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The following is my code, and I would like to get the dpear and ppear plots to appear on the histogram, just as I have done with the density curve, but I am running into some issues trying to do that. If someone could point me in the right direction, that would be great!

library(readr)
library(fitdistrplus)
library(moments)
library(PearsonDS)
library(ggplot2)

newdata <- read_csv("Downloads/newctdata - Sheet1.csv")

data <- rpearson(1000, moments = c(mean = 0.5205263, variance = 0.3940497, skewness = 1.747905, kurtosis = 5.706342))

ppar <- pearsonFitML(data)
print(unlist(ppar))

print(unlist(pearsonFitM(moments = empMoments(data))))

pIpars <- list(a = 0.44883385, b = 2.22621271, location = 0.04565093, scale = 3.20779382)

t <- seq(0, 2.5, by = 0.1)

dpearsonI(t, params = pIpars)
dpear <- plot(t, dpearsonI(t, params = pIpars), type = "l")

ppearsonI(t, params = pIpars)
dpear <- plot(t, ppearsonI(t, params = pIpars), type = "l")

hist(newdata$Mean, prob = TRUE, xlab = "Mean Duration of Asymptomatic Infection in Women", ylab = "Frequency", col = "steelblue", breaks = 12, cex.main = 1.3, cex.axis = 1.5, cex.lab = 1.5)
lines(density(newdata$Mean), col = "tomato", lwd = 4)
cbjdfall
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    It's easier to help you if you provide a [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. We can't read CSV files that are only on your computer so we can't run and test the code. – MrFlick Jun 28 '22 at 13:59

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