mix2exp: Mixture of Two Exponential Distributions
In VGAM: Vector Generalized Linear and Additive Models

Description Usage Arguments Details Value Warning Note Author(s) See Also Examples

Estimates the three parameters of a mixture of two exponential distributions by maximum likelihood estimation.

1 2	mix2exp(lphi = "logitlink", llambda = "loglink", iphi = 0.5, il1 = NULL, il2 = NULL, qmu = c(0.8, 0.2), nsimEIM = 100, zero = "phi")

`lphi, llambda`	Link functions for the parameters phi and lambda. The latter is the rate parameter and note that the mean of an ordinary exponential distribution is 1 / λ. See `Links` for more choices.
`iphi, il1, il2`	Initial value for phi, and optional initial value for lambda1 and lambda2. The last two have values that must be positive. The default is to compute initial values internally using the argument `qmu`.
`qmu`	Vector with two values giving the probabilities relating to the sample quantiles for obtaining initial values for lambda1 and lambda2. The two values are fed in as the `probs` argument into `quantile`.
`nsimEIM, zero`	See `CommonVGAMffArguments`.

The probability density function can be loosely written as

f(y) = phi * Exponential(lambda1) + (1-phi) * Exponential(lambda2)

where phi is the probability an observation belongs to the first group, and y>0. The parameter phi satisfies 0 < phi < 1. The mean of Y is phi/lambda1 + (1-phi)/lambda2 and this is returned as the fitted values. By default, the three linear/additive predictors are (logit(phi), log(lambda1), log(lambda2))^T.

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm and vgam.

This VGAM family function requires care for a successful application. In particular, good initial values are required because of the presence of local solutions. Therefore running this function with several different combinations of arguments such as iphi, il1, il2, qmu is highly recommended. Graphical methods such as hist can be used as an aid.

This VGAM family function is experimental and should be used with care.

Fitting this model successfully to data can be difficult due to local solutions, uniqueness problems and ill-conditioned data. It pays to fit the model several times with different initial values and check that the best fit looks reasonable. Plotting the results is recommended. This function works better as lambda1 and lambda2 become more different. The default control argument trace = TRUE is to encourage monitoring convergence.

T. W. Yee

rexp, exponential, mix2poisson.

## Not run:  lambda1 <- exp(1); lambda2 <- exp(3)
(phi <- logitlink(-1, inverse = TRUE))
mdata <- data.frame(y1 = rexp(nn <- 1000, lambda1))
mdata <- transform(mdata, y2 = rexp(nn, lambda2))
mdata <- transform(mdata, Y  = ifelse(runif(nn) < phi, y1, y2))
fit <- vglm(Y ~ 1, mix2exp, data = mdata, trace = TRUE)
coef(fit, matrix = TRUE)

# Compare the results with the truth
round(rbind('Estimated' = Coef(fit),
            'Truth' = c(phi, lambda1, lambda2)), digits = 2)

with(mdata, hist(Y, prob = TRUE, main = "Orange = estimate, blue = truth"))
abline(v = 1 / Coef(fit)[c(2, 3)],  lty = 2, col = "orange", lwd = 2)
abline(v = 1 / c(lambda1, lambda2), lty = 2, col = "blue", lwd = 2)

## End(Not run)