Description Usage Arguments Details Value References See Also Examples
gemmEst
is called by gemm
to fit general monotone models.
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input.data |
must be data frame, first column is treated as dependent variable. |
output |
string argument for use in naming file
output. |
n.beta |
number of beta vectors to generate per generation. |
n.chains |
number of times the fitting process will be repeated. |
n.gens |
number of generations per chain. |
save.results |
logical value to determine whether the resulting gemm object is saved to a .RData file. |
k.pen |
penalty term for BIC, as calculated by
|
seed.metric |
logical value to control whether
|
check.convergence |
logical value to indicate
whether BIC for each generation is retained, mostly
useful for checking performance of |
roe |
logical value to determine whether region of equivalence data are retained. |
fit.metric |
value used to order models. |
correction |
placeholder for correction transformations on the fit statistic, (r or tau). |
oclo |
logical for Ordered Constrained Linear Optimization. If
|
isTauB |
logical for whether to include ties in the denominator of the tau calculation. |
Formula syntax and interaction penalty terms can be avoided by fitting data
directly using gemmEst
.
A list with class "gemm
" containing the following components:
date |
system time and date for model completion. |
call |
the matched call. |
coefficients |
matrix of best weights with one row for each chain. |
fitted.values |
model predictions for criterion generated from weights associated with best chain. |
residuals |
metric values for response minus fitted. |
rank.residuals |
rank response minus rank criterion. |
bic |
vector of Bayesian Information Criteria for estimation sample of each chain. |
r |
vector of Pearson's |
tau |
vector of Kendall's |
tau.par |
vector containing the correction, incorrect, criterion ties, predictor ties, and both between the weighted cues and outcome used for model fit. |
metric.betas |
regression weights derived using |
p.vals |
p-values associated with ordinary least squares regression weights. |
model |
data frame including modeled data. |
fit.metric |
sorting metric used. |
cross.val.bic |
vector of Bayesian Information Criteria for cross-validation sample of each chain. |
cross.val.r |
vector of Pearson's |
cross.val.tau |
vector of Kendall's |
cross.val.tau.par |
vector containing the correction, incorrect, criterion ties, predictor ties, and both between the weighted cues and outcome used for crossvalidation. |
converge.fit.metric |
matrix of " |
converge.beta |
matrix derived weights for each generation within each chain, column for each predictor. |
converge.r |
generations by chains matrix of Pearson's |
formula |
|
Dougherty, M. R., & Thomas, R. P. (2012). Robust decision making in a nonlinear world. Psychological review, 119(2), 321.
gemm
for normal use. genAlg
for search,
gemmFitRcppI
for fitting routine, tauTest
for
O*(N log N)
scale Kendall's tau
, convergencePlot
for the optional plot pane when check.convergence = TRUE
.
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