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> # File src/library/stats/tests/nls.R
> # Part of the R package, https://www.R-project.org
> #
> # This program is free software; you can redistribute it and/or modify
> # it under the terms of the GNU General Public License as published by
> # the Free Software Foundation; either version 2 of the License, or
> # (at your option) any later version.
> #
> # This program is distributed in the hope that it will be useful,
> # but WITHOUT ANY WARRANTY; without even the implied warranty of
> # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
> # GNU General Public License for more details.
> #
> # A copy of the GNU General Public License is available at
> # https://www.R-project.org/Licenses/
>
> ## tests of nls, especially of weighted fits
>
> library(stats)
> options(digits = 5) # to avoid trivial printed differences
> options(useFancyQuotes = FALSE) # avoid fancy quotes in o/p
> options(show.nls.convergence = FALSE) # avoid non-diffable output
> options(warn = 1)
>
> have_MASS <- requireNamespace('MASS', quietly = TRUE)
>
> pdf("nls-test.pdf")
>
> ## utility for comparing nls() results: [TODO: use more often below]
> .n <- function(r) r[names(r) != "call"]
>
> ## selfStart.default() w/ no parameters:
> logist <- deriv( ~Asym/(1+exp(-(x-xmid)/scal)), c("Asym", "xmid", "scal"),
+ function(x, Asym, xmid, scal){} )
> logistInit <- function(mCall, LHS, data) {
+ xy <- sortedXyData(mCall[["x"]], LHS, data)
+ if(nrow(xy) < 3) stop("Too few distinct input values to fit a logistic")
+ Asym <- max(abs(xy[,"y"]))
+ if (Asym != max(xy[,"y"])) Asym <- -Asym # negative asymptote
+ xmid <- NLSstClosestX(xy, 0.5 * Asym)
+ scal <- NLSstClosestX(xy, 0.75 * Asym) - xmid
+ setNames(c(Asym, xmid, scal),
+ mCall[c("Asym", "xmid", "scal")])
+ }
> logist <- selfStart(logist, initial = logistInit) ##-> Error in R 1.5.0
> str(logist)
function (x, Asym, xmid, scal)
- attr(*, "initial")=function (mCall, LHS, data)
- attr(*, "class")= chr "selfStart"
>
> ## lower and upper in algorithm="port"
> set.seed(123)
> x <- runif(200)
> a <- b <- 1; c <- -0.1
> y <- a+b*x+c*x^2+rnorm(200, sd=0.05)
> plot(x,y)
> curve(a+b*x+c*x^2, add = TRUE)
> nls(y ~ a+b*x+c*I(x^2), start = c(a=1, b=1, c=0.1), algorithm = "port")
Nonlinear regression model
model: y ~ a + b * x + c * I(x^2)
data: parent.frame()
a b c
1.0058 0.9824 -0.0897
residual sum-of-squares: 0.46
Algorithm "port", convergence message: relative convergence (4)
> (fm <- nls(y ~ a+b*x+c*I(x^2), start = c(a=1, b=1, c=0.1),
+ algorithm = "port", lower = c(0, 0, 0)))
Nonlinear regression model
model: y ~ a + b * x + c * I(x^2)
data: parent.frame()
a b c
1.02 0.89 0.00
residual sum-of-squares: 0.468
Algorithm "port", convergence message: both X-convergence and relative convergence (5)
> if(have_MASS) print(confint(fm))
Waiting for profiling to be done...
2.5% 97.5%
a 1.00875 1.037847
b 0.84138 0.914645
c NA 0.042807
>
> ## weighted nls fit: unsupported < 2.3.0
> set.seed(123)
> y <- x <- 1:10
> yeps <- y + rnorm(length(y), sd = 0.01)
> wts <- rep(c(1, 2), length = 10); wts[5] <- 0
> fit0 <- lm(yeps ~ x, weights = wts)
> summary(fit0, cor = TRUE)
Call:
lm(formula = yeps ~ x, weights = wts)
Weighted Residuals:
Min 1Q Median 3Q Max
-0.01562 -0.00723 -0.00158 0.00403 0.02413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.00517 0.00764 0.68 0.52
x 0.99915 0.00119 841.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0132 on 7 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 7.08e+05 on 1 and 7 DF, p-value: <2e-16
Correlation of Coefficients:
(Intercept)
x -0.89
> cf0 <- coef(summary(fit0))[, 1:2]
> fit <- nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321),
+ weights = wts, trace = TRUE)
112.14 : 0.12345 0.54321
0.0012128 : 0.0051705 0.9991529
> summary(fit, cor = TRUE)
Formula: yeps ~ a + b * x
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 0.00517 0.00764 0.68 0.52
b 0.99915 0.00119 841.37 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0132 on 7 degrees of freedom
Correlation of Parameter Estimates:
a
b -0.89
> stopifnot(all.equal(residuals(fit), residuals(fit0), tolerance = 1e-5,
+ check.attributes = FALSE))
> stopifnot(df.residual(fit) == df.residual(fit0))
> cf1 <- coef(summary(fit))[, 1:2]
> fit2 <- nls(yeps ~ a + b*x, start = list(a = 0.12345, b = 0.54321),
+ weights = wts, trace = TRUE, algorithm = "port")
0: 56.070572: 0.123450 0.543210
1: 6.3964587: 1.34546 0.700840
2: 0.00060639084: 0.00517053 0.999153
3: 0.00060639084: 0.00517051 0.999153
> summary(fit2, cor = TRUE)
Formula: yeps ~ a + b * x
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 0.00517 0.00764 0.68 0.52
b 0.99915 0.00119 841.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0132 on 7 degrees of freedom
Correlation of Parameter Estimates:
a
b -0.89
Algorithm "port", convergence message: both X-convergence and relative convergence (5)
> cf2 <- coef(summary(fit2))[, 1:2]
> rownames(cf0) <- c("a", "b")
> # expect relative errors ca 2e-08
> stopifnot(all.equal(cf1, cf0, tolerance = 1e-6),
+ all.equal(cf1, cf0, tolerance = 1e-6))
> stopifnot(all.equal(residuals(fit2), residuals(fit0), tolerance = 1e5,
+ check.attributes = FALSE))
>
>
> DNase1 <- subset(DNase, Run == 1)
> DNase1$wts <- rep(8:1, each = 2)
> fm1 <- nls(density ~ SSlogis(log(conc), Asym, xmid, scal),
+ data = DNase1, weights = wts)
> summary(fm1)
Formula: density ~ SSlogis(log(conc), Asym, xmid, scal)
Parameters:
Estimate Std. Error t value Pr(>|t|)
Asym 2.3350 0.0966 24.2 3.5e-12 ***
xmid 1.4731 0.0947 15.6 8.8e-10 ***
scal 1.0385 0.0304 34.1 4.2e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0355 on 13 degrees of freedom
>
> ## directly
> fm2 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
+ data = DNase1, weights = wts,
+ start = list(Asym = 3, xmid = 0, scal = 1))
> summary(fm2)
Formula: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
Parameters:
Estimate Std. Error t value Pr(>|t|)
Asym 2.3350 0.0966 24.2 3.5e-12 ***
xmid 1.4731 0.0947 15.6 8.8e-10 ***
scal 1.0385 0.0304 34.1 4.2e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0355 on 13 degrees of freedom
> stopifnot(all.equal(coef(summary(fm2)), coef(summary(fm1)), tolerance = 1e-6))
> stopifnot(all.equal(residuals(fm2), residuals(fm1), tolerance = 1e-5))
> stopifnot(all.equal(fitted(fm2), fitted(fm1), tolerance = 1e-6))
> fm2a <- nls(density ~ Asym/(1 + exp((xmid - log(conc)))),
+ data = DNase1, weights = wts,
+ start = list(Asym = 3, xmid = 0))
> anova(fm2a, fm2)
Analysis of Variance Table
Model 1: density ~ Asym/(1 + exp((xmid - log(conc))))
Model 2: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
1 14 0.0186
2 13 0.0164 1 0.00212 1.68 0.22
>
> ## and without using weights
> fm3 <- nls(~ sqrt(wts) * (density - Asym/(1 + exp((xmid - log(conc))/scal))),
+ data = DNase1, start = list(Asym = 3, xmid = 0, scal = 1))
> summary(fm3)
Formula: 0 ~ sqrt(wts) * (density - Asym/(1 + exp((xmid - log(conc))/scal)))
Parameters:
Estimate Std. Error t value Pr(>|t|)
Asym 2.3350 0.0966 24.2 3.5e-12 ***
xmid 1.4731 0.0947 15.6 8.8e-10 ***
scal 1.0385 0.0304 34.1 4.2e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0355 on 13 degrees of freedom
> stopifnot(all.equal(coef(summary(fm3)), coef(summary(fm1)), tolerance = 1e-6))
> ft <- with(DNase1, density - fitted(fm3)/sqrt(wts))
> stopifnot(all.equal(ft, fitted(fm1), tolerance = 1e-6))
> # sign of residuals is reversed
> r <- with(DNase1, -residuals(fm3)/sqrt(wts))
> all.equal(r, residuals(fm1), tolerance = 1e-5)
[1] TRUE
> fm3a <- nls(~ sqrt(wts) * (density - Asym/(1 + exp((xmid - log(conc))))),
+ data = DNase1, start = list(Asym = 3, xmid = 0))
> anova(fm3a, fm3)
Analysis of Variance Table
Model 1: 0 ~ sqrt(wts) * (density - Asym/(1 + exp((xmid - log(conc)))))
Model 2: 0 ~ sqrt(wts) * (density - Asym/(1 + exp((xmid - log(conc))/scal)))
Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
1 14 0.0186
2 13 0.0164 1 0.00212 1.68 0.22
>
> ## using conditional linearity
> fm4 <- nls(density ~ 1/(1 + exp((xmid - log(conc))/scal)),
+ data = DNase1, weights = wts,
+ start = list(xmid = 0, scal = 1), algorithm = "plinear")
> summary(fm4)
Formula: density ~ 1/(1 + exp((xmid - log(conc))/scal))
Parameters:
Estimate Std. Error t value Pr(>|t|)
xmid 1.4731 0.0947 15.6 8.8e-10 ***
scal 1.0385 0.0304 34.1 4.2e-14 ***
.lin 2.3350 0.0966 24.2 3.5e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0355 on 13 degrees of freedom
> cf <- coef(summary(fm4))[c(3,1,2), ]
> rownames(cf)[2] <- "Asym"
> stopifnot(all.equal(cf, coef(summary(fm1)), tolerance = 1e-6,
+ check.attributes = FALSE))
> stopifnot(all.equal(residuals(fm4), residuals(fm1), tolerance = 1e-5))
> stopifnot(all.equal(fitted(fm4), fitted(fm1), tolerance = 1e-6))
> fm4a <- nls(density ~ 1/(1 + exp((xmid - log(conc)))),
+ data = DNase1, weights = wts,
+ start = list(xmid = 0), algorithm = "plinear")
> anova(fm4a, fm4)
Analysis of Variance Table
Model 1: density ~ 1/(1 + exp((xmid - log(conc))))
Model 2: density ~ 1/(1 + exp((xmid - log(conc))/scal))
Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
1 14 0.0186
2 13 0.0164 1 0.00212 1.68 0.22
>
> ## using 'port'
> fm5 <- nls(density ~ Asym/(1 + exp((xmid - log(conc))/scal)),
+ data = DNase1, weights = wts,
+ start = list(Asym = 3, xmid = 0, scal = 1),
+ algorithm = "port")
> summary(fm5)
Formula: density ~ Asym/(1 + exp((xmid - log(conc))/scal))
Parameters:
Estimate Std. Error t value Pr(>|t|)
Asym 2.3350 0.0966 24.2 3.5e-12 ***
xmid 1.4731 0.0947 15.6 8.8e-10 ***
scal 1.0385 0.0304 34.1 4.2e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0355 on 13 degrees of freedom
Algorithm "port", convergence message: relative convergence (4)
> stopifnot(all.equal(coef(summary(fm5)), coef(summary(fm1)), tolerance = 1e-6))
> stopifnot(all.equal(residuals(fm5), residuals(fm1), tolerance = 1e-5))
> stopifnot(all.equal(fitted(fm5), fitted(fm1), tolerance = 1e-6))
>
> ## check profiling
> pfm1 <- profile(fm1)
> pfm3 <- profile(fm3)
> for(m in names(pfm1))
+ stopifnot(all.equal(pfm1[[m]], pfm3[[m]], tolerance = 1e-5))
> pfm5 <- profile(fm5)
> for(m in names(pfm1))
+ stopifnot(all.equal(pfm1[[m]], pfm5[[m]], tolerance = 1e-5))
> if(have_MASS) {
+ print(c1 <- confint(fm1))
+ print(c4 <- confint(fm4, 1:2))
+ stopifnot(all.equal(c1[2:3, ], c4, tolerance = 1e-3))
+ }
Waiting for profiling to be done...
2.5% 97.5%
Asym 2.14936 2.5724
xmid 1.28535 1.6966
scal 0.97526 1.1068
Waiting for profiling to be done...
2.5% 97.5%
xmid 1.2866 1.6949
scal 0.9757 1.1063
>
> ## some low-dimensional examples
> npts <- 1000
> set.seed(1001)
> x <- runif(npts)
> b <- 0.7
> y <- x^b+rnorm(npts, sd=0.05)
> a <- 0.5
> y2 <- a*x^b+rnorm(npts, sd=0.05)
> c <- 1.0
> y3 <- a*(x+c)^b+rnorm(npts, sd=0.05)
> d <- 0.5
> y4 <- a*(x^d+c)^b+rnorm(npts, sd=0.05)
> m1 <- c(y ~ x^b, y2 ~ a*x^b, y3 ~ a*(x+exp(logc))^b)
> s1 <- list(c(b=1), c(a=1,b=1), c(a=1,b=1,logc=0))
> for(p in 1:3) {
+ fm <- nls(m1[[p]], start = s1[[p]])
+ print(fm)
+ if(have_MASS) print(confint(fm))
+ fm <- nls(m1[[p]], start = s1[[p]], algorithm = "port")
+ print(fm)
+ if(have_MASS) print(confint(fm))
+ }
Nonlinear regression model
model: y ~ x^b
data: parent.frame()
b
0.695
residual sum-of-squares: 2.39
Waiting for profiling to be done...
2.5% 97.5%
0.68704 0.70281
Nonlinear regression model
model: y ~ x^b
data: parent.frame()
b
0.695
residual sum-of-squares: 2.39
Algorithm "port", convergence message: relative convergence (4)
Waiting for profiling to be done...
2.5% 97.5%
0.68704 0.70281
Nonlinear regression model
model: y2 ~ a * x^b
data: parent.frame()
a b
0.502 0.724
residual sum-of-squares: 2.51
Waiting for profiling to be done...
2.5% 97.5%
a 0.49494 0.50893
b 0.70019 0.74767
Nonlinear regression model
model: y2 ~ a * x^b
data: parent.frame()
a b
0.502 0.724
residual sum-of-squares: 2.51
Algorithm "port", convergence message: relative convergence (4)
Waiting for profiling to be done...
2.5% 97.5%
a 0.49494 0.50893
b 0.70019 0.74767
Nonlinear regression model
model: y3 ~ a * (x + exp(logc))^b
data: parent.frame()
a b logc
0.558 0.603 -0.176
residual sum-of-squares: 2.44
Waiting for profiling to be done...
2.5% 97.5%
a 0.35006 0.66057
b 0.45107 0.91473
logc -0.64627 0.40946
Nonlinear regression model
model: y3 ~ a * (x + exp(logc))^b
data: parent.frame()
a b logc
0.558 0.603 -0.176
residual sum-of-squares: 2.44
Algorithm "port", convergence message: relative convergence (4)
Waiting for profiling to be done...
2.5% 97.5%
a 0.35006 0.66057
b 0.45107 0.91473
logc -0.64627 0.40946
>
> if(have_MASS) {
+ fm <- nls(y2~x^b, start=c(b=1), algorithm="plinear")
+ print(confint(profile(fm)))
+ fm <- nls(y3 ~ (x+exp(logc))^b, start=c(b=1, logc=0), algorithm="plinear")
+ print(confint(profile(fm)))
+ }
2.5% 97.5%
0.70019 0.74767
2.5% 97.5%
b 0.45105 0.91471
logc -0.64625 0.40933
>
>
> ## more profiling with bounds
> op <- options(digits=3)
> npts <- 10
> set.seed(1001)
> a <- 2
> b <- 0.5
> x <- runif(npts)
> y <- a*x/(1+a*b*x) + rnorm(npts, sd=0.2)
> gfun <- function(a,b,x) {
+ if(a < 0 || b < 0) stop("bounds violated")
+ a*x/(1+a*b*x)
+ }
> m1 <- nls(y ~ gfun(a,b,x), algorithm = "port",
+ lower = c(0,0), start = c(a=1, b=1))
> (pr1 <- profile(m1))
$a
tau par.vals.a par.vals.b
1 -3.869 0.706 0.000
2 -3.114 0.802 0.000
3 -0.863 1.124 0.000
4 0.000 1.538 0.263
5 0.590 1.952 0.446
6 1.070 2.423 0.592
7 1.534 3.082 0.737
8 1.969 4.034 0.878
9 2.376 5.502 1.014
10 2.751 7.929 1.144
11 3.090 12.263 1.264
12 3.375 20.845 1.373
$b
tau par.vals.a par.vals.b
1 -0.673 1.2087 0.0272
2 0.000 1.5381 0.2633
3 0.707 2.0026 0.4994
4 1.365 2.6295 0.7236
5 1.994 3.5762 0.9522
6 2.611 5.1820 1.1962
7 3.225 8.2162 1.4614
8 3.820 17.3946 1.7512
attr(,"original.fit")
Nonlinear regression model
model: y ~ gfun(a, b, x)
data: parent.frame()
a b
1.538 0.263
residual sum-of-squares: 0.389
Algorithm "port", convergence message: relative convergence (4)
attr(,"summary")
Formula: y ~ gfun(a, b, x)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.538 0.617 2.49 0.037 *
b 0.263 0.352 0.75 0.476
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.221 on 8 degrees of freedom
Algorithm "port", convergence message: relative convergence (4)
attr(,"class")
[1] "profile.nls" "profile"
> if(have_MASS) print(confint(pr1))
2.5% 97.5%
a 0.96 5.20
b NA 1.07
>
> gfun <- function(a,b,x) {
+ if(a < 0 || b < 0 || a > 1.5 || b > 1) stop("bounds violated")
+ a*x/(1+a*b*x)
+ }
> m2 <- nls(y ~ gfun(a,b,x), algorithm = "port",
+ lower = c(0, 0), upper=c(1.5, 1), start = c(a=1, b=1))
> profile(m2)
$a
tau par.vals.a par.vals.b
1 -3.681 0.729 0.000
2 -2.945 0.823 0.000
3 -0.977 1.099 0.000
4 0.000 1.500 0.243
$b
tau par.vals.a par.vals.b
1 -0.733 1.18200 0.00395
2 0.000 1.50000 0.24263
3 1.645 1.50000 0.48132
4 2.154 1.50000 0.57869
5 2.727 1.50000 0.70706
6 3.288 1.50000 0.85748
attr(,"original.fit")
Nonlinear regression model
model: y ~ gfun(a, b, x)
data: parent.frame()
a b
1.500 0.243
residual sum-of-squares: 0.39
Algorithm "port", convergence message: relative convergence (4)
attr(,"summary")
Formula: y ~ gfun(a, b, x)
Parameters:
Estimate Std. Error t value Pr(>|t|)
a 1.500 0.598 2.51 0.036 *
b 0.243 0.356 0.68 0.514
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.221 on 8 degrees of freedom
Algorithm "port", convergence message: relative convergence (4)
attr(,"class")
[1] "profile.nls" "profile"
> if(have_MASS) print(confint(m2))
Waiting for profiling to be done...
2.5% 97.5%
a 0.907 NA
b NA 0.611
> options(op)
>
> ## scoping problems
> test <- function(trace=TRUE)
+ {
+ x <- seq(0,5,len=20)
+ n <- 1
+ y <- 2*x^2 + n + rnorm(x)
+ xy <- data.frame(x=x,y=y)
+ myf <- function(x,a,b,c) a*x^b+c
+ list(with.start=
+ nls(y ~ myf(x,a,b,n), data=xy, start=c(a=1,b=1), trace=trace),
+ no.start= ## cheap auto-init to 1
+ suppressWarnings(
+ nls(y ~ myf(x,A,B,n), data=xy)))
+ }
> t1 <- test(); t1$with.start
8291.9 : 1 1
726.02 : 0.80544 2.42971
552.85 : 1.290 2.129
70.431 : 1.9565 1.9670
26.555 : 1.9788 2.0064
26.503 : 1.9798 2.0046
26.503 : 1.9799 2.0046
Nonlinear regression model
model: y ~ myf(x, a, b, n)
data: xy
a b
1.98 2.00
residual sum-of-squares: 26.5
> ##__with.start:
> ## failed to find n in 2.2.x
> ## found wrong n in 2.3.x
> ## finally worked in 2.4.0
> ##__no.start: failed in 3.0.2
> ## 2018-09 fails on macOS with Accelerate framework.
> stopifnot(all.equal(.n(t1[[1]]), .n(t1[[2]])))
> rm(a,b)
> t2 <- test(FALSE)
> stopifnot(all.equal(lapply(t1, .n),
+ lapply(t2, .n), tolerance = 0.16))# different random error
>
>
> ## list 'start'
> set.seed(101)# (remain independent of above)
> getExpmat <- function(theta, t)
+ {
+ conc <- matrix(nrow = length(t), ncol = length(theta))
+ for(i in 1:length(theta)) conc[, i] <- exp(-theta[i] * t)
+ conc
+ }
> expsum <- as.vector(getExpmat(c(.05,.005), 1:100) %*% c(1,1))
> expsumNoisy <- expsum + max(expsum) *.001 * rnorm(100)
> expsum.df <-data.frame(expsumNoisy)
>
> ## estimate decay rates, amplitudes with default Gauss-Newton
> summary (nls(expsumNoisy ~ getExpmat(k, 1:100) %*% sp, expsum.df,
+ start = list(k = c(.6,.02), sp = c(1,2))))
Formula: expsumNoisy ~ getExpmat(k, 1:100) %*% sp
Parameters:
Estimate Std. Error t value Pr(>|t|)
k1 5.00e-02 2.73e-04 183 <2e-16 ***
k2 4.97e-03 4.77e-05 104 <2e-16 ***
sp1 1.00e+00 3.96e-03 253 <2e-16 ***
sp2 9.98e-01 4.43e-03 225 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.00182 on 96 degrees of freedom
>
> ## didn't work with port in 2.4.1
> summary (nls(expsumNoisy ~ getExpmat(k, 1:100) %*% sp, expsum.df,
+ start = list(k = c(.6,.02), sp = c(1,2)),
+ algorithm = "port"))
Formula: expsumNoisy ~ getExpmat(k, 1:100) %*% sp
Parameters:
Estimate Std. Error t value Pr(>|t|)
k1 5.00e-02 2.73e-04 183 <2e-16 ***
k2 4.97e-03 4.77e-05 104 <2e-16 ***
sp1 1.00e+00 3.96e-03 253 <2e-16 ***
sp2 9.98e-01 4.43e-03 225 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.00182 on 96 degrees of freedom
Algorithm "port", convergence message: both X-convergence and relative convergence (5)
>
>
> ## PR13540
>
> x <- runif(200)
> b0 <- c(rep(0,100),runif(100))
> b1 <- 1
> fac <- as.factor(rep(c(0,1), each = 100))
> y <- b0 + b1*x + rnorm(200, sd=0.05)
> # next failed in 2.8.1
> fit <- nls(y~b0[fac] + b1*x, start = list(b0=c(1,1), b1=1),
+ algorithm ="port", upper = c(100, 100, 100))
> # next did not "fail" in proposed fix:
> fiB <- nls(y~b0[fac] + b1*x, start = list(b0=c(1,1), b1=101),
+ algorithm ="port", upper = c(100, 100, 100),
+ control = list(warnOnly=TRUE))# warning ..
Warning in nls(y ~ b0[fac] + b1 * x, start = list(b0 = c(1, 1), b1 = 101), :
Convergence failure: initial par violates constraints
> with(fiB$convInfo, ## start par. violates constraints
+ stopifnot(isConv == FALSE, stopCode == 300))
>
>
> ## PR#17367 -- nls() quoting non-syntactical variable names
> ##
> op <- options(warn = 2)# no warnings allowed from here
> ##
> dN <- data.frame('NO [µmol/l]' = c(1,3,8,17), t = 1:4, check.names=FALSE)
> fnN <- `NO [µmol/l]` ~ a + k* exp(t)
> ## lm() works, nls() should too
> lm.N <- lm(`NO [µmol/l]` ~ exp(t) , data = dN)
> summary(lm.N) -> slmN
> nm. <- nls(`NO [µmol/l]` ~ a + k*exp(t), start=list(a=0,k=1), data = dN)
> ## In R <= 3.4.x : Error in eval(predvars, data, env) : object 'NO' not found
> nmf <- nls(fnN, start=list(a=0,k=1), data = dN)
> ## (ditto; gave identical error)
> noC <- function(L) L[-match("call", names(L))]
> stopifnot(all.equal(noC (nm.), noC (nmf)))
> ##
> ## with list for which as.data.frame() does not work [-> different branch, not using model.frame!]
> ## list version (has been valid "forever", still doubtful, rather give error [FIXME] ?)
> lsN <- c(as.list(dN), list(foo="bar")); lsN[["t"]] <- 1:8
> nmL <- nls(`NO [µmol/l]` ~ a + k*exp(t), start=list(a=0,k=1), data = lsN)
> stopifnot(all.equal(coef(nmL), c(a = 5.069866, k = 0.003699669), tol = 4e-7))# seen 4.2e-8
>
> ## trivial RHS -- should work even w/o 'start='
> fi1 <- nls(y ~ a, start = list(a=1))
> ## -> 2 deprecation warnings "length 1 in vector-arithmetic" from nlsModel() in R 3.4.x ..
> options(op) # warnings about missing 'start' ok:
> f.1 <- nls(y ~ a) # failed in R 3.4.x
Warning in nls(y ~ a) :
No starting values specified for some parameters.
Initializing 'a' to '1.'.
Consider specifying 'start' or using a selfStart model
> stopifnot(all.equal(noC(f.1), noC(fi1)),
+ all.equal(coef(f.1), c(a = mean(y))))
>
> proc.time()
user system elapsed
1.437 0.067 1.488