| Type: | Package |
| Title: | Crossed Classification Credibility Model |
| Version: | 0.1.0 |
| Date: | 2022-05-05 |
| Maintainer: | Muhlis Ozdemir <muhlisozdemir@gazi.edu.tr> |
| Description: | Calculates the credit debt for the next period based on the available data using the cross-classification credibility model. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | dplyr, rlang |
| RoxygenNote: | 7.1.2 |
| NeedsCompilation: | no |
| Packaged: | 2022-05-27 11:43:29 UTC; Gazi |
| Author: | Muhlis Ozdemir 
[aut, cre],
Seda Tugce Altan
[aut, ctb],
Meral Ebegil
[aut, ctb, ths] |
| Depends: | R (≥ 3.5.0) |
| Repository: | CRAN |
| Date/Publication: | 2022-05-30 09:00:02 UTC |
Crossed Classification Credibility Model.
Description
Estimation of premium credibility for Crossed Classification Credibility Model. In this model an insurance portfolio is subdivided by two qualitative risk factors, modeled in symmetrical way. Especially this model presents an alternative way when data is not classifiable in a hierarchical manner and to determine main effects of both risk factors. Also this model more useful to calculate co-effect both risk factors. Dannenburg et al., (1995, ISBN:90-802117-3-7)
Author(s)
Muhlis Ozdemir muhlisozdemir@gazi.edu.tr Seda Tugce Altan stugce.altan@gazi.edu.tr Meral Ebegil mdemirel@gazi.edu.tr
Examples
raw_data <- debt
categorical_columns = c(1,2)
weights_column = 3
debt_column = 4
calculate_generalMean(raw_data, categorical_columns, weights_column, debt_column)
calculate_variance_and_std(raw_data, categorical_columns, weights_column, debt_column)
calculate_group_averages_matrix(raw_data, categorical_columns, weights_column, debt_column)
calculate_weights_of_obs_matrix(raw_data, categorical_columns, weights_column, debt_column)
calculate_varianceComponents(raw_data, categorical_columns, weights_column, debt_column)
estimate_credibility(raw_data, categorical_columns, weights_column, debt_column)
General Mean
Description
General Mean
Usage
calculate_generalMean(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
general mean
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_generalMean(raw_data, categorical_columns, weights_column, debt_column)
Group Averages Matrix
Description
Group Averages Matrix
Usage
calculate_group_averages_matrix(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
group averages matrix
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_group_averages_matrix(raw_data, categorical_columns, weights_column, debt_column)
Repeats of observations
Description
Repeats of observations
Usage
calculate_obs_and_group_weights(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
This function returns categorical group sizes.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_obs_and_group_weights(raw_data, categorical_columns, weights_column, debt_column)
Variance Components
Description
Variance Components
Usage
calculate_varianceComponents(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
variance components
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_varianceComponents(raw_data, categorical_columns, weights_column, debt_column)
Variance and Standard Deviation
Description
Variance and Standard Deviation
Usage
calculate_variance_and_std(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
variance and sd.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_variance_and_std(raw_data, categorical_columns, weights_column, debt_column)
Weights of observation matrix
Description
Weights of observation matrix
Usage
calculate_weights_of_obs_matrix(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
Weights of observation matrix.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
calculate_weights_of_obs_matrix(raw_data, categorical_columns, weights_column, debt_column)
Column Wise Matrix Diff
Description
This function returns of the column wise difference between the m matrix and the vector v
Usage
col_diff_matrix_with_vector(m, vec)
Arguments
m |
is a matrix |
vec |
is a vector |
Value
This function returns a num matrix.
Data checker
Description
Throws an error message if at least 2 features is not in categorical format.
Usage
control_data(x)
Arguments
x |
a dataset. |
Value
This function checks whether dataset has at least 2 features in categorical format.
Debt Data
Description
A real data which published by Turkey Banking Regulation and Supervisory Board <https://www.bddk.org.tr/BultenAylik/en>.
Usage
debt
Format
A data frame of 106 rows and 4 columns
- bank
categorical data of bank type. Bank type includes four subcategory such as State Banks, Deposit Banks, Foreign Banks and Privately Owned Deposit Banks
- loan
categorical data of dept type. Loan type includes three subcategory such as non-performing vehicle, home, and consumer loan.
- weights
Numeric values of weights
- debt
Numeric values of debt
Column Wise Matrix Division
Description
This function returns of the column wise division of the m matrix and the vector v.
Usage
div_matrix_cols_with_vector(m, vec)
Arguments
m |
is a matrix |
vec |
is a vector |
Value
This function returns a num matrix.
Row Wise Matrix Division
Description
This function returns of the row wise division of the m matrix and the vector v.
Usage
div_matrix_rows_with_vector(m, vec)
Arguments
m |
is a matrix |
vec |
is a vector |
Value
This function returns a num matrix.
The Credibility Premium Estimates
Description
The Credibility Premium Estimates
Usage
estimate_credibility(
raw_data,
categorical_columns,
weights_column,
debt_column
)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit dept column of data set. |
Value
returns premium estimation of credibility.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
estimate_credibility(raw_data, categorical_columns, weights_column, debt_column)
Column Wise Matrix Multiplication
Description
This function returns of the column wise multiplication of the m matrix and the vector v.
Usage
mult_matrix_cols_with_vector(m, vec)
Arguments
m |
is a matrix |
vec |
is a vector |
Value
This function returns a num matrix.
Row Wise Matrix Diff
Description
This function returns of the row wise difference between the m matrix and the vector v
Usage
row_diff_matrix_with_vector(m, vec)
Arguments
m |
is a matrix |
vec |
is a vector |
Value
This function returns a num matrix.
Get names
Description
Get names
Usage
save_names(raw_data, categorical_columns)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
Value
returns categorical variables' unique values and column names of data set.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
save_names(raw_data, categorical_columns)
Data prep
Description
Data prep
Usage
set_data(raw_data, categorical_columns, weights_column, debt_column)
Arguments
raw_data |
a data set of credibility. |
categorical_columns |
categorical column of data set. |
weights_column |
weights column of data set. |
debt_column |
credit debt column of data set. |
Value
This function returns a tibble as prepared_data by using raw_data. Adds new columns to raw data as weighted_obs, group_average_weights, variance_column.
Examples
raw_data <- debt
categorical_columns <- c(1,2)
weights_column <- 3
debt_column <- 4
prepared_data <- set_data(raw_data, categorical_columns, weights_column, debt_column)