regFun: Fitness function from linear regressions based on mean...

In evola: Evolutionary Algorithm

Fitness function from linear regressions based on mean squared error.

Description

Simple function for fitness where the mean squared error is computed when the user provides y and X and b are the average allelic effects of the population in the genetic algorithm.

Usage

regFun(Y,b,Q,D,a,lambda,X,y)

Arguments

Y	A matrix of trait values (internal matrix). See details.
b	A vector of trait weights (provided by users). See details.
Q	A QTL matrix (internal matrix). See details.
D	An LD matrix. See details.
a	A named list with vectors of average allelic effects per trait (internal matrix). See details.
lambda	A numeric value to weight the Q'DQ portion of the objective function (to be provided by the user with the lambda argument). See details.
X	A matrix of covariates or explanatory variables (to be provided by the user in the ... arguments). See details.
y	A vector of the response variable (to be provided by the user in the ... arguments). See details.

Details

A simple apply function of a regular mean squared error.

( y - X%*%b ) ^ 2

Internally, we use this function in the following way:

The y vector and X matrix are provided by the user and are fixed values that do not change across iterations. The evolutionary algorithm optimizes the b values which are the QTLs and associated average allelic effects that are evolving. The 'b' coefficients in the formula come from the GA and are computed as:

b[j] = a[[1]][p[[j]]]

where a[[1]] is the list of QTL average allelic effects per trait provided in the original dataset, whereas p is a list (with length equal to the number of solutions) that indicates which QTLs are activated in each solution and the j variable is just a counter so each solution is tested.

Value

$res

a vector of values

References

Giovanny Covarrubias-Pazaran (2024). evola: a simple evolutionary algorithm for complex problems. To be submitted to Bioinformatics.

See Also

evolafit – the core function of the package

Examples

y <- rnorm(40) # 4 responses
X=matrix(rnorm(120),40,3) # covariates
Q=matrix(0,40,30) # QTL matrix with 30 QTLs
for(i in 1:nrow(Q)){Q[i,sample(1:ncol(Q),3)]=1}
a <- matrix(rnorm(30),ncol=1) # 30 average allelic effects in trait 1

mse = regFun(y=y, X=X, Q=Q, a=a, # used
             # ignored, Y is normally available in the evolafit routine
             Y=X) 

evola documentation built on Aug. 8, 2025, 7:54 p.m.