英文:
Fitting a GEV Distribution: dataset and results
问题
我尝试调整一个GEV分布以适应数据集，使用"fitdistrplus"包中的"fitdistr"函数。
对于另一个数据集，我在解释p值结果方面也遇到了问题。"gofstat"函数中的"chisqpvalue"返回一个空向量。
非常感谢您的提前帮助。
我尝试了以下脚本：
library(fitdistrplus)
library(evd)
x <- c(11, 6, 3, 3, 4, 4, 5, 9, 4, 16, 8, 9, 7, 8, 16, 11, 5, 8, 9, 4, 17, 6, 7, 7, 6, 6)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev", start=par)
resultX <- gofstat(fitX)
它返回以下消息：函数mle无法估计参数，错误代码100
当我从数据集中删除一些值时，它可以运行而没有错误：
x <- c(11, 6, 3, 3, 5, 9, 4, 16, 8, 9, 7, 8, 16, 11, 5, 8, 9, 4, 17, 6, 7, 7, 6, 6)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev", start=par)
resultX <- gofstat(fitX)
我认为问题可能是我选择的起始参数。但是，有一种正确的方法来估计它吗？
我还有另一个问题是关于p值结果。在下面的示例中（另一个数据集），代码正在运行，拟合GEV分布，但p值（chisqpvalue）结果为空：
x <- c(10, 21, 7, 17, 18, 16, 17, 12, 22, 19, 12, 49, 11, 9)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev", start=par)
resultX <- gofstat(fitX)
英文:
I'm trying to adjust a GEV Distribution to a dataset, using the "fitdistr" function from "fitdistrplus" package.
I'm also having trouble in the interpretation of the p-value result for another dataset. The "chisqpvalue" from "gofstat" function is returning a null vector.
Thank you very much in advance.
I tried the following script:
library(fitdistrplus)
library(evd)
x <- c(11, 6, 3, 3, 4, 4, 5, 9, 4, 16, 8, 9, 7, 8, 16, 11, 5, 8, 9, 4, 17, 6, 7, 7, 6, 6)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev",start=par)
resultX <- gofstat(fitX)
It's returning the following message: the function mle failed to estimate the parameters, with the error code 100
When i remove some values from the dataset, it runs withour error:
x <- c(11, 6, 3, 3, 5, 9, 4, 16, 8, 9, 7, 8, 16, 11, 5, 8, 9, 4, 17, 6, 7, 7, 6, 6)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev",start=par)
resultX <- gofstat(fitX)
I think the problem could be the starting parameters that i've choose. But, there is a correct way to estimate it?
Another problem of mine is about the p-value resuits. In the example below (another dataset), the code is running, fitting the GEV distribution, but the p-value(chisqpvalue) result is Null
x <- c(10, 21, 7, 17, 18, 16, 17, 12, 22, 19, 12, 49, 11, 9)
par <- list(loc = 0.0, scale = 1.0, shape = 0)
fitX <- fitdist(abs(x), "gev",start=par)
resultX <- gofstat(fitX)
答案1
得分: 1
Using bbmle::mle2, which is a little more flexible and lets us fit the scale parameter on the log scale so we can stay out of trouble:
使用 `bbmle::mle2`，这个函数更加灵活，可以让我们在对数尺度上拟合尺度参数，以便避免问题：
```r
library(bbmle)
m1 <- mle2(x ~ dgev(loc, exp(logscale), shape), data = data.frame(x),
start = list(loc = 0, logscale = 0, shape = 0), method = "Nelder-Mead")
Using these starting values is good enough to let fitdist succeed:
使用这些初始值就足够让 fitdist 成功：
fitX <- fitdist(x, "gev",
start= with(as.list(coef(m1)), list(loc = loc, scale = exp(logscale), shape = shape)))
英文:
Using bbmle::mle2, which is a little more flexible and lets us fit the scale parameter on the log scale so we can stay out of trouble:
library(bbmle)
m1 <- mle2(x ~ dgev(loc, exp(logscale), shape), data = data.frame(x),
start = list(loc = 0, logscale = 0, shape = 0), method = "Nelder-Mead")
Using these starting values is good enough to let fitdist succeed:
fitX <- fitdist(x, "gev",
start= with(as.list(coef(m1)), list(loc = loc, scale = exp(logscale), shape = shape)))
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