| N2G {AnalyzeFMRI} | R Documentation |
Fits the N2G model (1 Normal and 2 Gamma's mixture model) to a dataset using Maximum Likelihhod.
N2G(data, par.start = c(4, 2, 4, 2, 0.9, 0.05))
data |
The dataset. |
par.start |
The starting values for the optimization to maximize
the likelihood. The parameters of the model are ordered in the
vector par.start in the following way (refer to the model below)
c(a, b, c, d, p1, p2) |
The mixture model considered is a mixture of a standard normal distribution and two Gamma functions. This model is denoted N2G.
x ~ p1 * N(0, 1) + p2 * Gamma(a, b) + (1 - p1 - p2) * -Gamma(c, d)
A list with components
par |
The fitted parameter values. |
lims |
The upper and lower thresholds for the Normal component of the fitted model |
J. L. Marchini
N2G.Class.Probability, N2G.Likelihood.Ratio, N2G.Spatial.Mixture,
N2G.Density , N2G.Likelihood , N2G.Transform,
N2G.Fit ,
N2G.Inverse , N2G.Region
par <- c(3, 2, 3, 2, .3, .4) data <- c(rnorm(10000), rgamma(2000, 10, 1), -rgamma(1400, 10, 1)) hist(data, n = 100, freq = FALSE) q <- N2G.Fit(data, par, maxit = 10000, method = "BFGS") p <- seq(-50, 50, .1) lines(p, N2G.Density(p, q), col = 2)