cal_block_energy {autoFC} R Documentation

## Calculation of Item Block "Energy"

### Description

Calculates the total "energy" of one or multiple paired item blocks, which is a linear combination of different functions applied to different item characteristics of interest.

### Usage

cal_block_energy(block, item_chars, weights, FUN)


### Arguments

 block An n by k integer matrix, where n is the number of item blocks and k is the number of items per block. item_chars An m by r data frame, where m is the total number of items to sample from, whether it is included in the block or not, whereas r is the number of item characteristics. weights A vector of length r with weights for each item characteristics in item_chars. Should provide a weight of 0 for specific characteristics not of interest, such as item ID. FUN A vector of customized function names for optimizing each item characteristic within each block, with length r.

### Details

This energy calculation function serves as the core for determining the acceptance or rejection of a newly built block over the previous one.

Higher energy is considered more preferable in this case.

Items in the same block can be paired based on characteristics such as:

Mean score, Item Factor, Factor loading, Item IRT Parameters, Reverse Coding, etc.

Pairings of different characteristics can be optimized in different way, by determining the customized function vector FUN and the corresponding weights.

### Value

A numeric value indicating the total energy for the given item block(s).

### Note

Use cal_block_energy_with_iia if inter-item agreement (IIA) metrics are needed.

Mengtong Li

### Examples

## Simulate 60 items loading on different Big Five dimensions,
## with different mean and item difficulty

item_dims <- sample(c("Openness","Conscientiousness","Neuroticism",
"Extraversion","Agreeableness"), 60, replace = TRUE)
item_mean <- rnorm(60, 5, 2)
item_difficulty <- runif(60, -1, 1)

## Construct data frame for item characteristics and produce
## 20 random triplet blocks with these 60 items

item_df <- data.frame(Dimensions = item_dims, Mean = item_mean,
Difficulty = item_difficulty)
solution <- make_random_block(60, 60, 3)

## See ?facfun for its use.
cal_block_energy(solution, item_chars = item_df,
weights = c(1,1,1), FUN = c("facfun", "var", "var"))



[Package autoFC version 0.1.2 Index]