对于双曲线分布，如何确定 R 中事件的概率？

Question

我想知道如何计算 P ≥ -0.0001。我在数学、统计学和 R 方面都是新手。

我目前正在 R 中对一些传播数据进行建模。我已经能够将数据建模为特定分布（在下面的示例中，这是双曲线的）并执行似然比检验以确认可行性使用指定的分布。

在我的脑海中，一旦我获得了分布，我现在能够更可靠地计算我的概率，因为我更准确地了解曲线下方的区域？

我原以为一旦分布匹配，我就可以对 t 统计量进行类似的检验，但是我不知道如何做。

我看过 pnorm(x) 函数，也看过 phyper 但是不知道如何简单地计算 P ≥ -0.0001.

以下是我的代码，非常感谢您的帮助；

`# 图书馆（GYP）库（时间序列）

# Coverting to Time Series 
E <- timeSeries(A[,"Spread"])

# Fitting
ef <- (density(E))
ghdfit <- fit.ghypuv(E,symmetric = FALSE, control = list(maxit = 1000))
hypfit <- fit.hypuv(E,symmetric = FALSE, control = list(maxit = 1000))
nigfit <- fit.NIGuv(E,symmetric = FALSE, control = list(maxit = 1000))

# Density
ghddens <- dghyp(ef$x, ghdfit)
    hypdens <- dghyp(ef$x, hypfit)
nigdens <- dghyp(ef$x, nigfit)
    nordens <- dnorm(ef$x, mean = mean(E),sd = sd(c(E[,1])))
col.def <- c("black","red","green","orange")
plot(ef, xlab = " Spread ", ylab = expression(f(x)),ylim = c(0,50), main ='CABLE - 3 Day Comparison across 28 Years')
lines(ef$x, ghddens, col = "red")
    lines(ef$x, hypdens, col = "blue")
lines(ef$x, nigdens, col = "green")
    lines(ef$x, nordens, col = "orange")
legend("topleft", legend = c("Empirical","GHD","HYP","NIG","NORM"), col = col.def, lty = 1)



# QQ Plot
        qqghyp(ghdfit, line = TRUE, ghyp.col = "red", plot.legend = FALSE, gaussian = FALSE, main = " ", cex = 0.8)
        qqghyp(hypfit, add = TRUE, ghyp.pch = 2, ghyp.col = "green", gaussian = FALSE, line = FALSE, cex = 0.8)
        qqghyp(nigfit,add = TRUE, ghyp.pch = 3, ghyp.col = "orange", gaussian = FALSE, line = FALSE, cex = 0.8)
        legend("topleft", legend = c("GHD","HYP","NIG"), col = col.def[-c(1,5)], pch = 1:3)

       # Diagnostic
        options(scipen=999)
        AIC <- stepAIC.ghyp(E, dist = c("ghyp","hyp","NIG"), symmetric = FALSE, control = list(maxit=1000))
        LRghdnig <- lik.ratio.test(ghdfit,nigfit)
        LRghdhyp <- lik.ratio.test(ghdfit,hypfit)

LRghdhyp $statistic L 0.07005745

$p.value 1 0.0211198

$df 1 1

$H0 1 错误

所以，我知道正确的分布以及如何适应它。我如何确定 > - 0.0001 发生的概率？

Answer 1

答案已发布 here 并涉及 pghyp 函数 @fg nu

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