params: Parameter Descriptions for ssdtools Functions
In ssdtools: Species Sensitivity Distributions

params

R Documentation

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.