The R Project SVN – R – Diff – /branches/R-groups/tests/Examples/stats-Ex.Rout.save

-R Under development (unstable) (2021-09-02 r80849) -- "Unsuffered Consequences"
+R Under development (unstable) (2021-10-13 r81047) -- "Unsuffered Consequences"
 Copyright (C) 2021 The R Foundation for Statistical Computing
 Platform: x86_64-pc-linux-gnu (64-bit)
 R is free software and comes with ABSOLUTELY NO WARRANTY.
 You are welcome to redistribute it under certain conditions.
 > try( # maxiter=10: store less garbage
 +         nls(conc ~ SSbiexp(time, A1, lrc1, A2, lrc2), data = datN,
 +             trace=TRUE, control = list(maxiter = 10)) )
 .01722077  (5.34e+02): par = (0.6168807 -1.783839 2.050204 0.2004597)
 .308944e-06 (1.13e+04): par = (0.5798674 -1.784335 2.028943 0.1920502)
-.571093e-11 (7.68e+06): par = (0.5793882 -1.78778 2.029276 0.1915479)
+.571095e-11 (7.68e+06): par = (0.5793882 -1.78778 2.029276 0.1915479)
-.633875e-23 (4.26e+03): par = (0.5793887 -1.787785 2.029277 0.1915474)
+.619248e-23 (5.90e+03): par = (0.5793887 -1.787785 2.029277 0.1915474)
-.883240e-29 (1.32e+01): par = (0.5793887 -1.787785 2.029277 0.1915474)
+.243570e-28 (8.88e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
-.292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
+.599121e-29 (1.38e+01): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .292713e-29 (1.71e+00): par = (0.5793887 -1.787785 2.029277 0.1915474)
 .01722077    (6.55e-02): par = (0.6168807 -1.783839 2.050204 0.2004597)
   It.   1, fac=           1, eval (no.,total): ( 1,  1): new dev = 3.30894e-06
 .308942e-06  (9.08e-04): par = (0.5798674 -1.784335 2.028943 0.1920502)
   It.   2, fac=           1, eval (no.,total): ( 1,  2): new dev = 2.57108e-11
 .571082e-11  (2.53e-06): par = (0.5793882 -1.78778 2.029276 0.1915479)
-  It.   3, fac=           1, eval (no.,total): ( 1,  3): new dev = 1.66922e-23
+  It.   3, fac=           1, eval (no.,total): ( 1,  3): new dev = 1.66996e-23
-.669220e-23  (2.04e-12): par = (0.5793887 -1.787785 2.029277 0.1915474)
+.669964e-23  (2.04e-12): par = (0.5793887 -1.787785 2.029277 0.1915474)
-.670353e-23  (1.67e-13): par = (2.029277 0.5793887 0.1915474 -1.787785)
+.671287e-23  (1.67e-13): par = (2.029277 0.5793887 0.1915474 -1.787785)
 > all.equal(coef(fm1), coef(fmX1), tolerance=0) # ... rel.diff.: 1.57e-6
-[1] "Mean relative difference: 1.574125e-06"
+[1] "Mean relative difference: 1.574124e-06"
 > all.equal(coef(fm1), coef(fmX),  tolerance=0) # ... rel.diff.: 1.03e-12
-[1] "Mean relative difference: 1.031784e-12"
+[1] "Mean relative difference: 1.032014e-12"
 > ## IGNORE_RDIFF_END
 > stopifnot(all.equal(coef(fm1), coef(fmX1), tolerance = 6e-6),
 +           all.equal(coef(fm1), coef(fmX ), tolerance = 1e-11))
+>
+>
+>
 > ### ** Examples
+>
 > asOneSidedFormula("age")
 ~age
-<environment: 0x564b7d38cbb0>
+<environment: 0x32a3fb8>
 > asOneSidedFormula(~ age)
 ~age
+>
+>
+>
     r <- V
     r[] <- Is * V * rep(Is, each = p)
     r[cbind(1L:p, 1L:p)] <- 1
+    r
+}
-<bytecode: 0x564b798dd788>
+<bytecode: 0x5b09fd0>
 <environment: namespace:stats>
 > stopifnot(all.equal(Cl, cov2cor(cov(longley))),
 +           all.equal(cor(longley, method = "kendall"),
 +             cov2cor(cov(longley, method = "kendall"))))
+>
 alternative hypothesis: two.sided
 > # Exactly the same p-value, as Cochran's conditions are never met:
 > fisher.test(MP6, hybrid=TRUE)
-	Fisher's Exact Test for Count Data hybrid using asym.chisq. iff
+	Fisher's Exact Test for Count Data hybrid using asym.chisq. iff (exp=5,
-	(exp=5, perc=80, Emin=1)
+	perc=80, Emin=1)
 data:  MP6
 p-value = 0.03929
 alternative hypothesis: two.sided
 > environment(fo)
 <environment: R_GlobalEnv>
 > environment(as.formula("y ~ x"))
 <environment: R_GlobalEnv>
 > environment(as.formula("y ~ x", env = new.env()))
-<environment: 0x564b7a1d19e8>
+<environment: 0x691c4e8>
+>
+>
 > ## Create a formula for a model with a large number of variables:
 > xnam <- paste0("x", 1:25)
 > (fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+"))))
 > try(nlm1 <- update(nlmod, control = list(tol = 1e-7)))
 Warning in nls(formula = y ~ Const + A * exp(B * x), algorithm = "default",  :
   No starting values specified for some parameters.
 Initializing ‘Const’, ‘A’, ‘B’ to '1.'.
 Consider specifying 'start' or using a selfStart model
-Error in nls(formula = y ~ Const + A * exp(B * x), algorithm = "default",  :
-  step factor 0.000488281 reduced below 'minFactor' of 0.000976562
 > o2 <- options(digits = 10) # more accuracy for 'trace'
 > ## central differencing works here typically (PR#18165: not converging on *some*):
 > ctr2 <- nls.control(nDcentral=TRUE, tol = 8e-8, # <- even smaller than above
++    warnOnly =
++         TRUE || # << work around; e.g. needed on some ATLAS-Lapack setups
-+    warnOnly = (grepl("^aarch64.*linux", R.version$platform) && grepl("^NixOS", osVersion)
++         (grepl("^aarch64.*linux", R.version$platform) && grepl("^NixOS", osVersion)
 +               ))
 > (nlm2 <- update(nlmod, control = ctr2, trace = TRUE)); options(o2)
 Warning in nls(formula = y ~ Const + A * exp(B * x), algorithm = "default",  :
   No starting values specified for some parameters.
 Initializing ‘Const’, ‘A’, ‘B’ to '1.'.
 Consider specifying 'start' or using a selfStart model
 1017460.306    (4.15e+02): par = (1 1 1)
-.7503    (2.34e+02): par = (13.42031396 1.961485 0.05947543745)
+.7503    (2.34e+02): par = (13.42031396 1.961485 0.05947543746)
-.3537    (3.23e+02): par = (51.75719817 -13.09155958 0.8428607712)
+.3540    (3.23e+02): par = (51.75719814 -13.09155954 0.8428607699)
-.21891    (1.03e+02): par = (76.0006985 -1.93522675 1.0190858)
+.21900    (1.03e+02): par = (76.00069849 -1.93522673 1.019085799)
-.3672224    (1.29e+00): par = (100.3761515 8.624648408 5.104490252)
+.3672239    (1.29e+00): par = (100.3761515 8.62464841 5.104490279)
-.4400170    (9.39e+00): par = (100.6344391 4.913490999 0.2849209664)
+.4400266    (9.39e+00): par = (100.6344391 4.913490966 0.284920948)
-.08739445    (7.24e+00): par = (100.6830407 6.899303393 0.4637755095)
+.08740235    (7.24e+00): par = (100.6830408 6.899303242 0.4637755057)
-.344478582    (5.97e-01): par = (100.0368306 9.897714144 0.5169294926)
+.344478691    (5.97e-01): par = (100.0368306 9.89771414 0.5169294949)
-.9908415908   (1.55e-02): par = (100.0300625 9.9144191 0.5023516843)
+.9908415909   (1.55e-02): par = (100.0300625 9.9144191 0.5023516842)
-.9906046057   (1.84e-05): par = (100.0288724 9.916224018 0.5025207336)
+.9906046057   (1.84e-05): par = (100.0288724 9.916224018 0.5025207337)
 .9906046054   (9.94e-08): par = (100.028875 9.916228366 0.50252165)
-.9906046054   (5.00e-08): par = (100.028875 9.916228377 0.5025216525)
+.9906046054   (5.06e-10): par = (100.028875 9.916228388 0.5025216549)
 Nonlinear regression model
   model: y ~ Const + A * exp(B * x)
    data: parent.frame()
       Const           A           B
 .0288750   9.9162284   0.5025217
 [1] "link-glm"
 > quasi(link = power(1/3))[c("linkfun", "linkinv")]
 $linkfun
 function (mu)
 mu^lambda
-<bytecode: 0x564b7bd79308>
+<bytecode: 0x59e1648>
-<environment: 0x564b7c2a9ac0>
+<environment: 0x59e9c68>
 $linkinv
 function (eta)
 pmax(eta^(1/lambda), .Machine$double.eps)
-<bytecode: 0x564b7bd791b8>
+<bytecode: 0x59e14f8>
-<environment: 0x564b7c2a9ac0>
+<environment: 0x59e9c68>
+>
+>
+>
 > cleanEx()
+>
 > smokers  <- c( 83, 90, 129, 70 )
 > patients <- c( 86, 93, 136, 82 )
 > prop.test(smokers, patients)
--sample test for equality of proportions without continuity
+-sample test for equality of proportions without continuity correction
-	correction
 data:  smokers out of patients
 X-squared = 12.6, df = 3, p-value = 0.005585
 alternative hypothesis: two.sided
 sample estimates:
+>
 > smokers  <- c( 83, 90, 129, 70 )
 > patients <- c( 86, 93, 136, 82 )
 > prop.test(smokers, patients)
--sample test for equality of proportions without continuity
+-sample test for equality of proportions without continuity correction
-	correction
 data:  smokers out of patients
 X-squared = 12.6, df = 3, p-value = 0.005585
 alternative hypothesis: two.sided
 sample estimates:
+>
 > ## look at the internal structure:
 > unclass(sfun0)
 function (v)
 .approxfun(x, y, v, method, yleft, yright, f, na.rm)
-<bytecode: 0x564b7a0c9b98>
+<bytecode: 0x5b2ea30>
-<environment: 0x564b7c1c1e38>
+<environment: 0x7496e48>
 attr(,"call")
 stepfun(1:3, y0, f = 0)
 > ls(envir = environment(sfun0))
 [1] "f"      "method" "na.rm"  "x"      "y"      "yleft"  "yright"
+>
 > ### * <FOOTER>
 > ###
 > cleanEx()
 > options(digits = 7L)
 > base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed:  4.67 0.127 4.801 0 0
+Time elapsed:  7.667 0.249 7.957 0 0
 > grDevices::dev.off()
 null device
 > ###
 > ### Local variables: ***

The R Project SVN R

(root)/branches/R-groups/tests/Examples/stats-Ex.Rout.save – Rev 80874 → 81060