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
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