rigff.RdEstimation of the parameters of a reciprocal inverse Gaussian distribution.
rigff(lmu = "identitylink", llambda = "loglink", imu = NULL,
ilambda = 1)Link functions for mu and lambda.
See Links for more choices.
Initial values for mu and lambda.
A NULL means a value is computed internally.
See Jorgensen (1997) for details.
An object of class "vglmff"
(see vglmff-class).
The object is used by modelling functions
such as vglm,
and vgam.
Jorgensen, B. (1997). The Theory of Dispersion Models. London: Chapman & Hall
This distribution is potentially useful for dispersion modelling.
rdata <- data.frame(y = rchisq(100, df = 14)) # Not 'proper' data!!
fit <- vglm(y ~ 1, rigff, rdata, trace = TRUE)
#> Iteration 1: loglikelihood = -204.66436
#> Iteration 2: loglikelihood = -203.31288
#> Iteration 3: loglikelihood = -203.28874
#> Iteration 4: loglikelihood = -203.28873
#> Iteration 5: loglikelihood = -203.28873
fit <- vglm(y ~ 1, rigff, rdata, trace = TRUE, crit = "c")
#> Iteration 1: coefficients = 12.70634373, -0.32424612
#> Iteration 2: coefficients = 12.31959523, -0.47208282
#> Iteration 3: coefficients = 12.25397505, -0.49405914
#> Iteration 4: coefficients = 12.25250125, -0.49452809
#> Iteration 5: coefficients = 12.25250056, -0.49452831
#> Iteration 6: coefficients = 12.25250056, -0.49452831
summary(fit)
#>
#> Call:
#> vglm(formula = y ~ 1, family = rigff, data = rdata, trace = TRUE,
#> crit = "c")
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept):1 12.2525 0.4482 27.335 < 2e-16 ***
#> (Intercept):2 -0.4945 0.1414 -3.497 0.000471 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Names of linear predictors: mu, loglink(lambda)
#>
#> Log-likelihood: -203.2887 on 198 degrees of freedom
#>
#> Number of Fisher scoring iterations: 6
#>
#> No Hauck-Donner effect found in any of the estimates
#>