All these settings are passed to the Expectation Maximization
fitting algorithm. All unspecified settings are set to the default value.
Setting model = "EM"
sets all settings to the default
value irrespective of any other setting and fits z-curve as described in
Bartoš and Schimmack (2022)
A type of model to be fitted, defaults to "EM"
for a z-curve with 7 z-scores centered components.
An alpha level of the test statistics, defaults to
.05
A beginning of fitting interval, defaults to
qnorm(sig_level/2,lower.tail = F)
An end of fitting interval, defaults to 5
Means of the components, defaults to
0:6
A standard deviation of the components, defaults to
rep(1, length(mu))
A vector of alpha parameters of a Dirichlet distribution
for generating random starting values for the weights, defaults to
rep(.5, length(mu))
Upper limits for weights, defaults to
rep(1,length(mu))
A criterion to terminate the EM algorithm,
defaults to 1e-6
A criterion to terminate the starting phase
of the EM algorithm, defaults to 1e-3
A criterion to terminate the bootstrapping phase
of the EM algorithm, defaults to 1e-5
A maximum number of iterations of the EM algorithm
(not including the starting iterations) defaults to 10000
A maximum number of iterations for the
starting phase of EM algorithm, defaults to 100
A maximum number of iterations for the
booting phase of EM algorithm, defaults to 100
A number of starting fits to get the initial
position for the EM algorithm, defaults to 100
Bartoš F, Schimmack U (2022). “Z-curve 2.0: Estimating replication rates and discovery rates.” Meta-Psychology, 6. doi:10.15626/MP.2021.2720 .