Spatial Survey Data Modeling - Sampling Spatial Units for Agricultural Surveys - page 316

svyglm(formula ¼ yobs ~ poly(xc, 2) + poly(yc, 2), dsrs)

Survey design:
svydesign(id ¼ ~1, data ¼ framesrs, fpc ¼ ~rep(n/N, n))

Coefficients:

Estimate Std. Error t value Pr( > |t|)

(Intercept) 93.2114 0.4471 208.459 < 2e-16 ***

poly(xc, 2)1 -9.1317 4.4408 -2.056 0.0425 *

poly(xc, 2)2 -144.3066 4.1652 -34.646 < 2e-16 ***

poly(yc, 2)1 7.4200 4.7834 1.551 0.1242

poly(yc, 2)2 -141.5748 4.9076 -28.848 < 2e-16 ***

---
Signif. codes: 0 ' *** ' 0.001 ' ** ' 0.01 ' * ' 0.05 ' . ' 0.1 '' 1

(Dispersion parameter for gaussian family taken to be 22.2153)

Number of Fisher Scoring iterations: 2

> regsrs < - lm(yobs ~ poly(xc,2)+poly(yc,2), data ¼ framesrs)
> summary(regsrs)

Call:
lm(formula ¼ yobs ~ poly(xc, 2) + poly(yc, 2), data ¼ framesrs)

Residuals:
Min 1Q Median 3Q Max
-11.0966 -3.4837 -0.2616 2.8503 11.9855

Coefficients:

Estimate Std. Error t value Pr( > |t|)

(Intercept) 93.2114 0.4812 193.726 < 2e-16 ***

poly(xc, 2)1 -9.1317 4.8372 -1.888 0.0621 .

poly(xc, 2)2 -144.3066 4.8620 -29.680 < 2e-16 ***

poly(yc, 2)1 7.4200 4.8523 1.529 0.1295

poly(yc, 2)2 -141.5748 4.8469 -29.209 < 2e-16 ***

---
Signif. codes: 0 ' *** ' 0.001 ' ** ' 0.01 ' * ' 0.05 ' . ' 0.1 '' 1

Residual standard error: 4.812 on 95 degrees of freedom
Multiple R-squared: 0.9543, Adjusted R-squared: 0.9524
F-statistic: 495.8 on 4 and 95 DF, p-value: < 2.2e-16

> ypps < - exp(yobs/10)
> probinc < - inclusionprobabilities(ypps,n)