# Score_algorithms_interval: Score a vector of error metrics for algorithms by the... In bishun945/FCMm: Fuzzy Cluster Method Based on the Optimized m Value (Fuzzifier)

## Description

Score a vector of error metrics for algorithms by the interval

## Usage

 ```1 2 3 4 5 6 7``` ```Score_algorithms_interval( x, trim = FALSE, reward.punishment = TRUE, decreasing = TRUE, hundred.percent = FALSE ) ```

## Arguments

 `x` Input vector of error metrics (NA values are allowed) `trim` whether to run a trim process to calculate mean and standard deviation of input vector x (Default as `FALSE`) `reward.punishment` Whether to conduct the reward and punishment mechanism in scoring system (Default as `TRUE`) `decreasing` the order of the good metric to be evaluated. For instance, MAE should use `decreasing = TRUE` (Default) since the algorithm performs better when MAE becomes smaller. However, when comes to `Rsquare` from linear regression (maximum is 1), it should be `FALSE` `hundred.percent` A variable constrain for metrics that the maximun of input `x` should not be greater than 100.

## Value

Results of `Score_algorithms_interval()` are returned as a list including:

 `p` A ggplot list of the scoring result. `score` The final score from by the interval score. `u` Trimmed mean of input x with `NA` values removed. `bds` Up and low boundaries for determining scores. `x` The input x.

Other Algorithm assessment: `Assessment_via_cluster()`, `Getting_Asses_results()`, `Sampling_via_cluster()`, `Score_algorithms_sort()`, `Scoring_system()`
 ```1 2 3``` ```set.seed(1234) x = runif(10) result = Score_algorithms_interval(x) ```