Nothing
tests/MMnpcensreg-savedvalues.R
In robustloggamma: Robust Estimation of the Generalized log Gamma Model
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0.292752667443011), .Dim = c(150L, 5L)), kappa = 0, Jstar = 89L,
Kstar = 89L, message = "Normal (1277) Iter=Maxit (0) f(a)*f(b)>0 (0) |f(a)-f(b)|<tl (471) ",
control = structure(list(method = "MM", Srefine = FALSE,
N = 150, q = 8, sigma0 = 1, max.it = 100, tol = 0.001,
ialg = 3, seed = 123, bb = 0.5, tuning.rho = 1.547647,
tuning.psi = 3.444, cint = "Nonpar", tolB = 0.001,
tolS = 0.01, maxit.Srefine = 20, d = NULL, lower = NULL,
upper = NULL, verbose = FALSE, fast.s.large.n = Inf), .Names = c("method",
"Srefine", "N", "q", "sigma0", "max.it", "tol", "ialg",
"seed", "bb", "tuning.rho", "tuning.psi", "cint", "tolB",
"tolS", "maxit.Srefine", "d", "lower", "upper", "verbose",
"fast.s.large.n")), converged = TRUE), .Names = c("coefficients",
"scale", "fitted.values", "residuals", "init", "gama", "kappa",
"Jstar", "Kstar", "message", "control", "converged")), init.Sr = NULL,
init.M = structure(list(coefficients = c(1.08634354367636,
2.13666984791791, 1.97840427649739, 2.09318107474757, 2.03558002533076
), gamma = c(-0.173567007155455, -0.171426111291191, 0.0338729466160611,
-0.131235737553546, 0.0707373659473647), init.MM = 0.240627754802471,
final.MM = 0.2349805878988, iter = 17L, message = "relative convergence (4)"), .Names = c("coefficients",
"gamma", "init.MM", "final.MM", "iter", "message")), control = structure(list(
method = "MM", Srefine = FALSE, N = 150, q = 8, sigma0 = 1,
max.it = 100, tol = 0.001, ialg = 3, seed = 123, bb = 0.5,
tuning.rho = 1.547647, tuning.psi = 3.444, cint = "Nonpar",
tolB = 0.001, tolS = 0.01, maxit.Srefine = 20, d = NULL,
lower = NULL, upper = NULL, verbose = FALSE), .Names = c("method",
"Srefine", "N", "q", "sigma0", "max.it", "tol", "ialg", "seed",
"bb", "tuning.rho", "tuning.psi", "cint", "tolB", "tolS",
"maxit.Srefine", "d", "lower", "upper", "verbose")), converged = TRUE), .Names = c("coefficients",
"scale", "fitted.values", "residuals", "init", "init.S", "init.Sr",
"init.M", "control", "converged"))
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robustloggamma documentation built on May 1, 2019, 9:20 p.m.
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0.292752667443011), .Dim = c(150L, 5L)), kappa = 0, Jstar = 89L,
Kstar = 89L, message = "Normal (1277) Iter=Maxit (0) f(a)*f(b)>0 (0) |f(a)-f(b)|<tl (471) ",
control = structure(list(method = "MM", Srefine = FALSE,
N = 150, q = 8, sigma0 = 1, max.it = 100, tol = 0.001,
ialg = 3, seed = 123, bb = 0.5, tuning.rho = 1.547647,
tuning.psi = 3.444, cint = "Nonpar", tolB = 0.001,
tolS = 0.01, maxit.Srefine = 20, d = NULL, lower = NULL,
upper = NULL, verbose = FALSE, fast.s.large.n = Inf), .Names = c("method",
"Srefine", "N", "q", "sigma0", "max.it", "tol", "ialg",
"seed", "bb", "tuning.rho", "tuning.psi", "cint", "tolB",
"tolS", "maxit.Srefine", "d", "lower", "upper", "verbose",
"fast.s.large.n")), converged = TRUE), .Names = c("coefficients",
"scale", "fitted.values", "residuals", "init", "gama", "kappa",
"Jstar", "Kstar", "message", "control", "converged")), init.Sr = NULL,
init.M = structure(list(coefficients = c(1.08634354367636,
2.13666984791791, 1.97840427649739, 2.09318107474757, 2.03558002533076
), gamma = c(-0.173567007155455, -0.171426111291191, 0.0338729466160611,
-0.131235737553546, 0.0707373659473647), init.MM = 0.240627754802471,
final.MM = 0.2349805878988, iter = 17L, message = "relative convergence (4)"), .Names = c("coefficients",
"gamma", "init.MM", "final.MM", "iter", "message")), control = structure(list(
method = "MM", Srefine = FALSE, N = 150, q = 8, sigma0 = 1,
max.it = 100, tol = 0.001, ialg = 3, seed = 123, bb = 0.5,
tuning.rho = 1.547647, tuning.psi = 3.444, cint = "Nonpar",
tolB = 0.001, tolS = 0.01, maxit.Srefine = 20, d = NULL,
lower = NULL, upper = NULL, verbose = FALSE), .Names = c("method",
"Srefine", "N", "q", "sigma0", "max.it", "tol", "ialg", "seed",
"bb", "tuning.rho", "tuning.psi", "cint", "tolB", "tolS",
"maxit.Srefine", "d", "lower", "upper", "verbose")), converged = TRUE), .Names = c("coefficients",
"scale", "fitted.values", "residuals", "init", "init.S", "init.Sr",
"init.M", "control", "converged"))
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