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git-svn-id: https://svn.r-project.org/R/trunk@62385 00db46b3-68df-0310-9c12-caf00c1e9a41
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1 parent 60ceea8 commit fcaeb74f4d4e5c798b42f2a65ac7a62b27d2a602 ripley committed Mar 24, 2013
Showing with 66 additions and 66 deletions.
  1. +19 −19 src/library/stats/tests/ks-test.Rout.save
  2. +47 −47 src/library/stats/tests/nls.Rout.save
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38 src/library/stats/tests/ks-test.Rout.save
@@ -1,8 +1,8 @@
-R Under development (unstable) (2011-08-04 r56621)
-Copyright (C) 2011 The R Foundation for Statistical Computing
+R Under development (unstable) (2013-03-23 r62384) -- "Unsuffered Consequences"
+Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
-Platform: x86_64-unknown-linux-gnu (64-bit)
+Platform: x86_64-apple-darwin12.3.0 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
@@ -24,9 +24,9 @@ Type 'q()' to quit R.
One-sample Kolmogorov-Smirnov test
-data: ds1
+data: ds1
D = 0.274, p-value = 0.4407
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216) :
@@ -38,9 +38,9 @@ In ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216) :
One-sample Kolmogorov-Smirnov test
-data: ds1
+data: ds1
D = 0.274, p-value = 0.3715
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE) :
@@ -53,26 +53,26 @@ In ks.test(ds1, "pnorm", mean = 3.3, sd = 1.55216, exact = TRUE) :
One-sample Kolmogorov-Smirnov test
-data: ds2
+data: ds2
D = 0.0194, p-value = 0.8452
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
> ks.test(ds2, "pnorm", exact = TRUE)
One-sample Kolmogorov-Smirnov test
-data: ds2
+data: ds2
D = 0.0194, p-value = 0.8379
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
> ## next two are still close
> ks.test(round(ds2, 2), "pnorm")
One-sample Kolmogorov-Smirnov test
-data: round(ds2, 2)
+data: round(ds2, 2)
D = 0.0192, p-value = 0.856
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(round(ds2, 2), "pnorm") :
@@ -81,9 +81,9 @@ In ks.test(round(ds2, 2), "pnorm") :
One-sample Kolmogorov-Smirnov test
-data: round(ds2, 2)
+data: round(ds2, 2)
D = 0.0192, p-value = 0.8489
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(round(ds2, 2), "pnorm", exact = TRUE) :
@@ -93,9 +93,9 @@ In ks.test(round(ds2, 2), "pnorm", exact = TRUE) :
One-sample Kolmogorov-Smirnov test
-data: round(ds2, 1)
+data: round(ds2, 1)
D = 0.0367, p-value = 0.1344
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(round(ds2, 1), "pnorm") :
@@ -104,9 +104,9 @@ In ks.test(round(ds2, 1), "pnorm") :
One-sample Kolmogorov-Smirnov test
-data: round(ds2, 1)
+data: round(ds2, 1)
D = 0.0367, p-value = 0.1311
-alternative hypothesis: two-sided
+alternative hypothesis: two-sided
Warning message:
In ks.test(round(ds2, 1), "pnorm", exact = TRUE) :
View
94 src/library/stats/tests/nls.Rout.save
@@ -1,6 +1,6 @@
-R Under development (unstable) (2012-10-24 r61009) -- "Unsuffered Consequences"
-Copyright (C) 2012 The R Foundation for Statistical Computing
+R Under development (unstable) (2013-03-23 r62384) -- "Unsuffered Consequences"
+Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-unknown-linux-gnu (64-bit)
@@ -69,23 +69,23 @@ function (x, Asym, xmid, scal)
> 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()
+ 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)
+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()
+ 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)
+Algorithm "port", convergence message: both X-convergence and relative convergence (5)
> confint(fm)
Waiting for profiling to be done...
2.5% 97.5%
@@ -115,8 +115,8 @@ Coefficients:
x 0.99915 0.00119 841.38 <2e-16
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
+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)
@@ -125,8 +125,8 @@ 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
+112.14 : 0.12345 0.54321
+0.0012128 : 0.0051705 0.9991529
> summary(fit, cor = TRUE)
Formula: yeps ~ a + b * x
@@ -167,7 +167,7 @@ Correlation of Parameter Estimates:
a
b -0.89
-Algorithm "port", convergence message: both X-convergence and relative convergence (5)
+Algorithm "port", convergence message: both X-convergence and relative convergence (5)
> cf2 <- coef(summary(fit2))[, 1:2]
> rownames(cf0) <- c("a", "b")
@@ -309,7 +309,7 @@ scal 1.0385 0.0304 34.1 4.2e-14
Residual standard error: 0.0355 on 13 degrees of freedom
-Algorithm "port", convergence message: relative convergence (4)
+Algorithm "port", convergence message: relative convergence (4)
> stopifnot(all.equal(coef(summary(fm5)), coef(summary(fm1)), tol = 1e-6))
> stopifnot(all.equal(residuals(fm5), residuals(fm1), tol = 1e-5))
@@ -358,28 +358,28 @@ scal 0.9757 1.1063
+ print(confint(fm))
+ }
Nonlinear regression model
- model: y ~ x^b
- data: parent.frame()
+ 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()
+ model: y ~ x^b
+ data: parent.frame()
b
0.695
residual sum-of-squares: 2.39
-Algorithm "port", convergence message: relative convergence (4)
+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()
+ model: y2 ~ a * x^b
+ data: parent.frame()
a b
0.502 0.724
residual sum-of-squares: 2.51
@@ -388,20 +388,20 @@ Waiting for profiling to be done...
a 0.49494 0.50893
b 0.70019 0.74767
Nonlinear regression model
- model: y2 ~ a * x^b
- data: parent.frame()
+ 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)
+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()
+ 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
@@ -411,13 +411,13 @@ 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()
+ 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)
+Algorithm "port", convergence message: relative convergence (4)
Waiting for profiling to be done...
2.5% 97.5%
a 0.35006 0.66057
@@ -478,13 +478,13 @@ $b
attr(,"original.fit")
Nonlinear regression model
- model: y ~ gfun(a, b, x)
- data: parent.frame()
+ 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)
+Algorithm "port", convergence message: relative convergence (4)
attr(,"summary")
Formula: y ~ gfun(a, b, x)
@@ -496,7 +496,7 @@ b 0.263 0.352 0.75 0.476
Residual standard error: 0.221 on 8 degrees of freedom
-Algorithm "port", convergence message: relative convergence (4)
+Algorithm "port", convergence message: relative convergence (4)
attr(,"class")
[1] "profile.nls" "profile"
@@ -530,13 +530,13 @@ $b
attr(,"original.fit")
Nonlinear regression model
- model: y ~ gfun(a, b, x)
- data: parent.frame()
+ 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)
+Algorithm "port", convergence message: relative convergence (4)
attr(,"summary")
Formula: y ~ gfun(a, b, x)
@@ -548,7 +548,7 @@ b 0.243 0.356 0.68 0.514
Residual standard error: 0.221 on 8 degrees of freedom
-Algorithm "port", convergence message: relative convergence (4)
+Algorithm "port", convergence message: relative convergence (4)
attr(,"class")
[1] "profile.nls" "profile"
@@ -570,16 +570,16 @@ b NA 0.611
+ nls(y ~ myf(x,a,b,n), data=xy, start=c(a=1,b=1), trace=TRUE)
+ }
> test()
-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
+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
+ model: y ~ myf(x, a, b, n)
+ data: xy
a b
1.98 2.00
residual sum-of-squares: 26.5
@@ -632,7 +632,7 @@ sp2 9.99e-01 5.00e-03 199.8 <2e-16
Residual standard error: 0.00205 on 96 degrees of freedom
-Algorithm "port", convergence message: both X-convergence and relative convergence (5)
+Algorithm "port", convergence message: both X-convergence and relative convergence (5)
>
>

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