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``` r
library(sf)

## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE

library(tidyverse)

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library(scales)

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library(ggplot2)
library(dplyr)
library(tmap)

## Breaking News: tmap 3.x is retiring. Please test v4, e.g. with
## remotes::install_github('r-tmap/tmap')

library(mapview)
remotes::install_github('r-tmap/tmap')

## Downloading GitHub repo r-tmap/tmap@HEAD

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library(magrittr)

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setwd(‘D:/isair_r/data/haiti’) adm2 <- st_read(‘adm2.geojson’, stringsAsFactors = F, quiet = TRUE)

#plot1

adm2_urb_plot <- ggplot(data = adm2, aes(x = urbanpct, y = dtp3coverage2016/100)) + geom_point() + xlab(“Population living in urban areas”) + ylab(“Population vaccinated with DTP3”) + scale_y_continuous(limits = c(0 , 1), labels = percent_format()) + scale_x_continuous(limits = c(0 , 1) ,labels = percent_format()) + theme_minimal()

adm2 <- mutate(adm2, vacc_fail = vacc_denom - vacc_num)
adm2_binom <- glm(data = adm2, cbind(vacc_num, vacc_fail) ~ urbanpct, family = ‘binomial’)

summary(adm2_binom)

library (broom) tidy(adm2_binom, conf.int = TRUE) glance(adm2_binom)

head(fitted.values(adm2_binom))# this is the first six of 138 head(predict(adm2_binom, type = ‘response’))

setwd(‘D:/isair_r/data/rats’)

rats_aug <- augment(rats_lm) head(rats_aug)

head(predict(adm2_binom))

#Exercise: Add the predicted values (on the response scale) to the adm2 data frame and make aplot showing the predicted and observed values.

adm2 <- mutate(adm2, coverage_pred = predict(object = adm2_binom, newdata = adm2, type = ‘response’)) ggplot(data= adm2, aes(x = vacc_num/vacc_denom, y = coverage_pred)) + geom_point() + xlab(‘Observed’) + ylab(‘Predicted’) + theme_minimal() + geom_abline(intercept = 0 , slope = 1) + scale_x_continuous(limits = c(0,1), labels = percent_format()) + scale_y_continuous(limits = c(0,1), labels = percent_format()) + coord_equal()

adm2_pred <- data.frame( urbanpct = seq(from = 0, to = 1, length.out = 101))

adm2_pred %<>% mutate(coverage_pred = predict(object = adm2_binom, newdata = ., type =‘response’))

adm2_urb_plot + geom_line(data = adm2_pred, aes(x = urbanpct,y = coverage_pred), color =‘purple’) + coord_equal()

#Exercise: Create a spatial visualisation of the residuals (on the logit scale). Discuss whetherthey appear to be IID with correlation of 0.

adm2_binom_resid <- mutate(adm2, residuals = residuals(adm2_binom))

ggplot(data = adm2_binom_resid) + geom_sf(aes(fill = residuals)) + scale_fill_gradient2(midpoint = 0, name= “Residuals”) + theme_void() + theme(axis.text = element_blank())

Extension exercise: Calculate the relative risk of vaccine coverage for a completely rural vs acompletely urban population, given by fi nding the ratio of the probabilities in each.

RR <- head(adm2_pred\(coverage_pred, 1)/ tail(adm2_pred\)coverage_pred,1)

round(RR, 2)

Extension exercise: it a Poisson GLM to the vaccine coverage data. The off set variable to beused is the vacc_exp, which is derived from the proportion of vaccinees expected to be found ineach arrondissement if the coverage was equal.

adm2_poisson <- glm(data = adm2, vacc_num ~ offset(log(vacc_exp)) + urbanpct, family = ‘poisson’)

summary(adm2_poisson)

#Extension exercise: #Calculate the change in risk (relative) associated with a change fromurbanicity of 1 to 0 by exponentiating the coeffi cient.

tidy(adm2_poisson, conf.int = TRUE ) %>% filter(term == ‘urbanpct’) %>% select(estimate, conf.low, conf.high) %>% mutate_all(.funs = ~{1 - exp(-.)})

GLM_VR

Venkat

2025-01-27

R Markdown

Extension exercise: Calculate the relative risk of vaccine coverage for a completely rural vs acompletely urban population, given by fi nding the ratio of the probabilities in each.

Extension exercise: it a Poisson GLM to the vaccine coverage data. The off set variable to beused is the vacc_exp, which is derived from the proportion of vaccinees expected to be found ineach arrondissement if the coverage was equal.