tolfind.boxcox {boxcoxmix} | R Documentation |
A grid search over the parameter tol
, to set the initial values of
the EM algorithm.
tolfind.boxcox( formula, groups = 1, data, K = 3, lambda = 1, EMdev.change = 1e-04, plot.opt = 2, s = 15, steps = 500, find.in.range = c(0, 1.5), start = "gq", verbose = FALSE, noformat = FALSE, ... )
formula |
a formula describing the transformed response and the fixed effect model (e.g. y ~ x). |
groups |
the random effects. To fit overdispersion models , set |
data |
a data frame containing variables used in the fixed and random effect models. |
K |
the number of mass points. |
lambda |
a transformation parameter, setting |
EMdev.change |
a small scalar, with default 0.0001, used to determine when to stop EM algorithm. |
plot.opt |
Set |
s |
number of points in the grid search of |
steps |
maximum number of iterations for the EM algorithm. |
find.in.range |
search in a range of |
start |
a description of the initial values to be used in the fitted model, Quantile-based version "quantile" or Gaussian Quadrature "gq" can be set. |
verbose |
If set to FALSE, no printed output on progress. |
noformat |
Set |
... |
extra arguments will be ignored. |
A grid search over tol
can be performed using tolfind.boxcox()
function, which works for np.boxcoxmix()
to find the
optimal solution.
MinDisparity |
the minimum disparity found. |
Mintol |
the
value of |
AllDisparities |
a vector containing all disparities calculated on the grid. |
Alltol |
list of |
AllEMconverged |
1 is TRUE, means the EM algorithm converged. |
aic |
the Akaike information criterion of the fitted regression model. |
bic |
the Bayesian information criterion of the fitted regression model. |
Amani Almohaimeed and Jochen Einbeck
# The Pennsylvanian Hospital Stay Data data(hosp, package = "npmlreg") test1 <- tolfind.boxcox(duration ~ age , data = hosp, K = 2, lambda = 0, find.in.range = c(0, 2), s = 10, start = "gq") # Minimal Disparity: 137.8368 at tol= 2 # Minimal Disparity with EM converged: 137.8368 at tol= 2 # Effect of Phenylbiguanide on Blood Pressure data(PBG, package = "nlme") test2 <- tolfind.boxcox(deltaBP ~ dose , groups = PBG$Rabbit, find.in.range = c(0, 2), data = PBG, K = 2, lambda = -1, s = 15, start = "quantile", plot.opt = 0) test2$Mintol # [1] 1.6 test2$MinDisparity # [1] 449.5876