constants {robust2sls} | R Documentation |
Calculate constants across estimation
Description
constants
calculates various values that do not change across the
estimation and records them in a list.
Usage
constants(
call,
formula,
data,
reference = c("normal"),
sign_level,
estimator,
split,
shuffle,
shuffle_seed,
iter,
criterion,
max_iter,
user_model,
verbose
)
Arguments
call |
A record of the original function call. |
formula |
The regression formula specified in the function call. |
data |
The dataframe used in the function call. |
reference |
A character vector of length 1 that denotes a valid reference distribution. |
sign_level |
A numeric value between 0 and 1 that determines the cutoff in the reference distribution against which observations are judged as outliers or not. |
estimator |
A character vector specifying which initial estimator was used. |
split |
A numeric value strictly between 0 and 1 that specifies how the
sample is split in case of saturated 2SLS. |
shuffle |
A logical value whether the sample is re-arranged in random
order before splitting the sample in case of saturated 2SLS. |
shuffle_seed |
A numeric value setting the seed for the shuffling of the
sample. Only used if |
iter |
An integer value setting the number of iterations of the outlier-detection algorithm. |
criterion |
A numeric value that determines when the iterated outlier-detection algorithm stops by comparing it to the sum of squared differences between the m- and (m-1)-step parameter estimates. NULL if convergence criterion should not be used. |
max_iter |
A numeric value that determines after which iteration the algorithm stops in case it does not converge. |
user_model |
A model object of class ivreg. Only
required if argument |
verbose |
A logical value whether progress during estimation should be reported. |
Value
Returns a list that stores values that are constant across the
estimation. It is used to fill parts of the "robust2sls"
class object,
which is returned by outlier_detection.
$call
The captured function call.
$verbose
The verbose argument (TRUE/FALSE).
$formula
The formula argument.
$data
The original data set.
$reference
The chosen reference distribution to classify outliers.
$sign_level
The significance level determining the cutoff.
$psi
The probability that an observation is not classified as an outlier under the null hypothesis of no outliers.
$cutoff
The cutoff used to classify outliers if their standardised residuals are larger than that value.
$bias_corr
A numeric bias correction factor to account for potential false positives (observations classified as outliers even though they are not).
$initial
A list storing settings about the initial estimator:
$estimator
is the type of the initial estimator (e.g. robustified or saturated),$split
how the sample is split (NULL
if argument not used),$shuffle
whether the sample is shuffled before splitting (NULL
if argument not used),$shuffle_seed
the value of the random seed (NULL
if argument not used),$user
the user-specified initial model (NULL
if not used).$convergence
A list storing information about the convergence of the outlier-detection algorithm:
$criterion
is the user-specified convergence criterion (NULL
if argument not used),$difference
is initialised asNULL
.$converged
is initialised asNULL
.$iter
is initialised asNULL
.$max_iter
the maximum number of iterations if does not converge (NULL
if not used or applicable).$iterations
A list storing information about the iterations of the algorithm.
$setting
stores the user-specifiediterations
argument.$actual
is initialised asNULL
and will store the actual number of iterations done.