Using Stratified Exposures
strata.RmdEXPOSURE
Set up a stratified exposure
boston_exposure <- subset(ma_exposure, TOWN20 == 'BOSTON')
head(boston_exposure)
#> date tmax_C TOWN20 COUNTY20
#> 137783 2010-01-01 -0.3825 BOSTON SUFFOLK
#> 137784 2010-01-02 1.4337 BOSTON SUFFOLK
#> 137785 2010-01-03 -1.4163 BOSTON SUFFOLK
#> 137786 2010-01-04 -0.4483 BOSTON SUFFOLK
#> 137787 2010-01-05 0.6565 BOSTON SUFFOLK
#> 137788 2010-01-06 1.2098 BOSTON SUFFOLK
# convert tmax_C into a factor
boston_exposure$tmax_C_fct <- as.numeric(cut(boston_exposure$tmax_C,
breaks = c(-50, 25, 30, 50),
include.lowest = T))
boston_exposure$tmax_C_fct
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [25] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [49] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1
#> [73] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
#> [97] 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3
#> [121] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 3 3
#> [145] 1 2 2 2 1 2 2 2 2 3 1 1 1 1 1 1 1 1 2 2 1 1 3 3
#> [169] 3 3 2 2 3 2 2 2 3 3 2 1 2 3 3 3 3 3 3 3 2 3 3 1
#> [193] 2 3 3 3 3 2 3 2 2 3 3 2 3 3 3 2 2 2 2 3 3 3 3 2
#> [217] 3 3 3 3 2 2 2 2 2 3 2 3 2 2 1 1 1 1 2 2 2 3 3 3
#> [241] 3 3 2 2 1 2 2 2 1 1 1 1 1 1 1 NA 1 1 1 1 1 2 1 2
#> [265] 3 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [289] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [313] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [337] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [361] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [385] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [409] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [433] NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
#> [481] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 3 2 3
#> [505] 3 2 3 1 1 1 2 3 3 3 1 1 1 1 NA 1 3 1 2 2 2 2 1 1
#> [529] 1 1 2 2 2 2 2 2 2 2 3 3 3 2 2 2 2 3 3 3 2 2 3 3
#> [553] 2 3 3 3 3 3 3 1 2 2 2 3 NA 3 3 2 2 2 2 2 2 2 2 2
#> [577] 1 1 1 2 3 3 3 3 1 2 2 NA 2 2 1 2 2 2 2 1 2 2 2 1
#> [601] 1 1 2 1 1 2 2 2 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 1
#> [625] 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA
#> [649] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1
#> [673] 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [697] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [721] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [745] 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [769] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
#> [793] 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [817] 1 2 3 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [841] 1 1 1 1 2 NA 1 1 1 1 1 2 2 1 1 2 2 1 2 1 1 1 2 2
#> [865] 1 1 1 1 1 1 1 2 2 2 1 2 1 1 1 1 1 1 1 3 3 3 3 2
#> [889] 1 1 1 2 3 3 3 2 2 3 2 3 2 3 2 2 3 3 3 3 3 3 3 3
#> [913] 2 1 2 3 3 3 2 2 1 2 1 2 2 2 3 3 2 2 3 3 2 2 2 2
#> [937] 2 2 2 3 1 1 2 2 2 3 2 2 2 2 2 1 2 3 2 2 2 1 2 2
#> [961] 3 2 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [985] 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1009] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1
#> [1033] 1 NA 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1
#> [1057] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1081] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1105] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1129] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1153] NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1177] 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1
#> [1201] 2 1 1 1 1 1 2 1 1 1 2 1 1 1 1 NA 1 1 1 1 3 3 3 3
#> [1225] 2 1 1 1 1 1 NA 1 1 1 1 1 2 1 2 1 1 2 2 2 3 3 3 2
#> [1249] 1 2 2 2 2 2 3 3 3 3 1 2 2 2 2 3 3 3 3 3 NA 2 2 2
#> [1273] 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 NA 2 2
#> [1297] 2 2 2 3 2 2 2 2 2 2 1 2 2 2 2 2 2 1 1 2 1 1 1 3
#> [1321] 3 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
#> [1345] 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1369] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1393] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1417] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1441] 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1465] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1
#> [1489] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1513] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1
#> [1537] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1
#> [1561] 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 1 2
#> [1585] 2 NA 2 2 1 1 1 1 2 2 3 2 2 1 1 1 2 2 3 2 2 2 3 NA
#> [1609] 3 3 3 1 2 2 3 3 3 2 2 2 2 3 3 1 2 2 1 1 2 3 3 2
#> [1633] 2 2 2 2 2 2 2 2 1 1 2 3 2 1 2 2 2 3 2 1 1 1 1 2
#> [1657] 1 2 2 1 1 1 2 3 3 3 2 1 2 2 3 3 2 2 3 1 1 1 1 1
#> [1681] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1
#> [1705] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1729] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1753] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1777] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1801] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1825] 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1849] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1873] 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [1897] 1 1 NA 1 1 1 1 1 1 1 1 2 2 1 2 1 2 3 1 2 1 1 1 NA
#> [1921] 2 1 1 1 1 1 1 2 2 3 2 3 2 2 1 1 1 1 1 1 1 1 1 2
#> [1945] 2 2 2 2 1 1 1 2 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 1
#> [1969] 2 2 2 2 2 2 2 3 2 2 1 2 2 3 3 3 2 2 1 1 2 2 3 3
#> [1993] 3 2 2 2 3 2 2 2 2 2 1 2 1 2 2 2 3 3 NA 3 3 2 2 2
#> [2017] 1 2 2 2 2 2 2 2 3 2 3 3 1 2 3 3 3 3 1 NA 2 1 1 2
#> [2041] 2 3 3 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1
#> [2065] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2089] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2113] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2137] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2161] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2185] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2209] 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2233] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1
#> [2257] 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
#> [2281] 2 1 1 1 1 1 1 1 1 2 1 3 2 2 3 1 2 2 1 1 1 2 1 2
#> [2305] 2 1 1 1 1 1 1 NA 2 2 2 2 3 3 2 1 2 2 2 3 2 2 2 2
#> [2329] 2 2 2 3 2 3 2 1 1 1 2 3 3 2 3 3 3 3 2 2 3 3 3 3
#> [2353] 3 3 3 2 3 1 1 2 2 3 3 3 3 3 2 3 3 2 3 2 2 2 3 2
#> [2377] 2 1 2 3 2 3 3 2 2 2 2 2 2 NA 1 1 1 1 2 3 2 2 1 2
#> [2401] 3 1 1 2 2 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2425] 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2449] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2473] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2497] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2521] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2545] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2569] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2593] 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 1 1 1 1 1 1 1
#> [2617] 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3
#> [2641] 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3
#> [2665] 3 3 2 2 1 1 2 3 2 2 2 3 2 2 2 1 2 2 3 2 2 2 2 NA
#> [2689] 2 2 2 2 3 1 1 2 3 3 2 3 3 3 2 1 1 1 2 1 2 1 2 2
#> [2713] 3 2 2 2 2 1 1 1 2 3 2 2 2 2 1 2 2 1 2 2 2 3 2 2
#> [2737] 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2
#> [2761] 1 1 1 1 1 2 3 3 2 3 1 1 1 1 1 2 2 1 2 1 1 1 1 1
#> [2785] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2809] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2833] 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2857] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2881] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2905] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA
#> [2929] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2953] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [2977] 1 1 1 1 1 1 3 3 2 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1
#> [3001] 2 2 1 2 1 3 3 1 1 3 2 2 2 2 1 1 1 1 1 2 NA 2 1 2
#> [3025] 2 1 1 2 3 2 2 NA 2 1 2 1 2 1 1 3 3 3 3 3 3 3 2 2
#> [3049] 2 3 3 2 2 2 2 2 3 3 2 2 2 2 2 2 3 2 3 3 3 2 2 3
#> [3073] 2 3 3 2 3 3 3 3 3 2 1 1 1 2 2 3 2 2 1 1 2 1 2 2
#> [3097] 3 3 3 2 1 1 2 3 3 3 3 1 1 1 1 1 1 1 2 1 3 2 1 1
#> [3121] 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 NA 1 1 1 2 2 1 1 1
#> [3145] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3169] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3193] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3217] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3241] 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1
#> [3265] 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1
#> [3289] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3313] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3337] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3361] 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1
#> [3385] 1 2 1 2 1 1 1 1 2 2 2 1 2 2 3 2 1 2 2 3 3 3 3 2
#> [3409] 2 3 3 2 2 2 2 2 2 3 1 3 3 3 2 1 2 2 2 2 3 3 3 3
#> [3433] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 2 3 2 1
#> [3457] 1 1 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 2 1 1 1 2 1 1
#> [3481] 1 1 2 2 2 3 1 1 2 1 2 1 1 1 1 1 1 1 1 2 1 1 1 1
#> [3505] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3529] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3553] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3577] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3601] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3625] 1 1 NA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3649] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3673] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3697] 1 1 1 1 1 1 NA 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1
#> [3721] 1 1 1 1 2 2 1 1 1 2 3 2 2 2 1 1 1 2 2 3 1 2 1 1
#> [3745] 1 2 1 1 1 1 2 3 3 3 2 3 2 2 2 2 2 1 1 2 3 1 2 2
#> [3769] 2 2 3 3 2 3 3 2 2 1 1 1 3 3 3 3 2 3 2 3 3 3 3 3
#> [3793] 3 3 3 2 3 2 3 2 2 3 3 3 3 3 3 2 1 1 2 2 2 2 3 2
#> [3817] 3 3 3 1 1 2 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1
#> [3841] 1 1 1 1 1 1 2 2 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2
#> [3865] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1
#> [3889] 1 1 1 1 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1 1 1 1
#> [3913] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#> [3937] 1 1 1 1 1 1 1 1 1
# remove old exposure column
boston_exposure$tmax_C <- NULL
# create exposure matrix
exposure_columns <- list(
"date" = "date",
"exposure" = "tmax_C_fct",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
boston_exposure_mat <- make_exposure_matrix(boston_exposure, exposure_columns)
#> Warning in make_exposure_matrix(boston_exposure, exposure_columns): check about any NA, some corrections for this later,
#> but only in certain columnsOUTCOME
Outcome processing should be the same
boston_deaths <- subset(ma_deaths, TOWN20 == 'BOSTON')
head(boston_deaths)
#> date TOWN20 daily_deaths age_grp sex COUNTY20
#> <Date> <char> <int> <char> <char> <char>
#> 1: 2010-05-01 BOSTON 385 0-17 M SUFFOLK
#> 2: 2010-05-02 BOSTON 367 0-17 M SUFFOLK
#> 3: 2010-05-03 BOSTON 431 0-17 M SUFFOLK
#> 4: 2010-05-04 BOSTON 431 0-17 M SUFFOLK
#> 5: 2010-05-05 BOSTON 456 0-17 M SUFFOLK
#> 6: 2010-05-06 BOSTON 400 0-17 M SUFFOLK
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = 'age_grp',
"factor" = 'sex',
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
boston_deaths_tbl <- make_outcome_table(boston_deaths, outcome_columns)
head(boston_deaths_tbl)
#> date TOWN20 COUNTY20 daily_deaths strata
#> <IDat> <char> <char> <int> <char>
#> 1: 2010-05-01 BOSTON SUFFOLK 2238 BOSTON:yr2010:mn05:dow07
#> 2: 2010-05-02 BOSTON SUFFOLK 2089 BOSTON:yr2010:mn05:dow01
#> 3: 2010-05-03 BOSTON SUFFOLK 2374 BOSTON:yr2010:mn05:dow02
#> 4: 2010-05-04 BOSTON SUFFOLK 2354 BOSTON:yr2010:mn05:dow03
#> 5: 2010-05-05 BOSTON SUFFOLK 2489 BOSTON:yr2010:mn05:dow04
#> 6: 2010-05-06 BOSTON SUFFOLK 2191 BOSTON:yr2010:mn05:dow05
#> strata_total match_strata
#> <num> <char>
#> 1: 11312 BOSTON:2010-05-01
#> 2: 10929 BOSTON:2010-05-02
#> 3: 11435 BOSTON:2010-05-03
#> 4: 9372 BOSTON:2010-05-04
#> 5: 9193 BOSTON:2010-05-05
#> 6: 8657 BOSTON:2010-05-06MODEL
You need to take care in setting breaks. if factor is 1,2,3 breaks should be 1.5, 2.5
# run the model
m1 <- condPois_1stage(
exposure_matrix = boston_exposure_mat,
outcomes_tbl = boston_deaths_tbl,
argvar = list(fun = 'strata', breaks = c(1.5, 2.5))
)
#>
#> crossbasis args:
#>
#> maxlag: 5
#>
#> argvar:
#> List of 2
#> $ fun : chr "strata"
#> $ breaks: num [1:2] 1.5 2.5
#>
#> arglag:
#> List of 2
#> $ fun : chr "ns"
#> $ knots: num [1:2] 0.878 2.095
#>
#> strata:
#> BOSTON:yr2010:mn05:dow07
#> strata_min: 0
plot(m1)