0, 2.0, 3.0, 4.0) > > error <- rnorm(n = n, mean = 0, sd = 1) > > y <- as.data.frame(beta[1]*rep(1, n) + beta[2]*x.clean[,1] + > > beta[3]*x.clean[,2] + beta[4]*x.clean[,3] + error) > > xy.clean <- cbind(x.clean, y) > > colnames(xy.clean) <- c("x1", "x2", "x3", "y") > > > > Then, I pass the following formula to lmrob: f <- y ~ x1 + x2 + x3 > > > > Finally, I run lmrob: lmrob(f, data = data, cov = ".vcov.w") > > and this results in NA coefficients. > > It would also be more courteous to specify the package where you are > getting lmrob. > > > > > It would be great if anyone can help me out. Thanks in advance. > > > > Regards, > > Christien > > > > [[alternative HTML version deleted]] > > This is a plain text mailing list although it doesn't seem to have created > problems this time. > > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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. > > David Winsemius > Alameda, CA, USA > > 'Any technology distinguishable from magic is insufficiently advanced.' > -Gehm's Corollary to Clarke's Third Law > > > > > >[[alternative HTML version deleted]]
What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote:> Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a multivariate normal distribution > where the covariance matrix of the variables is defined by Sigma in my > code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y > is created by y = 1 - 2*x1 + 3*x3 + 4*x4 + error and the error term is > standard normal distributed. > > Hope this helps. > > Regards, > Christien > > In this section, we provide a simulation study to illustrate the > performance of four estimators, the (GLS), S, MM and MM ridge estimator for > SUR model. This simulation process is executed to generate data for the > following equation Where In this simulation, we set the initial value > for ?= [1,2,3] for k=3. The explanatory variables are generated by > multivariate normal distribution MNNk=3 (0,?x) where diag(?x)=1, > off-diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high > interdependency. Where ?x is correlation between explanatory variables. We > chose two sample size 25 for small sample and 100 for large sample. The > specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0, > ??), ?? the variance covariance matrix of errors, diag(??)= 1, > off-diag(??)= ??= 0.15. To investigate the robustness of the estimators > against outliers, we chosen different percentages of outliers ( 20%, 45%). > We choose shrink parameter in (12) by minimize the new robust Cross > Validation (CVMM) criterion which avoided > > 2018-03-04 0:52 GMT+01:00 David Winsemius <dwinsemius at comcast.net>: > > > > > > On Mar 3, 2018, at 3:04 PM, Christien Kerbert < > > christienkerbert at gmail.com> wrote: > > > > > > Dear list members, > > > > > > I want to perform an MM-regression. This seems an easy task using the > > > function lmrob(), however, this function provides me with NA > > coefficients. > > > My data generating process is as follows: > > > > > > rho <- 0.15 # low interdependency > > > Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 > > > x.clean <- mvrnorm(n, rep(0,d), Sigma) > > > > Which package are you using for mvrnorm? > > > > > beta <- c(1.0, 2.0, 3.0, 4.0) > > > error <- rnorm(n = n, mean = 0, sd = 1) > > > y <- as.data.frame(beta[1]*rep(1, n) + beta[2]*x.clean[,1] + > > > beta[3]*x.clean[,2] + beta[4]*x.clean[,3] + error) > > > xy.clean <- cbind(x.clean, y) > > > colnames(xy.clean) <- c("x1", "x2", "x3", "y") > > > > > > Then, I pass the following formula to lmrob: f <- y ~ x1 + x2 + x3 > > > > > > Finally, I run lmrob: lmrob(f, data = data, cov = ".vcov.w") > > > and this results in NA coefficients. > > > > It would also be more courteous to specify the package where you are > > getting lmrob. > > > > > > > > It would be great if anyone can help me out. Thanks in advance. > > > > > > Regards, > > > Christien > > > > > > [[alternative HTML version deleted]] > > > > This is a plain text mailing list although it doesn't seem to have > created > > problems this time. > > > > > > > > ______________________________________________ > > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > > 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. > > > > David Winsemius > > Alameda, CA, USA > > > > 'Any technology distinguishable from magic is insufficiently advanced.' > > -Gehm's Corollary to Clarke's Third Law > > > > > > > > > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. >[[alternative HTML version deleted]]
d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>:> What is 'd'? What is 'n'? > > > On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > >> Thanks for your reply. >> >> I use mvrnorm from the *MASS* package and lmrob from the *robustbase* >> package. >> >> To further explain my data generating process, the idea is as follows. The >> explanatory variables are generated my a multivariate normal distribution >> where the covariance matrix of the variables is defined by Sigma in my >> code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y >> is created by y = 1 - 2*x1 + 3*x3 + 4*x4 + error and the error term is >> standard normal distributed. >> >> Hope this helps. >> >> Regards, >> Christien >> >> In this section, we provide a simulation study to illustrate the >> performance of four estimators, the (GLS), S, MM and MM ridge estimator >> for >> SUR model. This simulation process is executed to generate data for the >> following equation Where In this simulation, we set the initial value >> >> for ?= [1,2,3] for k=3. The explanatory variables are generated by >> multivariate normal distribution MNNk=3 (0,?x) where diag(?x)=1, >> off-diag(?x)= ?X= 0.15 for low interdependency and ?x= 0.70 for high >> interdependency. Where ?x is correlation between explanatory variables. We >> chose two sample size 25 for small sample and 100 for large sample. The >> specific error in equations ?i, i=1,2,?..,n, we generated by MVNk=3 (0, >> ??), ?? the variance covariance matrix of errors, diag(??)= 1, >> off-diag(??)= ??= 0.15. To investigate the robustness of the estimators >> against outliers, we chosen different percentages of outliers ( 20%, 45%). >> We choose shrink parameter in (12) by minimize the new robust Cross >> Validation (CVMM) criterion which avoided >> >> 2018-03-04 0:52 GMT+01:00 David Winsemius <dwinsemius at comcast.net>: >> >> > >> > > On Mar 3, 2018, at 3:04 PM, Christien Kerbert < >> > christienkerbert at gmail.com> wrote: >> > > >> > > Dear list members, >> > > >> > > I want to perform an MM-regression. This seems an easy task using the >> > > function lmrob(), however, this function provides me with NA >> > coefficients. >> > > My data generating process is as follows: >> > > >> > > rho <- 0.15 # low interdependency >> > > Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 >> > > x.clean <- mvrnorm(n, rep(0,d), Sigma) >> > >> > Which package are you using for mvrnorm? >> > >> > > beta <- c(1.0, 2.0, 3.0, 4.0) >> > > error <- rnorm(n = n, mean = 0, sd = 1) >> > > y <- as.data.frame(beta[1]*rep(1, n) + beta[2]*x.clean[,1] + >> > > beta[3]*x.clean[,2] + beta[4]*x.clean[,3] + error) >> > > xy.clean <- cbind(x.clean, y) >> > > colnames(xy.clean) <- c("x1", "x2", "x3", "y") >> > > >> > > Then, I pass the following formula to lmrob: f <- y ~ x1 + x2 + x3 >> > > >> > > Finally, I run lmrob: lmrob(f, data = data, cov = ".vcov.w") >> > > and this results in NA coefficients. >> > >> > It would also be more courteous to specify the package where you are >> > getting lmrob. >> > >> > > >> > > It would be great if anyone can help me out. Thanks in advance. >> > > >> > > Regards, >> > > Christien >> > > >> > > [[alternative HTML version deleted]] >> > >> > This is a plain text mailing list although it doesn't seem to have >> created >> > problems this time. >> > >> > > >> > > ______________________________________________ >> > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> > > 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. >> > >> > David Winsemius >> > Alameda, CA, USA >> > >> > 'Any technology distinguishable from magic is insufficiently advanced.' >> > -Gehm's Corollary to Clarke's Third Law >> > >> > >> > >> > >> > >> > >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posti >> ng-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > >[[alternative HTML version deleted]]