I am trying to plot the model predictions using ggplot2 in r of the quasipoisson glm model below. Some sample data is given below:
Rabbit_ACT = structure(list(Occurrence_ID = c(628437L, 668157L, 658431L, 623576L,
683597L, 629915L, 682595L, 683739L, 683502L, 645019L, 627926L,
630368L, 630595L, 629778L, 628486L, 678084L, 25909L, 683462L,
630460L, 658385L, 167L, 733L, 627921L, 628208L, 627820L, 629402L,
654144L, 679478L, 660806L, 628352L, 684863L, 629369L, 628186L,
679270L, 679285L, 654213L, 627689L, 669989L, 629540L, 629117L,
630666L, 629645L, 653212L, 629375L, 686819L, 662562L, 644488L,
683672L, 648112L, 337L, 688L, 630133L, 629846L, 683339L, 679402L,
670064L, 628215L, 628952L, 763L, 648919L, 628507L, 679518L, 650769L,
648981L, 648261L, 684892L, 630539L, 650628L, 628598L, 683627L,
628098L, 679430L, 679298L, 628815L, 668723L, 630227L, 683776L,
222L, 629792L, 668887L, 669247L, 688104L, 660411L, 630734L, 627837L,
682613L, 484L, 628584L, 659998L, 628792L, 629186L, 668881L, 630721L,
630346L, 661689L, 630637L, 627809L, 684913L, 688100L, 689276L
), Lat = c(-32.25, -37.65, -38.35, -25.75, -37.45, -32.25, -40.95,
-37.25, -38.25, -33.95, -32.25, -32.25, -32.25, -32.25, -32.25,
-32.95, -37.65, -37.35, -32.25, -37.45, -31.95, -22.64, -32.25,
-32.25, -32.25, -32.25, -38.05, -34.55, -37.45, -32.25, -36.25,
-32.25, -32.25, -37.45, -37.35, -37.25, -32.25, -24.55, -32.25,
-32.25, -32.25, -32.25, -36.65, -32.25, -37.65, -34.55, -34.85,
-37.25, -36.65, -31.95, -27.45, -32.25, -32.25, -38.45, -36.85,
-24.75, -32.25, -32.25, -20.75, -37.75, -32.25, -35.65, -35.35,
-36.85, -31.55, -36.75, -32.25, -38.25, -32.25, -37.95, -32.25,
-35.85, -37.45, -32.25, -42.25, -32.25, -37.85, -31.95, -32.25,
-38.25, -35.15, -36.65, -29.65, -32.25, -32.25, -33.65, -33.55,
-32.25, -37.55, -32.25, -32.25, -38.05, -32.25, -32.25, -34.45,
-32.25, -32.25, -36.35, -36.55, -32.55), Long = c(138.75, 145.15,
146.15, 118.85, 146.85, 138.75, 148.05, 149.35, 147.25, 147.95,
138.75, 138.75, 138.75, 138.75, 138.75, 146.15, 144.35, 146.95,
138.75, 141.15, 116.35, 113.68, 138.75, 138.75, 138.75, 138.75,
141.15, 147.45, 149.95, 138.75, 147.65, 138.75, 138.75, 149.95,
149.95, 143.25, 138.75, 134.25, 138.75, 138.75, 138.75, 138.75,
146.45, 138.75, 145.25, 147.45, 149.05, 147.95, 149.45, 116.35,
114.55, 138.75, 138.75, 145.55, 149.05, 133.65, 138.75, 138.75,
118.85, 144.25, 138.75, 148.65, 138.75, 141.65, 129.15, 146.45,
138.75, 145.75, 138.75, 147.25, 138.75, 147.55, 149.95, 138.75,
146.95, 138.75, 146.05, 116.35, 138.75, 147.25, 141.35, 141.65,
152.95, 138.75, 138.75, 115.95, 115.05, 138.75, 148.25, 138.75,
138.75, 147.65, 138.75, 138.75, 147.45, 138.75, 138.75, 146.25,
141.55, 149.55), Occurences = c(5121L, 111L, 5L, 1L, 5L, 5121L,
7L, 4L, 15L, 15L, 5121L, 5121L, 5121L, 5121L, 5121L, 8L, 17410L,
8L, 5121L, 12L, 259L, 2L, 5121L, 5121L, 5121L, 5121L, 1L, 46L,
66L, 5121L, 6L, 5121L, 5121L, 66L, 22L, 11L, 5121L, 16L, 5121L,
5121L, 5121L, 5121L, 3L, 5121L, 95L, 46L, 12L, 3L, 109L, 259L,
1L, 5121L, 5121L, 14L, 4L, 21L, 5121L, 5121L, 1L, 849L, 5121L,
8L, 8L, 5L, 7L, 2L, 5121L, 4L, 5121L, 15L, 5121L, 6L, 66L, 5121L,
1L, 5121L, 7L, 259L, 5121L, 15L, 3209L, 6L, 8L, 5121L, 5121L,
24L, 95L, 5121L, 8L, 5121L, 5121L, 6L, 5121L, 5121L, 8L, 5121L,
5121L, 4L, 6L, 5L), Abund.1 = c(5121L, 111L, 5L, 1L, 5L, 5121L,
7L, 4L, 15L, 15L, 5121L, 5121L, 5121L, 5121L, 5121L, 8L, 17410L,
8L, 5121L, 12L, 259L, 2L, 5121L, 5121L, 5121L, 5121L, 1L, 46L,
66L, 5121L, 6L, 5121L, 5121L, 66L, 22L, 11L, 5121L, 16L, 5121L,
5121L, 5121L, 5121L, 3L, 5121L, 95L, 46L, 12L, 3L, 109L, 259L,
1L, 5121L, 5121L, 14L, 4L, 21L, 5121L, 5121L, 1L, 849L, 5121L,
8L, 8L, 5L, 7L, 2L, 5121L, 4L, 5121L, 15L, 5121L, 6L, 66L, 5121L,
1L, 5121L, 7L, 259L, 5121L, 15L, 3209L, 6L, 8L, 5121L, 5121L,
24L, 95L, 5121L, 8L, 5121L, 5121L, 6L, 5121L, 5121L, 8L, 5121L,
5121L, 4L, 6L, 5L), Abund.2 = c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_),
Abund.3 = c(256.05, 0.001727626, 7.78e-05, 0.000769231, 7.78e-05,
256.05, 0.000108949, 6.23e-05, 0.000233463, 0.000233463,
256.05, 256.05, 256.05, 256.05, 256.05, 0.000124514, 1741,
0.000124514, 256.05, 0.00018677, 25.9, 0.000961538, 256.05,
256.05, 256.05, 256.05, 1.56e-05, 0.000715953, 0.001027237,
256.05, 9.34e-05, 256.05, 256.05, 0.001027237, 0.000342412,
0.000171206, 256.05, 0.000249027, 256.05, 256.05, 256.05,
256.05, 4.67e-05, 256.05, 0.001478599, 0.000715953, 0.00018677,
4.67e-05, 0.001696498, 25.9, 0.000480769, 256.05, 256.05,
0.000217899, 6.23e-05, 0.000326848, 256.05, 256.05, 0.000480769,
0.013214008, 256.05, 0.000124514, 0.000124514, 7.78e-05,
0.000108949, 3.11e-05, 256.05, 6.23e-05, 256.05, 0.000233463,
256.05, 9.34e-05, 0.001027237, 256.05, 1.56e-05, 256.05,
0.000108949, 25.9, 256.05, 0.000233463, 0.049945525, 9.34e-05,
0.000124514, 256.05, 256.05, 0.000373541, 9.5, 256.05, 0.000124514,
256.05, 256.05, 9.34e-05, 256.05, 256.05, 0.000124514, 256.05,
256.05, 6.23e-05, 9.34e-05, 0.005681818), No.of.10km.cells = c(2L,
6425L, 6425L, 130L, 6425L, 2L, 6425L, 6425L, 6425L, 6425L,
2L, 2L, 2L, 2L, 2L, 6425L, 1L, 6425L, 2L, 6425L, 1L, 208L,
2L, 2L, 2L, 2L, 6425L, 6425L, 6425L, 2L, 6425L, 2L, 2L, 6425L,
6425L, 6425L, 2L, 6425L, 2L, 2L, 2L, 2L, 6425L, 2L, 6425L,
6425L, 6425L, 6425L, 6425L, 1L, 208L, 2L, 2L, 6425L, 6425L,
6425L, 2L, 2L, 208L, 6425L, 2L, 6425L, 6425L, 6425L, 6425L,
6425L, 2L, 6425L, 2L, 6425L, 2L, 6425L, 6425L, 2L, 6425L,
2L, 6425L, 1L, 2L, 6425L, 6425L, 6425L, 6425L, 2L, 2L, 6425L,
1L, 2L, 6425L, 2L, 2L, 6425L, 2L, 2L, 6425L, 2L, 2L, 6425L,
6425L, 88L), Year = c(1972L, 1980L, 1967L, 1981L, 1978L,
1979L, 1965L, 1977L, 1978L, 1978L, 1969L, 1981L, 1982L, 1978L,
1972L, 1981L, 1982L, 1978L, 1981L, 1979L, 1974L, 1981L, 1969L,
1970L, 1969L, 1976L, 1980L, 1981L, 1971L, 1971L, 1982L, 1976L,
1970L, 1980L, 1980L, 1975L, 1968L, 1980L, 1977L, 1975L, 1982L,
1977L, 1981L, 1976L, 1979L, 1982L, 1982L, 1977L, 1900L, 1974L,
1981L, 1980L, 1978L, 1981L, 1981L, 1980L, 1970L, 1974L, 1981L,
1969L, 1972L, 1964L, 1968L, 1980L, 1950L, 1981L, 1981L, 1981L,
1972L, 1978L, 1970L, 1979L, 1980L, 1973L, 1979L, 1980L, 1967L,
1974L, 1978L, 1978L, 1980L, 1978L, 1978L, 1982L, 1969L, 1966L,
1975L, 1972L, 1979L, 1973L, 1975L, 1978L, 1982L, 1980L, 1975L,
1982L, 1969L, 1978L, 1978L, 1883L), Day = c(19L, NA, NA,
NA, NA, 1L, NA, NA, NA, NA, 9L, 4L, 24L, 21L, 29L, NA, 15L,
NA, 22L, NA, 9L, NA, 9L, 15L, 10L, 8L, NA, NA, NA, 15L, NA,
9L, 3L, NA, NA, NA, 12L, NA, 6L, 26L, 1L, 16L, NA, 28L, NA,
NA, NA, NA, NA, 1L, NA, 24L, 13L, NA, NA, NA, 15L, 16L, NA,
NA, 3L, NA, NA, NA, NA, NA, 26L, NA, 12L, NA, 17L, NA, NA,
31L, NA, 10L, NA, 13L, 21L, NA, NA, NA, NA, 10L, 29L, NA,
28L, 12L, NA, 12L, 31L, NA, 23L, 18L, NA, 20L, 10L, NA, NA,
NA), A_Prec_Avg30Yr = c(299.76, 708.74, NA, 246.95, 1176.45,
299.76, NA, 875.46, 579.65, 580.43, NA, 299.76, 299.76, 299.76,
299.76, 382.24, 575.45, 832.4, 299.76, 623.53, 732.58, 243.46,
NA, 299.76, NA, 299.76, 784.35, 542.49, 909.03, 299.76, 1003.98,
299.76, 299.76, 909.03, 902.49, 803.54, NA, 243.56, 299.76,
299.76, 299.76, 299.76, 958.2, 299.76, 769.46, 542.49, 668.97,
793.91, NA, 732.58, 308.18, 299.76, 299.76, 959.99, 511.97,
241.04, 299.76, 299.76, 354.01, NA, 299.76, NA, NA, 502.69,
NA, 1099.01, 299.76, 961.28, 299.76, 643.55, 299.76, 964.26,
909.03, 299.76, 602.15, 299.76, NA, 732.58, 299.76, 579.65,
299.88, 449.9, 1025.68, 299.76, NA, NA, 854.37, 299.76, 849.48,
299.76, 299.76, 594.14, 299.76, 299.76, 529.02, 299.76, NA,
618.71, 440.67, NA), A_Psea_Avg30Yr = c(21.19, 16.34, NA,
54.98, 27.29, 21.19, NA, 15.32, 12.77, 14.38, NA, 21.19,
21.19, 21.19, 21.19, 16.01, 20.69, 21.15, 21.19, 44.64, 79.51,
74.18, NA, 21.19, NA, 21.19, 44.32, 16.54, 13.41, 21.19,
34.18, 21.19, 21.19, 13.41, 13.36, 32.69, NA, 48.96, 21.19,
21.19, 21.19, 21.19, 34.46, 21.19, 18.09, 16.54, 15.76, 16.55,
NA, 79.51, 74.64, 21.19, 21.19, 25.09, 24.33, 47.77, 21.19,
21.19, 116.17, NA, 21.19, NA, NA, 41.58, NA, 36.53, 21.19,
22.21, 21.19, 16.23, 21.19, 31.83, 13.41, 21.19, 16.98, 21.19,
NA, 79.51, 21.19, 12.77, 29.83, 39.68, 46.99, 21.19, NA,
NA, 85.12, 21.19, 16.45, 21.19, 21.19, 14.12, 21.19, 21.19,
15.36, 21.19, NA, 26.3, 39.78, NA), A_TAvg_Avg30Yr = c(15.86,
14.33, NA, 22.5, 8.94, 15.86, NA, 13.06, 14.17, 15.84, NA,
15.86, 15.86, 15.86, 15.86, 17.91, 13.37, 11.39, 15.86, 14,
17.45, 24.34, NA, 15.86, NA, 15.86, 13.67, 15.45, 14.9, 15.86,
12.63, 15.86, 15.86, 14.9, 14.43, 11.96, NA, 21.66, 15.86,
15.86, 15.86, 15.86, 14.1, 15.86, 14.11, 15.45, 13.52, 10.54,
NA, 17.45, 21.1, 15.86, 15.86, 13.62, 11.55, 21.7, 15.86,
15.86, 27.13, NA, 15.86, NA, NA, 14.15, NA, 13.42, 15.86,
13.77, 15.86, 14.38, 15.86, 12.94, 14.9, 15.86, 9.31, 15.86,
NA, 17.45, 15.86, 14.17, 16.34, 14.37, 19.26, 15.86, NA,
NA, 16.52, 15.86, 14.32, 15.86, 15.86, 14.65, 15.86, 15.86,
15.7, 15.86, NA, 15.01, 14.45, NA), A_TMax_Avg30Yr = c(31.24,
27.18, NA, 38.82, 20.86, 31.24, NA, 24.84, 23.85, 31.86,
NA, 31.24, 31.24, 31.24, 31.24, 33.59, 26.82, 24.4, 31.24,
27.36, 32.04, 35.4, NA, 31.24, NA, 31.24, 23.95, 31.32, 23.56,
31.24, 27.9, 31.24, 31.24, 23.56, 22.98, 25.55, NA, 37.66,
31.24, 31.24, 31.24, 31.24, 29.76, 31.24, 27.02, 31.32, 28.48,
22.81, NA, 32.04, 36.93, 31.24, 31.24, 24.84, 25.04, 37.64,
31.24, 31.24, 39.96, NA, 31.24, NA, NA, 28.87, NA, 28.69,
31.24, 25.6, 31.24, 26.1, 31.24, 28.24, 23.56, 31.24, 21.03,
31.24, NA, 32.04, 31.24, 23.85, 31.84, 29.34, 29.89, 31.24,
NA, NA, 27.39, 31.24, 26.09, 31.24, 31.24, 23.6, 31.24, 31.24,
31.59, 31.24, NA, 30.88, 29.41, NA), A_TMin_Avg30Yr = c(2.78,
4.59, NA, 5.97, -0.13, 2.78, NA, 2.26, 4.92, 2.52, NA, 2.78,
2.78, 2.78, 2.78, 3.92, 3.06, 1.29, 2.78, 4.71, 6.65, 12.96,
NA, 2.78, NA, 2.78, 5.31, 2.15, 6.09, 2.78, 1.06, 2.78, 2.78,
6.09, 5.65, 2.81, NA, 3.81, 2.78, 2.78, 2.78, 2.78, 2.5,
2.78, 4.48, 2.15, 0.74, 0.82, NA, 6.65, 7.76, 2.78, 2.78,
4.88, -0.65, 3.92, 2.78, 2.78, 11.98, NA, 2.78, NA, NA, 3.92,
NA, 2.24, 2.78, 4.44, 2.78, 3.67, 2.78, 1.25, 6.09, 2.78,
0.04, 2.78, NA, 6.65, 2.78, 4.92, 4.06, 3.76, 5.94, 2.78,
NA, NA, 8.76, 2.78, 4.15, 2.78, 2.78, 5.85, 2.78, 2.78, 2.2,
2.78, NA, 2.69, 3.8, NA), A_TSea_Avg30Yr = c(563.7, 405.82,
NA, 663.22, 438.27, 563.7, NA, 416.92, 330.39, 588.51, NA,
563.7, 563.7, 563.7, 563.7, 612.28, 416.04, 438.13, 563.7,
376.52, 488.8, 417.94, NA, 563.7, NA, 563.7, 303.9, 596.8,
320.72, 563.7, 537.25, 563.7, 563.7, 320.72, 321.8, 441.32,
NA, 651.46, 563.7, 563.7, 563.7, 563.7, 515.65, 563.7, 404.93,
596.8, 543.86, 422.81, NA, 488.8, 529.02, 563.7, 563.7, 345.9,
468.25, 650.8, 563.7, 563.7, 479.28, NA, 563.7, NA, NA, 438.45,
NA, 505.71, 563.7, 363, 563.7, 380.24, 563.7, 550.8, 320.72,
563.7, 358.72, 563.7, NA, 488.8, 563.7, 330.39, 501.96, 449.81,
412.56, 563.7, NA, NA, 320.65, 563.7, 380.63, 563.7, 563.7,
310.08, 563.7, 563.7, 598.64, 563.7, NA, 544.19, 449.76,
NA), A_TWet_Avg30Yr = c(12.47, 13.7, NA, 27.06, 8.19, 12.47,
NA, 12.71, 13.56, 9.6, NA, 12.47, 12.47, 12.47, 12.47, 11.27,
12.65, 10.17, 12.47, 9.54, 11.75, 20.15, NA, 12.47, NA, 12.47,
9.98, 9.15, 15.87, 12.47, 6.08, 12.47, 12.47, 15.87, 15.37,
6.7, NA, 28.21, 12.47, 12.47, 12.47, 12.47, 7.95, 12.47,
13.25, 9.15, 9.98, 10.06, NA, 11.75, 15.99, 12.47, 12.47,
9.42, 15.96, 28.25, 12.47, 12.47, 31.83, NA, 12.47, NA, NA,
8.96, NA, 7.37, 12.47, 11.34, 12.47, 13.91, 12.47, 6.26,
15.87, 12.47, 7.09, 12.47, NA, 11.75, 12.47, 13.56, 10.2,
9.03, 23.67, 12.47, NA, NA, 12.95, 12.47, 13.91, 12.47, 12.47,
15.69, 12.47, 12.47, 9.37, 12.47, NA, 9.05, 9.1, NA), A_TWrm_Avg30Yr = c(22.87,
19.34, NA, 30.4, 14.29, 22.87, NA, 18.1, 18.23, 23.22, NA,
22.87, 22.87, 22.87, 22.87, 25.47, 18.55, 16.76, 22.87, 18.72,
23.51, 29.35, NA, 22.87, NA, 22.87, 17.47, 22.94, 18.85,
22.87, 19.32, 22.87, 22.87, 18.85, 18.38, 17.47, NA, 29.22,
22.87, 22.87, 22.87, 22.87, 20.61, 22.87, 19.11, 22.94, 20.3,
15.7, NA, 23.51, 27.6, 22.87, 22.87, 17.96, 17.28, 29.25,
22.87, 22.87, 32.23, NA, 22.87, NA, NA, 19.7, NA, 19.78,
22.87, 18.27, 22.87, 18.98, 22.87, 19.84, 18.85, 22.87, 13.74,
22.87, NA, 23.51, 22.87, 18.23, 22.62, 20.06, 24, 22.87,
NA, NA, 20.66, 22.87, 18.93, 22.87, 22.87, 18.45, 22.87,
22.87, 23.21, 22.87, NA, 21.9, 20.13, NA), A_Prec_AvgAutumn30Yr = c(19.88,
57.41, NA, 26.9, 83.64, 19.88, NA, 69.05, 47.86, 39.89, NA,
19.88, 19.88, 19.88, 19.88, 29.31, 42.5, 56.51, 19.88, 45.19,
47.28, 29.61, NA, 19.88, NA, 19.88, 59.33, 39.38, 79.74,
19.88, 67.46, 19.88, 19.88, 79.74, 78.05, 57.85, NA, 20.29,
19.88, 19.88, 19.88, 19.88, 66.85, 19.88, 60.98, 39.38, 48.04,
60.31, NA, 47.28, 27.14, 19.88, 19.88, 75.63, 39.77, 19.61,
19.88, 19.88, 33.34, NA, 19.88, NA, NA, 35.13, NA, 73.9,
19.88, 73.36, 19.88, 54.09, 19.88, 67.42, 79.74, 19.88, 45.96,
19.88, NA, 47.28, 19.88, 47.86, 21.98, 31.63, 101.12, 19.88,
NA, NA, 59.49, 19.88, 63.58, 19.88, 19.88, 49.92, 19.88,
19.88, 39.45, 19.88, NA, 47.15, 31.11, NA), A_Prec_AvgSummer30Yr = c(25.99,
55.28, NA, 34.05, 77.66, 25.99, NA, 68.99, 47.85, 49.57,
NA, 25.99, 25.99, 25.99, 25.99, 35.61, 49.96, 58.93, 25.99,
29.85, 13.97, 19.69, NA, 25.99, NA, 25.99, 35.63, 44.06,
71.62, 25.99, 61.43, 25.99, 25.99, 71.62, 74.09, 47.48, NA,
32.21, 25.99, 25.99, 25.99, 25.99, 58.55, 25.99, 57.47, 44.06,
56.79, 66.65, NA, 13.97, 12.32, 25.99, 25.99, 54.2, 51.59,
30.96, 25.99, 25.99, 72.09, NA, 25.99, NA, NA, 26.11, NA,
63.04, 25.99, 62.38, 25.99, 54.9, 25.99, 61.34, 71.62, 25.99,
45.53, 25.99, NA, 13.97, 25.99, 47.85, 20.77, 24.7, 131.36,
25.99, NA, NA, 11.64, 25.99, 68.81, 25.99, 25.99, 49.93,
25.99, 25.99, 43.77, 25.99, NA, 45.04, 24.02, NA), A_Prec_AvgSpring30Yr = c(28.46,
70.69, NA, 7.71, 128.29, 28.46, NA, 79.69, 55.23, 54.13,
NA, 28.46, 28.46, 28.46, 28.46, 33.6, 65.37, 83.85, 28.46,
58.34, 51.89, 3.39, NA, 28.46, NA, 28.46, 68.15, 49.5, 74.25,
28.46, 91.9, 28.46, 28.46, 74.25, 74.26, 74.53, NA, 17.47,
28.46, 28.46, 28.46, 28.46, 81.92, 28.46, 76.34, 49.5, 66.25,
83.35, NA, 51.89, 12.17, 28.46, 28.46, 82.69, 49.07, 18.62,
28.46, 28.46, 1.21, NA, 28.46, NA, NA, 47.15, NA, 91.76,
28.46, 93.91, 28.46, 62.61, 28.46, 83.59, 74.25, 28.46, 58.5,
28.46, NA, 51.89, 28.46, 55.23, 30.91, 43.08, 69.22, 28.46,
NA, NA, 55.02, 28.46, 80.65, 28.46, 28.46, 55.46, 28.46,
28.46, 48.66, 28.46, NA, 57.2, 42.15, NA), A_Prec_AvgWinter30Yr = c(26.81,
59.12, NA, 18.1, 103.8, 26.81, NA, 73.29, 45.33, 49.58, NA,
26.81, 26.81, 26.81, 26.81, 30.62, 48.06, 75.28, 26.81, 83.67,
132.13, 32.53, NA, 26.81, NA, 26.81, 107.33, 47.63, 73.48,
26.81, 113.92, 26.81, 26.81, 73.48, 69.56, 89.1, NA, 10.36,
26.81, 26.81, 26.81, 26.81, 106.49, 26.81, 65.63, 47.63,
61.61, 65.62, NA, 132.13, 51.46, 26.81, 26.81, 93.42, 32.25,
11.31, 26.81, 26.81, 12.17, NA, 26.81, NA, NA, 60.19, NA,
121.9, 26.81, 87.78, 26.81, 49.24, 26.81, 105.6, 73.48, 26.81,
55.64, 26.81, NA, 132.13, 26.81, 45.33, 31.48, 52.89, 42.98,
26.81, NA, NA, 148.58, 26.81, 72.5, 26.81, 26.81, 47.51,
26.81, 26.81, 45.99, 26.81, NA, 66.13, 51.99, NA), DistPermWater = c(5.35,
2.93, NA, 12.82, 8.21, 5.35, NA, 2.09, 2.48, 2.08, NA, 5.35,
5.35, 5.35, 5.35, 19.6, 0.41, 2.54, 5.35, 1.04, 7.11, 3.75,
NA, 5.35, NA, 5.35, 1.81, 1.37, 2.36, 5.35, 0.71, 5.35, 5.35,
2.36, 3.15, 0.92, NA, 5.81, 5.35, 5.35, 5.35, 5.35, 0.51,
5.35, 3.27, 1.37, 0.31, 3.59, NA, 7.11, 5.21, 5.35, 5.35,
9.53, 2.99, 5.66, 5.35, 5.35, 15.74, NA, 5.35, NA, NA, 0.52,
NA, 0.61, 5.35, 2.56, 5.35, 2.87, 5.35, 2.34, 2.36, 5.35,
2.41, 5.35, NA, 7.11, 5.35, 2.48, 5.9, 0.48, 0.89, 5.35,
NA, NA, 3.91, 5.35, 3.14, 5.35, 5.35, 0.68, 5.35, 5.35, 0.88,
5.35, NA, 0.32, 1.66, NA), DistAgriLand = c(35.73, 4.7, NA,
362.03, 28.52, 35.73, NA, 6.52, 1.53, 1.74, NA, 35.73, 35.73,
35.73, 35.73, 5.49, 1.33, 42.58, 35.73, 5.82, 6.66, 531.66,
NA, 35.73, NA, 35.73, 7.14, 0.66, 22.83, 35.73, 12.29, 35.73,
35.73, 22.83, 11.03, 8.58, NA, 649.51, 35.73, 35.73, 35.73,
35.73, 3.05, 35.73, 6.96, 0.66, 10.14, 19.12, NA, 6.66, 27.47,
35.73, 35.73, 2.14, 9.5, 687.88, 35.73, 35.73, 861.68, NA,
35.73, NA, NA, 1.92, NA, 6.16, 35.73, 3.27, 35.73, 4.57,
35.73, 3.75, 22.83, 35.73, 4.24, 35.73, NA, 6.66, 35.73,
1.53, 1.57, 1, 3.46, 35.73, NA, NA, 3.2, 35.73, 5.49, 35.73,
35.73, 6.66, 35.73, 35.73, 0.72, 35.73, NA, 3.02, 5.6, NA
), PercSoilClay = c(27.6, 23.22, NA, 29.98, 25.71, 27.6,
NA, 20.54, 8.61, 19.46, NA, 27.6, 27.6, 27.6, 27.6, 29.74,
24.42, 25.06, 27.6, 44.6, 5.96, 7.09, NA, 27.6, NA, 27.6,
22.83, 18.75, 19.83, 27.6, 17.76, 27.6, 27.6, 19.83, 20.01,
15, NA, 10.96, 27.6, 27.6, 27.6, 27.6, 16.19, 27.6, 30.81,
18.75, 15, 20.35, NA, 5.96, 13.33, 27.6, 27.6, 16.08, 26.25,
10.93, 27.6, 27.6, 20, NA, 27.6, NA, NA, 12.46, NA, 20.23,
27.6, 28.55, 27.6, 15.21, 27.6, 19.29, 19.83, 27.6, 15.74,
27.6, NA, 5.96, 27.6, 8.61, 21.61, 29.17, 23.97, 27.6, NA,
NA, 14.86, 27.6, 19.05, 27.6, 27.6, 16.11, 27.6, 27.6, 17.67,
27.6, NA, 14.51, 11.71, NA), MinDayLength = c(10.03, 9.56,
NA, 10.53, 9.57, 10.03, NA, 9.59, 9.5, 9.89, NA, 10.03, 10.03,
10.03, 10.03, 9.97, 9.56, 9.58, 10.03, 9.57, 10.06, 10.74,
NA, 10.03, NA, 10.03, 9.52, 9.84, 9.57, 10.03, 9.69, 10.03,
10.03, 9.57, 9.58, 9.59, NA, 10.61, 10.03, 10.03, 10.03,
10.03, 9.65, 10.03, 9.56, 9.84, 9.81, 9.59, NA, 10.06, 10.4,
10.03, 10.03, 9.48, 9.63, 10.6, 10.03, 10.03, 10.87, NA,
10.03, NA, NA, 9.63, NA, 9.64, 10.03, 9.5, 10.03, 9.53, 10.03,
9.72, 9.57, 10.03, 9.08, 10.03, NA, 10.06, 10.03, 9.5, 9.78,
9.65, 10.24, 10.03, NA, NA, 9.92, 10.03, 9.56, 10.03, 10.03,
9.52, 10.03, 10.03, 9.85, 10.03, NA, 9.68, 9.66, NA), VarDayLength = c(2.14,
3.22, NA, 1.24, 3.18, 2.14, NA, 3.13, 3.37, 2.44, NA, 2.14,
2.14, 2.14, 2.14, 2.26, 3.22, 3.15, 2.14, 3.18, 2.09, 0.92,
NA, 2.14, NA, 2.14, 3.32, 2.55, 3.18, 2.14, 2.9, 2.14, 2.14,
3.18, 3.15, 3.13, NA, 1.11, 2.14, 2.14, 2.14, 2.14, 2.99,
2.14, 3.22, 2.55, 2.61, 3.13, NA, 2.09, 1.44, 2.14, 2.14,
3.42, 3.04, 1.13, 2.14, 2.14, 0.76, NA, 2.14, NA, NA, 3.04,
NA, 3.01, 2.14, 3.37, 2.14, 3.3, 2.14, 2.82, 3.18, 2.14,
4.52, 2.14, NA, 2.09, 2.14, 3.37, 2.67, 2.99, 1.73, 2.14,
NA, NA, 2.37, 2.14, 3.2, 2.14, 2.14, 3.31, 2.14, 2.14, 2.53,
2.14, NA, 2.93, 2.97, NA)), row.names = c(1665L, 6514L, 503L,
7208L, 4855L, 4989L, 411L, 4103L, 4761L, 4327L, 899L, 7332L,
8054L, 4203L, 1714L, 7775L, 7986L, 4721L, 7424L, 5392L, 2292L,
6992L, 894L, 1234L, 793L, 3365L, 6434L, 7836L, 1616L, 1497L,
8606L, 3332L, 1212L, 6651L, 6656L, 3188L, 590L, 6553L, 3669L,
3007L, 8125L, 3774L, 7634L, 3338L, 5797L, 8372L, 8238L, 4037L,
165L, 2462L, 6947L, 5999L, 4271L, 7861L, 7822L, 6618L, 1241L,
2640L, 7022L, 995L, 1735L, 382L, 690L, 6361L, 284L, 7896L, 7503L,
7581L, 1826L, 4885L, 1124L, 5671L, 6658L, 2140L, 5580L, 6093L,
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3200L, 8096L, 782L, 4896L, 4960L, 108L), class = "data.frame")
Here is the fully simplified model that I fitted to the data
final_ACT2 = glm(Occurences ~ A_Prec_Avg30Yr DistAgriLand DistPermWater A_TAvg_Avg30Yr,
family = quasipoisson(link = "log"), data = Rabbit_ACT)
I also wanted to plot 95% confidence intervals around the fitted line and used a function from a different question given below:
pois_ci = function(pred, data){
fit = pred$fit
lower = fit - 1.96*pred$se.fit
upper = fit 1.96*pred$se.fit
return(data.frame(fit = exp(fit), lwr = exp(lower), upr = exp(upper), data))
}
To plot the predictions I used the following code:
ACT_rain = data.frame(A_Prec_Avg30Yr =
seq(from = min(Rabbit_ACT$A_Prec_Avg30Yr, na.rm = TRUE),
to = max(Rabbit_ACT$A_Prec_Avg30Yr, na.rm = TRUE),
length.out = length(Rabbit_ACT$A_Prec_Avg30Yr)),
DistAgriLand = Rabbit_ACT$DistAgriLand,
DistPermWater = Rabbit_ACT$DistPermWater,
A_TAvg_Avg30Yr = Rabbit_ACT$A_TAvg_Avg30Yr,
Occurences = Rabbit_ACT$Occurences)
preds.ACT_rain = predict(final_ACT2, newdata = ACT_rain, se.fit = TRUE, type = "link")
preds.ACT_rain.ci = pois_ci(preds.ACT_rain, ACT_rain)
ACT.a = ggplot(data = na.omit(preds.ACT_rain.ci))
geom_point(aes(x = A_Prec_Avg30Yr, y = Occurences), alpha = 0.3)
geom_line(aes(x = A_Prec_Avg30Yr, fit))
geom_ribbon(aes(ymin = lwr, ymax = upr, x = A_Prec_Avg30Yr, y = Occurences))
labs(x = "Mean Average 30yr AVerage Percipitation (mL)", "No.of Rabbit Occurences")
ylim(0, 5000)
theme_classic()
ACT.a
But the output I got was a graph where the fitted line was zig-zagging between data points when it should be a smooth line but I don't know what I have done that is causing this. I know there are NA values in the data set but unsure if this is causing the issue. I have tried looking at other questions but I was struggling to understand what was going on and would be grateful if anyone has a solution. Thanks in advance.
Heres the plot that was produced (not it was done using the full dataset)
CodePudding user response：
There are two things causing this.
- You're adding the observed values of
Occurencesto your prediction dataset, then using those observed values as youryaesthetic ingeom_ribbon(). You should instead sety = fitto use the predicted values. - You're generating predictions using the observed values of your covariates, which will introduce noise. Instead, set each covariate at its mean in your prediction set.
Also - are you trying to plot the observed values using geom_point()? If so, set data = Rabbit_ACT to use the original data there for that geom. Otherwise, to plot the predicted values, change the mapping to y = fit there as well.
library(ggplot2)
final_ACT2 = glm(Occurences ~ A_Prec_Avg30Yr DistAgriLand DistPermWater A_TAvg_Avg30Yr,
family = quasipoisson(link = "log"), data = Rabbit_ACT)
pois_ci = function(pred, data){
fit = pred$fit
lower = fit - 1.96*pred$se.fit
upper = fit 1.96*pred$se.fit
return(data.frame(fit = exp(fit), lwr = exp(lower), upr = exp(upper), data))
}
ACT_rain = data.frame(A_Prec_Avg30Yr =
seq(from = min(Rabbit_ACT$A_Prec_Avg30Yr, na.rm = TRUE),
to = max(Rabbit_ACT$A_Prec_Avg30Yr, na.rm = TRUE),
length.out = length(Rabbit_ACT$A_Prec_Avg30Yr)),
# set covariates to mean
DistAgriLand = mean(Rabbit_ACT$DistAgriLand, na.rm = TRUE),
DistPermWater = mean(Rabbit_ACT$DistPermWater, na.rm = TRUE),
A_TAvg_Avg30Yr = mean(Rabbit_ACT$A_TAvg_Avg30Yr, na.rm = TRUE)
# Occurences = Rabbit_ACT$Occurences) # not needed
)
preds.ACT_rain = predict(final_ACT2, newdata = ACT_rain, se.fit = TRUE, type = "link")
preds.ACT_rain.ci = pois_ci(preds.ACT_rain, ACT_rain)
ACT.a = ggplot(data = na.omit(preds.ACT_rain.ci))
geom_point(
data = Rabbit_ACT, # use observed data for points
aes(x = A_Prec_Avg30Yr, y = Occurences),
alpha = 0.3
)
geom_line(aes(x = A_Prec_Avg30Yr, fit))
geom_ribbon(
aes(ymin = lwr, ymax = upr, x = A_Prec_Avg30Yr, y = fit), # set `y` to `fit`
alpha = .2
)
labs(x = "Mean Average 30yr AVerage Percipitation (mL)", "No.of Rabbit Occurences")
# ylim(0, 5000) # remove or adjust `ylim` to avoid cutting off CI
theme_classic()
ACT.a
