| Title: | Description of a Data Frame |
|---|---|
| Description: | Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>. |
| Authors: | Sandip Garai [aut, cre], Ranjit Kumar Paul [aut] |
| Maintainer: | Sandip Garai <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.1 |
| Built: | 2025-01-28 02:37:23 UTC |
| Source: | https://github.com/cran/DescribeDF |
Gives descriptive statistics for a data frame (df) excluding the first column. First column mentions different statistics. Other columns are the Statistics values of the particular column (series). "p_value <= 0.01: ***; p_value <= 0.05: **; p_value <= 0.1: *".
df_descstat(df)df_descstat(df)
df |
Data Frame with first column as serial number or date |
desc_df - A table contains 10 descriptive statistics rowwise
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
# create a vector of dates dates <- seq(as.Date("2021-01-01"), as.Date("2021-01-05"), by = "day") # create vectors of random numeric data for columns A through E A <- runif(5, 0, 1) B <- runif(5, 0, 1) C <- runif(5, 0, 1) D <- runif(5, 0, 1) E <- runif(5, 0, 1) # combine the vectors into a data frame df <- data.frame(Date = dates, A = A, B = B, C = C, D = D, E = E) # print the data frame print(df) # Data Description df_descstat(df)# create a vector of dates dates <- seq(as.Date("2021-01-01"), as.Date("2021-01-05"), by = "day") # create vectors of random numeric data for columns A through E A <- runif(5, 0, 1) B <- runif(5, 0, 1) C <- runif(5, 0, 1) D <- runif(5, 0, 1) E <- runif(5, 0, 1) # combine the vectors into a data frame df <- data.frame(Date = dates, A = A, B = B, C = C, D = D, E = E) # print the data frame print(df) # Data Description df_descstat(df)
Gives non linearity test result for a df excluding the first column (contains serial number or date). This will give a list of data frames. Data frames are named as the names of columns of the data frame. First column mentions different statistics (eps). Other columns are the Statistics values of the particular dimension. "p_value <= 0.01: ***; p_value <= 0.05: **; p_value <= 0.1: *".
df_nonlinearity(df)df_nonlinearity(df)
df |
Data Frame with first column as serial number or date |
result_list - List of data frames named as the column names of provided data frame. Each df is such that first column mentions different statistics and other columns are the Statistics values of the particular dimension.
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
# Create a sequence of numbers from 1 to 100 serial <- 1:100 # Create six vectors of random numbers, one for each column col1 <- rnorm(100) col2 <- rnorm(100) col3 <- rnorm(100) col4 <- rnorm(100) col5 <- rnorm(100) col6 <- rnorm(100) # Combine the vectors into a data frame df <- data.frame(serial, col1, col2, col3, col4, col5, col6) df_nonlinearity(df)# Create a sequence of numbers from 1 to 100 serial <- 1:100 # Create six vectors of random numbers, one for each column col1 <- rnorm(100) col2 <- rnorm(100) col3 <- rnorm(100) col4 <- rnorm(100) col5 <- rnorm(100) col6 <- rnorm(100) # Combine the vectors into a data frame df <- data.frame(serial, col1, col2, col3, col4, col5, col6) df_nonlinearity(df)
Gives a list of three data frames: 'ADF', 'PP', 'KPSS'. This will also indicate whether the data is stationary or not according to the null hypothesis of the corresponding tests. The data frame must contain serial number or date or anything in the 1st column.This function will exclude the 1st column of the data frame and will perform tests on other columns. "p_value <= 0.01: ***; p_value <= 0.05: **; p_value <= 0.1: *".
df_stationarity(df)df_stationarity(df)
df |
Data Frame with first column as serial number or date |
test_results - List of three data frames: 'ADF', 'PP', 'KPSS'
Garai, S., & Paul, R. K. (2023). Development of MCS based-ensemble models using CEEMDAN decomposition and machine intelligence. Intelligent Systems with Applications, 18, 200202.
Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.
# create a vector of dates dates <- seq(as.Date("2021-01-01"), as.Date("2021-01-05"), by = "day") # create vectors of random numeric data for columns A through E A <- runif(5, 0, 1) B <- runif(5, 0, 1) C <- runif(5, 0, 1) D <- runif(5, 0, 1) E <- runif(5, 0, 1) # combine the vectors into a data frame df <- data.frame(Date = dates, A = A, B = B, C = C, D = D, E = E) # print the data frame print(df) # stationarity results df_stationarity(df)# create a vector of dates dates <- seq(as.Date("2021-01-01"), as.Date("2021-01-05"), by = "day") # create vectors of random numeric data for columns A through E A <- runif(5, 0, 1) B <- runif(5, 0, 1) C <- runif(5, 0, 1) D <- runif(5, 0, 1) E <- runif(5, 0, 1) # combine the vectors into a data frame df <- data.frame(Date = dates, A = A, B = B, C = C, D = D, E = E) # print the data frame print(df) # stationarity results df_stationarity(df)