Permet de retirer des indicateurs (fonction remove_indicators()) ou de
n'en retenir que certains (fonction retain_indicators()) d'objets
QR_matrix ou mQR_matrix. Le nom des séries
(colonne "series") ne peut être enlevé.
Arguments
- x
objet de type
QR_matrixoumQR_matrix.- ...
noms des variables à retirer (ou conserver).
Value
remove_indicators() renvoie le même objet x réduit par
les drapeaux et les variables utilisés comme arguments ... Donc si l'entrée
x est une matrice QR_matrix, un objet de la classe QR_matrix est
renvoyé. Si le code d'entrée x est une matrice mQR, un objet de la
classe mQR_matrix est renvoyé.
Examples
# Chemin menant au fichier demetra_m.csv
demetra_path <- file.path(
system.file("extdata", package = "JDCruncheR"),
"WS/ws_ipi/Output/SAProcessing-1",
"demetra_m.csv"
)
# Extraire le bilan qualité à partir du fichier demetra_m.csv
QR <- extract_QR(demetra_path)
# Calculer le score
QR <- compute_score(x = QR, n_contrib_score = 5)
# Retenir certains indicateurs
retain_indicators(QR, "score", "m7") # Retiens les indicateurs "score" et "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")) # Pareil
#> 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
# Retirer des indicateurs
QR <- remove_indicators(QR, "score") # removing "score"
extract_score(QR) # est NULL car l'indicateur "score a été retiré
#> NULL