MAVF_sensitivity {KraljicMatrix}

Multi-attribute value function sensitivity analysis

Description

MAVF_sensitivity computes summary statistics for multi-attribute value scores of x and y given a range of swing weights for each attribute

Usage

MAVF_sensitivity(data, x, y, x_wt_min, x_wt_max, y_wt_min, y_wt_max)

Arguments

data

A data frame

x

Variable from data frame to represent x attribute values

y

Variable from data frame to represent y attribute values

x_wt_min

Lower bound anchor point for x attribute swing weight

x_wt_max

Upper bound anchor point for x attribute swing weight

y_wt_min

Lower bound anchor point for y attribute swing weight

y_wt_max

Upper bound anchor point for y attribute swing weight

Details

The sensitivity analysis performs a Monte Carlo simulation with 1000 trials for each product or service (row). Each trial randomly selects a weight from a uniform distribution between the lower and upper bound weight parameters and calculates the mult-attribute utility score. From these trials, summary statistics for each product or service (row) are calculated and reported for the final output.

Value

A data frame with added variables consisting of sensitivity analysis summary statistics for each product or service (row).

See Also

MAVF_score for computing the multi-attribute value score of x and y given their respective weights

SAVF_score for computing the exponential single attribute value score

Examples

# Given the following data frame that contains \code{x} and \code{y} attribute
# values for each product or service contract, we can compute how the range of
# swing weights for each \code{x} and \code{y} attribute influences the multi-
# attribute value score.

df <- data.frame(contract = 1:10,
                 x_attribute = c(0.92, 0.79, 1.00, 0.39, 0.68, 0.55, 0.73, 0.76, 1.00, 0.74),
                 y_attribute = c(0.52, 0.19, 0.62, 1.00, 0.55, 0.52, 0.53, 0.46, 0.61, 0.84))

MAVF_sensitivity(df, x_attribute, y_attribute, .55, .75, .25, .45)