Folks, This is so simple is driving me crazy. It's not really an R question is more an Statistics question. I applied a chemical in 3 different events during a growing season. I took 3 samples in each event to estimate mean applied chemical. Thus, I can easily estimate means and st.err by event. I can then estimate the total chemical applied for the whole season by adding the 3 means for each event. How can I estimate the standard error of this total applied chemical. Thanks in advance, Jose event <- c(1,1,1,2,2,2,3,3,3) chem <- c(131.75,125.92,130.09,42.67,48.26,54.56,85.05,91.17,90.86) d.1 <- data.frame(event,chem) attach(d.1) s.err <- function(x) sd(x)/(sqrt(length(x))) print(mean_event <- tapply(d.1$chem,factor(data.1$event),mean)) print(s.err_event <- tapply(d.1$chem,factor(data.1$event),s.err)) print(total_chem <- sum(mean_event)) s.err_total_chem <- -- Jose A. Hernandez Ph.D. Candidate Precision Agriculture Center Department of Soil, Water, and Climate University of Minnesota 1991 Upper Buford Circle St. Paul, MN 55108 Ph. (612) 625-0445, Fax. (612) 625-2208
The variance of a sum is the sum of all the elements of the variance-covariance matrix. If the summands are uncorrelated, then the variance of the sum is the sum of the variances. hope this helps. spencer graves Jose A. Hernandez wrote:> Folks, > > This is so simple is driving me crazy. It's not really an R question > is more an Statistics question. > > I applied a chemical in 3 different events during a growing season. I > took 3 samples in each event to estimate mean applied chemical. > > Thus, I can easily estimate means and st.err by event. I can then > estimate the total chemical applied for the whole season by adding the > 3 means for each event. How can I estimate the standard error of this > total applied chemical. > > Thanks in advance, > > Jose > > > event <- c(1,1,1,2,2,2,3,3,3) > chem <- c(131.75,125.92,130.09,42.67,48.26,54.56,85.05,91.17,90.86) > d.1 <- data.frame(event,chem) > attach(d.1) > s.err <- function(x) sd(x)/(sqrt(length(x))) > > print(mean_event <- tapply(d.1$chem,factor(data.1$event),mean)) > print(s.err_event <- tapply(d.1$chem,factor(data.1$event),s.err)) > > print(total_chem <- sum(mean_event)) > s.err_total_chem <- > > >
If you supose that each mean is independent then s.err_total_chem <- sqrt(sum(s.err_event^2)) If not you must estimate de var-covariance matrix to find this s.err. This estimation will be difficult given the scarce data available. Hope it will helps. Kenneth Jose A. Hernandez wrote:> Folks, > > This is so simple is driving me crazy. It's not really an R question > is more an Statistics question. > > I applied a chemical in 3 different events during a growing season. I > took 3 samples in each event to estimate mean applied chemical. > > Thus, I can easily estimate means and st.err by event. I can then > estimate the total chemical applied for the whole season by adding the > 3 means for each event. How can I estimate the standard error of this > total applied chemical. > > Thanks in advance, > > Jose > > > event <- c(1,1,1,2,2,2,3,3,3) > chem <- c(131.75,125.92,130.09,42.67,48.26,54.56,85.05,91.17,90.86) > d.1 <- data.frame(event,chem) > attach(d.1) > s.err <- function(x) sd(x)/(sqrt(length(x))) > > print(mean_event <- tapply(d.1$chem,factor(data.1$event),mean)) > print(s.err_event <- tapply(d.1$chem,factor(data.1$event),s.err)) > > print(total_chem <- sum(mean_event)) > s.err_total_chem <- > > >-- Kenneth Roy Cabrera Torres Celular +57 (315) 504 9339