Parameter Descriptions for ssdtools Functions

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.

all_estimates

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

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).

ci_method

A string specifying which method to use for estimating the standard error and confidence limits from the bootstrap samples. Possible values include ci_method = "multi_fixed" and ci_method = "multi_free" which generate the bootstrap samples using the model-averaged cumulative distribution function but differ in whether the model weights are fixed at the values for the original dataset or re-estimated for each bootstrap sample dataset. The value ci_method = "weighted_samples" takes bootstrap samples from each distribution proportional to its AICc based weights and calculates the confidence limits (and SE) from this single set. The value ci_method = "weighted_arithmetic" (was ci_method = "MACL" but has been soft-deprecated) which is only included for historical reasons takes the weighted arithmetic mean of the confidence limits and ci_method = MGCL which was included for a research paper takes the weighted geometric mean of the confidence limits. The values ci_method = "MAW1" and ci_method = "MAW2" use the two alternative equations of Burnham and Anderson to model average the weighted standard errors and then calculate the confidence limits using the Wald approach. Finally ci_method = "arithmetic" and ci_method = "geometric" take the weighted arithmetic or geometric mean of the values for each bootstrap iteration across all the distributions and then calculate the confidence limits (and SE) from the single set of samples.

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.

est_method

A string specifying whether to estimate directly from the model-averaged cumulative distribution function (est_method = 'multi') or to take the arithmetic mean of the estimates from the individual cumulative distribution functions weighted by the AICc derived weights (est_method = 'arithmetic') or or to use the geometric mean instead (est_method = 'geometric').

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.

multi_est

A flag specifying whether to estimate directly from the model-averaged cumulative distribution function (multi_est = TRUE) or to take the arithmetic mean of the estimates from the individual cumulative distribution functions weighted by the AICc derived weights (multi_est = FALSE).

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. #'

strict

A flag indicating whether all elements of select must be present.

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.

wt

A flag specifying whether to return the Akaike weight as "wt" instead of "weight".

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.