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Directory data to keep out ##

plotmap(casespopulation$smr01,LGA,nclr=9) title("raw standardised morbidity ratio-TIA") ## [1] "SpatialPolygonsDataFrame" ## attr(,"package") ## [1] "sp" ## [1] "SpatialPolygons" ## attr(,"package") ## [1] "sp" plotmap(results$RR,LGAsp,nclr=9,location="topleft")
title("Empirical Bayes Risk-Hypertension")

## Warning: package 'spdep' was built under R version 3.3.3
## Loading required package: Matrix

plot(LGAxy2,border="red")
title(main="subset of LGA and neighbours")

## Neighbour list object:
## Number of regions: 77
## Number of nonzero links: 312
## Percentage nonzero weights: 5.26227
## Average number of links: 4.051948
## 4 regions with no links:
## 32 33 71 72
##
##  0  1  2  3  4  5  6  7  8  9
##  4  4  8 17 10 14 13  4  2  1
plotmap(localEB$est,LGAxy2,nclr=9) title(main="Local moment estimator of TIA") ## The default for subtract_mean_in_numerator set TRUE from February 2016 ## ## Monte-Carlo simulation of Empirical Bayes Index (mean subtracted) ## ## data: cases: casespopulation$freq01, risk population: casespopulation$X2001 ## weights: nb2listw(xx2, style = "B", zero.policy = TRUE) ## number of simulations + 1: 1000 ## ## statistic = 0.1301, observed rank = 963, p-value = 0.037 ## alternative hypothesis: greater ## ## Monte-Carlo simulation of Moran I ## ## data: EBI.p ## weights: nb2listw(xx2, style = "B", zero.policy = TRUE) ## number of simulations + 1: 1000 ## ## statistic = 0.13596, observed rank = 964, p-value = 0.036 ## alternative hypothesis: greater plotmap(EBI.p,LGAxy2,nclr=9) title(main="Permutation test for Empirical Bayes") ## This is INLA 0.0-1485844051, dated 2017-01-31 (09:14:12+0300). ## See www.r-inla.org/contact-us for how to get help. ## ## Call: ## lm(formula = casespopulation$smr01 ~ casespopulation$age0175 + ## casespopulation$HTN01 + fitted(LGAFilt), data = casespopulation)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -1.6958 -0.4868 -0.0215  0.3185  3.6587
##
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.8731     0.4086   2.136  0.03619 *
## casespopulation$age0175 1.0346 0.7133 1.451 0.15145 ## casespopulation$HTN01    -0.6873     0.8061  -0.853  0.39678
## fitted(LGAFilt)vec3      -2.3033     0.8795  -2.619  0.01084 *
## fitted(LGAFilt)vec2      -2.0030     0.8795  -2.277  0.02586 *
## fitted(LGAFilt)vec14      2.4382     0.8795   2.772  0.00715 **
## fitted(LGAFilt)vec9      -1.2624     0.8795  -1.435  0.15571
## fitted(LGAFilt)vec4      -0.9890     0.8795  -1.125  0.26467
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8795 on 69 degrees of freedom
## Multiple R-squared:  0.2684, Adjusted R-squared:  0.1942
## F-statistic: 3.616 on 7 and 69 DF,  p-value: 0.002242

## Including Plots

#plot(TIA)
plotmap(LGAlm\$fitted.values,LGAxy2,nclr=9)
title(main="Spatial regression: TIA with covariates HT and Age")

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