View source: R/family.others.R
extlogF1  R Documentation 
Maximum likelihood estimation of the 1parameter extended logF distribution.
extlogF1(tau = c(0.25, 0.5, 0.75), parallel = TRUE ~ 0,
seppar = 0, tol0 = 0.001,
llocation = "identitylink", ilocation = NULL,
lambda.arg = NULL, scale.arg = 1, ishrinkage = 0.95,
digt = 4, idf.mu = 3, imethod = 1)
tau 
Numeric, the desired quantiles. A strictly increasing sequence,
each value must be in 
parallel 
Similar to Setting 
seppar, tol0 
Numeric, both of unit length and nonnegative,
the separation and shift parameters.
If If If avoiding the quantile crossing problem is of concern to you,
try increasing 
llocation, ilocation 
See 
lambda.arg 
Positive tuning parameter which controls the sharpness of the cusp.
The limit as it approaches 0 is probably very similar to

scale.arg 
Positive scale parameter and sometimes called 
ishrinkage, idf.mu, digt 
Similar to 
imethod 
Initialization method.
Either the value 1, 2, or ....
See 
This is an experimental family function for quantile regression.
Fasiolo et al. (2020) propose an extended logF distribution
(ELF)
however this family function only estimates the location parameter.
The distribution has a scale parameter which can be inputted
(default value is unity).
One location parameter is estimated for each tau
value
and these are the estimated quantiles.
For quantile regression it is not necessary to estimate
the scale parameter since the loglikelihood function is
triangle shaped.
The ELF is used as an approximation of the asymmetric Laplace
distribution (ALD).
The latter cannot be estimated properly using Fisher scoring/IRLS
but the ELF holds promise because it has continuous derivatives
and therefore fewer problems with the regularity conditions.
Because the ELF is fitted to data to obtain an
empirical result the convergence behaviour may not be gentle
and smooth.
Hence there is a functionspecific control function called
extlogF1.control
which has something like
stepsize = 0.5
and maxits = 100
.
It has been found that
slowing down the rate of convergence produces greater
stability during the estimation process.
Regardless, convergence should be monitored carefully always.
This function accepts a vector response but not a matrix response.
An object of class "vglmff"
(see vglmffclass
).
The object is used by modelling functions such as vglm
and vgam
.
Changes will occur in the future to finetune things.
In general
setting trace = TRUE
is strongly encouraged because it is
needful to check that convergence occurs properly.
If seppar > 0
then logLik(fit)
will return the
penalized loglikelihood.
Thomas W. Yee
Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R. and Goude, Y. (2020). Fast calibrated additive quantile regression. J. Amer. Statist. Assoc., in press.
Yee, T. W. (2020). On quantile regression based on the 1parameter extended logF distribution. In preparation.
dextlogF
,
is.crossing
,
fix.crossing
,
eCDF
,
vglm.control
,
logF
,
alaplace1
,
dalap
,
lms.bcn
.
nn < 1000; mytau < c(0.25, 0.75)
edata < data.frame(x2 = sort(rnorm(nn)))
edata < transform(edata, y1 = 1 + x2 + rnorm(nn, sd = exp(1)),
y2 = cos(x2) / (1 + abs(x2)) + rnorm(nn, sd = exp(1)))
fit1 < vglm(y1 ~ x2, extlogF1(tau = mytau), data = edata) # trace = TRUE
fit2 < vglm(y2 ~ bs(x2, 6), extlogF1(tau = mytau), data = edata)
coef(fit1, matrix = TRUE)
fit2@extra$percentile # Empirical percentiles here
summary(fit2)
c(is.crossing(fit1), is.crossing(fit2))
head(fitted(fit1))
## Not run: plot(y2 ~ x2, edata, col = "blue")
matlines(with(edata, x2), fitted(fit2), col="orange", lty = 1, lwd = 2)
## End(Not run)
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