mix2exp {VGAM} | R Documentation |
Estimates the three parameters of a mixture of two exponential distributions by maximum likelihood estimation.
mix2exp(lphi = "logit", llambda = "loge", ephi = list(), el1 = list(), el2 = list(), iphi = 0.5, il1 = NULL, il2 = NULL, qmu = c(0.8, 0.2), nsimEIM = 100, zero = 1)
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 |
ephi, el1, el2 |
List. Extra argument for each of the links.
See |
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 |
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 |
nsimEIM, zero |
The probability function can be loosely written as
P(Y=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.
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
.
lambda1 = exp(1); lambda2 = exp(3) (phi = logit(-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, mdata, trace = TRUE) coef(fit, matrix = TRUE) # Compare the results with the truth round(rbind('Estimated' = Coef(fit), 'Truth' = c(phi, lambda1, lambda2)), dig = 2) ## Not run: # Plot the results 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)