Revert to canonical install.packages("name") idiom
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@ -44,23 +44,23 @@
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#TOC> 2.2.2 Fitting functions 436
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#TOC> 2.2.2.1 Fit a normal distribution (using nls() ) 506
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#TOC> 2.2.2.2 Fit a normal distribution (using nlrob()) ) 525
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#TOC> 2.2.2.3 Extreme Value distributions: Gumbel 551
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#TOC> 2.2.2.4 Extreme Value distributions: Weibull 576
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#TOC> 2.2.2.5 Logistic distribution 618
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#TOC> 2.2.2.6 Log-Logistic distribution 647
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#TOC> 2.2.2.7 Fitting a negative binomial distribution 676
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#TOC> 2.2.2.8 Fitting a binomial distribution 729
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#TOC> 2.3 The uniform distribution 841
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#TOC> 2.4 The Normal Distribution 861
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#TOC> 3 quantile-quantile comparison 902
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#TOC> 3.1 qqnorm() 912
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#TOC> 3.2 qqplot() 978
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#TOC> 4 Quantifying the difference 995
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#TOC> 4.1 Chi2 test for discrete distributions 1029
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#TOC> 4.2 Kullback-Leibler divergence 1120
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#TOC> 4.2.1 An example from tossing dice 1131
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#TOC> 4.2.2 An example from lognormal distributions 1253
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#TOC> 4.3 Continuous distributions: Kolmogorov-Smirnov test 1296
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#TOC> 2.2.2.3 Extreme Value distributions: Gumbel 552
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#TOC> 2.2.2.4 Extreme Value distributions: Weibull 579
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#TOC> 2.2.2.5 Logistic distribution 621
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#TOC> 2.2.2.6 Log-Logistic distribution 650
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#TOC> 2.2.2.7 Fitting a negative binomial distribution 679
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#TOC> 2.2.2.8 Fitting a binomial distribution 732
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#TOC> 2.3 The uniform distribution 844
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#TOC> 2.4 The Normal Distribution 864
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#TOC> 3 quantile-quantile comparison 905
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#TOC> 3.1 qqnorm() 915
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#TOC> 3.2 qqplot() 981
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#TOC> 4 Quantifying the difference 998
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#TOC> 4.1 Chi2 test for discrete distributions 1032
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#TOC> 4.2 Kullback-Leibler divergence 1124
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#TOC> 4.2.1 An example from tossing dice 1135
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#TOC> 4.2.2 An example from lognormal distributions 1257
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#TOC> 4.3 Continuous distributions: Kolmogorov-Smirnov test 1300
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#TOC>
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#TOC> ==========================================================================
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@ -527,8 +527,9 @@ sum(resid(fit)^2)
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# There's a bit of an art to chosing starting parameters correctly and if the
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# nls() fit does not converge, more robust methods are called for.
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pkg <- "robustbase"
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if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) }
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if (! requireNamespace("robustbase", quietly = TRUE)) {
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install.packages("robustbase")
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}
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x <- 0:28
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plot(x, tu, type="s")
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@ -553,8 +554,10 @@ sum(resid(fit)^2)
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# Many processes that involve "best-of" choices are better modelled with
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# so-called extreme-value distributions: here is the Gumbel distribution
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# from the evd package.
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pkg <- "evd"
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if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) }
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if (! requireNamespace("evd", quietly = TRUE)) {
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install.packages("evd")
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}
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x <- 0:28
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plot(x, tu, type="s")
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@ -1055,8 +1058,9 @@ hist(rG1.5, breaks = myBreaks, col = myCols[4])
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# package information - plotrix has _many_ useful utilities to enhance
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# plots or produce informative visualizations.
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pkg <- "plotrix"
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if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) }
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if (! requireNamespace("plotrix", quietly = TRUE)) {
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install.packages("plotrix")
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}
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# Package information:
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# library(help = plotrix) # basic information
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