Package 'ThermalSampleR'

Title: Calculate Sample Sizes Required for Critical Thermal Limits Experiments
Description: We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs from these simulations. These simulations are presented in Owen, C., Sutton, G., Martin, G., van Steenderen, C., and Coetzee, J. Sample size determination for critical thermal limits studies. 2022. Physiological Entomology. Under review. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.
Authors: Guy Frederick Sutton [aut] (Wrote original R functions, and adapted the Duffy et. al. 2021 R function into this package., <https://orcid.org/0000-0003-2405-0945>), Clarke JM van Steenderen [aut, cre] (Compiled the R package, and the associated R Shiny application., <https://orcid.org/0000-0002-4219-446X>), Grant Duffy [cph] (Granted permission to use the source code for the Test of Total Equivalency function., <https://orcid.org/0000-0002-9031-8164>)
Maintainer: Clarke JM van Steenderen <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-02-02 03:41:03 UTC
Source: https://github.com/clarkevansteenderen/thermalsampler

Help Index

Bootstrap sampling to calculate summary statistics of CTL values

Description

Calculate mean and CI's of CTL for a single population

Arguments

data

Data frame contains raw data. Must contain a column with a population identifier (e.g. population ID), and a column containing critical thermal limit data (e.g. temperatures at which critical limits are reached).
groups_col

Factor. Column containing name(s) of population(s) of interest
groups_which

Character. Which population should be analysed?
n_max

Numeric. Maximum sample size to extrapolate simulations.
n_min

Numeric. Minimum sample size to extrapolate simulations. Defaults to 3.
iter

Numeric. Number of bootstrap samples. Defaults to 29.
response

Numeric. Column containing thermal limit data for individual samples

Value

A data frame of CTL summary statistics from bootstrap resamples

Examples

head(coreid_data)
sims <- boot_one(data = coreid_data,
                    groups_col = col,
                    groups_which = "Catorhintha schaffneri_APM",
                    response = response,
                    n_max = 49,
                    iter = 99)

Bootstrap sampling for difference in means between two groups

Description

Calculate difference in mean CT limits between two groups.

Arguments

data

Data frame contain raw data.
groups_col

Factor. Column containing names of two populations to compare
n_max

Numeric. Maximum sample size to extrapolate simulations.
n_min

Numeric. Minimum sample size to extrapolate simulations. Defaults to 3.
iter

Numeric. Number of bootstrap samples. Defaults to 29.
response

Numeric. Column containing thermal limit data for individual samples.
group1

String. Name of first population to compare.
group2

String. Name of second population to compare.
colour_exp

Colour of the experimental data. Defaults to "blue".
colour_extrap

Colour of the extrapolated data. Defaults to "red".
legend.position

Position of the legend. Defaults to "top". Can be "bottom", "left", "right", or "none".
ggtheme

The theme for the ggplot created. See ggplot2 themes for options. Default set to theme_classic().

Value

A data frame of bootstrap resamples

Examples

head(coreid_data)
sims <- boot_two(data = coreid_data,
                        groups_col = col,
                        response = response,
                        group1 = "Catorhintha schaffneri_APM",
                        group2 = "Catorhintha schaffneri_NPM",
                        n_max = 49,
                        iter = 99)

Example critical thermal limit data for ThermalSampleN package

Description

A dataset containing critical thermal limit (CTmin) data for the sap-sucking bug, Catorintha schaffneri.

Usage

coreid_data

Format

A data frame with 60 rows and 2 variables:

col

insect population tested, string

response

CTmin value for individual insect, in degrees celsius

...

Source

"Unpublished data provided by Philippa Muskett (Centre for Biological Control, Rhodes University, South Africa)

equiv_tost

Description

Perform a Test of Total Equivalence as adapted from Duffy et al. (2021) (<https://doi.org/10.1111/1365-2435.13928>)

Arguments

data

Data frame contains raw data. Must contain a column with a population identifier (e.g. population ID), and a column containing critical thermal limit data (e.g. temperatures at which critical limits are reached).
groups_col

Factor. Column containing the name of the population of interest (group ID)
groups_which

Character. Which population should be analysed?
response

Numeric. Column containing thermal limit data for individual samples
skews

Numeric. Vector containing skewness parameter(s). Defaults to 0, 1, 2, 10, 50.
equiv_margin

Numeric. Equivalence of subsets to full population CT estimate (unit = degree Celcius). Defaults to 1.
pop_n

Numeric. Size of population to sample (will test subsamples of size pop_n - x against pop_n for equivalence) Defaults to population size = 30
colrs

Character. Vector of colours for each skewness paramater value. E.g. if two skewness parameter values are set, choose two colours: colrs = c("blue", "red"). Defaults to "blue", "red", "orange", "forestgreen", "lightgrey".

Value

Two plots; (a) equivalence of means, and (b) equivalence of variances

Examples

head(coreid_data)
res <- equiv_tost(data = coreid_data,
                    groups_col = col,
                    groups_which = "Catorhintha schaffneri_APM",
                    response = response,
                    skews = c(1,10),
                    colrs = c("lightblue", "lightpink"),
                    equiv_margin = 1,
                    pop_n = 5)

Plot output from boot_sample

Description

Plot output from boot_one.

Arguments

x

Output from boot_one function.
n_max

Numeric. Maximum sample size to extrapolate simulations.
n_min

Numeric. Minimum sample size to extrapolate simulations. Defaults to 3.
colour_exp

Colour of the experimental data. Defaults to "blue".
colour_extrap

Colour of the extrapolated data. Defaults to "red".
legend.position

Position of the legend. Defaults to "top". Can be "bottom", "left", "right", or "none".
alpha_val

Change the degree of shading of the graphs. Default is 0.2.
ggtheme

The theme for the ggplot created. See ggplot2 themes for options. Default set to theme_classic().

Value

Two plots; (a) precision of the CTmin estimate across experimental and extrapolated sample sizes; (b) the sampling distribution (range of plausible CTmin values) across experimental and extrapolated sample sizes.

Examples

sims <- boot_one(coreid_data,
                    groups_col = col,
                    groups_which = "Catorhintha schaffneri_APM",
                    n_max = 30,
                    response = response)
plot_one_group(x = sims,
               n_min = 3,
               n_max = 15,
               colour_exp = "darkblue",
               colour_extrap = "green",
               legend.position = "right")

Plot output from boot_two_groups

Description

Plot output from boot_two.

Arguments

x

Output from boot_two_groups function. Defaults to 'sims'.
n_max

Numeric. Maximum sample size to extrapolate simulations.
n_min

Numeric. Minimum sample size to extrapolate simulations. Defaults to 3.
colour_exp

Colour of the experimental data. Defaults to "blue".
colour_extrap

Colour of the extrapolated data. Defaults to "red".
legend.position

Position of the legend. Defaults to "top". Can be "bottom", "left", "right", or "none".
alpha_val

Change the degree of shading of the graphs. Default is 0.2.
ggtheme

The theme for the ggplot created. See ggplot2 themes for options. Default set to theme_classic().

Value

Two plots: (a) the precision of the estimates for the difference in CTmin between the two selected groups across sample sizes; (b) the 95

Examples

sims <- boot_two(data = coreid_data,
                        groups_col = col,
                        response = response,
                        group1 = "Catorhintha schaffneri_APM",
                        group2 = "Catorhintha schaffneri_NPM",
                        n_max = 30,
                        iter = 99)

plots <- plot_two_groups(x = sims,
                         n_min = 3,
                         n_max = 30,
                         colour_exp = "gold",
                         colour_extrap = "darkgreen",
                         legend.position = "right")