From 4503993b98a3fc3caaa4d14d52fb472bd2f6d2f3 Mon Sep 17 00:00:00 2001 From: Chitu Okoli Date: Thu, 26 Sep 2024 14:24:55 +0200 Subject: [PATCH 1/3] Added support for the discrete Poisson distribution pois --- R/mlpois.R | 45 +++++++++++++++++++++++++++++++++++ tests/testthat/Rplots.pdf | Bin 278193 -> 278193 bytes tests/testthat/test_mlpois.R | 29 ++++++++++++++++++++++ 3 files changed, 74 insertions(+) create mode 100644 R/mlpois.R create mode 100644 tests/testthat/test_mlpois.R diff --git a/R/mlpois.R b/R/mlpois.R new file mode 100644 index 0000000..e06351a --- /dev/null +++ b/R/mlpois.R @@ -0,0 +1,45 @@ +#' Poisson distribution maximum likelihood estimation +#' +#' The maximum likelihood estimate of `lambda` is the empirical mean. +#' +#' For the density function of the Poisson distribution see +#' [Poisson][stats::Poisson]. +#' +#' @param x a (non-empty) numeric vector of data values. +#' @param na.rm logical. Should missing values be removed? +#' @param ... currently affects nothing. +#' @return `mlpois` returns an object of [class][base::class] `univariateML`. +#' This is a named numeric vector with maximum likelihood estimates for +#' `mean` and `sd` and the following attributes: +#' \item{`model`}{The name of the model.} +#' \item{`density`}{The density associated with the estimates.} +#' \item{`logLik`}{The loglikelihood at the maximum.} +#' \item{`support`}{The support of the density.} +#' \item{`n`}{The number of observations.} +#' \item{`call`}{The call as captured my `match.call`} +#' @examples +#' mlpois(precip) +#' @seealso [Poisson][stats::Poisson] for the Poisson density. +#' @references Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) +#' Continuous Univariate Distributions, Volume 1, Chapter 13. Wiley, New York. +#' @export + +mlpois <- function(x, na.rm = FALSE, ...) { + if (na.rm) x <- x[!is.na(x)] else assertthat::assert_that(!anyNA(x)) + ml_input_checker(x) + n <- length(x) + + lambda <- mean(x) + + object <- c(lambda = lambda) + class(object) <- "univariateML" + attr(object, "model") <- "Poisson" + attr(object, "continuous") = FALSE + attr(object, "density") <- "stats::dpois" + # logLik from ChatGPT; verified correct with sum(stats::dpois(x, lambda, log = TRUE)) + attr(object, "logLik") <- -n * lambda + sum(x) * log(lambda) - sum(lgamma(x + 1)) + attr(object, "support") <- c(0, Inf) + attr(object, "n") <- length(x) + attr(object, "call") <- match.call() + object +} diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf index 708003fac7e805caadadfb4f9d86fad4cf0aec39..58ec265ff45f713b3b6b4881989cd5f6de061084 100644 GIT binary patch delta 69 zcmdn^S775`feB`+mPTfVCWe-#=9*mkzWFIGi6yBD8ZK5w21X_ZaJh-mI?N_|hK*gV RU5u?=Os!qaTf11)?EvDT6mkFn delta 69 zcmdn^S775`feB`+hDK(FW(Ed^7Mfi8zWFIGi6yBD8ZK5w21X_ZaJh-mI?Tp;MvYyq RU5u?=Os!qaTf11)?Eu_a6ifgB diff --git a/tests/testthat/test_mlpois.R b/tests/testthat/test_mlpois.R new file mode 100644 index 0000000..35be4da --- /dev/null +++ b/tests/testthat/test_mlpois.R @@ -0,0 +1,29 @@ +context("mlpois") + +## Data generation. +set.seed(313) +small_data <- rpois(100, 20) +tiny_data <- rpois(10, 10) + +## Finds errors with na and data out of bounds. +expect_error(mlpois(c(tiny_data, NA))) + +## Checks that na.rm works as intended. +expect_equal( + coef(mlpois(small_data)), + coef(mlpois(c(small_data, NA), na.rm = TRUE)) +) + +## Is the log-likelihood correct? +est <- mlpois(small_data, na.rm = TRUE) +expect_equal( + sum(dpois(small_data, est['lambda'], log = TRUE)), + attr(est, "logLik") +) + +## Check class. +expect_equal(attr(est, "model"), "Poisson") +expect_equal(class(est), "univariateML") + +## Check support. +expect_equal(class(attr(est, "support")), "numeric") From 46a4f14351bfe2a77be77bb94dfdc09747bada47 Mon Sep 17 00:00:00 2001 From: Chitu Okoli Date: Thu, 26 Sep 2024 14:25:38 +0200 Subject: [PATCH 2/3] Rewrote test_functions.R to be generic for the normal and Poisson distributions. Template for generic tests for any distribution. --- tests/testthat/test_functions.R | 85 ++++++++++++++++++++++++++------- 1 file changed, 67 insertions(+), 18 deletions(-) diff --git a/tests/testthat/test_functions.R b/tests/testthat/test_functions.R index f9cc2bb..22a136c 100644 --- a/tests/testthat/test_functions.R +++ b/tests/testthat/test_functions.R @@ -1,25 +1,74 @@ context("functions") -set.seed(313) -x <- rnorm(10, 2, 3) -obj <- mlnorm(x) +# Verify that distributions with d*, r*, p*, and q* versions work correctly +seed <- 313 -expect_equal(dml(1, obj), dnorm(1, mean = obj[1], sd = obj[2])) +set.seed(seed) -expect_equal( - pml(1, obj, log.p = TRUE), - pnorm(1, mean = obj[1], sd = obj[2], log.p = TRUE) +# For all distributions to be tested here, follow the pattern of list entries: +# Where "dist" is the name of the distribution type function stem, enter: +# dist = rdist(10, ...) where ... are arbitrary values for the rdist parameters. +# This list must be constructed manually (rather than by automatically reading +# the list of ml*.R files) because each dist function has unique parameters. +dists <- list( + norm = rnorm(10, 2, 3), + pois = rpois(10, 3) ) -expect_equal( - qml(0.9, obj, lower.tail = FALSE), - qnorm(0.9, mean = obj[1], sd = obj[2], lower.tail = FALSE) -) +for (i in 1:length(dists)) { + dist <- names(dists)[i] + x <- dists[[i]] + + obj <- do.call(paste0("ml", dist), list(x)) + + # Validate ddist + expect_equal( + dml(1, obj), + do.call( + paste0("d", dist), # ddist() + c(1, as.list(obj)) # 1 as input plus all dist parameters + ) + ) + + # Validate pdist + expect_equal( + pml(1, obj, log.p = TRUE), + do.call( + paste0("p", dist), # pdist() + # 1 as input plus all dist parameters + c( + 1, log.p = TRUE, + as.list(obj) + ) + ) + ) + + # Validate qdist + expect_equal( + qml(0.9, obj, lower.tail = TRUE), + do.call( + paste0("q", dist), # qdist() + # 0.9 as input plus all dist parameters + c( + 0.9, lower.tail = TRUE, + as.list(obj) + ) + ) + ) -expect_equal((function() { - set.seed(313) - rml(1, obj) -})(), (function() { - set.seed(313) - rnorm(1, obj[1], obj[2]) -})()) + # Validate rdist. + # Seeds must be reset repeatedly to ensure perfect random replication. + expect_equal( + (function() { + set.seed(seed) + rml(1, obj) + })(), + (function() { + set.seed(seed) + do.call( + paste0("r", dist), # rdist() + c(1, as.list(obj)) # 1 as input plus all dist parameters + ) + })() + ) +} From eaca1c62dd7891ee590f5bbf7159fb59529cc82d Mon Sep 17 00:00:00 2001 From: Jonas Moss Date: Thu, 26 Sep 2024 15:57:37 +0200 Subject: [PATCH 3/3] :hammer: Plots for discrete distributions + plots for CDFs and quantile functions + bug fixes. * Fix bug in tests for model selection. * Add quantile and probability functionality to plots. * Add support for discrete distributions in plots, except PP-plots and QQ-plots, where errors are returned. * Fix documentation bug in logitnorm. * Fix spelling error ing lognormal. * Minor change in mlpois. * Update readme with poisson distribution + support for pmf. --- DESCRIPTION | 2 +- NAMESPACE | 1 + R/generics.R | 55 ++++++++++++++++++++------ R/helpers.R | 6 ++- R/mllnorm.R | 2 +- R/mllogitnorm.R | 2 +- R/mlpois.R | 3 -- R/probability_plots.R | 3 ++ README.Rmd | 3 +- README.md | 23 ++++++----- man/ProbabilityPlots.Rd | 3 ++ man/figures/README-weibull_plot-1.png | Bin 17601 -> 17625 bytes man/mllnorm.Rd | 2 +- man/mllogitnorm.Rd | 2 +- man/mlpois.Rd | 39 ++++++++++++++++++ man/plot.univariateML.Rd | 8 ++-- man/plot_wrangler.Rd | 2 +- man/ppqq_wrangler.Rd | 2 +- man/univariateML_models.Rd | 4 +- tests/testthat/Rplots.pdf | Bin 278193 -> 278122 bytes tests/testthat/test_model_select.R | 4 +- 21 files changed, 124 insertions(+), 42 deletions(-) create mode 100644 man/mlpois.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 6642cd5..abe6ac8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -29,7 +29,7 @@ Suggests: copula, dplyr, covr -RoxygenNote: 7.3.1 +RoxygenNote: 7.3.2 VignetteBuilder: knitr URL: https://github.com/JonasMoss/univariateML, https://jonasmoss.github.io/univariateML/ BugReports: https://github.com/JonasMoss/univariateML/issues diff --git a/NAMESPACE b/NAMESPACE index efa8e77..6b495e4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -31,6 +31,7 @@ export(mllomax) export(mlnaka) export(mlnorm) export(mlpareto) +export(mlpois) export(mlpower) export(mlrayleigh) export(mlsged) diff --git a/R/generics.R b/R/generics.R index 8bddc11..7f7a6c1 100644 --- a/R/generics.R +++ b/R/generics.R @@ -5,35 +5,60 @@ #' @param points Boolean; should points be plotted by default? #' @keywords internal -plot_wrangler <- function(x, range, points = FALSE, ...) { +plot_wrangler <- function(x, range, points = FALSE, kind, ...) { + continuous <- if(is.null(attr(x,"continuous"))) TRUE else FALSE support <- attr(x, "support") if (is.null(range)) { if (abs(support[1]) + abs(support[2]) < Inf) { limits <- support } else if (abs(support[1]) == 0 & abs(support[2]) == Inf) { - limits <- c(0, qml(0.99, x)) + limits <- c(0, qml(0.995, x)) } else { - limits <- qml(c(0.01, 0.99), x) + limits <- qml(c(0.005, 0.995), x) } - range <- seq(limits[1], limits[2], length.out = 1000) + limits_untransformed <- limits + if(kind == "q") limits <- pml(limits, x) + + range <- if (continuous) { + seq(limits[1], limits[2], length.out = 1000) + } else { + if(kind == "q") { + pml(seq(limits_untransformed[1], limits_untransformed[2]), x) + } else { + seq(limits[1], limits[2]) + } + } } + ylab <- list(d="Density",p="Cumulative probability",q="Quantile") + xlab <- list(d="x",p="Quantile",q="Cumulative probability") defaults <- list( type = if (points) "p" else "l", main = paste0(attr(x, "model"), " model"), - ylab = "Density", - xlab = "x", + ylab = ylab[[kind]], + xlab = xlab[[kind]], lwd = 1 ) + if(!continuous) { + defaults$pch = 20 + defaults$type = if (points) "p" else "b" + } + args <- listmerge( x = defaults, y = list(...) ) args$x <- range - args$y <- dml(args$x, x) + args$y <- if(kind == "d") { + dml(args$x, x) + } else if (kind == "p") { + pml(args$x, x) + } else { + qml(args$x, x) + } args } @@ -45,6 +70,7 @@ plot_wrangler <- function(x, range, points = FALSE, ...) { #' @export #' @param x a `univariateML` object. #' @param range range of `x` values to plot, i.e. `c(lower, upper)`. +#' @param kind can be `density`, `probability`, or `quantile`. #' @param ... parameters passed to `plot`, `lines`, or `points`. #' @return An invisible copy of `x`. #' @examples @@ -53,24 +79,27 @@ plot_wrangler <- function(x, range, points = FALSE, ...) { #' rug(datasets::precip) #' @export #' -plot.univariateML <- function(x, range = NULL, ...) { - args <- plot_wrangler(x, range, points = FALSE, ...) +plot.univariateML <- function(x, range = NULL, kind = c("d", "p", "q"), ...) { + kind <- match.arg(kind) + args <- plot_wrangler(x, range, points = FALSE, kind = kind, ...) do.call(graphics::plot, args) invisible(x) } #' @export #' @rdname plot.univariateML -lines.univariateML <- function(x, range = NULL, ...) { - args <- plot_wrangler(x, range, points = FALSE, ...) +lines.univariateML <- function(x, range = NULL, kind = c("d", "p", "q"), ...) { + kind <- match.arg(kind) + args <- plot_wrangler(x, range, points = FALSE, kind = kind, ...) do.call(graphics::lines, args) invisible(x) } #' @export #' @rdname plot.univariateML -points.univariateML <- function(x, range = NULL, ...) { - args <- plot_wrangler(x, range, points = TRUE, ...) +points.univariateML <- function(x, range = NULL, kind = c("d", "p", "q"), ...) { + kind <- match.arg(kind) + args <- plot_wrangler(x, range, points = TRUE, kind = kind,...) do.call(graphics::points, args) invisible(x) } diff --git a/R/helpers.R b/R/helpers.R index 68e8f29..a69b27f 100644 --- a/R/helpers.R +++ b/R/helpers.R @@ -64,12 +64,16 @@ to_univariateML <- function(y, obj) { #' Wrangles arguments for use in ppml and qqml functions. #' #' @param y The input data. -#' @param obj Function or `"univariateML"` object. +#' @param obj Function or continuous `"univariateML"` object. #' @param datax logical; if true, plots the data on the x axis. #' @param ... Arguments passed to `plot` or `points` down the line. #' @keywords internal ppqq_wrangler <- function(y, obj, datax, pp, ...) { + + if (!is.null(attr(obj, "continuous"))) { + stop("QQ and PP plots are only supported for continuous distributions.") + } ## Nas are removed by default in this function, following qqplot. y <- y[!is.na(y)] diff --git a/R/mllnorm.R b/R/mllnorm.R index 8674dec..f7e7806 100644 --- a/R/mllnorm.R +++ b/R/mllnorm.R @@ -11,7 +11,7 @@ #' @param x a (non-empty) numeric vector of data values. #' @param na.rm logical. Should missing values be removed? #' @param ... currently affects nothing. -#' @return `mllonorm` returns an object of [class][base::class] `univariateML`. +#' @return `mllnorm` returns an object of [class][base::class] `univariateML`. #' This is a named numeric vector with maximum likelihood estimates for #' `meanlog` and `sdlog` and the following attributes: #' \item{`model`}{The name of the model.} diff --git a/R/mllogitnorm.R b/R/mllogitnorm.R index be97a3b..46088c4 100644 --- a/R/mllogitnorm.R +++ b/R/mllogitnorm.R @@ -22,7 +22,7 @@ #' \item{`call`}{The call as captured my `match.call`} #' @examples #' AIC(mllogitnorm(USArrests$Rape / 100)) -#' @seealso link[dlogitnorm]{dlogitnorm}for the normal density. +#' @seealso [Normal][stats::dnorm] for the normal density. #' @references Atchison, J., & Shen, S. M. (1980). Logistic-normal #' distributions: Some properties and uses. Biometrika, 67(2), 261-272. #' @export diff --git a/R/mlpois.R b/R/mlpois.R index e06351a..2f87c10 100644 --- a/R/mlpois.R +++ b/R/mlpois.R @@ -20,8 +20,6 @@ #' @examples #' mlpois(precip) #' @seealso [Poisson][stats::Poisson] for the Poisson density. -#' @references Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) -#' Continuous Univariate Distributions, Volume 1, Chapter 13. Wiley, New York. #' @export mlpois <- function(x, na.rm = FALSE, ...) { @@ -36,7 +34,6 @@ mlpois <- function(x, na.rm = FALSE, ...) { attr(object, "model") <- "Poisson" attr(object, "continuous") = FALSE attr(object, "density") <- "stats::dpois" - # logLik from ChatGPT; verified correct with sum(stats::dpois(x, lambda, log = TRUE)) attr(object, "logLik") <- -n * lambda + sum(x) * log(lambda) - sum(lgamma(x + 1)) attr(object, "support") <- c(0, Inf) attr(object, "n") <- length(x) diff --git a/R/probability_plots.R b/R/probability_plots.R index 200e2af..2d321fa 100644 --- a/R/probability_plots.R +++ b/R/probability_plots.R @@ -18,6 +18,9 @@ #' #' This function is modeled after [qqnorm][stats::qqnorm]. #' +#' Quantile-quantile plots and probability-probability plots are only supported +#' for continuous distributions. +#' #' Graphical parameters may be given as arguments to all the functions below. #' #' @param y Numeric vector; The data to plot on the `y` axis when diff --git a/README.Rmd b/README.Rmd index 85e4f9a..a622994 100644 --- a/README.Rmd +++ b/README.Rmd @@ -30,7 +30,7 @@ set.seed(313) ## Overview [`univariateML`](https://jonasmoss.github.io/univariateML/index.html) is an `R`-package for user-friendly maximum likelihood estimation of a -[selection](https://jonasmoss.github.io/univariateML/articles/distributions.html) of parametric univariate densities. In addition to basic estimation capabilities, +[selection](https://jonasmoss.github.io/univariateML/articles/distributions.html) of parametric univariate densities and probability mass functions. In addition to basic estimation capabilities, this package support visualization through `plot` and `qqmlplot`, model selection by `AIC` and `BIC`, confidence sets through the parametric bootstrap with `bootstrapml`, and convenience functions such as the density, distribution @@ -99,6 +99,7 @@ lines(mlweibull(egypt$age)) | Logit-normal | `mllogitnorm` | logitnorm | | Uniform distribution | `mlunif` | stats | | Power distribution | `mlpower` | extraDistr | +| Poisson distribution | `mlpois` | stats | ## Implementations diff --git a/README.md b/README.md index f190018..ed839e4 100644 --- a/README.md +++ b/README.md @@ -17,12 +17,13 @@ developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.re [`univariateML`](https://jonasmoss.github.io/univariateML/index.html) is an `R`-package for user-friendly maximum likelihood estimation of a [selection](https://jonasmoss.github.io/univariateML/articles/distributions.html) -of parametric univariate densities. In addition to basic estimation -capabilities, this package support visualization through `plot` and -`qqmlplot`, model selection by `AIC` and `BIC`, confidence sets through -the parametric bootstrap with `bootstrapml`, and convenience functions -such as the density, distribution function, quantile function, and -random sampling at the estimated distribution parameters. +of parametric univariate densities and probability mass functions. In +addition to basic estimation capabilities, this package support +visualization through `plot` and `qqmlplot`, model selection by `AIC` +and `BIC`, confidence sets through the parametric bootstrap with +`bootstrapml`, and convenience functions such as the density, +distribution function, quantile function, and random sampling at the +estimated distribution parameters. ## Installation @@ -94,6 +95,7 @@ lines(mlweibull(egypt$age)) | Logit-normal | `mllogitnorm` | logitnorm | | Uniform distribution | `mlunif` | stats | | Power distribution | `mlpower` | extraDistr | +| Poisson distribution | `mlpois` | stats | ## Implementations @@ -110,11 +112,12 @@ x <- rbeta(500, 2, 7) microbenchmark::microbenchmark( univariateML = univariateML::mlbeta(x), - naive = nlm(function(p) -sum(dbeta(x, p[1], p[2], log = TRUE)), p = c(1, 1))) + naive = nlm(function(p) -sum(dbeta(x, p[1], p[2], log = TRUE)), p = c(1, 1)) +) #> Unit: microseconds -#> expr min lq mean median uq max neval -#> univariateML 259.2 348.75 557.959 447.05 536.40 5103.5 100 -#> naive 15349.1 15978.35 16955.165 16365.45 17082.25 48941.4 100 +#> expr min lq mean median uq max neval +#> univariateML 205.1 329.85 800.077 464.35 730.35 13938.4 100 +#> naive 10665.9 11773.30 21428.104 15233.70 28628.65 103372.1 100 ``` The maximum likelihood estimators in this package have all been subject diff --git a/man/ProbabilityPlots.Rd b/man/ProbabilityPlots.Rd index 3bea6bd..e22c8ec 100644 --- a/man/ProbabilityPlots.Rd +++ b/man/ProbabilityPlots.Rd @@ -69,6 +69,9 @@ quantile-quantile plot functions. This function is modeled after \link[stats:qqnorm]{qqnorm}. +Quantile-quantile plots and probability-probability plots are only supported +for continuous distributions. + Graphical parameters may be given as arguments to all the functions below. } \examples{ diff --git a/man/figures/README-weibull_plot-1.png b/man/figures/README-weibull_plot-1.png index 2ab782310ed05ca6005db5a05005789b834651ad..49dc2d09121a4f2966b54675a3ba8f050d37e454 100644 GIT binary patch literal 17625 zcmeIac{tQ<{4lCKNo3EGHBqEAT2S^Dq=i(-Hd-ttOetopQ`xf1TFH_nB6|$YFf~F* zvL%MmWXm?jI)gFu-e2me=Xag!T<4E-{&=tRPSeaa_ww17`~KYbSLk_TLmn=1E;cqc z9-~tyFR-y~Lb0*2|Kwx`Ef=pl`hu^~^Jg#VgKsuA4K_AMHVq9n4e%Mm=IF@gSOJ=# z2I%Fep%J6u=&0ccJ}We0Vl-mFC(&_jj-v(}=mvdIKlF-mi~;WoM=+qG!m$E;vSKvY zVjRI+BWArD0DwkS#8iMcF@{(XLj<3!3P-jIM~#XY@OG>K-B3Sx5i5w`&8lFrIyyRt zF&e}eM`8tdgL$9_Oa-6d#RC6`EEa&ms)%6`!MkFu0nCG%^WI&S1dMLEeaiAK8=Eu% z`oF>Li-!&y+YUCPlgBQho=^4rUGKP@n0}{<2p!oEes1-uBL?%d&1 ztBJ;hjm*h3Q(tCBl3p`EEHAl;lUO7^q-=7!Me_0!Ucv 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zK(%34gq|1xa8dxy9@|;!m*mnC{6$HZXNXbrB}F!w5D?C8NOMd{$WV8oko{NY#7lR; zKW;-@ZG7P&vIqP;f>k^$hy(cf)yANGd;&V?`*^q(P^d7KQ{ExFt%(CX!~RLHd2Wzs t4|P6H!KPMWci+4x_rLFtOyslq9dq2@lfQZFf_{obU)%6(?wP9({s)51Xk7pR diff --git a/man/mllnorm.Rd b/man/mllnorm.Rd index 79f0119..675baaf 100644 --- a/man/mllnorm.Rd +++ b/man/mllnorm.Rd @@ -14,7 +14,7 @@ mllnorm(x, na.rm = FALSE, ...) \item{...}{currently affects nothing.} } \value{ -\code{mllonorm} returns an object of \link[base:class]{class} \code{univariateML}. +\code{mllnorm} returns an object of \link[base:class]{class} \code{univariateML}. This is a named numeric vector with maximum likelihood estimates for \code{meanlog} and \code{sdlog} and the following attributes: \item{\code{model}}{The name of the model.} diff --git a/man/mllogitnorm.Rd b/man/mllogitnorm.Rd index 0edcc63..f2119f7 100644 --- a/man/mllogitnorm.Rd +++ b/man/mllogitnorm.Rd @@ -42,5 +42,5 @@ Atchison, J., & Shen, S. M. (1980). Logistic-normal distributions: Some properties and uses. Biometrika, 67(2), 261-272. } \seealso{ -link[dlogitnorm]{dlogitnorm}for the normal density. +\link[stats:Normal]{Normal} for the normal density. } diff --git a/man/mlpois.Rd b/man/mlpois.Rd new file mode 100644 index 0000000..47e5090 --- /dev/null +++ b/man/mlpois.Rd @@ -0,0 +1,39 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mlpois.R +\name{mlpois} +\alias{mlpois} +\title{Poisson distribution maximum likelihood estimation} +\usage{ +mlpois(x, na.rm = FALSE, ...) +} +\arguments{ +\item{x}{a (non-empty) numeric vector of data values.} + +\item{na.rm}{logical. Should missing values be removed?} + +\item{...}{currently affects nothing.} +} +\value{ +\code{mlpois} returns an object of \link[base:class]{class} \code{univariateML}. +This is a named numeric vector with maximum likelihood estimates for +\code{mean} and \code{sd} and the following attributes: +\item{\code{model}}{The name of the model.} +\item{\code{density}}{The density associated with the estimates.} +\item{\code{logLik}}{The loglikelihood at the maximum.} +\item{\code{support}}{The support of the density.} +\item{\code{n}}{The number of observations.} +\item{\code{call}}{The call as captured my \code{match.call}} +} +\description{ +The maximum likelihood estimate of \code{lambda} is the empirical mean. +} +\details{ +For the density function of the Poisson distribution see +\link[stats:Poisson]{Poisson}. +} +\examples{ +mlpois(precip) +} +\seealso{ +\link[stats:Poisson]{Poisson} for the Poisson density. +} diff --git a/man/plot.univariateML.Rd b/man/plot.univariateML.Rd index d074c2b..f5dd71e 100644 --- a/man/plot.univariateML.Rd +++ b/man/plot.univariateML.Rd @@ -6,17 +6,19 @@ \alias{points.univariateML} \title{Plot, Lines and Points Methods for Maximum Likelihood Estimates} \usage{ -\method{plot}{univariateML}(x, range = NULL, ...) +\method{plot}{univariateML}(x, range = NULL, kind = c("d", "p", "q"), ...) -\method{lines}{univariateML}(x, range = NULL, ...) +\method{lines}{univariateML}(x, range = NULL, kind = c("d", "p", "q"), ...) -\method{points}{univariateML}(x, range = NULL, ...) +\method{points}{univariateML}(x, range = NULL, kind = c("d", "p", "q"), ...) } \arguments{ \item{x}{a \code{univariateML} object.} \item{range}{range of \code{x} values to plot, i.e. \code{c(lower, upper)}.} +\item{kind}{can be \code{density}, \code{probability}, or \code{quantile}.} + \item{...}{parameters passed to \code{plot}, \code{lines}, or \code{points}.} } \value{ diff --git a/man/plot_wrangler.Rd b/man/plot_wrangler.Rd index 8f5db6c..caef54a 100644 --- a/man/plot_wrangler.Rd +++ b/man/plot_wrangler.Rd @@ -4,7 +4,7 @@ \alias{plot_wrangler} \title{Wrangles arguments for use in the plot, lines and points functions.} \usage{ -plot_wrangler(x, range, points = FALSE, ...) +plot_wrangler(x, range, points = FALSE, kind, ...) } \arguments{ \item{x}{The input data.} diff --git a/man/ppqq_wrangler.Rd b/man/ppqq_wrangler.Rd index f004bae..c46aba3 100644 --- a/man/ppqq_wrangler.Rd +++ b/man/ppqq_wrangler.Rd @@ -9,7 +9,7 @@ ppqq_wrangler(y, obj, datax, pp, ...) \arguments{ \item{y}{The input data.} -\item{obj}{Function or \code{"univariateML"} object.} +\item{obj}{Function or continuous \code{"univariateML"} object.} \item{datax}{logical; if true, plots the data on the x axis.} diff --git a/man/univariateML_models.Rd b/man/univariateML_models.Rd index 866f020..76e24f3 100644 --- a/man/univariateML_models.Rd +++ b/man/univariateML_models.Rd @@ -5,7 +5,7 @@ \alias{univariateML_models} \title{Implemented models} \format{ -An object of class \code{character} of length 29. +An object of class \code{character} of length 30. } \usage{ univariateML_models @@ -14,6 +14,6 @@ univariateML_models Vector of all supported models in \code{univariateML}. } \details{ -The currently supported models are \link{mlbeta}, \link{mlbetapr}, \link{mlcauchy}, \link{mlexp}, \link{mlgamma}, \link{mlged}, \link{mlgumbel}, \link{mlinvgamma}, \link{mlinvgauss}, \link{mlinvweibull}, \link{mlkumar}, \link{mllaplace}, \link{mllgamma}, \link{mlllogis}, \link{mllnorm}, \link{mllogis}, \link{mllogitnorm}, \link{mllomax}, \link{mlnaka}, \link{mlnorm}, \link{mlpareto}, \link{mlpower}, \link{mlrayleigh}, \link{mlsged}, \link{mlsnorm}, \link{mlsstd}, \link{mlstd}, \link{mlunif}, \link{mlweibull} +The currently supported models are \link{mlbeta}, \link{mlbetapr}, \link{mlcauchy}, \link{mlexp}, \link{mlgamma}, \link{mlged}, \link{mlgumbel}, \link{mlinvgamma}, \link{mlinvgauss}, \link{mlinvweibull}, \link{mlkumar}, \link{mllaplace}, \link{mllgamma}, 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