Felipe Carrillo
2008-Mar-07 22:44 UTC
[R] How to Estimate Covariance by Week based on a linear regression model
Hi all: I have always used SPSS to estimate weekly covariance based on a linear regression model but have to hard code the model Std. Error and the Mean-Square and then execute one week a the time. I was wondering if someone could give me an idea on how to estimate weekly(WK) covariance using the summary and anova of "dfr"(lineal model below). I have to do this for 52 weeks(WK) but I am providing a dataset with only two weeks below. The first week(WK 38 is missing values) dfr <- read.table(textConnection("percentQ Efficiency 1.565 0.0125 1.94 0.0213 0.876 0.003736 1.027 0.006 1.536 0.0148 1.536 0.0162 2.607 0.02 1.456 0.0157 2.16 0.0103 1.698 0.0196 1.64 0.0098684 1.814 0.0183 2.394 0.0107 2.469 0.0221 3.611 0.0197 3.466 0.0155 1.877 0.0283 2.893 0.0189 1.851 0.009772 2.834 0.0285 1.923 0.022 2.581 0.0159 2.361 0.0053591 2.43 0.0185 1.66 0.0151 2.285 0.0084034 2.285 0.0124 2.37 0.0122 2.392 0.0146 2.244 0.0175"), header=TRUE) # Linear model Reg<-lm(Efficiency~percentQ,data=dfr) summary(Reg) # Coefficients standard error Std=Betas[,"Std. Error"] Std[1]^2 Std[1]^2 # Analysis of Variance (ANOVA) MS <- anova(lm(Efficiency~percentQ,data=dfr)) MS # value of the Residual Mean-Square MS$"Mean Sq"[2] #I want to estimate weekly(WK) covariance of the dataset below using the linear model above. temp53 <- read.table(textConnection("XD TD PD WK 38 38 38 38 3.0259 0.022522 163299 38 2.2316 0.01724 120315 38 2.3374 0.017944 137874 38 2.2024 0.017046 160524 39 2.4216 0.018504 163565 39 1.4672 0.012157 143973 39 1.4817 0.012253 111956 39 1.4959 0.012348 89677 39 1.4431 0.011997 95269 39 1.5676 0.012825 81558 39"), header=TRUE) # I read about the cov function and tried it with my data but couldn't get the desired results..I would really appreciate any hints..Thanks Felipe D. Carrillo Fishery Biologist US Fish & Wildlife Service California, USA