ydeng/bch441-work-abc-units
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Revert to canonical install.packages("name") idiom

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hyginn 2021-10-12 12:17:53 +02:00
parent 6939bd1e88
commit bb2e2a4c85
1 changed files with 27 additions and 23 deletions
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FND-STA-Probability_distribution.R
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@ -44,23 +44,23 @@
#TOC> 2.2.2 Fitting functions 436 #TOC> 2.2.2 Fitting functions 436
#TOC> 2.2.2.1 Fit a normal distribution (using nls() ) 506 #TOC> 2.2.2.1 Fit a normal distribution (using nls() ) 506
#TOC> 2.2.2.2 Fit a normal distribution (using nlrob()) ) 525 #TOC> 2.2.2.2 Fit a normal distribution (using nlrob()) ) 525
#TOC> 2.2.2.3 Extreme Value distributions: Gumbel 551 #TOC> 2.2.2.3 Extreme Value distributions: Gumbel 552
#TOC> 2.2.2.4 Extreme Value distributions: Weibull 576 #TOC> 2.2.2.4 Extreme Value distributions: Weibull 579
#TOC> 2.2.2.5 Logistic distribution 618 #TOC> 2.2.2.5 Logistic distribution 621
#TOC> 2.2.2.6 Log-Logistic distribution 647 #TOC> 2.2.2.6 Log-Logistic distribution 650
#TOC> 2.2.2.7 Fitting a negative binomial distribution 676 #TOC> 2.2.2.7 Fitting a negative binomial distribution 679
#TOC> 2.2.2.8 Fitting a binomial distribution 729 #TOC> 2.2.2.8 Fitting a binomial distribution 732
#TOC> 2.3 The uniform distribution 841 #TOC> 2.3 The uniform distribution 844
#TOC> 2.4 The Normal Distribution 861 #TOC> 2.4 The Normal Distribution 864
#TOC> 3 quantile-quantile comparison 902 #TOC> 3 quantile-quantile comparison 905
#TOC> 3.1 qqnorm() 912 #TOC> 3.1 qqnorm() 915
#TOC> 3.2 qqplot() 978 #TOC> 3.2 qqplot() 981
#TOC> 4 Quantifying the difference 995 #TOC> 4 Quantifying the difference 998
#TOC> 4.1 Chi2 test for discrete distributions 1029 #TOC> 4.1 Chi2 test for discrete distributions 1032
#TOC> 4.2 Kullback-Leibler divergence 1120 #TOC> 4.2 Kullback-Leibler divergence 1124
#TOC> 4.2.1 An example from tossing dice 1131 #TOC> 4.2.1 An example from tossing dice 1135
#TOC> 4.2.2 An example from lognormal distributions 1253 #TOC> 4.2.2 An example from lognormal distributions 1257
#TOC> 4.3 Continuous distributions: Kolmogorov-Smirnov test 1296 #TOC> 4.3 Continuous distributions: Kolmogorov-Smirnov test 1300
#TOC> #TOC>
#TOC> ========================================================================== #TOC> ==========================================================================
@ -527,8 +527,9 @@ sum(resid(fit)^2)
# There's a bit of an art to chosing starting parameters correctly and if the # There's a bit of an art to chosing starting parameters correctly and if the
# nls() fit does not converge, more robust methods are called for. # nls() fit does not converge, more robust methods are called for.
pkg <- "robustbase" if (! requireNamespace("robustbase", quietly = TRUE)) {
if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) } install.packages("robustbase")
}
x <- 0:28 x <- 0:28
plot(x, tu, type="s") plot(x, tu, type="s")
@ -553,8 +554,10 @@ sum(resid(fit)^2)
# Many processes that involve "best-of" choices are better modelled with # Many processes that involve "best-of" choices are better modelled with
# so-called extreme-value distributions: here is the Gumbel distribution # so-called extreme-value distributions: here is the Gumbel distribution
# from the evd package. # from the evd package.
pkg <- "evd"
if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) } if (! requireNamespace("evd", quietly = TRUE)) {
install.packages("evd")
}
x <- 0:28 x <- 0:28
plot(x, tu, type="s") plot(x, tu, type="s")
@ -1055,8 +1058,9 @@ hist(rG1.5, breaks = myBreaks, col = myCols[4])
# package information - plotrix has _many_ useful utilities to enhance # package information - plotrix has _many_ useful utilities to enhance
# plots or produce informative visualizations. # plots or produce informative visualizations.
pkg <- "plotrix" if (! requireNamespace("plotrix", quietly = TRUE)) {
if (! requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) } install.packages("plotrix")
}
# Package information: # Package information:
# library(help = plotrix) # basic information # library(help = plotrix) # basic information