I am trying to store the results of the the code below, however I could only come up with a solution to save the results of the model with the smallest sum of squared residuals. This was useful until the results were in the limits of the range of both c and gamma, therefore I need to assess the characteristics of other points. For this I need to store the results of every iteration. Does anyone know how to do this in this case?
Thanks in advance!
dlpib1 <- info$dlpib1
scale <- sqrt(var(dlpib1))
RSS.m <- 10
for (c in seq(-0.03,0.05,0.001)){
for (gamma in seq(1,100,0.2))
{
trans <- (1+exp(-(gamma/scale)*(dlpib1-c)))^-1
grid.regre <-lm(dlpib ~ dlpib1 + dlpib8 + trans + trans*dlpib1 +
+ I(trans*dlpib4) ,data=info)
coef <- grid.regre$coefficients
RSS <- sum(grid.regre$residuals^2)
if (RSS < RSS.m){
RSS.m <- RSS
gamma.m <- gamma
c.m <- c
coef.m <- coef
}
}
}
grid <- c(RSS=RSS.m,gamma=gamma.m,c=c.m,coef.m)
grid`
The easiest way to store model results by iterations is in a list
:
List = list()
for(i in 1:100)
{
LM = lm(rnorm(10)~rnorm(10))
List[[length(List)+1]] = LM
}