问题
I have x-y scatter data, which exhibit bell-shaped (i.e. normal distribution shaped) behaviour over the course of a year. These are primary production data from high latitudes (more in detail here, the article is paywalled, but I hope that the figures are visible).
Question
How do I fit a normal distribution shaped curve on scatter data in ggplot2?
Example data
x <- structure(list(yday = c(238, 238, 238, 242, 242, 250, 250, 253,
254, 169, 199, 208, 230, 21, 37, 88, 94, 102, 125, 125, 95, 98,
100, 101, 103, 93, 96, 97, 97, 99, 291, 300, 316, 332, 363, 12,
27, 49, 68, 256, 263, 263, 264, 265, 266, 127, 127, 127, 127,
133, 123, 127, 133, 127, 133, 141, 148, 155, 112, 120, 127, 134,
169, 169, 169, 169, 169, 124, 124, 124, 124, 126, 126, 127, 130,
132, 134, 134, 136, 138, 140, 142, 144, 146, 149, 152, 154, 127,
130, 132, 134, 134, 136, 138, 140, 142, 144, 146, 149, 152, 154,
127, 130, 132, 134, 134, 136, 138, 140, 142, 144, 146, 149, 152,
154, 127, 130, 132, 134, 134, 136, 138, 140, 142, 144, 146, 149,
152, 154, 127, 130, 132, 134, 134, 136, 138, 140, 142, 144, 146,
149, 152, 154, 146, 154, 154, 154, 143, 156, 134, 162, 164, 168,
166, 71, 38, 39), value = c(48802.2, 28869.36, 46370.31, 67936,
91442, 89559.4, 46862.2, 7895.3, 19660.72, 273540.7, 254615,
268930, 264310.56, 72561, 114520, 42950, 149151.15, 530610, 53289.2,
45, 1776, 20504, 1768, 5740, 29497, 340762, 259837, 11576, 847238,
1773275, 1555.92, 3108.48, 8579.1, 25677.8, 17697.32, 2887.56,
5311.2, 13127.98, 38006.4, 135128, 71003, 10454.75, 41389.6,
15266.5, 58601.7, 206984.918282083, 265165.058077198, 90485.4790849673,
5705618.16993847, 1616527.31316346, 1610059.4788107, 5689427.93092749,
3261840.85863376, 1911057.16943202, 1023812.55301328, 1579191.31813709,
2241683.51873045, 4398531.75676259, 933143.183151504, 1771596.51236257,
3000366.86522064, 1219826.2208944, 247538.595548984, 353927.523573691,
323722.062546854, 278081.544235635, 601042.642308546, 2317070.57555887,
963348.671707912, 8125168.04401668, 1860334.91955526, 18673.3716353477,
682901.426071428, 348046.291238703, 1387947.38534056, 112176.06673827,
203778.898538342, 304593.428222028, 1015454.26894711, 384172.102208766,
1211065.9345086, 580449.092224899, 556147.163209095, 707840.652723421,
2919016.89462558, 878518.35266303, 760837.632557093, 437441.609086177,
3761984.12905246, 1008524.7172583, 153914.10863321, 209919.739543153,
5165174.16501832, 592152.070785338, 754878.057858348, 548774.607567716,
784679.488265372, 1191547.05905346, 867977.806474748, 1601076.47417622,
1059665.24406883, 509654.672768973, 1878007.77720015, 217773.469093887,
282571.399726361, 98438.9397685662, 889753.057501427, 564416.438455766,
1843608.78521975, 727213.52622083, 689307.464580901, 1018069.45500141,
1188687.56383149, 1352651.53225745, 726363.223839249, 420446.302222222,
856363.289847527, 18015.1056535911, 229628.041636759, 165657.605285714,
164394.955219614, 510449.457665504, 1778093.57209278, 610564.603533888,
889187.420481627, 2762856.39975472, 863978.618292937, 1697540.61924469,
859284.971006319, 197822.92972973, 389791.010663156, 144870.825015753,
196128.64471631, 133023.96688172, 70787.1033258229, 518223.208383732,
4051834.77590046, 628912.36192445, 378818.831615793, 413839.100579421,
1091509.82410159, 1187325.98867099, 331226.406610866, 1729022.69104484,
4663215.78870189, 2843159.21140248, 708207.236363227, 1436498.03405122,
1158173.94324553, 448469.915666212, 901903.855484778, 1599625.0472896,
1141633.00553421, 728670.952878351, 123982.148723477, 112304.540084388,
4011.30312056738)), .Names = c("yday", "value"), row.names = c(NA,
-157L), class = "data.frame")
Example
ggplot(x, aes(x = yday, y = value)) + geom_point() + xlab("Day of year")
The "curve" has been added manually.
What have I tried
Previous works have used normal distribution, Weibull distribution and log-normal disrtibution. I obviously cannot fit a density distribution directly to these data, as the data are two dimensional. This is where I struggle. I could adjust the height of the density distributions, but this would not be model fitting, and R has to have a better way to do it. My initial feeling is that I need to formulate a curve from the above-mentioned distributions and use the nls to fit these curves. I am, however, a bit lost with how to formulate the equations and function calls. Once the nls call is formulated, it could be passed to the stat_function or geom_smooth layers. Any help would be appreciated.
回答1:
You can easily fit the normal density function:
fit <- nls(value ~ a * dnorm(yday, mean, sd), data = x,
start = list(mean = 150, sd = 25, a = 1e8))
plot(value ~ yday, data = x)
curve(predict(fit, newdata = data.frame(yday = x)), from = 0, to = 400, add = TRUE)
If that is a sensible thing to to is a different question.
来源：https://stackoverflow.com/questions/49066677/how-to-fit-a-bell-shaped-curve-on-2-dimensional-scatter-data