非线性最小二乘拟合nls
x <- c(3.07119e-06, 4.29966e-06, 6.55186e-06, 9.41830e-06, 1.39227e-05, 2.04746e-05, 3.07119e-05, 4.29966e-05, 6.55186e-05, 9.41830e-05 ,1.39227e-04 ,2.04746e-04) #
y <- c(-0.009063872, 0.057893999, 0.111945847, 0.158933826, 0.217008712, 0.361448959, 0.420216381, 0.534205132, 0.659209119, 0.718999094, 0.745282917, 0.760975686)
fun <- 'y ~ 1/(1 + exp((-Alpha) - Beta * log10(x)))'
fit <- nls(fun, start = list(Alpha = 12, Beta = 2.5), algorithm="default")
yhat <- predict(fit, x)
ssr <- sum((yhat - mean(y))^2) #explained sum of squares
sst <- sum((y - mean(y))^2) # total sum of squares
res <- y - yhat # or summary(fit)$residuals, same values here
sse_1 <- sst - ssr
sse_2 <- sum(res^2)
为什么sse_1 和sse_2不相同 ? 应该以哪个sse为准?