R을 이용한 curve fitting

아주 간단하지만, 잘 정리되어 있는..

링크를 찾아서 기록을 남겨둡니다.

http://davetang.org/muse/2013/05/09/on-curve-fitting/

#fit first degree polynomial equation:

fit  <- lm(y~x)

#second degree

fit2 <- lm(y~poly(x,2,raw=TRUE))

#third degree

fit3 <- lm(y~poly(x,3,raw=TRUE))

#fourth degree

fit4 <- lm(y~poly(x,4,raw=TRUE))

#generate range of 50 numbers starting from 30 and ending at 160

xx <- seq(30,160, length=50)

plot(x,y,pch=19,ylim=c(0,150))

lines(xx, predict(fit, data.frame(x=xx)), col="red")

lines(xx, predict(fit2, data.frame(x=xx)), col="green")

lines(xx, predict(fit3, data.frame(x=xx)), col="blue")

lines(xx, predict(fit4, data.frame(x=xx)), col="purple")




#create function for the third order polynomial

third_order <- function(newdist, model) {

    coefs <- coef(model)

    #y = d + cx + bx^2 + ax^3

    res <- coefs[1] + (coefs[2] * newdist) + (coefs[3] * newdist^2) + (coefs[4] * newdist^3)

    return(res)

}

yy <- third_order(xx,fit3)

plot(xx,yy,type="l")

lines(x,y,col="red")







anova(fit2,fit3)










coef(fit)