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

From: Felipe Carrillo <mazatlanmexico_at_yahoo.com>
Date: Fri, 07 Mar 2008 14:44:08 -0800 (PST)

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)

```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
```

# 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
```

# 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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