R SSasympOrig 通过原点的自启动 NLS 渐近回归模型

说明

此selfStart 模型通过原点及其梯度评估渐近回归函数。它具有 initial 属性，用于评估给定数据集的参数 Asym 和 lrc 的初始估计。

用法

SSasympOrig(input, Asym, lrc)

参数

input	用于评估模型的数值向量。
Asym	表示水平渐近线的数字参数。
lrc	表示速率常数的自然对数的数字参数。

值

与 input 长度相同的数值向量。它是表达式 Asym*(1 - exp(-exp(lrc)*input)) 的值。如果所有参数 Asym 和 lrc 都是对象的名称，则与这些名称相关的梯度矩阵将作为名为 gradient 的属性附加。

例子

Lob.329 <- Loblolly[ Loblolly$Seed == "329", ]
SSasympOrig(Lob.329$age, 100, -3.2)  # response only
local({   Asym <- 100; lrc <- -3.2
  SSasympOrig(Lob.329$age, Asym, lrc) # response and gradient
})
getInitial(height ~ SSasympOrig(age, Asym, lrc), data = Lob.329)
## Initial values are in fact the converged values
fm1 <- nls(height ~ SSasympOrig(age, Asym, lrc), data = Lob.329)
summary(fm1)


## Visualize the SSasympOrig()  model  parametrization :

  xx <- seq(0, 5, length.out = 101)
  yy <- 5 * (1- exp(-xx * log(2)))
  stopifnot( all.equal(yy, SSasympOrig(xx, Asym = 5, lrc = log(log(2)))) )

  require(graphics)
  op <- par(mar = c(0, 0, 3.5, 0))
  plot(xx, yy, type = "l", axes = FALSE, ylim = c(0,5), xlim = c(-1/4, 5),
       xlab = "", ylab = "", lwd = 2,
       main = quote("Parameters in the SSasympOrig model"~~ f[phi](x)))
  mtext(quote(list(phi[1] == "Asym", phi[2] == "lrc")))
  usr <- par("usr")
  arrows(usr[1], 0, usr[2], 0, length = 0.1, angle = 25)
  arrows(0, usr[3], 0, usr[4], length = 0.1, angle = 25)
  text(usr[2] - 0.2, 0.1, "x", adj = c(1, 0))
  text(   -0.1,   usr[4], "y", adj = c(1, 1))
  abline(h = 5, lty = 3)
  axis(2, at = 5*c(1/2,1), labels= expression(frac(phi[1],2), phi[1]), pos=0, las=1)
  arrows(c(.3,.7), 5/2,
         c(0, 1 ), 5/2, length = 0.08, angle = 25)
  text(   0.5,     5/2, quote(t[0.5]))
  text(   1 +.4,   5/2,
       quote({f(t[0.5]) == frac(phi[1],2)}~{} %=>% {}~~{t[0.5] == frac(log(2), e^{phi[2]})}),
       adj = c(0, 0.5))
  par(op)

作者

José Pinheiro and Douglas Bates

也可以看看

nls , selfStart