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JVS matrix object

Source: R/JVS_classes.R
JVS_matrix.Rd

JVS_matrix() are creating a quality report based on the Eurostat JVS Plug-In.

Usage

JVS_matrix(x = list())

# S3 method for class 'data.frame'
JVS_matrix(x)

# S3 method for class 'JVS_matrix'
JVS_matrix(x)

# Default S3 method
JVS_matrix(x)

Arguments

x

a data.frame containing the output variables' values (test p-values, test statistics, etc.) and modalities (Yes/No).

Value

JVS_matrix() creates and returns a JVS_matrix object.

Details

AJVS_matrix object is a data.frame with 30 items:

  • Series

  • Method

  • Period

  • Nobs

  • Start

  • End

  • Adjustment

  • Presence of Seasonality in the Raw Series

  • Presence of TD effects

  • Log-Transformation

  • ARIMA Model

  • LeapYear

  • MovingHoliday

  • NbTD

  • Noutliers

  • Outlier1

  • Outlier2

  • Outlier3

  • Residual Seasonality in SA Series (F-test)

  • Residual TD Effect

  • Q-Stat (for X13)

  • Final Henderson Filter

  • Stage 2 Henderson Filter

  • Seasonal Filter

  • Quality

  • Autocorrelation of order 1 of the SA series

  • Ljung-Box Test (P-value)

  • Autocorrelation negative and significant

  • Irregular Standard-Deviation

  • Max-Adj

See also

Traduction française

Examples

JVS_data <- data.frame(
    Series = "Series 1",
    Method = "X13",
    Period = 12L,
    Nobs = 300L,
    Start = "2000-01-01",
    End = "2024-12-01",
    Adjustment = "SA",
    `Presence of Seasonality in the Raw Series` = "Yes",
    `Presence of TD effects` = "No",
    `Log-Transformation` = "No",
    `ARIMA Model` = "(0,1,1)(0,1,1)",
    LeapYear = "Yes",
    MovingHoliday = "No",
    NbTD = 0L,
    Noutliers = 1L,
    Outlier1 = "AO (2020-01)",
    Outlier2 = "AO (2018-11)",
    Outlier3 = NA_character_,
    `Residual Seasonality in SA Series (F-test)` = "No",
    `Residual TD Effect` = "No",
    `Q-Stat (for X13)` = "Good",
    `Final Henderson Filter` = "H13",
    `Stage 2 Henderson Filter` = "H13",
    `Seasonal Filter` = "S3X5",
    Quality = "Good",
    `Autocorrelation of order 1 of the SA series` = 0.2,
    `Ljung-Box Test (P-value)` = 0.8,
    `Autocorrelation negative and significant` = "",
    `Irregular Standard-Deviation` = 0.8,
    `Max-Adj` = 2.5
)

# Create a JVS_matrix object
JVS <- JVS_matrix(JVS_data)

# Check the class of the object
class(JVS)
#> [1] "JVS_matrix" "data.frame"