power<-table_all[,i+1]

 len <- nrow(power)

 y<-power

 ds <- data.frame(x = x, y = y)

 ds<-ds[order(ds[,1],decreasing=F),]

 plot(ds[,2])

 rhs <- function(x, b0, b1,b2,b3,b4) {b0*(b3-x)*(x<b3) + b1*(x-b4)*(x>b4)+b2 }

 m.2 <- nls(y ~ rhs(x, cold, hot,cons,L,U), data = ds, start = list(cold = 350, hot = 1600,cons = 5800,L=14,U=25), trace = T)

 parest <- as.data.frame(coef(m.2))

 b_result[i,]<-t(parest)

 summary(m.2)

 s <- seq(-10,35, length =len)

 plot(y ~ x, main = "Fitted power model, with intercept", sub = "Blue: fit; magenta: fit LSE ; green: known")

lines(s, predict(m.2, list(x = s)), lty = 1, col = "blue")

报错内容:step factor 0.000488281 reduced below `minFactor' of 0.000976563

nls.control参数解释：

Description

Allow the user to set some characteristics of the nls nonlinear least squares algorithm.

Usagenls.control(maxiter = 50, tol = 1e-05, minFactor = 1/1024,            printEval = FALSE, warnOnly = FALSE)Arguments

所以nls一般报错有两个可能，一个是maxiter次数用完还没有收敛，一个是step－size已经缩小的最小的极限minfactor还是没有是的tol下降。

简单的处理方法，可以是改minfactor，也可以将warnOnly设置为Ture。

设置方法：

 m.2 <- nls(y ~ rhs(x, cold, hot,cons,L,U), data = ds, start = list(cold = 350, hot = 1600,cons = 5800,L=14,U=25), trace = T,control=nls.control(warnOnly = T))

部分参考：https://stat.ethz.ch/R-manual/R-devel/library/stats/html/nls.control.html