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Weighted score calculation

Source: R/QR_functions.R
weighted_score.Rd

Function to weight a pre-calculated score

Usage

weighted_score(x, pond = 1)

Arguments

x

a QR_matrix or mQR_matrix object

pond

the weights to use. Can be an integer, a vector of integers, the name of one of the quality report variables or a list of weights for the mQR_matrix objects.

Value

the input with an additionnal weighted score

See also

Traduction française

Other QR_matrix functions: export_xlsx(), export_xlsx.QR_matrix(), export_xlsx.mQR_matrix(), extract_QR(), rbind.QR_matrix(), sort()

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)

# Weighted 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  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  residuals_independency  residuals_normality  residuals_homoskedasticity  residuals_skewness  residuals_kurtosis  oos_mean  oos_mse  m7  q  q_m2  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 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

# Extract the weighted score
QR$modalities$score_pond
#>  [1]  280  210  560  490  150  480  920 1010  920 1030  600  610 1020