[R] How to Estimate Covariance by Week based on a linear regression model

[Felipe Carrillo]

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