Hãy thử mixdist
Đây là một ví dụ:
library(mixdist)
#Build data vector "x" as a mixture of data from 3 Normal Distributions
x1 <- rnorm(1000, mean=0, sd=2.0)
x2 <- rnorm(500, mean=9, sd=1.5)
x3 <- rnorm(300, mean=13, sd=1.0)
x <- c(x1, x2, x3)
#Plot a histogram (you'll play around with the value for "breaks" as
#you zero-in on the fit). Then build a data frame that has the
#bucket midpoints and counts.
breaks <- 30
his <- hist(x, breaks=breaks)
df <- data.frame(mid=his$mids, cou=his$counts)
head(df)
#The above Histogram shows 3 peaks that might be represented by 3 Normal
#Distributions. Guess at the 3 Means in Ascending Order, with a guess for
#the associated 3 Sigmas and fit the distribution.
guemea <- c(3, 11, 14)
guesig <- c(1, 1, 1)
guedis <- "norm"
(fitpro <- mix(as.mixdata(df), mixparam(mu=guemea, sigma=guesig), dist=guedis))
#Plot the results
plot(fitpro, main="Fit a Probability Distribution")
grid()
legend("topright", lty=1, lwd=c(1, 1, 2), c("Original Distribution to be Fit", "Individual Fitted Distributions", "Fitted Distributions Combined"), col=c("blue", "red", rgb(0.2, 0.7, 0.2)), bg="white")
===========================
Parameters:
pi mu sigma
1 0.5533 -0.565 1.9671
2 0.2907 8.570 1.6169
3 0.1561 12.725 0.9987
Distribution:
[1] "norm"
Constraints:
conpi conmu consigma
"NONE" "NONE" "NONE"
