对于双曲线分布，如何确定R中事件的概率？

我想知道如何计算

p≥ -0.0001

。我对数学、统计学和R都是新手

我目前正在对R中的一些利差数据进行建模。我能够将数据建模为特定分布（在以下示例中，这是双曲线），并执行似然比测试，以确认使用指定分布的可行性

在我的头脑中，一旦我获得了分布，我现在能够更可靠地计算我的概率，因为我更准确地理解了曲线下的面积

我曾假设，一旦分布匹配，我将能够进行与t统计相似的测试，但我不知道如何做到这一点

我看到了

pnorm（x）

函数，也看到了

phyper

，但是我不知道如何简单地计算

p≥ -0.0001

下面是我的代码，非常感谢您的帮助

`# 库（ghyp） 图书馆（时间系列）

#转换为时间序列
E答案已发布，并涉及
pghyp
功能
@fg nu

# 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)