Sets the number of digits to display in the output of the different package functions.
Usage
set_display_digits(digits = list())Arguments
- digits
A list of numbers corresponding to the different display digits. The default value is
list().
Details
The parameter digits is a named list. It will be containing:
d1: contains the number of display digits for the values of probability estimates such as \(\alpha\), \(\delta\),pin,mpin,mpin(j),adjpin,psos, \(\theta\), and \(\theta'\).d2: contains the number of display digits for the values of \(\mu\), \(\epsilon\)b and \(\epsilon\)s, as well as information criteria:AIC,BIC, andAWE.d3: contains the number of display digits for the remaining values such asvpinstatistics andlikelihoodvalue .
If the function is called with no arguments, the display digits will be reset
to the default values, i.e., list(d1 = 6, d2 = 2, d3 = 3)).
If the argument digits is not omitted, the function will only accept a list
containing exactly three numerical values, each ranging
between 0 and 10. The list can be named or unnamed. If the numbers in the
argument digits are not integers, they will be rounded.
Examples
# There is a preloaded quarterly dataset called 'dailytrades' with 60
# observations. Each observation corresponds to a day and contains the total
# number of buyer-initiated transactions ('B') and seller-initiated
# transactions ('S') on that day. To know more, type ?dailytrades
xdata <- dailytrades
# We show the output of the function mpin_ml() using the default values
# of display digits. We then change these values using the function
# set_display_digits(), before displaying the same estimate.mpin object
# again to see the difference.
model <- mpin_ml(xdata, layers = 2, verbose = FALSE)
show(model)
#> ----------------------------------
#> MPIN estimation completed successfully
#> ----------------------------------
#> Likelihood factorization: Ersan (2016)
#> Estimation Algorithm : Maximum Likelihood Estimation
#> Initial parameter sets : Ersan (2016), Ersan and Alici (2016)
#> Info. layers in the data: provided by the user
#> ----------------------------------
#> 15 initial set(s) are used in the estimation
#> Type object@initialsets to see the initial parameter sets used
#>
#> MPIN model Sequential
#>
#>
#> ========== ==================
#> Variables Estimates
#> ========== ==================
#> alpha 0.266666, 0.483338
#> delta 0.312498, 0.034482
#> mu 677.93, 1512.38
#> eps.b 331.06
#> eps.s 338.2
#> ----
#> Likelihood (800.379)
#> mpin(j) 0.114343, 0.462349
#> mpin 0.576692
#> ========== ==================
#>
#> -------
#> Running time: 13.093 seconds
# Change the number of digits for d1 to 3, of d2 to 0 and of d3 to 2
set_display_digits(list(3, 0, 2))
#> Display digits updated successfully!
# No need to run the function mpin_ml() again to update the display of an
# estimate.mpin object.This holds for all estimate* S4 objects.
show(model)
#> ----------------------------------
#> MPIN estimation completed successfully
#> ----------------------------------
#> Likelihood factorization: Ersan (2016)
#> Estimation Algorithm : Maximum Likelihood Estimation
#> Initial parameter sets : Ersan (2016), Ersan and Alici (2016)
#> Info. layers in the data: provided by the user
#> ----------------------------------
#> 15 initial set(s) are used in the estimation
#> Type object@initialsets to see the initial parameter sets used
#>
#> MPIN model Sequential
#>
#>
#> ========== ============
#> Variables Estimates
#> ========== ============
#> alpha 0.267, 0.483
#> delta 0.312, 0.034
#> mu 678, 1512
#> eps.b 331
#> eps.s 338
#> ----
#> Likelihood (800.38)
#> mpin(j) 0.114, 0.462
#> mpin 0.577
#> ========== ============
#>
#> -------
#> Running time: 13.093 seconds