backtrack_lcwm {outlierMBC} | R Documentation |
Fit a linear cluster-weighted model to the backtrack solution.
Description
The backtrack function determines the number of outliers for the backtrack
solution and plot_backtrack plots this on a dissimilarity curve.
backtrack_gmm
fits the mixture model corresponding to the number of
outliers selected by the backtrack solution (or any manually specified number
of outliers).
Usage
backtrack_lcwm(
xy,
x,
ombc_lcwm_out,
max_total_rise = 0.1,
max_step_rise = 0.05,
init_z = NULL,
manual_outlier_num = NULL,
verbose = TRUE
)
Arguments
xy |
|
x |
Covariate data only. |
ombc_lcwm_out |
An |
max_total_rise |
Upper limit for the cumulative increase, as a proportion of the global minimum dissimilarity, from all backward steps. |
max_step_rise |
Upper limit for the increase, as a proportion of the global minimum dissimilarity, from each backward step. |
init_z |
Initial component assignment probability matrix. |
manual_outlier_num |
User-specified number of outliers. |
verbose |
Whether the iteration count is printed. |
Value
backtrack_gmm
returns a list with the following elements:
labels
Vector of component labels with outliers denoted by 0.
outlier_bool
Logical vector indicating if an observation has been classified as an outlier.
outlier_num
Number of observations classified as outliers.
lcwm
Output from flexCWM::cwm fitted to the non-outlier observations.
call
Arguments / parameter values used in this function call.
Examples
gross_lcwm_k3n1000o10 <- find_gross(lcwm_k3n1000o10, 20)
ombc_lcwm_k3n1000o10 <- ombc_lcwm(
xy = lcwm_k3n1000o10[, c("X1", "Y")],
x = lcwm_k3n1000o10$X1,
y_formula = Y ~ X1,
comp_num = 2,
max_out = 20,
mnames = "V",
gross_outs = gross_lcwm_k3n1000o10$gross_bool
)
backtrack_lcwm_k3n1000o10 <- backtrack_lcwm(
xy = lcwm_k3n1000o10[, c("X1", "Y")],
x = lcwm_k3n1000o10$X1,
ombc_lcwm_out = ombc_lcwm_k3n1000o10
)