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Type 'q()' to quit R. > #### Tests for "Functionals": Root finding, Optimization, Integration, etc > > stopifnot(require("Rmpfr")) Loading required package: Rmpfr Loading required package: gmp Attaching package: 'gmp' The following objects are masked from 'package:base': %*%, apply, crossprod, matrix, tcrossprod C code of R package 'Rmpfr': GMP using 64 bits per limb Attaching package: 'Rmpfr' The following object is masked from 'package:gmp': outer The following objects are masked from 'package:stats': dbinom, dgamma, dnbinom, dnorm, dpois, dt, pnorm The following objects are masked from 'package:base': cbind, pmax, pmin, rbind > > (f.chk <- system.file("check-tools.R", package="Rmpfr", mustWork=TRUE)) [1] "D:/RCompile/CRANincoming/R-devel/lib/Rmpfr/check-tools.R" > source(f.chk, keep.source=FALSE) > ## -> assert.EQ.() showSys.time() > > options(warn = 1)# warnings *immediately* > (doExtras <- Rmpfr:::doExtras()) [1] FALSE > > ### 1. Integration ----------------------------------------------- > > ## Example from Lauren K, June 2014 (~/R/MM/Pkg-ex/Rmpfr/integrateR-LaurenK.R): > beta0 <- 0.05 > beta1 <- 0.05 > (tau <- sqrt(0.01*0.05*0.95/0.99))# = 0.0219.. [1] 0.02190429 > ## > Z00 <- 9 > Z01 <- 1 > Z10 <- 18 > Z11 <- 2 > N <- Z00+Z01+Z10+Z11 > > integrand <- function(u) { + ee.u <- exp(-u^2/2)/(sqrt(2*pi)*tau) + b0u <- beta0 + tau*u + b1u <- beta1 + b0u # == beta0+beta1+ tau*u + + ee.u ^ (Z00+Z01 + Z10+Z11) * + (1-b0u)^Z00 * b0u ^ Z01 * + (1-b1u)^Z10 * b1u ^ Z11 + } > > ## MM: note how the integrand() function looks: > op <- par(mfcol=c(3,1), mgp=c(1.5, .6, 0), mar=.1+c(3,3,0,0)) > ax1 <- function(a,b) axis(1, at=c(a,b), col=2, col.axis=2, tcl= +3/4, mgp=c(3,0,0)) > curve(integrand, -5,5, n=1000) > cc <- adjustcolor(2, 1/4) ## look closer: > ep <- .01; rect(-3, -ep, 0, +ep, col=cc, border=cc); ax1(-3,0) > curve(integrand, -3,0, n=1000, ylim = c(-ep,ep)) > ## but now look really closely : > ep <- .001; rect(-3, -ep, -2, +ep, col=cc); ax1(-3,-2) > curve(integrand, -3,-2, n=1000, ylim = c(-ep, ep)) > par(op) > > (I1 <- integrate(integrand,lower = -100, upper = 100)) 1.396355e+33 with absolute error < 1.6e+29 > (I1. <- integrate(integrand,lower = -100, upper = 100, rel.tol = 1e-14)) 1.396355e+33 with absolute error < 8e+18 > > showSys.time(I2 <- integrateR(integrand, lower = -100, upper = 100)) Warning in integrateR(integrand, lower = -100, upper = 100) : no convergence up to order 13; last relative change = 1.285403e-05 Consider setting 'ord = ' (e.g. = 14). Time user system elapsed Time 0.01 0.00 0.02 > I2 ## ... Warning ‘no convergence up to order 13’ Non-convergence message 'no convergence up to order 13; last relative change = 1.285403e-05 Consider setting 'ord = ' (e.g. = 14).' 1.3964e+33 with absolute error < 1.7949e+28 > ## Solaris Sparc (2014-06, CRAN checks); thanks Brian: print(I2[1:2], digits=15) > I2.Solaris <- list(value = 1.3963550396006e+33, abs.error = 1.79487857486724e+28) > I.db <- list(value = 1.39635503960059e+33, abs.error = 1.79487857478077e+28) > stopifnot( + all.equal(I2[1:2], I.db, tol = 1e-10)# Solaris SPARC needs at least 4.8e-11 + ) > > ## Now using high accuracy > showSys.time(I3 <- integrateR(integrand, lower = mpfr(-100, precBits=256), upper = 100)) Warning in integrateR(integrand, lower = mpfr(-100, precBits = 256), upper = 100) : no convergence up to order 13; last relative change = 1.285403e-5 Consider setting 'ord = ' (e.g. = 14). Time user system elapsed Time 1.60 0.01 1.61 > ## much slower but not better (and not worse) > I3 Non-convergence message 'no convergence up to order 13; last relative change = 1.285403e-5 Consider setting 'ord = ' (e.g. = 14).' 1.3964e+33 with absolute error < 1.7949e+28 > assert.EQ.(sapply(I3[1:2], asNumeric), unlist(I.db)) Mean relative difference: 3.569039e-15 > ## Really get better when decreasing the integration interval > ## from [-100, 100] to [-10, 10] ... which should give "the same" > showSys.time(I4 <- integrateR(integrand, lower = mpfr(-10, precBits=256), upper = 10, + ord = 15, verbose=TRUE)) ord = 15; ==> evaluating integrand at 2^(ord+1)-2 = 65534 locations n= 1, 2^n= 2 | I = 4.085188566e+34, abs.err = 4.085189e+34 n= 2, 2^n= 4 | I = 8.170377132e+33, abs.err = 3.268151e+34 n= 3, 2^n= 8 | I = 4.712016441e+33, abs.err = 3.458361e+33 n= 4, 2^n= 16 | I = 2.335919214e+33, abs.err = 2.376097e+33 n= 5, 2^n= 32 | I = 1.181962279e+33, abs.err = 1.153957e+33 n= 6, 2^n= 64 | I = 1.220705480e+33, abs.err = 3.874320e+31 n= 7, 2^n= 128 | I = 1.410495454e+33, abs.err = 1.897900e+32 n= 8, 2^n= 256 | I = 1.396201858e+33, abs.err = 1.429360e+31 n= 9, 2^n= 512 | I = 1.396354229e+33, abs.err = 1.523703e+29 n=10, 2^n= 1024 | I = 1.396354970e+33, abs.err = 7.409143e+26 n=11, 2^n= 2048 | I = 1.396354964e+33, abs.err = 5.998797e+24 n=12, 2^n= 4096 | I = 1.396354964e+33, abs.err = 6.619581e+21 n=13, 2^n= 8192 | I = 1.396354964e+33, abs.err = 1.662622e+18 n=14, 2^n= 16384 | I = 1.396354964e+33, abs.err = 1.021861e+14 n=15, 2^n= 32768 | I = 1.396354964e+33, abs.err = 1.561794e+9 Time user system elapsed Time 6.57 0.11 6.69 > ## ~ 6.6 sec [lynne 2013] > I4 1.3964e+33 with absolute error < 1.5618e+9 > > ## on the left side, there is "nothing" (and negative, as we know!): > showSys.time(I0.1 <- integrateR(integrand, lower = mpfr(-1000, precBits=256), + upper = -10, ord= 11, verbose=TRUE)) ord = 11; ==> evaluating integrand at 2^(ord+1)-2 = 4094 locations n= 1, 2^n= 2 | I = -2.859606864e-613, abs.err = 5.719214e-613 n= 2, 2^n= 4 | I = -1.334483203e-613, abs.err = 1.525124e-613 n= 3, 2^n= 8 | I = -6.566504650e-614, abs.err = 6.778327e-614 n= 4, 2^n= 16 | I = -3.270376825e-614, abs.err = 3.296128e-614 n= 5, 2^n= 32 | I = -1.633589988e-614, abs.err = 1.636787e-614 n= 6, 2^n= 64 | I = -8.165955325e-615, abs.err = 8.169945e-615 n= 7, 2^n= 128 | I = -4.082728442e-615, abs.err = 4.083227e-615 n= 8, 2^n= 256 | I = -2.041333072e-615, abs.err = 2.041395e-615 n= 9, 2^n= 512 | I = -1.020662642e-615, abs.err = 1.020670e-615 n=10, 2^n= 1024 | I = -5.103308345e-616, abs.err = 5.103318e-616 n=11, 2^n= 2048 | I = -2.551653564e-616, abs.err = 2.551655e-616 Time user system elapsed Time 0.44 0.00 0.44 > showSys.time(I0.2 <- integrateR(integrand, lower = mpfr(10, precBits=256), + upper = 1000, ord= 11, verbose=TRUE)) ord = 11; ==> evaluating integrand at 2^(ord+1)-2 = 4094 locations n= 1, 2^n= 2 | I = 6.250128073e-618, abs.err = 1.250026e-617 n= 2, 2^n= 4 | I = 2.916726434e-618, abs.err = 3.333402e-618 n= 3, 2^n= 8 | I = 1.435214595e-618, abs.err = 1.481512e-618 n= 4, 2^n= 16 | I = 7.147931510e-619, abs.err = 7.204214e-619 n= 5, 2^n= 32 | I = 3.570472142e-619, abs.err = 3.577459e-619 n= 6, 2^n= 64 | I = 1.784800116e-619, abs.err = 1.785672e-619 n= 7, 2^n= 128 | I = 8.923455870e-620, abs.err = 8.924545e-620 n= 8, 2^n= 256 | I = 4.461659853e-620, abs.err = 4.461796e-620 n= 9, 2^n= 512 | I = 2.230821417e-620, abs.err = 2.230838e-620 n=10, 2^n= 1024 | I = 1.115409645e-620, abs.err = 1.115412e-620 n=11, 2^n= 2048 | I = 5.577046893e-621, abs.err = 5.577050e-621 Time user system elapsed Time 0.39 0.00 0.39 > I0.1 -2.5517e-616 with absolute error < 2.5517e-616 > I0.2 5.5770e-621 with absolute error < 5.5770e-621 > I4 1.3964e+33 with absolute error < 1.5618e+9 > > ## Integral [-1000, +1000 ] = Int[-1000, -10] + Int[-10, +10] + Int[+10, +1000]: > > I4 $value + I0.1 $value + I0.2 $value 1 'mpfr' number of precision 256 bits [1] 1396354963674017387015147490193159.224442394550727848837192551722726424516192161 > ## but this is really the same as just the middle: > stopifnot(I4 $value + I0.1 $value + I0.2 $value + == I4 $value) > > value <- I4$value; delta <- I4$abs.err > nDig <- -asNumeric(log10(delta/value)) > cat("Correct number of digits: ", round(nDig, 2),"\n", + "Integral I = ", format(I4$value, digits = ceiling(nDig)), + " (last change change= ", format(delta, digits = 7),")\n", + "integrate(.) = ", format(I1 $value, digits = 22),"\n", + "integrate(., rtol=1e-15)= ", format(I1.$value, digits = 22),"\n", sep="") Correct number of digits: 23.95 Integral I = 1.39635496367401738701515e+33 (last change change= 1.561794e+9) integrate(.) = 1.396354963673671429648e+33 integrate(., rtol=1e-15)= 1.396354963674017017894e+33 > > > > > ### 2. Root Finding ---------------------------------------------- > > ### 3. Optimization / Minimization, .. --------------------------- > > > > cat('Time elapsed: ', proc.time(),'\n') # "stats" Time elapsed: 10.1 0.21 10.31 NA NA > > proc.time() user system elapsed 10.10 0.21 10.31