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## We first use the plinear algorithm to fit an overall model,
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## We first use the plinear algorithm to fit an overall model,
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## ignoring that alpha and beta might vary with Strip.
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## ignoring that alpha and beta might vary with Strip.
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musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
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musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
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              start = list(th = 1), algorithm = "plinear")
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              start = list(th = 1), algorithm = "plinear")
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## IGNORE_RDIFF_BEGIN
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summary(musc.1)
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summary(musc.1)
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## IGNORE_RDIFF_END
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## Then we use nls' indexing feature for parameters in non-linear
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## Then we use nls' indexing feature for parameters in non-linear
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## models to use the conventional algorithm to fit a model in which
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## models to use the conventional algorithm to fit a model in which
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## alpha and beta vary with Strip.  The starting values are provided
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## alpha and beta vary with Strip.  The starting values are provided
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## by the previously fitted model.
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## by the previously fitted model.
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## Note that with indexed parameters, the starting values must be
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## Note that with indexed parameters, the starting values must be
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## given in a list (with names):
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## given in a list (with names):
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b <- coef(musc.1)
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b <- coef(musc.1)
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musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
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musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
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              start = list(a = rep(b[2], 21), b = rep(b[3], 21), th = b[1]))
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              start = list(a = rep(b[2], 21), b = rep(b[3], 21), th = b[1]))
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## IGNORE_RDIFF_BEGIN
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summary(musc.2)
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summary(musc.2)
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## IGNORE_RDIFF_END
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options(od)
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options(od)
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# princomp.Rd
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# princomp.Rd
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## Robust:
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## Robust:
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(pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))
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(pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))