Help for package ssdtools

Title:

Species Sensitivity Distributions

Version:

2.3.0

Description:

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

License:

Apache License (== 2.0) | file LICENSE

URL:

https://github.com/bcgov/ssdtools, https://bcgov.github.io/ssdtools/

BugReports:

https://github.com/bcgov/ssdtools/issues

Depends:

R (≥ 4.1)

Imports:

abind, chk, furrr, generics, ggplot2, ggtext, glue, goftest, graphics, grid, lifecycle, parallel, plyr, purrr, Rcpp, rlang, scales, ssddata, stats, stringr, tibble, TMB, universals, utils, withr

Suggests:

actuar, covr, doFuture, dplyr, EnvStats, extraDistr, fitdistrplus, foreach, future, grDevices, knitr, latex2exp, magrittr, mle.tools, patchwork, readr, reshape2, rmarkdown, testthat (≥ 3.0.0), tidyr, tidyselect, tinytex, VGAM

LinkingTo:

Rcpp, RcppEigen, TMB

VignetteBuilder:

knitr

Config/testthat/edition:

Encoding:

UTF-8

Language:

en-US

LazyData:

true

RoxygenNote:

7.3.2.9000

NeedsCompilation:

yes

Packaged:

2025-02-20 12:55:21 UTC; joe

Author:

Joe Thorley

[aut, cre], Rebecca Fisher [aut], David Fox [aut], Carl Schwarz [aut], Angeline Tillmanns [ctb], Seb Dalgarno

[ctb], Kathleen McTavish [ctb], Heather Thompson [ctb], Doug Spry [ctb], Rick van Dam [ctb], Graham Batley [ctb], Ali Azizishirazi [ctb], Nadine Hussein

[ctb], Sarah Lyons

[ctb], Duncan Kennedy

[ctb], Stephanie Hazlitt [ctb], Hadley Wickham [ctb], Sergio Ibarra Espinosa [ctb], Andy Teucher [ctb], Emilie Doussantousse [ctb], Nan-Hung Hsieh [ctb], Florencia D'Andrea [ctb], Province of British Columbia [fnd, cph], Environment and Climate Change Canada [fnd, cph], Australian Government Department of Climate Change, Energy, the Environment and Water [fnd, cph]

Maintainer:

Joe Thorley <joe@poissonconsulting.ca>

Repository:

CRAN

Date/Publication:

2025-02-20 13:20:02 UTC

ssdtools: Species Sensitivity Distributions

Description

Author(s)

Maintainer: Joe Thorley joe@poissonconsulting.ca (ORCID)

Authors:

Rebecca Fisher R.Fisher@aims.gov.au
David Fox david.fox@environmetrics.net.au
Carl Schwarz

Other contributors:

Angeline Tillmanns [contributor]
Seb Dalgarno seb@poissonconsulting.ca (ORCID) [contributor]
Kathleen McTavish [contributor]
Heather Thompson [contributor]
Doug Spry [contributor]
Rick van Dam [contributor]
Graham Batley [contributor]
Ali Azizishirazi [contributor]
Nadine Hussein nadine@poissonconsulting.ca (ORCID) [contributor]
Sarah Lyons sarah@poissonconsulting.ca (ORCID) [contributor]
Duncan Kennedy duncan@poissonconsulting.ca (ORCID) [contributor]
Stephanie Hazlitt [contributor]
Hadley Wickham [contributor]
Sergio Ibarra Espinosa [contributor]
Andy Teucher [contributor]
Emilie Doussantousse [contributor]
Nan-Hung Hsieh [contributor]
Florencia D'Andrea [contributor]
Province of British Columbia [funder, copyright holder]
Environment and Climate Change Canada [funder, copyright holder]
Australian Government Department of Climate Change, Energy, the Environment and Water [funder, copyright holder]

Augmented Data from fitdists Object

Description

Get a tibble of the original data with augmentation.

Usage

## S3 method for class 'fitdists'
augment(x, ...)

Arguments

x

The object.

...

Unused.

Value

A tibble of the agumented data.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
augment(fits)

Plot a fitdists Object

Description

A wrapper on ssd_plot_cdf().

Usage

## S3 method for class 'fitdists'
autoplot(object, ...)

Arguments

object

The object.

...

Unused.

Value

A ggplot object.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
autoplot(fits)

Model Averaged Predictions for CCME Boron Data

Description

A data frame of the predictions based on 1,000 bootstrap iterations.

Usage

boron_pred

Format

An object of class tbl_df (inherits from tbl, data.frame) with 99 rows and 11 columns.

Details

proportion: The proportion of species affected (int).
est: The estimated concentration (dbl).
se: The standard error of the estimate (dbl).
lcl: The lower confidence limit (dbl).
se: The upper confidence limit (dbl).
dist: The distribution (chr).

Examples

## Not run: 
fits <- ssd_fit_dists(ssddata::ccme_boron)
set.seed(99)
boron_pred <- predict(fits, ci = TRUE)

## End(Not run)
head(boron_pred)

Turn a fitdists Object into a Tidy Tibble

Description

A wrapper on tidy.fitdists().

Usage

## S3 method for class 'fitdists'
coef(object, ...)

Arguments

object

The object.

...

Unused.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
coef(fits)

Comma and Significance Formatter

Description

Deprecated for ssd_label_comma()

Usage

comma_signif(x, digits = 3, ..., big.mark = ",")

Arguments

x

A numeric vector to format.

digits

A whole number specifying the number of significant figures.

...

Unused.

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

Value

A character vector.

Examples

## Not run: 
comma_signif(c(0.1, 1, 10, 1000, 10000))

## End(Not run)

Gompertz Probability Density

Description

Gompertz Probability Density

Usage

dgompertz(x, llocation = 0, lshape = 0, log = FALSE)

Arguments

x

A numeric vector of values.

llocation

location parameter on the log scale.

lshape

shape parameter on the log scale.

log

logical; if TRUE, probabilities p are given as log(p).

Value

A numeric vector.

Distribution Data

Description

A data frame of information on the implemented distributions.

Usage

dist_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 10 rows and 5 columns.

Details

dist: The distribution (chr).
bcanz: Whether the distribution belongs to the set of distributions approved by BC, Canada, Australia and New Zealand for official guidelines (flag).
tails: Whether the distribution has both tails (flag).
npars: The number of parameters (int).
valid: Whether the distribution has a valid likelihood that allows it to be fit with other distributions for modeling averaging (flag).

Examples

dist_data

Log-Gumbel (Inverse Weibull) Probability Density

Description

Log-Gumbel (Inverse Weibull) Probability Density

Usage

dlgumbel(x, locationlog = 0, scalelog = 1, log = FALSE)

Arguments

x

A numeric vector of values.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

log

logical; if TRUE, probabilities p are given as log(p).

Value

A numeric vector.

Estimates for fitdists Object

Description

Gets a named list of the estimated weights and parameters.

Usage

## S3 method for class 'fitdists'
estimates(x, all_estimates = FALSE, ...)

Arguments

x

The object.

all_estimates

A flag specifying whether to calculate estimates for all implemented distributions.

...

Unused.

Value

A named list of the estimates.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
estimates(fits)

Species Sensitivity Hazard Concentration Intersection

Description

Plots the intersection between each xintercept and yintercept value.

Usage

geom_hcintersect(
  mapping = NULL,
  data = NULL,
  ...,
  xintercept,
  yintercept,
  na.rm = FALSE,
  show.legend = NA
)

Arguments

mapping

Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

xintercept

The x-value for the intersect.

yintercept

The y-value for the intersect.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

Examples

ggplot2::ggplot(ssddata::ccme_boron, ggplot2::aes(x = Conc)) +
  geom_ssdpoint() +
  geom_hcintersect(xintercept = 1.5, yintercept = 0.05)

Species Sensitivity Data Points

Description

Deprecated for geom_ssdpoint().

Usage

geom_ssd(
  mapping = NULL,
  data = NULL,
  stat = "ssdpoint",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

stat

The statistical transformation to use on the data for this layer. When using a ⁠geom_*()⁠ function to construct a layer, the stat argument can be used the override the default coupling between geoms and stats. The stat argument accepts the following:

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

Species Sensitivity Data Points

Description

Uses the empirical cumulative distribution to create scatterplot of points x.

Usage

geom_ssdpoint(
  mapping = NULL,
  data = NULL,
  stat = "ssdpoint",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

Examples

ggplot2::ggplot(ssddata::ccme_boron, ggplot2::aes(x = Conc)) +
  geom_ssdpoint()

Species Sensitivity Censored Segments

Description

Uses the empirical cumulative distribution to draw lines between points x and xend.

Usage

geom_ssdsegment(
  mapping = NULL,
  data = NULL,
  stat = "ssdsegment",
  position = "identity",
  ...,
  arrow = NULL,
  arrow.fill = NULL,
  lineend = "butt",
  linejoin = "round",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

arrow

specification for arrow heads, as created by grid::arrow().

arrow.fill

fill colour to use for the arrow head (if closed). NULL means use colour aesthetic.

lineend

Line end style (round, butt, square).

linejoin

Line join style (round, mitre, bevel).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

Examples

ggplot2::ggplot(ssddata::ccme_boron, ggplot2::aes(x = Conc, xend = Conc * 2)) +
  geom_ssdsegment()

Ribbon on X-Axis

Description

Plots the x interval defined by xmin and xmax.

Usage

geom_xribbon(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

stat

A Stat ggproto subclass, for example StatCount.
A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as "count".
For more information and other ways to specify the stat, see the layer stat documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

Examples

gp <- ggplot2::ggplot(boron_pred) +
  geom_xribbon(ggplot2::aes(xmin = lcl, xmax = ucl, y = proportion))

Get a tibble summarizing each distribution

Description

Gets a tibble with a single row for each distribution.

Usage

## S3 method for class 'fitdists'
glance(x, ...)

Arguments

x

The object.

...

Unused.

Value

A tidy tibble of the distributions.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
glance(fits)

Is fitdists Object

Description

Tests whether x is a fitdists Object.

Usage

is.fitdists(x)

Arguments

x

The object.

Value

A flag specifying whether x is a fitdists Object.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
is.fitdists(fits)

Is Censored

Description

Deprecated for ssd_is_censored().

Usage

is_censored(x)

Arguments

x

A fitdists object.

Value

A flag indicating if the data is censored.

Parameter Descriptions for ssdtools Functions

Description

Parameter Descriptions for ssdtools Functions

Arguments

...

Unused.

add_x

The value to add to the label x values (before multiplying by shift_x).

all

A flag specifying whether to also return transformed parameters.

all_dists

A flag specifying whether all the named distributions must fit successfully.

at_boundary_ok

A flag specifying whether a model with one or more parameters at the boundary should be considered to have converged (default = FALSE).

average

A flag specifying whether to provide model averaged values as opposed to a value for each distribution.

bcanz

A flag or NULL specifying whether to only include distributions in the set that is approved by BC, Canada, Australia and New Zealand for official guidelines.

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

breaks

A character vector

bounds

A named non-negative numeric vector of the left and right bounds for uncensored missing (0 and Inf) data in terms of the orders of magnitude relative to the extremes for non-missing values.

chk

A flag specifying whether to check the arguments.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

censoring

A numeric vector of the left and right censoring values.

color

A string of the column in data for the color aesthetic.

computable

A flag specifying whether to only return fits with numerically computable standard errors.

conc

A numeric vector of concentrations to calculate the hazard proportions for.

control

A list of control parameters passed to stats::optim().

data

A data frame.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

digits

A whole number specifying the number of significant figures.

dists

A character vector of the distribution names.

fitdists

An object of class fitdists.

hc

A value between 0 and 1 indicating the proportion hazard concentration (or NULL).

hc_value

A number of the hazard concentration value to offset.

label

A string of the column in data with the labels.

label_size

A number for the size of the labels.

left

A string of the column in data with the concentrations.

level

A number between 0 and 1 of the confidence level of the interval.

linecolor

A string of the column in pred to use for the line color.

linetype

A string of the column in pred to use for the linetype.

llocation

location parameter on the log scale.

location

location parameter.

locationlog

location on the log scale parameter.

locationlog1

locationlog1 parameter.

locationlog2

locationlog2 parameter.

log

logical; if TRUE, probabilities p are given as log(p).

log.p

logical; if TRUE, probabilities p are given as log(p).

lscale

scale parameter on the log scale.

lshape

shape parameter on the log scale.

lshape1

shape1 parameter on the log scale.

lshape2

shape2 parameter on the log scale.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

meanlog

mean on log scale parameter.

meanlog1

mean on log scale parameter.

meanlog2

mean on log scale parameter.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

min_pmix

A number between 0 and 0.5 specifying the minimum proportion in mixture models.

npars

A whole numeric vector specifying which distributions to include based on the number of parameters.

all_estimates

A flag specifying whether to calculate estimates for all implemented distributions.

ci_method

A string specifying which method to use for estimating the bootstrap values. Possible values are "multi_free" and "multi_fixed" which treat the distributions as constituting a single distribution but differ in whether the model weights are fixed and "weighted_samples" and "weighted_arithmetic" take bootstrap samples from each distribution proportional to its weight versus calculating the weighted arithmetic means of the lower and upper confidence limits.

multi_est

A flag specifying whether to treat the distributions as constituting a single distribution (as opposed to taking the mean) when calculating model averaged estimates.

na.rm

A flag specifying whether to silently remove missing values or remove them with a warning.

n

positive number of observations.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

nrow

A positive whole number of the minimum number of non-missing rows.

nsim

A positive whole number of the number of simulations to generate.

object

The object.

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

p

vector of probabilities.

percent

A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.

pmix

Proportion mixture parameter.

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

pvalue

A flag specifying whether to return p-values or the statistics (default) for the various tests.

pred

A data frame of the predictions.

q

vector of quantiles.

range_shape1

A numeric vector of length two of the lower and upper bounds for the shape1 parameter.

range_shape2

shape2 parameter.

reweight

A flag specifying whether to reweight weights by dividing by the largest weight.

rescale

A flag specifying whether to rescale concentration values by dividing by the geometric mean of the minimum and maximum positive finite values.

ribbon

A flag indicating whether to plot the confidence interval as a grey ribbon as opposed to green solid lines.

right

A string of the column in data with the right concentration values.

save_to

NULL or a string specifying a directory to save where the bootstrap datasets and parameter estimates (when successfully converged) to.

samples

A flag specfying whether to include a numeric vector of the bootstrap samples as a list column in the output.

scale

scale parameter.

scalelog1

scalelog1 parameter.

scalelog2

scalelog2 parameter.

scalelog

scale on log scale parameter.

sdlog

standard deviation on log scale parameter.

sdlog1

standard deviation on log scale parameter.

sdlog2

standard deviation on log scale parameter.

select

A character vector of the distributions to select.

shape

shape parameter.

shape1

shape1 parameter.

shape2

shape2 parameter.

shift_x

The value to multiply the label x values by (after adding add_x).

silent

A flag indicating whether fits should fail silently.

size

A number for the size of the labels. Deprecated for label_size. #'

suffix

Additional text to display after the number on the y-axis.

tails

A flag or NULL specifying whether to only include distributions with both tails.

text_size

A number for the text size.

theme_classic

A flag specifying whether to use the classic theme or the default.

trans

A string of which transformation to use. Accepted values include "log10", "log", and "identity" ("log10" by default).

valid

A flag or NULL specifying whether to include distributions with valid likelihoods that allows them to be fit with other distributions for modeling averaging.

weight

A string of the numeric column in data with positive weights less than or equal to 1,000 or NULL.

x

The object.

xbreaks

The x-axis breaks as one of:

NULL for no breaks
waiver() for the default breaks
A numeric vector of positions

xlimits

The x-axis limits as one of:

NULL to use the default scale range
A numeric vector of length two providing the limits. Use NA to refer to the existing minimum or maximum limits.

xintercept

The x-value for the intersect.

xlab

A string of the x-axis label.

yintercept

The y-value for the intersect.

ylab

A string of the x-axis label.

burrIII3.weight

weight parameter for the Burr III distribution.

burrIII3.shape1

shape1 parameter for the Burr III distribution.

burrIII3.shape2

shape2 parameter for the Burr III distribution.

burrIII3.scale

scale parameter for the Burr III distribution.

gamma.weight

weight parameter for the gamma distribution.

gamma.shape

shape parameter for the gamma distribution.

gamma.scale

scale parameter for the gamma distribution.

gompertz.weight

weight parameter for the Gompertz distribution.

gompertz.location

location parameter for the Gompertz distribution.

gompertz.shape

shape parameter for the Gompertz distribution.

invpareto.weight

weight parameter for the inverse Pareto distribution.

invpareto.shape

shape parameter for the inverse Pareto distribution.

invpareto.scale

scale parameter for the inverse Pareto distribution.

lgumbel.weight

weight parameter for the log-Gumbel distribution.

lgumbel.locationlog

location parameter for the log-Gumbel distribution.

lgumbel.scalelog

scale parameter for the log-Gumbel distribution.

llogis.weight

weight parameter for the log-logistic distribution.

llogis.locationlog

location parameter for the log-logistic distribution.

llogis.scalelog

scale parameter for the log-logistic distribution.

llogis_llogis.weight

weight parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog1

locationlog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog1

scalelog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog2

locationlog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog2

scalelog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.pmix

pmix parameter for the log-logistic log-logistic mixture distribution.

lnorm.weight

weight parameter for the log-normal distribution.

lnorm.meanlog

meanlog parameter for the log-normal distribution.

lnorm.sdlog

sdlog parameter for the log-normal distribution.

lnorm_lnorm.weight

weight parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog1

meanlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog1

sdlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog2

meanlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog2

sdlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.pmix

pmix parameter for the log-normal log-normal mixture distribution.

weibull.weight

weight parameter for the Weibull distribution.

weibull.shape

shape parameter for the Weibull distribution.

weibull.scale

scale parameter for the Weibull distribution.

Cumulative Distribution Function for Gompertz Distribution

Description

Deprecated for ssd_pgompertz().

Usage

pgompertz(q, llocation = 0, lshape = 0, lower.tail = TRUE, log.p = FALSE)

Arguments

q

vector of quantiles.

llocation

location parameter on the log scale.

lshape

shape parameter on the log scale.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

Cumulative Distribution Function for Log-Gumbel Distribution

Description

Deprecated for ssd_plgumbel().

Usage

plgumbel(q, locationlog = 0, scalelog = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

q

vector of quantiles.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

Predict Hazard Concentrations of fitburrlioz Object

Description

A wrapper on ssd_hc() that by default calculates all hazard concentrations from 1 to 99%.

Usage

## S3 method for class 'fitburrlioz'
predict(
  object,
  percent,
  proportion = 1:99/100,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  parametric = TRUE,
  ...
)

Arguments

object

The object.

percent

A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

level

A number between 0 and 1 of the confidence level of the interval.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

...

Unused.

Details

It is useful for plotting purposes.

Examples

fits <- ssd_fit_burrlioz(ssddata::ccme_boron)
predict(fits)

Predict Hazard Concentrations of fitdists Object

Description

A wrapper on ssd_hc() that by default calculates all hazard concentrations from 1 to 99%.

Usage

## S3 method for class 'fitdists'
predict(
  object,
  percent,
  proportion = 1:99/100,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  multi_est = TRUE,
  ci_method = "weighted_samples",
  parametric = TRUE,
  delta = 9.21,
  control = NULL,
  ...
)

Arguments

object

The object.

percent

A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

average

A flag specifying whether to provide model averaged values as opposed to a value for each distribution.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

level

A number between 0 and 1 of the confidence level of the interval.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

multi_est

A flag specifying whether to treat the distributions as constituting a single distribution (as opposed to taking the mean) when calculating model averaged estimates.

ci_method

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

control

A list of control parameters passed to stats::optim().

...

Unused.

Details

It is useful for plotting purposes.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
predict(fits)

Quantile Function for Gompertz Distribution

Description

Deprecated for ssd_qgompertz().

Usage

qgompertz(p, llocation = 0, lshape = 0, lower.tail = TRUE, log.p = FALSE)

Arguments

p

vector of probabilities.

llocation

location parameter on the log scale.

lshape

shape parameter on the log scale.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

Quantile Function for Log-Gumbel Distribution

Description

Deprecated for ssd_qlgumbel().

Usage

qlgumbel(p, locationlog = 0, scalelog = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

p

vector of probabilities.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics: augment, glance, tidy
ggplot2: autoplot, waiver
graphics: plot
stats: coef, logLik, nobs, predict
universals: estimates, npars

Random Generation for Gompertz Distribution

Description

Deprecated for ssd_rgompertz().

Usage

rgompertz(n, llocation = 0, lshape = 0)

Arguments

n

positive number of observations.

llocation

location parameter on the log scale.

lshape

shape parameter on the log scale.

Random Generation for log-Gumbel Distribution

Description

Deprecated for ssd_rlgumbel().

Usage

rlgumbel(n, locationlog = 0, scalelog = 1)

Arguments

n

positive number of observations.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

Details

Discrete color-blind scale for SSD Plots

Description

The functions were designed for coloring different groups in a plot of SSD data.

Usage

scale_colour_ssd(...)

scale_color_ssd(...)

scale_fill_ssd(...)

Arguments

...

Arguments passed to ggplot2::discrete_scale().

Functions

scale_color_ssd(): Discrete color-blind scale for SSD Plots
scale_fill_ssd(): Discrete color-blind scale for SSD Plots

Examples

# Use the color-blind palette for a SSD plot
ssd_plot(ssddata::ccme_boron, boron_pred, shape = "Group", color = "Group") +
  scale_colour_ssd()
# Use the color-blind palette for a histogram of concentrations
ggplot2::ggplot(ssddata::ccme_boron, ggplot2::aes(x = Species, y = Conc, fill = Group)) +
  ggplot2::geom_col() +
  scale_fill_ssd() +
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1))

Censor Data

Description

Censors data to a specified range based on the censoring argument. The function is useful for creating test data sets.

Usage

ssd_censor_data(data, left = "Conc", ..., right = left, censoring = c(0, Inf))

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

...

Unused.

right

A string of the column in data with the right concentration values.

censoring

A numeric vector of the left and right censoring values.

Value

A tibble of the censored data.

Examples

ssd_censor_data(ssddata::ccme_boron, censoring = c(2.5, Inf))

Data from fitdists Object

Description

Get a tibble of the original data.

Usage

ssd_data(x)

Arguments

x

The object.

Value

A tibble of the original data.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_data(fits)

Species Sensitivity Distributions

Description

Gets a character vector of the names of the available distributions.

Usage

ssd_dists(bcanz = NULL, tails = NULL, npars = 2:5, valid = TRUE)

Arguments

bcanz

A flag or NULL specifying whether to only include distributions in the set that is approved by BC, Canada, Australia and New Zealand for official guidelines.

tails

A flag or NULL specifying whether to only include distributions with both tails.

npars

A whole numeric vector specifying which distributions to include based on the number of parameters.

valid

A flag or NULL specifying whether to include distributions with valid likelihoods that allows them to be fit with other distributions for modeling averaging.

Value

A unique, sorted character vector of the distributions.

Examples

ssd_dists()
ssd_dists(bcanz = TRUE)
ssd_dists(tails = FALSE)
ssd_dists(npars = 5)

All Species Sensitivity Distributions

Description

Gets a character vector of the names of all the available distributions.

Usage

ssd_dists_all(valid = TRUE)

Arguments

valid

A flag or NULL specifying whether to include distributions with valid likelihoods that allows them to be fit with other distributions for modeling averaging.

Value

A unique, sorted character vector of the distributions.

Examples

ssd_dists_all()

BCANZ Distributions

Description

Gets a character vector of the names of the distributions adopted by BC, Canada, Australia and New Zealand for official guidelines.

Usage

ssd_dists_bcanz(npars = c(2L, 5L))

Arguments

npars

A whole numeric vector specifying which distributions to include based on the number of parameters.

Value

A unique, sorted character vector of the distributions.

Examples

ssd_dists_bcanz()
ssd_dists_bcanz(npars = 2)

All Shiny Species Sensitivity Distributions

Description

Gets a character vector of the names of all the available distributions in the shinyssdtools.

Usage

ssd_dists_shiny()

Value

A unique, sorted character vector of the distributions.

Examples

ssd_dists_shiny()

Default Parameter Estimates

Description

Default Parameter Estimates

Usage

ssd_eburrIII3()

ssd_egamma()

ssd_egompertz()

ssd_einvpareto()

ssd_elgumbel()

ssd_elgumbel()

ssd_ellogis_llogis()

ssd_ellogis()

ssd_elnorm_lnorm()

ssd_elnorm()

ssd_emulti()

ssd_eweibull()

Functions

ssd_eburrIII3(): Default Parameter Values for BurrIII Distribution
ssd_egamma(): Default Parameter Values for Gamma Distribution
ssd_egompertz(): Default Parameter Values for Gompertz Distribution
ssd_einvpareto(): Default Parameter Values for Inverse Pareto Distribution
ssd_elgumbel(): Default Parameter Values for Log-Gumbel Distribution
ssd_elgumbel(): Default Parameter Values for log-Gumbel Distribution
ssd_ellogis_llogis(): Default Parameter Values for Log-Logistic/Log-Logistic Mixture Distribution
ssd_ellogis(): Default Parameter Values for Log-Logistic Distribution
ssd_elnorm_lnorm(): Default Parameter Values for Log-Normal/Log-Normal Mixture Distribution
ssd_elnorm(): Default Parameter Values for Log-Normal Distribution
ssd_emulti(): Default Parameter Values for Multiple Distributions
ssd_eweibull(): Default Parameter Values for Log-Normal Distribution

Examples


ssd_eburrIII3()

ssd_egamma()

ssd_egompertz()

ssd_einvpareto()

ssd_einvpareto()

ssd_elgumbel()

ssd_ellogis_llogis()

ssd_ellogis()

ssd_elnorm_lnorm()

ssd_elnorm()

ssd_emulti()

ssd_eweibull()

Empirical Cumulative Density

Description

Empirical Cumulative Density

Usage

ssd_ecd(x, ties.method = "first")

Arguments

x

a numeric, complex, character or logical vector.

ties.method

a character string specifying how ties are treated, see ‘Details’; can be abbreviated.

Value

A numeric vector of the empirical cumulative density.

Examples

ssd_ecd(1:10)

Empirical Cumulative Density for Species Sensitivity Data

Description

Empirical Cumulative Density for Species Sensitivity Data

Usage

ssd_ecd_data(
  data,
  left = "Conc",
  right = left,
  bounds = c(left = 1, right = 1)
)

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

bounds

A named non-negative numeric vector of the left and right bounds for uncensored missing (0 and Inf) data in terms of the orders of magnitude relative to the extremes for non-missing values.

Value

A numeric vector of the empirical cumulative density for the rows in data.

Examples

ssd_ecd_data(ssddata::ccme_boron)

Proportion Exposure

Description

Calculates average proportion exposed based on log-normal distribution of concentrations.

Usage

ssd_exposure(x, meanlog = 0, sdlog = 1, nboot = 1000)

Arguments

x

The object.

meanlog

The mean of the exposure concentrations on the log scale.

sdlog

The standard deviation of the exposure concentrations on the log scale.

nboot

The number of samples to use to calculate the exposure.

Value

The proportion exposed.

Examples

## Not run: 
fits <- ssd_fit_dists(ssddata::ccme_boron, dists = "lnorm")
set.seed(10)
ssd_exposure(fits)
ssd_exposure(fits, meanlog = 1)
ssd_exposure(fits, meanlog = 1, sdlog = 1)

## End(Not run)

Fit BCANZ Distributions

Description

Fits distributions using settings adopted by BC, Canada, Australia and New Zealand for official guidelines.

Usage

ssd_fit_bcanz(data, left = "Conc", dists = ssd_dists_bcanz())

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

dists

A character vector of the distribution names.

Value

An object of class fitdists.

Examples

ssd_fit_bcanz(ssddata::ccme_boron)

Fit Burrlioz Distributions

Description

Fits 'burrIII3' distribution. If shape1 parameter is at boundary returns 'lgumbel' (which is equivalent to inverse Weibull). Else if shape2 parameter is at a boundary returns 'invpareto'. Otherwise returns 'burrIII3'

Usage

ssd_fit_burrlioz(
  data,
  left = "Conc",
  rescale = FALSE,
  control = list(),
  silent = FALSE
)

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

rescale

A flag specifying whether to rescale concentration values by dividing by the geometric mean of the minimum and maximum positive finite values.

control

A list of control parameters passed to stats::optim().

silent

A flag indicating whether fits should fail silently.

Value

An object of class fitdists.

Examples

ssd_fit_burrlioz(ssddata::ccme_boron)

Fit Distributions

Description

Fits one or more distributions to species sensitivity data.

Usage

ssd_fit_dists(
  data,
  left = "Conc",
  right = left,
  weight = NULL,
  dists = ssd_dists_bcanz(),
  nrow = 6L,
  rescale = FALSE,
  reweight = FALSE,
  computable = FALSE,
  at_boundary_ok = TRUE,
  all_dists = FALSE,
  min_pmix = ssd_min_pmix(nrow(data)),
  range_shape1 = c(0.05, 20),
  range_shape2 = range_shape1,
  control = list(),
  silent = FALSE
)

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

weight

A string of the numeric column in data with positive weights less than or equal to 1,000 or NULL.

dists

A character vector of the distribution names.

nrow

A positive whole number of the minimum number of non-missing rows.

rescale

A flag specifying whether to rescale concentration values by dividing by the geometric mean of the minimum and maximum positive finite values.

reweight

A flag specifying whether to reweight weights by dividing by the largest weight.

computable

A flag specifying whether to only return fits with numerically computable standard errors.

at_boundary_ok

A flag specifying whether a model with one or more parameters at the boundary should be considered to have converged (default = FALSE).

all_dists

A flag specifying whether all the named distributions must fit successfully.

min_pmix

A number between 0 and 0.5 specifying the minimum proportion in mixture models.

range_shape1

A numeric vector of length two of the lower and upper bounds for the shape1 parameter.

range_shape2

shape2 parameter.

control

A list of control parameters passed to stats::optim().

silent

A flag indicating whether fits should fail silently.

Details

By default the 'gamma', 'lgumbel', 'llogis', 'lnorm', 'lnorm_lnorm' and 'weibull' distributions are fitted to the data. For a complete list of the distributions that are currently implemented in ssdtools see ssd_dists_all().

If weight specifies a column in the data frame with positive numbers, weighted estimation occurs. However, currently only the resultant parameter estimates are available.

If the right argument is different to the left argument then the data are considered to be censored.

Value

An object of class fitdists.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
fits
ssd_plot_cdf(fits)
ssd_hc(fits)

Goodness of Fit

Description

Returns a tbl data frame with the following columns

dist: The distribution name (chr)
aic: Akaike's Information Criterion (dbl)
bic: Bayesian Information Criterion (dbl)

and if the data are non-censored

aicc: Akaike's Information Criterion corrected for sample size (dbl)

and if there are 8 or more samples

ad: Anderson-Darling statistic (dbl)
ks: Kolmogorov-Smirnov statistic (dbl)
cvm: Cramer-von Mises statistic (dbl)

In the case of an object of class fitdists the function also returns

delta: The Information Criterion differences (dbl)
weight: The Information Criterion weights (dbl)

where delta and weight are based on aic for censored data and aicc for non-censored data.

Usage

ssd_gof(x, ...)

## S3 method for class 'fitdists'
ssd_gof(x, pvalue = FALSE, ...)

Arguments

x

The object.

...

Unused.

pvalue

A flag specifying whether to return p-values or the statistics (default) for the various tests.

Value

A tbl data frame of the gof statistics.

Methods (by class)

ssd_gof(fitdists): Goodness of Fit

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_gof(fits)
ssd_gof(fits, pvalue = TRUE)

Hazard Concentrations for Species Sensitivity Distributions

Description

Calculates concentration(s) with bootstrap confidence intervals that protect specified proportion(s) of species for individual or model-averaged distributions using parametric or non-parametric bootstrapping.

Usage

ssd_hc(x, ...)

## S3 method for class 'list'
ssd_hc(x, percent, proportion = 0.05, ...)

## S3 method for class 'fitdists'
ssd_hc(
  x,
  percent,
  proportion = 0.05,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  multi_est = TRUE,
  ci_method = "weighted_samples",
  parametric = TRUE,
  delta = 9.21,
  samples = FALSE,
  save_to = NULL,
  control = NULL,
  ...
)

## S3 method for class 'fitburrlioz'
ssd_hc(
  x,
  percent,
  proportion = 0.05,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  parametric = FALSE,
  samples = FALSE,
  save_to = NULL,
  ...
)

Arguments

x

The object.

...

Unused.

percent

A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

average

A flag specifying whether to provide model averaged values as opposed to a value for each distribution.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

level

A number between 0 and 1 of the confidence level of the interval.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

multi_est

A flag specifying whether to treat the distributions as constituting a single distribution (as opposed to taking the mean) when calculating model averaged estimates.

ci_method

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

samples

A flag specfying whether to include a numeric vector of the bootstrap samples as a list column in the output.

save_to

NULL or a string specifying a directory to save where the bootstrap datasets and parameter estimates (when successfully converged) to.

control

A list of control parameters passed to stats::optim().

Details

Model-averaged estimates and/or confidence intervals (including standard error) can be calculated by treating the distributions as constituting a single mixture distribution versus 'taking the mean'. When calculating the model averaged estimates treating the distributions as constituting a single mixture distribution ensures that ssd_hc() is the inverse of ssd_hp().

If treating the distributions as constituting a single mixture distribution when calculating model average confidence intervals then weighted specifies whether to use the original model weights versus re-estimating for each bootstrap sample unless 'taking the mean' in which case weighted specifies whether to take bootstrap samples from each distribution proportional to its weight (so that they sum to nboot) versus calculating the weighted arithmetic means of the lower and upper confidence limits based on nboot samples for each distribution.

Distributions with an absolute AIC difference greater than a delta of by default 7 have considerably less support (weight < 0.01) and are excluded prior to calculation of the hazard concentrations to reduce the run time.

Value

A tibble of corresponding hazard concentrations.

Methods (by class)

ssd_hc(list): Hazard Concentrations for Distributional Estimates
ssd_hc(fitdists): Hazard Concentrations for fitdists Object
ssd_hc(fitburrlioz): Hazard Concentrations for fitburrlioz Object

References

Burnham, K.P., and Anderson, D.R. 2002. Model Selection and Multimodel Inference. Springer New York, New York, NY. doi:10.1007/b97636.

Examples


ssd_hc(ssd_match_moments())

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_hc(fits)

fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hc(fit)

BCANZ Hazard Concentrations

Description

Gets hazard concentrations with confidence intervals that protect 1, 5, 10 and 20% of species using settings adopted by BC, Canada, Australia and New Zealand for official guidelines. This function can take several minutes to run with recommended 10,000 iterations.

Usage

ssd_hc_bcanz(x, nboot = 10000, min_pboot = 0.95)

Arguments

x

The object.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

Value

A tibble of corresponding hazard concentrations.

Examples

fits <- ssd_fit_bcanz(ssddata::ccme_boron)
ssd_hc_bcanz(fits, nboot = 100)

Hazard Concentrations for Burrlioz Fit

Description

Deprecated for ssd_hc().

Usage

ssd_hc_burrlioz(
  x,
  percent,
  proportion = 0.05,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  parametric = FALSE
)

Arguments

x

The object.

percent

A numeric vector of percent values to estimate hazard concentrations for. Deprecated for proportion = 0.05.

proportion

A numeric vector of proportion values to estimate hazard concentrations for.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

level

A number between 0 and 1 of the confidence level of the interval.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

Value

A tibble of corresponding hazard concentrations.

Hazard Proportion

Description

Calculates proportion of species affected at specified concentration(s) with quantile based bootstrap confidence intervals for individual or model-averaged distributions using parametric or non-parametric bootstrapping. For more information see the inverse function ssd_hc().

Usage

ssd_hp(x, ...)

## S3 method for class 'fitdists'
ssd_hp(
  x,
  conc = 1,
  average = TRUE,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  multi_est = TRUE,
  ci_method = "weighted_samples",
  parametric = TRUE,
  delta = 9.21,
  samples = FALSE,
  save_to = NULL,
  control = NULL,
  ...
)

## S3 method for class 'fitburrlioz'
ssd_hp(
  x,
  conc = 1,
  ci = FALSE,
  level = 0.95,
  nboot = 1000,
  min_pboot = 0.95,
  parametric = FALSE,
  samples = FALSE,
  save_to = NULL,
  ...
)

Arguments

x

The object.

...

Unused.

conc

A numeric vector of concentrations to calculate the hazard proportions for.

average

A flag specifying whether to provide model averaged values as opposed to a value for each distribution.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

level

A number between 0 and 1 of the confidence level of the interval.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

multi_est

A flag specifying whether to treat the distributions as constituting a single distribution (as opposed to taking the mean) when calculating model averaged estimates.

ci_method

parametric

A flag specifying whether to perform parametric bootstrapping as opposed to non-parametrically resampling the original data with replacement.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

samples

A flag specfying whether to include a numeric vector of the bootstrap samples as a list column in the output.

save_to

NULL or a string specifying a directory to save where the bootstrap datasets and parameter estimates (when successfully converged) to.

control

A list of control parameters passed to stats::optim().

Value

A tibble of corresponding hazard proportions.

Methods (by class)

ssd_hp(fitdists): Hazard Proportions for fitdists Object
ssd_hp(fitburrlioz): Hazard Proportions for fitburrlioz Object

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_hp(fits, conc = 1)

fit <- ssd_fit_burrlioz(ssddata::ccme_boron)
ssd_hp(fit)

BCANZ Hazard Proportion

Description

Gets proportion of species affected at specified concentration(s) using settings adopted by BC, Canada, Australia and New Zealand for official guidelines. This function can take several minutes to run with recommended 10,000 iterations.

Usage

ssd_hp_bcanz(x, conc = 1, nboot = 10000, min_pboot = 0.95)

Arguments

x

The object.

conc

A numeric vector of concentrations to calculate the hazard proportions for.

nboot

A count of the number of bootstrap samples to use to estimate the confidence limits. A value of 10,000 is recommended for official guidelines.

min_pboot

A number between 0 and 1 of the minimum proportion of bootstrap samples that must successfully fit (return a likelihood) to report the confidence intervals.

Value

A tibble of corresponding hazard concentrations.

Examples

fits <- ssd_fit_bcanz(ssddata::ccme_boron)
ssd_hp_bcanz(fits, nboot = 100)

Is Censored

Description

Tests if an object has censored data.

Test if a data frame is censored.

Test if a fitdists object is censored.

Usage

ssd_is_censored(x, ...)

## S3 method for class 'data.frame'
ssd_is_censored(x, left = "Conc", right = left, ...)

## S3 method for class 'fitdists'
ssd_is_censored(x, ...)

Arguments

x

The object.

...

Unused.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

Value

A flag indicating whether an object is censored.

Examples


ssd_is_censored(ssddata::ccme_boron)
ssd_is_censored(data.frame(Conc = 1, right = 2), right = "right")

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_is_censored(fits)

Label numbers with significant digits and comma

Description

Label numbers with significant digits and comma

Usage

ssd_label_comma(digits = 3, ..., big.mark = ",")

Arguments

digits

A whole number specifying the number of significant figures.

...

Unused.

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

Value

A "labelling" function that takes a vector x and returns a character vector of length(x) giving a label for each input value.

Examples

ggplot2::ggplot(data = ssddata::anon_e, ggplot2::aes(x = Conc / 10)) +
  geom_ssdpoint() +
  ggplot2::scale_x_log10(labels = ssd_label_comma())

Label numbers with significant digits and comma. If `hc_value` is present in breaks, put on new line and make bold.

Description

Label numbers with significant digits and comma. If hc_value is present in breaks, put on new line and make bold.

Usage

ssd_label_comma_hc(hc_value, digits = 3, ..., big.mark = ",")

Arguments

hc_value

A number of the hazard concentration value to offset.

digits

A whole number specifying the number of significant figures.

...

Unused.

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

Value

A "labelling" function that takes a vector x and returns a character vector of length(x) giving a label for each input value.

Examples

ggplot2::ggplot(data = ssddata::anon_e, ggplot2::aes(x = Conc / 10)) +
  geom_ssdpoint() +
  ggplot2::scale_x_log10(labels = ssd_label_comma_hc(1.26))

Licensing Markdown

Description

A string of markdown code indicating the licensing of the code and documentation

Usage

ssd_licensing_md()

Examples

ssd_licensing_md()

Match Moments

Description

Gets a named list of the values that produce the moment values (meanlog and sdlog) by distribution and term.

Usage

ssd_match_moments(
  dists = ssd_dists_bcanz(),
  meanlog = 1,
  sdlog = 1,
  nsim = 1e+05
)

Arguments

dists

A character vector of the distribution names.

meanlog

The mean on the log scale.

sdlog

The standard deviation on the log scale.

nsim

A positive whole number of the number of simulations to generate.

Value

a named list of the values that produce the moment values by distribution and term.

Examples

moments <- ssd_match_moments()
print(moments)
ssd_hc(moments)
ssd_plot_cdf(moments)

Calculate Minimum Proportion in Mixture Models

Description

Calculate Minimum Proportion in Mixture Models

Usage

ssd_min_pmix(n)

Arguments

n

positive number of observations.

Value

A number between 0 and 0.5 of the minimum proportion in mixture models.

Examples

ssd_min_pmix(6)
ssd_min_pmix(50)

Color-blind Palette for SSD Plots

Description

Color-blind Palette for SSD Plots

Usage

ssd_pal()

Value

A character vector of a color blind palette with 8 colors.

Examples

ssd_pal()

Cumulative Distribution Function

Description

Cumulative Distribution Function

Usage

ssd_pburrIII3(
  q,
  shape1 = 1,
  shape2 = 1,
  scale = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_pgamma(q, shape = 1, scale = 1, lower.tail = TRUE, log.p = FALSE)

ssd_pgompertz(q, location = 1, shape = 1, lower.tail = TRUE, log.p = FALSE)

ssd_pinvpareto(q, shape = 3, scale = 1, lower.tail = TRUE, log.p = FALSE)

ssd_plgumbel(
  q,
  locationlog = 0,
  scalelog = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_pllogis_llogis(
  q,
  locationlog1 = 0,
  scalelog1 = 1,
  locationlog2 = 1,
  scalelog2 = 1,
  pmix = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_pllogis(q, locationlog = 0, scalelog = 1, lower.tail = TRUE, log.p = FALSE)

ssd_plnorm_lnorm(
  q,
  meanlog1 = 0,
  sdlog1 = 1,
  meanlog2 = 1,
  sdlog2 = 1,
  pmix = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_plnorm(q, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)

ssd_pmulti(
  q,
  burrIII3.weight = 0,
  burrIII3.shape1 = 1,
  burrIII3.shape2 = 1,
  burrIII3.scale = 1,
  gamma.weight = 0,
  gamma.shape = 1,
  gamma.scale = 1,
  gompertz.weight = 0,
  gompertz.location = 1,
  gompertz.shape = 1,
  lgumbel.weight = 0,
  lgumbel.locationlog = 0,
  lgumbel.scalelog = 1,
  llogis.weight = 0,
  llogis.locationlog = 0,
  llogis.scalelog = 1,
  llogis_llogis.weight = 0,
  llogis_llogis.locationlog1 = 0,
  llogis_llogis.scalelog1 = 1,
  llogis_llogis.locationlog2 = 1,
  llogis_llogis.scalelog2 = 1,
  llogis_llogis.pmix = 0.5,
  lnorm.weight = 0,
  lnorm.meanlog = 0,
  lnorm.sdlog = 1,
  lnorm_lnorm.weight = 0,
  lnorm_lnorm.meanlog1 = 0,
  lnorm_lnorm.sdlog1 = 1,
  lnorm_lnorm.meanlog2 = 1,
  lnorm_lnorm.sdlog2 = 1,
  lnorm_lnorm.pmix = 0.5,
  weibull.weight = 0,
  weibull.shape = 1,
  weibull.scale = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_pmulti_fitdists(q, fitdists, lower.tail = TRUE, log.p = FALSE)

ssd_pweibull(q, shape = 1, scale = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

q

vector of quantiles.

shape1

shape1 parameter.

shape2

shape2 parameter.

scale

scale parameter.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

shape

shape parameter.

location

location parameter.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

locationlog1

locationlog1 parameter.

scalelog1

scalelog1 parameter.

locationlog2

locationlog2 parameter.

scalelog2

scalelog2 parameter.

pmix

Proportion mixture parameter.

meanlog1

mean on log scale parameter.

sdlog1

standard deviation on log scale parameter.

meanlog2

mean on log scale parameter.

sdlog2

standard deviation on log scale parameter.

meanlog

mean on log scale parameter.

sdlog

standard deviation on log scale parameter.

burrIII3.weight

weight parameter for the Burr III distribution.

burrIII3.shape1

shape1 parameter for the Burr III distribution.

burrIII3.shape2

shape2 parameter for the Burr III distribution.

burrIII3.scale

scale parameter for the Burr III distribution.

gamma.weight

weight parameter for the gamma distribution.

gamma.shape

shape parameter for the gamma distribution.

gamma.scale

scale parameter for the gamma distribution.

gompertz.weight

weight parameter for the Gompertz distribution.

gompertz.location

location parameter for the Gompertz distribution.

gompertz.shape

shape parameter for the Gompertz distribution.

lgumbel.weight

weight parameter for the log-Gumbel distribution.

lgumbel.locationlog

location parameter for the log-Gumbel distribution.

lgumbel.scalelog

scale parameter for the log-Gumbel distribution.

llogis.weight

weight parameter for the log-logistic distribution.

llogis.locationlog

location parameter for the log-logistic distribution.

llogis.scalelog

scale parameter for the log-logistic distribution.

llogis_llogis.weight

weight parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog1

locationlog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog1

scalelog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog2

locationlog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog2

scalelog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.pmix

pmix parameter for the log-logistic log-logistic mixture distribution.

lnorm.weight

weight parameter for the log-normal distribution.

lnorm.meanlog

meanlog parameter for the log-normal distribution.

lnorm.sdlog

sdlog parameter for the log-normal distribution.

lnorm_lnorm.weight

weight parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog1

meanlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog1

sdlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog2

meanlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog2

sdlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.pmix

pmix parameter for the log-normal log-normal mixture distribution.

weibull.weight

weight parameter for the Weibull distribution.

weibull.shape

shape parameter for the Weibull distribution.

weibull.scale

scale parameter for the Weibull distribution.

fitdists

An object of class fitdists.

Functions

ssd_pburrIII3(): Cumulative Distribution Function for BurrIII Distribution
ssd_pgamma(): Cumulative Distribution Function for Gamma Distribution
ssd_pgompertz(): Cumulative Distribution Function for Gompertz Distribution
ssd_pinvpareto(): Cumulative Distribution Function for Inverse Pareto Distribution
ssd_plgumbel(): Cumulative Distribution Function for Log-Gumbel Distribution
ssd_pllogis_llogis(): Cumulative Distribution Function for Log-Logistic/Log-Logistic Mixture Distribution
ssd_pllogis(): Cumulative Distribution Function for Log-Logistic Distribution
ssd_plnorm_lnorm(): Cumulative Distribution Function for Log-Normal/Log-Normal Mixture Distribution
ssd_plnorm(): Cumulative Distribution Function for Log-Normal Distribution
ssd_pmulti(): Cumulative Distribution Function for Multiple Distributions
ssd_pmulti_fitdists(): Cumulative Distribution Function for Multiple Distributions
ssd_pweibull(): Cumulative Distribution Function for Weibull Distribution

Examples


ssd_pburrIII3(1)

ssd_pgamma(1)

ssd_pgompertz(1)

ssd_pinvpareto(1)

ssd_plgumbel(1)

ssd_pllogis_llogis(1)

ssd_pllogis(1)

ssd_plnorm_lnorm(1)

ssd_plnorm(1)

# multi
ssd_pmulti(1, gamma.weight = 0.5, lnorm.weight = 0.5)

# multi fitdists
fit <- ssd_fit_dists(ssddata::ccme_boron)
ssd_pmulti_fitdists(1, fit)

ssd_pweibull(1)

Plot Species Sensitivity Data and Distributions

Description

Plots species sensitivity data and distributions.

Usage

ssd_plot(
  data,
  pred,
  left = "Conc",
  right = left,
  ...,
  label = NULL,
  shape = NULL,
  color = NULL,
  size,
  linetype = NULL,
  linecolor = NULL,
  xlab = "Concentration",
  ylab = "Species Affected",
  ci = TRUE,
  ribbon = TRUE,
  hc = 0.05,
  shift_x = 3,
  add_x = 0,
  bounds = c(left = 1, right = 1),
  big.mark = ",",
  suffix = "%",
  trans = "log10",
  xbreaks = waiver(),
  xlimits = NULL,
  text_size = 11,
  label_size = 2.5,
  theme_classic = FALSE
)

Arguments

data

A data frame.

pred

A data frame of the predictions.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

...

Unused.

label

A string of the column in data with the labels.

shape

A string of the column in data for the shape aesthetic.

color

A string of the column in data for the color aesthetic.

size

A number for the size of the labels. Deprecated for label_size. #'

linetype

A string of the column in pred to use for the linetype.

linecolor

A string of the column in pred to use for the line color.

xlab

A string of the x-axis label.

ylab

A string of the x-axis label.

ci

A flag specifying whether to estimate confidence intervals (by bootstrapping).

ribbon

A flag indicating whether to plot the confidence interval as a grey ribbon as opposed to green solid lines.

hc

A value between 0 and 1 indicating the proportion hazard concentration (or NULL).

shift_x

The value to multiply the label x values by (after adding add_x).

add_x

The value to add to the label x values (before multiplying by shift_x).

bounds

A named non-negative numeric vector of the left and right bounds for uncensored missing (0 and Inf) data in terms of the orders of magnitude relative to the extremes for non-missing values.

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

suffix

Additional text to display after the number on the y-axis.

trans

A string of which transformation to use. Accepted values include "log10", "log", and "identity" ("log10" by default).

xbreaks

The x-axis breaks as one of:

NULL for no breaks
waiver() for the default breaks
A numeric vector of positions

xlimits

The x-axis limits as one of:

NULL to use the default scale range
A numeric vector of length two providing the limits. Use NA to refer to the existing minimum or maximum limits.

text_size

A number for the text size.

label_size

A number for the size of the labels.

theme_classic

A flag specifying whether to use the classic theme or the default.

Examples

ssd_plot(ssddata::ccme_boron, boron_pred, label = "Species", shape = "Group")

Plot Cumulative Distribution Function (CDF)

Description

Generic function to plots the cumulative distribution function (CDF).

Usage

ssd_plot_cdf(x, ...)

## S3 method for class 'fitdists'
ssd_plot_cdf(x, average = FALSE, delta = 9.21, ...)

## S3 method for class 'list'
ssd_plot_cdf(x, ...)

Arguments

x

The object.

...

Additional arguments passed to ssd_plot().

average

A flag specifying whether to provide model averaged values as opposed to a value for each distribution or if NA provides model averaged and individual values.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

Methods (by class)

ssd_plot_cdf(fitdists): Plot CDF for fitdists object
ssd_plot_cdf(list): Plot CDF for named list of distributional parameter values

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
ssd_plot_cdf(fits)
ssd_plot_cdf(fits, average = NA)

ssd_plot_cdf(list(
  llogis = c(locationlog = 2, scalelog = 1),
  lnorm = c(meanlog = 2, sdlog = 2)
))

Cullen and Frey Plot

Description

Plots a Cullen and Frey graph of the skewness and kurtosis for non-censored data.

Usage

ssd_plot_cf(data, left = "Conc")

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

Details

Deprecated for fitdistrplus::descdist().

Plot Species Sensitivity Data

Description

Plots species sensitivity data.

Usage

ssd_plot_data(
  data,
  left = "Conc",
  right = left,
  ...,
  label = NULL,
  shape = NULL,
  color = NULL,
  size = 2.5,
  xlab = "Concentration",
  ylab = "Species Affected",
  shift_x = 3,
  add_x = 0,
  big.mark = ",",
  suffix = "%",
  bounds = c(left = 1, right = 1),
  trans = "log10",
  xbreaks = waiver()
)

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

...

Unused.

label

A string of the column in data with the labels.

shape

A string of the column in data for the shape aesthetic.

color

A string of the column in data for the color aesthetic.

size

A number for the size of the labels. Deprecated for label_size. #'

xlab

A string of the x-axis label.

ylab

A string of the x-axis label.

shift_x

The value to multiply the label x values by (after adding add_x).

add_x

The value to add to the label x values (before multiplying by shift_x).

big.mark

A string specifying used between every 3 digits to separate thousands on the x-axis.

suffix

Additional text to display after the number on the y-axis.

bounds

A named non-negative numeric vector of the left and right bounds for uncensored missing (0 and Inf) data in terms of the orders of magnitude relative to the extremes for non-missing values.

trans

A string of which transformation to use. Accepted values include "log10", "log", and "identity" ("log10" by default).

xbreaks

The x-axis breaks as one of:

NULL for no breaks
waiver() for the default breaks
A numeric vector of positions

Examples

ssd_plot_data(ssddata::ccme_boron, label = "Species", shape = "Group")

Quantile Function

Description

Quantile Function

Usage

ssd_qburrIII3(
  p,
  shape1 = 1,
  shape2 = 1,
  scale = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_qgamma(p, shape = 1, scale = 1, lower.tail = TRUE, log.p = FALSE)

ssd_qgompertz(p, location = 1, shape = 1, lower.tail = TRUE, log.p = FALSE)

ssd_qinvpareto(p, shape = 3, scale = 1, lower.tail = TRUE, log.p = FALSE)

ssd_qlgumbel(
  p,
  locationlog = 0,
  scalelog = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_qllogis_llogis(
  p,
  locationlog1 = 0,
  scalelog1 = 1,
  locationlog2 = 1,
  scalelog2 = 1,
  pmix = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_qllogis(p, locationlog = 0, scalelog = 1, lower.tail = TRUE, log.p = FALSE)

ssd_qlnorm_lnorm(
  p,
  meanlog1 = 0,
  sdlog1 = 1,
  meanlog2 = 1,
  sdlog2 = 1,
  pmix = 0.5,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_qlnorm(p, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE)

ssd_qmulti(
  p,
  burrIII3.weight = 0,
  burrIII3.shape1 = 1,
  burrIII3.shape2 = 1,
  burrIII3.scale = 1,
  gamma.weight = 0,
  gamma.shape = 1,
  gamma.scale = 1,
  gompertz.weight = 0,
  gompertz.location = 1,
  gompertz.shape = 1,
  lgumbel.weight = 0,
  lgumbel.locationlog = 0,
  lgumbel.scalelog = 1,
  llogis.weight = 0,
  llogis.locationlog = 0,
  llogis.scalelog = 1,
  llogis_llogis.weight = 0,
  llogis_llogis.locationlog1 = 0,
  llogis_llogis.scalelog1 = 1,
  llogis_llogis.locationlog2 = 1,
  llogis_llogis.scalelog2 = 1,
  llogis_llogis.pmix = 0.5,
  lnorm.weight = 0,
  lnorm.meanlog = 0,
  lnorm.sdlog = 1,
  lnorm_lnorm.weight = 0,
  lnorm_lnorm.meanlog1 = 0,
  lnorm_lnorm.sdlog1 = 1,
  lnorm_lnorm.meanlog2 = 1,
  lnorm_lnorm.sdlog2 = 1,
  lnorm_lnorm.pmix = 0.5,
  weibull.weight = 0,
  weibull.shape = 1,
  weibull.scale = 1,
  lower.tail = TRUE,
  log.p = FALSE
)

ssd_qmulti_fitdists(p, fitdists, lower.tail = TRUE, log.p = FALSE)

ssd_qweibull(p, shape = 1, scale = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

p

vector of probabilities.

shape1

shape1 parameter.

shape2

shape2 parameter.

scale

scale parameter.

lower.tail

logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

log.p

logical; if TRUE, probabilities p are given as log(p).

shape

shape parameter.

location

location parameter.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

locationlog1

locationlog1 parameter.

scalelog1

scalelog1 parameter.

locationlog2

locationlog2 parameter.

scalelog2

scalelog2 parameter.

pmix

Proportion mixture parameter.

meanlog1

mean on log scale parameter.

sdlog1

standard deviation on log scale parameter.

meanlog2

mean on log scale parameter.

sdlog2

standard deviation on log scale parameter.

meanlog

mean on log scale parameter.

sdlog

standard deviation on log scale parameter.

burrIII3.weight

weight parameter for the Burr III distribution.

burrIII3.shape1

shape1 parameter for the Burr III distribution.

burrIII3.shape2

shape2 parameter for the Burr III distribution.

burrIII3.scale

scale parameter for the Burr III distribution.

gamma.weight

weight parameter for the gamma distribution.

gamma.shape

shape parameter for the gamma distribution.

gamma.scale

scale parameter for the gamma distribution.

gompertz.weight

weight parameter for the Gompertz distribution.

gompertz.location

location parameter for the Gompertz distribution.

gompertz.shape

shape parameter for the Gompertz distribution.

lgumbel.weight

weight parameter for the log-Gumbel distribution.

lgumbel.locationlog

location parameter for the log-Gumbel distribution.

lgumbel.scalelog

scale parameter for the log-Gumbel distribution.

llogis.weight

weight parameter for the log-logistic distribution.

llogis.locationlog

location parameter for the log-logistic distribution.

llogis.scalelog

scale parameter for the log-logistic distribution.

llogis_llogis.weight

weight parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog1

locationlog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog1

scalelog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog2

locationlog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog2

scalelog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.pmix

pmix parameter for the log-logistic log-logistic mixture distribution.

lnorm.weight

weight parameter for the log-normal distribution.

lnorm.meanlog

meanlog parameter for the log-normal distribution.

lnorm.sdlog

sdlog parameter for the log-normal distribution.

lnorm_lnorm.weight

weight parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog1

meanlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog1

sdlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog2

meanlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog2

sdlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.pmix

pmix parameter for the log-normal log-normal mixture distribution.

weibull.weight

weight parameter for the Weibull distribution.

weibull.shape

shape parameter for the Weibull distribution.

weibull.scale

scale parameter for the Weibull distribution.

fitdists

An object of class fitdists.

Functions

ssd_qburrIII3(): Quantile Function for BurrIII Distribution
ssd_qgamma(): Quantile Function for Gamma Distribution
ssd_qgompertz(): Quantile Function for Gompertz Distribution
ssd_qinvpareto(): Quantile Function for Inverse Pareto Distribution
ssd_qlgumbel(): Quantile Function for Log-Gumbel Distribution
ssd_qllogis_llogis(): Cumulative Distribution Function for Log-Logistic/Log-Logistic Mixture Distribution
ssd_qllogis(): Cumulative Distribution Function for Log-Logistic Distribution
ssd_qlnorm_lnorm(): Cumulative Distribution Function for Log-Normal/Log-Normal Mixture Distribution
ssd_qlnorm(): Cumulative Distribution Function for Log-Normal Distribution
ssd_qmulti(): Quantile Function for Multiple Distributions
ssd_qmulti_fitdists(): Quantile Function for Multiple Distributions
ssd_qweibull(): Cumulative Distribution Function for Weibull Distribution

Examples


ssd_qburrIII3(0.5)

ssd_qgamma(0.5)

ssd_qgompertz(0.5)

ssd_qinvpareto(0.5)

ssd_qlgumbel(0.5)

ssd_qllogis_llogis(0.5)

ssd_qllogis(0.5)

ssd_qlnorm_lnorm(0.5)

ssd_qlnorm(0.5)

# multi
ssd_qmulti(0.5, gamma.weight = 0.5, lnorm.weight = 0.5)

# multi fitdists
fit <- ssd_fit_dists(ssddata::ccme_boron)
ssd_qmulti_fitdists(0.5, fit)

ssd_qweibull(0.5)

Random Number Generation

Description

Random Number Generation

Usage

ssd_rburrIII3(n, shape1 = 1, shape2 = 1, scale = 1, chk = TRUE)

ssd_rgamma(n, shape = 1, scale = 1, chk = TRUE)

ssd_rgompertz(n, location = 1, shape = 1, chk = TRUE)

ssd_rinvpareto(n, shape = 3, scale = 1, chk = TRUE)

ssd_rlgumbel(n, locationlog = 0, scalelog = 1, chk = TRUE)

ssd_rllogis_llogis(
  n,
  locationlog1 = 0,
  scalelog1 = 1,
  locationlog2 = 1,
  scalelog2 = 1,
  pmix = 0.5,
  chk = TRUE
)

ssd_rllogis(n, locationlog = 0, scalelog = 1, chk = TRUE)

ssd_rlnorm_lnorm(
  n,
  meanlog1 = 0,
  sdlog1 = 1,
  meanlog2 = 1,
  sdlog2 = 1,
  pmix = 0.5,
  chk = TRUE
)

ssd_rlnorm(n, meanlog = 0, sdlog = 1, chk = TRUE)

ssd_rmulti(
  n,
  burrIII3.weight = 0,
  burrIII3.shape1 = 1,
  burrIII3.shape2 = 1,
  burrIII3.scale = 1,
  gamma.weight = 0,
  gamma.shape = 1,
  gamma.scale = 1,
  gompertz.weight = 0,
  gompertz.location = 1,
  gompertz.shape = 1,
  lgumbel.weight = 0,
  lgumbel.locationlog = 0,
  lgumbel.scalelog = 1,
  llogis.weight = 0,
  llogis.locationlog = 0,
  llogis.scalelog = 1,
  llogis_llogis.weight = 0,
  llogis_llogis.locationlog1 = 0,
  llogis_llogis.scalelog1 = 1,
  llogis_llogis.locationlog2 = 1,
  llogis_llogis.scalelog2 = 1,
  llogis_llogis.pmix = 0.5,
  lnorm.weight = 0,
  lnorm.meanlog = 0,
  lnorm.sdlog = 1,
  lnorm_lnorm.weight = 0,
  lnorm_lnorm.meanlog1 = 0,
  lnorm_lnorm.sdlog1 = 1,
  lnorm_lnorm.meanlog2 = 1,
  lnorm_lnorm.sdlog2 = 1,
  lnorm_lnorm.pmix = 0.5,
  weibull.weight = 0,
  weibull.shape = 1,
  weibull.scale = 1,
  chk = TRUE
)

ssd_rmulti_fitdists(n, fitdists, chk = TRUE)

ssd_rweibull(n, shape = 1, scale = 1, chk = TRUE)

Arguments

n

positive number of observations.

shape1

shape1 parameter.

shape2

shape2 parameter.

scale

scale parameter.

chk

A flag specifying whether to check the arguments.

shape

shape parameter.

location

location parameter.

locationlog

location on the log scale parameter.

scalelog

scale on log scale parameter.

locationlog1

locationlog1 parameter.

scalelog1

scalelog1 parameter.

locationlog2

locationlog2 parameter.

scalelog2

scalelog2 parameter.

pmix

Proportion mixture parameter.

meanlog1

mean on log scale parameter.

sdlog1

standard deviation on log scale parameter.

meanlog2

mean on log scale parameter.

sdlog2

standard deviation on log scale parameter.

meanlog

mean on log scale parameter.

sdlog

standard deviation on log scale parameter.

burrIII3.weight

weight parameter for the Burr III distribution.

burrIII3.shape1

shape1 parameter for the Burr III distribution.

burrIII3.shape2

shape2 parameter for the Burr III distribution.

burrIII3.scale

scale parameter for the Burr III distribution.

gamma.weight

weight parameter for the gamma distribution.

gamma.shape

shape parameter for the gamma distribution.

gamma.scale

scale parameter for the gamma distribution.

gompertz.weight

weight parameter for the Gompertz distribution.

gompertz.location

location parameter for the Gompertz distribution.

gompertz.shape

shape parameter for the Gompertz distribution.

lgumbel.weight

weight parameter for the log-Gumbel distribution.

lgumbel.locationlog

location parameter for the log-Gumbel distribution.

lgumbel.scalelog

scale parameter for the log-Gumbel distribution.

llogis.weight

weight parameter for the log-logistic distribution.

llogis.locationlog

location parameter for the log-logistic distribution.

llogis.scalelog

scale parameter for the log-logistic distribution.

llogis_llogis.weight

weight parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog1

locationlog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog1

scalelog1 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.locationlog2

locationlog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.scalelog2

scalelog2 parameter for the log-logistic log-logistic mixture distribution.

llogis_llogis.pmix

pmix parameter for the log-logistic log-logistic mixture distribution.

lnorm.weight

weight parameter for the log-normal distribution.

lnorm.meanlog

meanlog parameter for the log-normal distribution.

lnorm.sdlog

sdlog parameter for the log-normal distribution.

lnorm_lnorm.weight

weight parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog1

meanlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog1

sdlog1 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.meanlog2

meanlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.sdlog2

sdlog2 parameter for the log-normal log-normal mixture distribution.

lnorm_lnorm.pmix

pmix parameter for the log-normal log-normal mixture distribution.

weibull.weight

weight parameter for the Weibull distribution.

weibull.shape

shape parameter for the Weibull distribution.

weibull.scale

scale parameter for the Weibull distribution.

fitdists

An object of class fitdists.

Functions

ssd_rburrIII3(): Random Generation for BurrIII Distribution
ssd_rgamma(): Random Generation for Gamma Distribution
ssd_rgompertz(): Random Generation for Gompertz Distribution
ssd_rinvpareto(): Random Generation for Inverse Pareto Distribution
ssd_rlgumbel(): Random Generation for log-Gumbel Distribution
ssd_rllogis_llogis(): Random Generation for Log-Logistic/Log-Logistic Mixture Distribution
ssd_rllogis(): Random Generation for Log-Logistic Distribution
ssd_rlnorm_lnorm(): Random Generation for Log-Normal/Log-Normal Mixture Distribution
ssd_rlnorm(): Random Generation for Log-Normal Distribution
ssd_rmulti(): Random Generation for Multiple Distributions
ssd_rmulti_fitdists(): Random Generation for Multiple Distributions
ssd_rweibull(): Random Generation for Weibull Distribution

Examples


set.seed(50)
hist(ssd_rburrIII3(10000), breaks = 1000)

set.seed(50)
hist(ssd_rgamma(10000), breaks = 1000)

set.seed(50)
hist(ssd_rgompertz(10000), breaks = 1000)

set.seed(50)
hist(ssd_rinvpareto(10000), breaks = 1000)

set.seed(50)
hist(ssd_rlgumbel(10000), breaks = 1000)

set.seed(50)
hist(ssd_rllogis_llogis(10000), breaks = 1000)

set.seed(50)
hist(ssd_rllogis(10000), breaks = 1000)

set.seed(50)
hist(ssd_rlnorm_lnorm(10000), breaks = 1000)

set.seed(50)
hist(ssd_rlnorm(10000), breaks = 1000)

# multi
set.seed(50)
hist(ssd_rmulti(1000, gamma.weight = 0.5, lnorm.weight = 0.5), breaks = 100)

# multi fitdists
fit <- ssd_fit_dists(ssddata::ccme_boron)
ssd_rmulti_fitdists(2, fit)

set.seed(50)
hist(ssd_rweibull(10000), breaks = 1000)

Sort Species Sensitivity Data

Description

Sorts Species Sensitivity Data by empirical cumulative density (ECD).

Usage

ssd_sort_data(data, left = "Conc", right = left)

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

right

A string of the column in data with the right concentration values.

Details

Useful for sorting data before using geom_ssdpoint() and geom_ssdsegment() to construct plots for censored data with stat = identity to ensure order is the same for the various components.

Value

data sorted by the empirical cumulative density.

Examples

ssd_sort_data(ssddata::ccme_boron)

Water Quality Guideline for British Columbia

Description

Calculates the 5% Hazard Concentration using ssd_fit_bcanz() and ssd_hc().

Usage

ssd_wqg_bc(data, left = "Conc")

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

Value

A tibble of the 5% hazard concentration with 95% confidence intervals.

Examples

## Not run: 
ssd_wqg_bc(ssddata::ccme_boron)

## End(Not run)

Water Quality Guideline for Burrlioz

Description

Calculates the 5% Hazard Concentration using ssd_fit_burrlioz() and ssd_hc().

Usage

ssd_wqg_burrlioz(data, left = "Conc")

Arguments

data

A data frame.

left

A string of the column in data with the concentrations.

Value

A tibble of the 5% hazard concentration with 95% confidence intervals.

Examples

## Not run: 
ssd_wqg_burrlioz(ssddata::ccme_boron)

## End(Not run)

ggproto Classes for Plotting Species Sensitivity Data and Distributions

Description

ggproto Classes for Plotting Species Sensitivity Data and Distributions

Usage

StatSsdpoint

StatSsdsegment

GeomSsdpoint

GeomSsdsegment

GeomHcintersect

GeomXribbon

Format

An object of class StatSsdpoint (inherits from Stat, ggproto, gg) of length 4.

An object of class StatSsdsegment (inherits from Stat, ggproto, gg) of length 4.

An object of class GeomSsdpoint (inherits from GeomPoint, Geom, ggproto, gg) of length 1.

An object of class GeomSsdsegment (inherits from GeomSegment, Geom, ggproto, gg) of length 1.

An object of class GeomHcintersect (inherits from Geom, ggproto, gg) of length 5.

An object of class GeomXribbon (inherits from Geom, ggproto, gg) of length 6.

Plot Species Sensitivity Data

Description

Uses the empirical cumulative density/distribution to visualize species sensitivity data.

Usage

stat_ssd(
  mapping = NULL,
  data = NULL,
  geom = "point",
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

geom

The geometric object to use to display the data for this layer. When using a ⁠stat_*()⁠ function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following:

A Geom ggproto subclass, for example GeomPoint.
A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as "point".
For more information and other ways to specify the geom, see the layer geom documentation.

position

A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following:

The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position.
A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter".
For more information and other ways to specify the position, see the layer position documentation.

...

Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.
When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.
Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.
The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

inherit.aes

Subset fitdists Object

Description

Select a subset of distributions from a fitdists object. The Akaike Information-theoretic Criterion differences are calculated after selecting the distributions named in select.

Usage

## S3 method for class 'fitdists'
subset(x, select = names(x), delta = Inf, ...)

Arguments

x

The object.

select

A character vector of the distributions to select.

delta

A non-negative number specifying the maximum absolute AIC difference cutoff. Distributions with an absolute AIC difference greater than delta are excluded from the calculations.

...

Unused.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
subset(fits, c("gamma", "lnorm"))

Turn a fitdists Object into a Tibble

Description

Turns a fitdists object into a tidy tibble of the estimates (est) and standard errors (se) by the terms (term) and distributions (dist).

Usage

## S3 method for class 'fitdists'
tidy(x, all = FALSE, ...)

Arguments

x

The object.

all

A flag specifying whether to also return transformed parameters.

...

Unused.

Value

A tidy tibble of the estimates and standard errors.

Examples

fits <- ssd_fit_dists(ssddata::ccme_boron)
tidy(fits)
tidy(fits, all = TRUE)

ssdtools: Species Sensitivity Distributions

Description

Author(s)

See Also

Augmented Data from fitdists Object

Description

Usage

Arguments

Value

See Also

Examples

Plot a fitdists Object

Description

Usage

Arguments

Value

See Also

Examples

Model Averaged Predictions for CCME Boron Data

Description

Usage

Format

Details

Examples

Turn a fitdists Object into a Tidy Tibble

Description

Usage

Arguments

See Also

Examples

Comma and Significance Formatter

Description

Usage

Arguments

Value

See Also

Examples

Gompertz Probability Density

Description

Usage

Arguments

Value

Distribution Data

Description

Usage

Format

Details

See Also

Examples

Log-Gumbel (Inverse Weibull) Probability Density

Description

Usage

Arguments

Value

Estimates for fitdists Object

Description

Usage

Arguments

Value

See Also

Examples

Species Sensitivity Hazard Concentration Intersection

Description

Usage

Arguments

See Also

Examples

Species Sensitivity Data Points

Description

Usage

Arguments

Species Sensitivity Data Points

Description

Usage

Arguments

See Also

Examples

Species Sensitivity Censored Segments

Description

Usage