Permet de pondérer un score déjà calculé en fonction de variables.
Arguments
- x
objet de type
QR_matrixoumQR_matrix.- pond
pondération à appliquer au score. Il peut s'agir d'un nombre, d'un vecteur de nombres, du nom d'une des variables du bilan qualité ou d'une liste de pondérations pour les objets
mQR_matrix.
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)
#> Multiple column found for extraction of q statistic
#> first column selected
# Calculer le score
QR <- compute_score(QR, n_contrib_score = 2)
print(QR)
#> The quality report matrix has 13 observations
#> There are 19 indicators in the modalities matrix and 23 indicators in the values matrix
#>
#> The quality report matrix contains the following variables:
#> series residuals_homoskedasticity residuals_skewness residuals_kurtosis residuals_normality residuals_independency qs_residual_sa_on_sa f_residual_sa_on_sa qs_residual_sa_on_i f_residual_sa_on_i f_residual_td_on_sa f_residual_td_on_i oos_mean oos_mse q q_m2 m7 pct_outliers frequency arima_model score 1_highest_contrib_score 2_highest_contrib_score
#>
#> The variables exclusively found in the values matrix are:
#> frequency arima_model 1_highest_contrib_score 2_highest_contrib_score
#>
#> The smallest score is 30 and the greatest is 560
#> The average score is 330.385 and its standard deviation is 194.866
#>
#> 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
# Pondérer le score
QR <- weighted_score(QR, 2)
print(QR)
#> The quality report matrix has 13 observations
#> There are 20 indicators in the modalities matrix and 24 indicators in the values matrix
#>
#> The quality report matrix contains the following variables:
#> series residuals_homoskedasticity residuals_skewness residuals_kurtosis residuals_normality residuals_independency qs_residual_sa_on_sa f_residual_sa_on_sa qs_residual_sa_on_i f_residual_sa_on_i f_residual_td_on_sa f_residual_td_on_i oos_mean oos_mse q q_m2 m7 pct_outliers frequency arima_model score 1_highest_contrib_score 2_highest_contrib_score score_pond
#>
#> The variables exclusively found in the values matrix are:
#> frequency arima_model 1_highest_contrib_score 2_highest_contrib_score
#>
#> The smallest score is 30 and the greatest is 560
#> The average score is 330.385 and its standard deviation is 194.866
#>
#> 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
# Extraire le score pondéré
QR[["modalities"]][["score_pond"]]
#> [1] 290 90 600 620 60 390 1120 1120 1010 1090 510 620 1070