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Editing the indicators list

Source: R/QR_functions.R
QR_var_manipulation.Rd

Functions to remove indicators (remove_indicators()) or retrain some indicators only (retain_indicators()) from QR_matrix or mQR_matrix objects. The series names (column "series") cannot be removed.

Usage

remove_indicators(x, ...)

retain_indicators(x, ...)

Arguments

x

a QR_matrix or mQR_matrix object.

...

names of the variable to remove (or keep)

Value

remove_indicators() returns the same object x reduced by the flags and variables used as arguments ... So if the input x is a QR_matrix, an object of class QR_matrix is returned. If the input x is a mQR_matrix, an object of class mQR_matrix is returned.

See also

Traduction française

Other var QR_matrix manipulation: add_indicator(), recode_indicator_num()

Examples

# Path of matrix demetra_m
demetra_path <- file.path(
    system.file("extdata", package = "JDCruncheR"),
    "WS/ws_ipi/Output/SAProcessing-1",
    "demetra_m.csv"
)

# Extract the quality report from the demetra_m file
QR <- extract_QR(demetra_path)

# Compute the score
QR <- compute_score(QR, n_contrib_score = 2)

# Retain indicators
retain_indicators(QR, "score", "m7") # retaining "score" and "m7"
#> The quality report matrix has 13 observations
#> There are 3 indicators in the modalities matrix and 3 indicators in the values matrix
#> 
#> The quality report matrix contains the following variables:
#> series  m7  score
#> 
#> There's no additionnal variable in the values matrix
#> 
#> The smallest score is 75 and the greatest is 515
#> The average score is 318.462 and its standard deviation is 158.224
#> 
#> The following formula was used to calculate the score:
#> 30 * qs_residual_sa_on_sa + 30 * f_residual_sa_on_sa + 20 * qs_residual_sa_on_i + 20 * f_residual_sa_on_i + 30 * f_residual_td_on_sa + 20 * f_residual_td_on_i + 15 * oos_mean + 10 * oos_mse + 15 * residuals_independency + 5 * residuals_homoskedasticity + 5 * residuals_skewness + 5 * m7 + 5 * q_m2
retain_indicators(QR, c("score", "m7")) # Same
#> The quality report matrix has 13 observations
#> There are 3 indicators in the modalities matrix and 3 indicators in the values matrix
#> 
#> The quality report matrix contains the following variables:
#> series  m7  score
#> 
#> There's no additionnal variable in the values matrix
#> 
#> The smallest score is 75 and the greatest is 515
#> The average score is 318.462 and its standard deviation is 158.224
#> 
#> The following formula was used to calculate the score:
#> 30 * qs_residual_sa_on_sa + 30 * f_residual_sa_on_sa + 20 * qs_residual_sa_on_i + 20 * f_residual_sa_on_i + 30 * f_residual_td_on_sa + 20 * f_residual_td_on_i + 15 * oos_mean + 10 * oos_mse + 15 * residuals_independency + 5 * residuals_homoskedasticity + 5 * residuals_skewness + 5 * m7 + 5 * q_m2

# Remove indicators
QR <- remove_indicators(QR, "score") # removing "score"

extract_score(QR) # is NULL because we removed the score indicator
#> NULL