Hi R-help, How do you perform least median square regression in R? Here is what I have but received no output. LMSRegression <- function(df, indices){ sample <- df[indices, ] LMS_NAR_NIC_relation <- lm(sample$NAR~sample$NIC, data = sample, method = "lms") rsquared_lms_nar_nic <- summary(LMS_NAR_NIC_relation)$r.square LMS_SQRTNAR_SQRTNIC_relation <- lm(sample$SQRTNAR~sample$SQRTNIC, data = sample, method = "lms") rsquared_lms_sqrtnar_sqrtnic <- summary(LMS_SQRTNAR_SQRTNIC_relation)$r.square out <- c(rsquared_lms_nar_nic, rsquared_lms_sqrtnar_sqrtnic) return(out) } Also, which value should be looked at decide whether this is best regression model to use? Bryan Mac bryanmac.24 at gmail.com [[alternative HTML version deleted]]
Hello, Use package quantreg, function rq(). install.packages("quantreg") ?rq Hope this helps, Rui Barradas Citando Bryan Mac <bryanmac.24 at gmail.com>:> Hi R-help, > > How do you perform least median square regression in R? Here is what > I have but received no output. > > LMSRegression <- function(df, indices){ > sample <- df[indices, ] > LMS_NAR_NIC_relation <- lm(sample$NAR~sample$NIC, data = sample, > method = "lms") > rsquared_lms_nar_nic <- summary(LMS_NAR_NIC_relation)$r.square > > LMS_SQRTNAR_SQRTNIC_relation <- lm(sample$SQRTNAR~sample$SQRTNIC, > data = sample, method = "lms") > rsquared_lms_sqrtnar_sqrtnic <- > summary(LMS_SQRTNAR_SQRTNIC_relation)$r.square > > out <- c(rsquared_lms_nar_nic, rsquared_lms_sqrtnar_sqrtnic) > return(out) > } > > Also, which value should be looked at decide whether this is best > regression model to use? > > Bryan Mac > bryanmac.24 at gmail.com > > > > > [[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.
On Sat, 08 Oct 2016, Bryan Mac <bryanmac.24 at gmail.com> writes:> Hi R-help, > > How do you perform least median square regression in R? Here is what I have but received no output. > > LMSRegression <- function(df, indices){ > sample <- df[indices, ] > LMS_NAR_NIC_relation <- lm(sample$NAR~sample$NIC, data = sample, method = "lms") > rsquared_lms_nar_nic <- summary(LMS_NAR_NIC_relation)$r.square > > LMS_SQRTNAR_SQRTNIC_relation <- lm(sample$SQRTNAR~sample$SQRTNIC, data = sample, method = "lms") > rsquared_lms_sqrtnar_sqrtnic <- summary(LMS_SQRTNAR_SQRTNIC_relation)$r.square > > out <- c(rsquared_lms_nar_nic, rsquared_lms_sqrtnar_sqrtnic) > return(out) > } > > Also, which value should be looked at decide whether this is best regression model to use? > > Bryan Mac > bryanmac.24 at gmail.com >A tutorial on how to run such regressions is included in the NMOF package. https://cran.r-project.org/package=NMOF/vignettes/PSlms.pdf -- Enrico Schumann Lucerne, Switzerland http://enricoschumann.net
I am confused reading the document. I have installed and added the package (MASS). What is the function for LMS Regression? Bryan Mac bryanmac.24 at gmail.com> On Oct 8, 2016, at 6:17 AM, Enrico Schumann <es at enricoschumann.net> wrote: > > On Sat, 08 Oct 2016, Bryan Mac <bryanmac.24 at gmail.com> writes: > >> Hi R-help, >> >> How do you perform least median square regression in R? Here is what I have but received no output. >> >> LMSRegression <- function(df, indices){ >> sample <- df[indices, ] >> LMS_NAR_NIC_relation <- lm(sample$NAR~sample$NIC, data = sample, method = "lms") >> rsquared_lms_nar_nic <- summary(LMS_NAR_NIC_relation)$r.square >> >> LMS_SQRTNAR_SQRTNIC_relation <- lm(sample$SQRTNAR~sample$SQRTNIC, data = sample, method = "lms") >> rsquared_lms_sqrtnar_sqrtnic <- summary(LMS_SQRTNAR_SQRTNIC_relation)$r.square >> >> out <- c(rsquared_lms_nar_nic, rsquared_lms_sqrtnar_sqrtnic) >> return(out) >> } >> >> Also, which value should be looked at decide whether this is best regression model to use? >> >> Bryan Mac >> bryanmac.24 at gmail.com >> > > A tutorial on how to run such regressions is included > in the NMOF package. > > https://cran.r-project.org/package=NMOF/vignettes/PSlms.pdf > > > -- > Enrico Schumann > Lucerne, Switzerland > http://enricoschumann.net