R中的变点检测

Question

R中的变点检测

4

有人有解决这个R问题的想法吗？它是要在关系中找到一个变化点，比如下面数据中的x=5。

fitDat <- data.frame(x=1:10, y=c(rnorm(5, sd=0.2),(1:5)+rnorm(5, sd=0.2)))
plot(fitDat$x, fitDat$y)

SAS表现出色;

/*simulated example*/
data test;
INPUT id $ x y;
cards;
1   1 -0.22711769
2   2 -0.08712909
3   3  0.06922072
4   4 -0.12940913
5   5 -0.43152927
6   6  1.17685016
7   7  1.83410448
8   8  2.88528795
9   9  4.30078012
10 10  4.84517101
;
run;

proc nlmixed data=test;
    parms B0 = 0 B1=1 t0=5 sigma_r=1;
    mu1 = (x <= t0)*B0;
    mu2 = (x > t0)*(B0 + B1*(x - t0));
    mu=mu1+mu2;
    model y~normal(mu,sigma_r);
run;

R失败了;

changePointOptim <- function(x, int, slope, delta){ # code adapted from https://stats.stackexchange.com/questions/7527/change-point-analysis-using-rs-nls
       int + (pmax(delta-x, 0) * slope)
}

# nls
nls(y ~ changePointOptim(x, b0, b1, delta), 
              data = fitDat, 
              start = c(b0 = 0, b1 = 1, delta = 5))

# optimization
sqerror <- function (par, x, y)
       sum((y - changePointOptim(x, par[1], par[2], par[3]))^2)
minObj <- optim(par = c(0, 1, 3), fn = sqerror,
              x = fitDat$x, y = fitDat$y, method = "BFGS")
minObj$par

# nlme
library(nlme)
nlmeChgpt <- nlme(y ~ changePointOptim(b0, b1, delta,x), 
              data = fitDat, 
              fixed = b0 + b1 + delta, 
              start = c(b0=0, b1=1, delta=5)) 
summary(nlmeChgpt)

作为一个简单的案例，信号很清晰，R应该可以工作。我想知道我在R中做错了什么（我使用了一些来自https://stats.stackexchange.com/questions/7527/change-point-analysis-using-rs-nls的代码）。有人有建议/解决方案吗？

谢谢！

Willem

- user2941283

请提供可重现的输入和结果。使用 set.seed，以便我们都可以使用相同的输入数据集进行工作。解释一下您如何确定 SAS“有效”，而 R 则“无效”。 - Carl Witthoft

1个回答

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- Miff · Accepted Answer

您在changePointOptim中有一个符号错误。然后：

set.seed(0)
fitDat <- data.frame(x=1:10, y=c(rnorm(5, sd=0.2),(1:5)+rnorm(5, sd=0.2)))
plot(fitDat$x, fitDat$y)


changePointOptim <- function(x, int, slope, delta){ # code adapted from http://stats.stackexchange.com/questions/7527/change-point-analysis-using-rs-nls
       int + (pmax(x-delta, 0) * slope)  ####Chamnged sign here
}

# optimization
sqerror <- function (par, x, y)
       sum((y - changePointOptim(x, par[1], par[2], par[3]))^2)
minObj <- optim(par = c(0, 1, 3), fn = sqerror,
              x = fitDat$x, y = fitDat$y, method = "BFGS")

提供：

> minObj$par
[1] 0.1581436 1.1762401 5.5963392

这是与你最初开始的内容相关的。

# nls
nls(y ~ changePointOptim(x, b0, b1, delta),
              data = fitDat,
              start = list(b0 = 0, b1 = 1, delta = 5)) ##Note it is a list

提供相同的结果：

0.1581 1.1762 5.5963

目前对NLME还不确定