Extract PCLM-2D Deviance Residuals
Usage
# S3 method for class 'pclm2D'
residuals(object, ...)Examples
Dx <- ungroup.data$Dx[, 1:10]
# Aggregate data to ungroup it in the example below
x <- c(0, 1, seq(5, 85, by = 5))
nlast <- 26
n <- c(diff(x), nlast)
group <- rep(x, n)
y <- aggregate(Dx, by = list(group), FUN = "sum")[, -1]
# Example
P1 <- pclm2D(x, y, nlast)
#> Ungrouping data
residuals(P1)
#> 1980 1981 1982 1983 1984 1985
#> [0,1) 81.312576 95.686239 100.20803 125.084286 86.082352 149.600160
#> [1,5) -155.367332 -169.506678 -125.65420 -137.622218 -143.620595 -117.177123
#> [5,10) 65.887221 57.088615 50.20705 55.429327 42.893897 40.700208
#> [10,15) 2.146195 -4.142730 -34.56040 -26.059794 -6.599258 -13.142998
#> [15,20) -9.229870 16.343544 -13.30634 16.178922 -19.835060 -4.978672
#> [20,25) 21.980314 9.421463 -40.59738 -2.500662 1.262400 -7.757827
#> [25,30) 41.107851 -24.374348 3.26980 12.634879 17.214362 -32.571806
#> [30,35) 16.932056 -26.921423 -22.80252 -45.303163 -12.083816 5.136224
#> [35,40) -13.321378 -9.094032 92.12480 4.308084 40.294327 35.832854
#> [40,45) 19.250121 -72.244488 -48.81181 -13.194314 17.524511 -14.879096
#> [45,50) 58.724087 49.390747 10.88812 -35.887869 -67.854654 -58.397471
#> [50,55) -23.387153 -17.166428 -18.53549 88.118484 -44.471979 8.792365
#> [55,60) 50.153325 -24.089565 18.71250 -181.609595 -56.753161 46.140846
#> [60,65) -56.994335 54.305083 132.55193 108.555825 259.333449 172.242583
#> [65,70) -135.387122 23.459841 -40.30626 -265.443035 -563.523160 -49.325029
#> [70,75) -18.983825 448.259376 229.21654 115.024731 -110.641818 83.609802
#> [75,80) -152.892634 -49.423762 -188.59577 -270.861844 16.963918 352.580599
#> [80,85) -48.257638 267.919469 79.58150 5.196754 -293.497802 182.069393
#> [85,111) -101.063705 368.548993 -280.20263 127.934643 -532.594448 481.242048
#> 1986 1987 1988 1989
#> [0,1) 79.705691 99.151542 85.367064 69.428248
#> [1,5) -105.091366 -115.415590 -129.460354 -138.825641
#> [5,10) 41.914908 33.575476 38.691475 30.243359
#> [10,15) 10.338312 -7.133098 -1.542695 -13.884174
#> [15,20) -23.886561 -3.210832 -10.638742 33.086136
#> [20,25) 20.004227 12.136205 45.245598 -30.052031
#> [25,30) -1.355760 13.195032 5.390816 3.529469
#> [30,35) -37.413296 4.452748 15.879900 -14.021910
#> [35,40) 48.618805 -39.680138 -13.404218 -18.888900
#> [40,45) -8.612828 -11.747284 88.870843 -22.333360
#> [45,50) 43.890729 -1.590522 42.702845 -51.434708
#> [50,55) -20.439122 65.170062 13.745533 5.276019
#> [55,60) -39.452825 42.595710 41.095334 30.546074
#> [60,65) -129.016762 -137.516829 16.687265 -348.723251
#> [65,70) 138.412891 -20.085430 498.001264 330.485308
#> [70,75) -7.701461 -66.245364 -17.867757 -562.157391
#> [75,80) -153.427268 -79.797604 431.579425 25.343771
#> [80,85) -63.772519 -351.827442 515.000615 -268.506225
#> [85,111) -135.741049 -337.161924 841.895060 -431.439798