Calculates lethal time (LT) and its fiducial confidence limits (CL) using a logit analysis according to Finney 1971, Wheeler et al. 2006, and Robertson et al. 2007.
LT_logit(formula, data, p = NULL, weights = NULL,
subset = NULL, log_base = NULL, log_x = TRUE, het_sig = NULL,
conf_level = NULL, long_output = TRUE)an object of class formula or one that can be coerced to that class: a symbolic description of the model to be fitted.
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which LT_logit is called.
Lethal time (LT) values for given p, example will return a LT50 value if p equals 50. If more than one LT value wanted specify by creating a vector. LT values can be calculated down to the 1e-16 of a percentage (e.g. LT99.99).However, the tibble produced can and will round to nearest whole number.
vector of 'prior weights' to be used in the fitting process. Only needs to be supplied if you are taking the response / total for your response variable within the formula call of LC_probit. Otherwise if you use cbind(response, non-response) method you do not need to supply weights. If you do the model will be incorrect. If you don't supply weights there is a warning that will help you to make sure you are using one method or the other. See the following StackExchange post about differences cbind() function in R for a logistic regression.
allows for the data to be subseted if desired. Default set to NULL.
default is 10 and will be used to calculate results using the anti of log10() given that the x variable has been log10 transformed. If FALSE results will not be back transformed.
default is TRUE and will calculate results using the antilog of determined by log_base given that the x variable has been log() transformed. If FALSE results will not be back transformed.
significance level from person's chi square goodness-of-fit test that is used to decide if a heterogeneity factor is used. NULL is set to 0.15.
Adjust confidence level as necessary or NULL set at 0.95.
default is TRUE which will return a tibble with all 17 variables. If FALSE the tibble returned will consist of the p level, n, the predicted LC for given p level, lower and upper confidence limits.
Returns a tibble with predicted LT for given p level, lower CL (LCL), upper CL (UCL), LCL, Pearson's chi square goodness-of-fit test (pgof), slope, intercept, slope and intercept p values and standard error, and LT variance.
Finney, D.J., 1971. Probit Analysis, Cambridge University Press, Cambridge, England, ISBN: 052108041X
Wheeler, M.W., Park, R.M., and Bailey, A.J., 2006. Comparing median lethal concentration values using confidence interval overlap or ratio tests, Environ. Toxic. Chem. 25(5), 1441-1444.10.1897/05-320R.1
Robertson, J.L., Savin, N.E., Russell, R.M. and Preisler, H.K., 2007. Bioassays with arthropods. CRC press. ISBN: 9780849323317
# NOT RUN {
head(lamprey_time)
results <- LT_logit((response / total) ~ log10(hour),
p = c(50, 99),
weights = total,
data = lamprey_time,
subset = c(month == "May"))
# view calculated LT50 and LT99 for seasonal
# toxicity of a piscicide, 3-trifluoromethyl-4-nitrophenol, to lamprey in 2011
results
# dose-response curve can be plotted using 'ggplot2'
# }
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