Package 'ecotox' reference manual

Title:	Analysis of Ecotoxicology
Description:	A simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of 'ecotox' comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312).
Authors:	Benjamin L Hlina [aut, cre]
Maintainer:	Benjamin L Hlina <[email protected]>
License:	GPL-3 \| file LICENSE
Version:	1.4.4
Built:	2024-11-06 03:10:53 UTC
Source:	https://github.com/benjaminhlina/ecotox

Lethal time data for seasonal exposure to TFM for larval sea lamprey

Description

This dataset contains the amount of responding/dead lamprey from 1 to 12 h of exposure to the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM), for the calculated LC50s at each month for the 2011 seasonal larval sea lamprey TFM toxicity data.

Usage

lamprey_time
lamprey_time

Format

A tibble containing 44 rows and 7 columns.

nominal.dose: nominal TFM dose(mg/L)
dose: averaged measured TFM dose (mg/L) over 12 h of exposures
month: month in which toxicity test occurred
hour: duration of exposure in hours
response: number of lamprey that responded over 12 h exposure to TFM
survive: number of lamprey that survived over 12 h exposure to TFM
total: total number of lamprey that were exposed to TFM over 12 h

Details

For each month, new lamprey were collected from the deer creek, MI, and were randomly assigned to a tank for exposure to the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM). The LC50 at each month was then calculated and the amount of responding/dead lamprey from 1 to 12 h was recorded.

Source

Hlina, B.L., Birceanu, O., Robinson, C.S., Dhiyebi, H., Wilkie, M.P. In Reivew. Seasonal Variation in the Sensitivity of Invasive Sea Lampreys to the Lampricide TFM: Importance of Energy Reserves and Temperature. North American Journal of Fisheries Management

Lethal concentration data for seasonal exposure to TFM for larval sea lamprey

Description

This dataset contains 2011 seasonal larval sea lamprey exposed to the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM) toxicity data.

Usage

lamprey_tox
lamprey_tox

Format

A tibble containing 64 rows and 7 columns.

nominal_dose: nominal TFM dose (mg/L)
tank: tank assignment
month: month in which toxicity test occurred
dose: averaged measured TFM dose (mg/L) over 12 h of exposure
response: number of lamprey that responded over 12 h exposure to TFM
survive: number of lamprey that survived over 12 h exposure to TFM
total: total number of lamprey that were exposed to TFM over 12 h

Details

For each month, new lamprey were collected from the Deer Creek, MI, and were randomly assigned to a tank for exposure to varying doses of the piscicide, 3-trifluoromethyl-4-nitrophenol (TFM).

Source

Lethal Concentration Logit

Description

Calculates lethal concentration (LC) and its fiducial confidence limits (CL) using a logit analysis according to Finney 1971, Wheeler et al. 2006, and Robertson et al. 2007.

Usage

LC_logit(formula, data, p = NULL, weights = NULL,
         subset = NULL, log_base = NULL,
         log_x = TRUE, het_sig = NULL,
         conf_level = NULL, conf_type = NULL,
         long_output = TRUE)
LC_logit(formula, data, p = NULL, weights = NULL,
         subset = NULL, log_base = NULL,
         log_x = TRUE, het_sig = NULL,
         conf_level = NULL, conf_type = NULL,
         long_output = TRUE)

Arguments

`formula`	an object of class `formula` or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under Details.
`data`	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 `LC_logit` is called.
`p`	Lethal Concentration (LC) values for given p, example will return a LC50 value if p equals 50. If more than one LC value wanted specify by creating a vector. LC values can be calculated down to the 1e-16 of a percentage (e.g. LC99.99). However, the tibble produced can round to nearest whole number.
`weights`	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.
`subset`	allows for the data to be subseted if desired. Default set to `NULL`.
`log_base`	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.
`log_x`	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.
`het_sig`	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.
`conf_level`	adjust confidence level as necessary or `NULL` set at 0.95.
`conf_type`	default is `"fl"` which will calculate fudicial confidence limits per Finney 1971. If set to `"dm"` the delta method will be used instead.
`long_output`	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.

Value

Returns a tibble with predicted LC for given p level, lower CL (LCL), upper CL (UCL), Pearson's chi square goodness-of-fit test (pgof), slope, intercept, slope and intercept p values and standard error, and LC variance.

References

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

Examples

head(lamprey_tox)

# within the dataframe used, control dose, unless produced a value
# during experimentation, are removed from the dataframe,
# as glm cannot handle values of infinite. Other statistical programs
# make note of the control dose but do not include within analysis


# calculate LC50 and LC99 for May

m <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
         weights = total,
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "May"))


# OR

m1 <- LC_logit(cbind(response, survive) ~ log10(dose), p = c(50, 99),
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))


# view calculated LC50 and LC99 for seasonal toxicity of a pisicide,
# to lamprey in 2011

m

# they are the same

m1

# dose-response curve can be plotted using 'ggplot2'
# Uncomment the below lines to run create plots

# library(ggplot2)

# lc_may <- subset(lamprey_tox, month %in% c("May"))

# p1 <- ggplot(data = lc_may[lc_may$nominal_dose != 0, ],
#              aes(x = log10(dose), y = (response / total))) +
# geom_point() +
# geom_smooth(method = "glm",
#             method.args = list(family = binomial(link = "logit")),
#             aes(weight = total), colour = "#FF0000", se = TRUE)

# p1

# calculate LC50s and LC99s for multiple toxicity tests, June, August, and September

j <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "June"))

a <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "August"))

s <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "September"))

# group results together in a dataframe to plot with 'ggplot2'

results <- rbind(m[, c(1, 3:8, 11)], j[,c(1, 3:8, 11)],
                 a[, c(1, 3:8, 11)], s[, c(1, 3:8, 11)])
results$month <- factor(c(rep("May", 2), rep("June", 2),
                          rep("August", 2), rep("September", 2)),
                        levels = c("May", "June", "August", "September"))


# p2 <- ggplot(data = results, aes(x = month, y = dose,
#                              group = factor(p), fill = factor(p))) +
#   geom_col(position = position_dodge(width = 0.9), colour = "#000000") +
#   geom_errorbar(aes(ymin = LCL, ymax = UCL),
#                 size = 0.4, width = 0.06,
#                 position = position_dodge(width = 0.9))

# p2
head(lamprey_tox)

# within the dataframe used, control dose, unless produced a value
# during experimentation, are removed from the dataframe,
# as glm cannot handle values of infinite. Other statistical programs
# make note of the control dose but do not include within analysis


# calculate LC50 and LC99 for May

m <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
         weights = total,
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "May"))


# OR

m1 <- LC_logit(cbind(response, survive) ~ log10(dose), p = c(50, 99),
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))


# view calculated LC50 and LC99 for seasonal toxicity of a pisicide,
# to lamprey in 2011

m

# they are the same

m1

# dose-response curve can be plotted using 'ggplot2'
# Uncomment the below lines to run create plots

# library(ggplot2)

# lc_may <- subset(lamprey_tox, month %in% c("May"))

# p1 <- ggplot(data = lc_may[lc_may$nominal_dose != 0, ],
#              aes(x = log10(dose), y = (response / total))) +
# geom_point() +
# geom_smooth(method = "glm",
#             method.args = list(family = binomial(link = "logit")),
#             aes(weight = total), colour = "#FF0000", se = TRUE)

# p1

# calculate LC50s and LC99s for multiple toxicity tests, June, August, and September

j <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "June"))

a <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "August"))

s <- LC_logit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "September"))

# group results together in a dataframe to plot with 'ggplot2'

results <- rbind(m[, c(1, 3:8, 11)], j[,c(1, 3:8, 11)],
                 a[, c(1, 3:8, 11)], s[, c(1, 3:8, 11)])
results$month <- factor(c(rep("May", 2), rep("June", 2),
                          rep("August", 2), rep("September", 2)),
                        levels = c("May", "June", "August", "September"))


# p2 <- ggplot(data = results, aes(x = month, y = dose,
#                              group = factor(p), fill = factor(p))) +
#   geom_col(position = position_dodge(width = 0.9), colour = "#000000") +
#   geom_errorbar(aes(ymin = LCL, ymax = UCL),
#                 size = 0.4, width = 0.06,
#                 position = position_dodge(width = 0.9))

# p2

Lethal Concentration Probit

Description

Calculates lethal concentration (LC) and its fiducial confidence limits (CL) using a probit analysis according to Finney 1971, Wheeler et al. 2006, and Robertson et al. 2007.

Usage

LC_probit(formula, data, p = NULL, weights = NULL,
          subset = NULL, log_base = NULL, log_x = TRUE,
          het_sig = NULL, conf_level = NULL, conf_type = NULL,
          long_output = TRUE)
LC_probit(formula, data, p = NULL, weights = NULL,
          subset = NULL, log_base = NULL, log_x = TRUE,
          het_sig = NULL, conf_level = NULL, conf_type = NULL,
          long_output = TRUE)

Arguments

`formula`	an object of class `formula` or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under Details.
`data`	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 `LC_probit` is called.
`p`	Lethal Concentration (LC) values for given p, example will return a LC50 value if p equals 50. If more than one LC value wanted specify by creating a vector. LC values can be calculated down to the 1e-16 of a percentage (e.g. LC99.99). However, the tibble produced can round to nearest whole number.
`weights`	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 StackExchanges post about differences cbind() function in R for a logistic regression and input format for binomial glm.
`subset`	allows for the data to be subseted if desired. Default set to `NULL`.
`log_base`	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.
`log_x`	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.
`het_sig`	significance level from person's chi square goodness-of-fit test (pgof) that is used to decide if a heterogeneity factor is used. `NULL` is set to 0.15.
`conf_level`	adjust confidence level as necessary or `NULL` set at 0.95.
`conf_type`	default is `"fl"` which will calculate fudicial confidence limits per Finney 1971. If set to `"dm"` the delta method will be used instead.
`long_output`	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.

Value

References

Finney, D.J., 1971. Probit Analysis, Cambridge University Press, Cambridge, England, ISBN: 052108041X

Robertson, J.L., Savin, N.E., Russell, R.M. and Preisler, H.K., 2007. Bioassays with arthropods. CRC press. ISBN: 9780849323317

Examples

head(lamprey_tox)

# within the dataframe used, control dose, unless produced a value
# during experimentation, are removed from the dataframe,
# as glm cannot handle values of infinite. Other statistical programs
# make note of the control dose but do not include within analysis

# calculate LC50 and LC99

m <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
               weights = total,
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))
# OR

m1 <- LC_probit(cbind(response, survive) ~ log10(dose), p = c(50, 99),
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))

# view calculated LC50 and LC99 for seasonal toxicity of a pisicide,
# to lamprey in 2011

m

# these are the same

m1

# dose-response curve can be plotted using 'ggplot2'

# library(ggplot2)

# lc_may <- subset(lamprey_tox, month %in% c("May"))

# p1 <- ggplot(data = lc_may[lc_may$nominal_dose != 0, ],
#              aes(x = log10(dose), y = (response / total))) +
#   geom_point() +
#   geom_smooth(method = "glm",
#               method.args = list(family = binomial(link = "probit")),
#               aes(weight = total), colour = "#FF0000", se = TRUE)

# p1

# calculate LC50s and LC99s for multiple toxicity tests, June, August, and September

j <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "June"))

a <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "August"))

s <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "September"))

# group results together in a dataframe to plot with 'ggplot2'

results <- rbind(m[, c(1, 3:8, 11)], j[,c(1, 3:8, 11)],
                 a[, c(1, 3:8, 11)], s[, c(1, 3:8, 11)])
results$month <- factor(c(rep("May", 2), rep("June", 2),
                          rep("August", 2), rep("September", 2)),
                        levels = c("May", "June", "August", "September"))

# p2 <- ggplot(data = results, aes(x = month, y = dose,
#                              group = factor(p), fill = factor(p))) +
#   geom_col(position = position_dodge(width = 0.9), colour = "#000000") +
#   geom_errorbar(aes(ymin = LCL, ymax = UCL),
#                 size = 0.4, width = 0.06,
#                 position = position_dodge(width = 0.9))

# p2
head(lamprey_tox)

# within the dataframe used, control dose, unless produced a value
# during experimentation, are removed from the dataframe,
# as glm cannot handle values of infinite. Other statistical programs
# make note of the control dose but do not include within analysis

# calculate LC50 and LC99

m <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
               weights = total,
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))
# OR

m1 <- LC_probit(cbind(response, survive) ~ log10(dose), p = c(50, 99),
               data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
               subset = c(month == "May"))

# view calculated LC50 and LC99 for seasonal toxicity of a pisicide,
# to lamprey in 2011

m

# these are the same

m1

# dose-response curve can be plotted using 'ggplot2'

# library(ggplot2)

# lc_may <- subset(lamprey_tox, month %in% c("May"))

# p1 <- ggplot(data = lc_may[lc_may$nominal_dose != 0, ],
#              aes(x = log10(dose), y = (response / total))) +
#   geom_point() +
#   geom_smooth(method = "glm",
#               method.args = list(family = binomial(link = "probit")),
#               aes(weight = total), colour = "#FF0000", se = TRUE)

# p1

# calculate LC50s and LC99s for multiple toxicity tests, June, August, and September

j <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "June"))

a <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "August"))

s <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
        weights = total,
        data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
        subset = c(month == "September"))

# group results together in a dataframe to plot with 'ggplot2'

results <- rbind(m[, c(1, 3:8, 11)], j[,c(1, 3:8, 11)],
                 a[, c(1, 3:8, 11)], s[, c(1, 3:8, 11)])
results$month <- factor(c(rep("May", 2), rep("June", 2),
                          rep("August", 2), rep("September", 2)),
                        levels = c("May", "June", "August", "September"))

# p2 <- ggplot(data = results, aes(x = month, y = dose,
#                              group = factor(p), fill = factor(p))) +
#   geom_col(position = position_dodge(width = 0.9), colour = "#000000") +
#   geom_errorbar(aes(ymin = LCL, ymax = UCL),
#                 size = 0.4, width = 0.06,
#                 position = position_dodge(width = 0.9))

# p2

Lethal Time Logit

Description

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.

Usage

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)
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)

Arguments

`formula`	an object of class `formula` or one that can be coerced to that class: a symbolic description of the model to be fitted.
`data`	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.
`p`	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.
`weights`	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.
`subset`	allows for the data to be subseted if desired. Default set to `NULL`.
`log_base`	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.
`log_x`	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.
`het_sig`	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.
`conf_level`	Adjust confidence level as necessary or `NULL` set at 0.95.
`long_output`	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.

Value

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.

References

Finney, D.J., 1971. Probit Analysis, Cambridge University Press, Cambridge, England, ISBN: 052108041X

Robertson, J.L., Savin, N.E., Russell, R.M. and Preisler, H.K., 2007. Bioassays with arthropods. CRC press. ISBN: 9780849323317

Examples

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'

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'

Lethal Time Probit

Description

Calculates lethal time (LT) and its fiducial confidence limits (CL) using a probit analysis according to Finney 1971, Wheeler et al. 2006, and Robertson et al. 2007.

Usage

LT_probit(
  formula,
  data,
  p = NULL,
  weights = NULL,
  subset = NULL,
  log_base = NULL,
  log_x = TRUE,
  het_sig = NULL,
  conf_level = NULL,
  conf_type = NULL,
  long_output = TRUE
)
LT_probit(
  formula,
  data,
  p = NULL,
  weights = NULL,
  subset = NULL,
  log_base = NULL,
  log_x = TRUE,
  het_sig = NULL,
  conf_level = NULL,
  conf_type = NULL,
  long_output = TRUE
)

Arguments

`formula`	an object of class `formula` or one that can be coerced to that class: a symbolic description of the model to be fitted.
`data`	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_probit` is called.
`p`	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.
`weights`	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.
`subset`	allows for the data to be subseted if desired. Default set to `NULL`.
`log_base`	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.
`log_x`	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.
`het_sig`	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.
`conf_level`	Adjust confidence level as necessary or `NULL` set at 0.95.
`conf_type`	default is `"fl"` which will calculate fudicial confidence limits per Finney 1971. If set to `"dm"` the delta method will be used instead.
`long_output`	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.

Value

References

Finney, D.J., 1971. Probit Analysis, Cambridge University Press, Cambridge, England, ISBN: 052108041X

Robertson, J.L., Savin, N.E., Russell, R.M. and Preisler, H.K., 2007. Bioassays with arthropods. CRC press. ISBN: 9780849323317

Examples

head(lamprey_time)

results <- LT_probit((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'
head(lamprey_time)

results <- LT_probit((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'

Ratio test

Description

Calculates a ratio test to compare two LC or LT values from two separate probit or logit models. This function is based on the ratio test developed in Wheeler et al. 2006. 10.1897/05-320R.1 which has been suggested as a replacement to the common method of comparing confidence intervals to determine differences.

Usage

ratio_test(model_1, model_2, percentage = NULL,
           type = NULL, compare = NULL, log_base = NULL, log_x = TRUE,
           obj_type = NULL, conf_type = NULL)
ratio_test(model_1, model_2, percentage = NULL,
           type = NULL, compare = NULL, log_base = NULL, log_x = TRUE,
           obj_type = NULL, conf_type = NULL)

Arguments

`model_1`	first model used in the ratio test. Should be an object of either a probit or logit model created using the `glm()` function. See example.
`model_2`	second model used in the ratio test. Should be an object of either a probit or logit model created using the `glm()` function. See example.
`percentage`	either a single value or a vector for given LC or LT percentage desired to compare. Percentage is the same value used for the argument `p` in all `LC_` and `LT_` functions. For example, 50 will return and compare LC50 values for the two models. If more than one LC value is desired specify by creating a vector. LC values can be calculated down to the 1e-16 of a percentage (e.g. LC99.99). However, the tibble produced can and will round to nearest whole number.
`type`	Link type needs to be specified to either `"probit"` which is default and will return and used in calculations for a probit model for the desired LCs or LTs. If specified to `"logit"` then `ratio_test` will return and calculate using a logit model for the desired LCs or LTs.
`compare`	Supply a character string to be used in the output letting the user know what models the LCs or LTs are being compared. Default output is "Model 1 - Model 2". See example.
`log_base`	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.
`log_x`	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.
`obj_type`	default is `"list"` which requires both `model_1` and `model_2` arguments to be model objects, in the form of a list, from `glm()` functions. Alternatively `"df"` can be used which will require both `model_1` and `model_2` arguments to be data.frame objects created when running either `LC_` or `LT_` functions.
`conf_type`	default is `"fl"` which if `"df"` is supplied will correct covariance values if h is above 1 as fudicial confidence limits use a heterogeneity factor, h, to correct variances when chi-square p value is less than 0.15. `conf_type` can also be `"dm"`, delta method, which doesn't use a heterogeneity correction factor therefore covariance will not be uncorrected. This argument is only needed if you are using the dataframe objects from `LC_` or `LT_` functions and have used the delta method in that analysis.

Value

A tibble with percentage for the LC or LT value desired for the above percentage argument, dose_1 and dose_2 displayed calculated backtransformed or untransformed doses for the desired LC or LT values. Standard Error (se), Z test statistic (test_stat) and p_value determined using Z test statistic as determined using formulas in Wheeler et al. 2006. .

References

Examples

# view lamprey_tox data

head(lamprey_tox)

# using glm() to detemine LC values using probit model for May and June

m <- glm((response / total) ~ log10(dose),
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "May"),
         weights = total,
         family = binomial(link = "probit"))


j <- glm((response / total) ~ log10(dose),
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "June"),
         weights = total,
         family = binomial(link = "probit"))

# now that both May and June models have been made. use ratio_test to
# compare LC50 values or whatever LC values of interest.

ratios <- ratio_test(model_1 = m, model_2 = j, percentage = 50,
compare = "May - June")

# view ratio test results

ratios

# you can also use LC_probit to create the models and use ratio test

m_1 <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
weights = total,
data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
subset = c(month == "May"))



j_1 <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
weights = total,
data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
subset = c(month == "June"))



ratios_2 <- ratio_test(model_1 = m_1, model_2 = j_1, percentage = 50,
compare = "May - June", obj_type = "df")

ratios_2

# view lamprey_tox data

head(lamprey_tox)

# using glm() to detemine LC values using probit model for May and June

m <- glm((response / total) ~ log10(dose),
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "May"),
         weights = total,
         family = binomial(link = "probit"))


j <- glm((response / total) ~ log10(dose),
         data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
         subset = c(month == "June"),
         weights = total,
         family = binomial(link = "probit"))

# now that both May and June models have been made. use ratio_test to
# compare LC50 values or whatever LC values of interest.

ratios <- ratio_test(model_1 = m, model_2 = j, percentage = 50,
compare = "May - June")

# view ratio test results

ratios

# you can also use LC_probit to create the models and use ratio test

m_1 <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
weights = total,
data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
subset = c(month == "May"))



j_1 <- LC_probit((response / total) ~ log10(dose), p = c(50, 99),
weights = total,
data = lamprey_tox[lamprey_tox$nominal_dose != 0, ],
subset = c(month == "June"))



ratios_2 <- ratio_test(model_1 = m_1, model_2 = j_1, percentage = 50,
compare = "May - June", obj_type = "df")

ratios_2

Package 'ecotox'

Help Index

Lethal time data for seasonal exposure to TFM for larval sea lamprey

Description

Usage

Format

Details

Source

Lethal concentration data for seasonal exposure to TFM for larval sea lamprey

Description

Usage

Format

Details

Source

Lethal Concentration Logit

Description

Usage

Arguments

Value

References

Examples

Lethal Concentration Probit

Description

Usage

Arguments

Value

References

Examples

Lethal Time Logit

Description

Usage

Arguments

Value

References

Examples

Lethal Time Probit

Description

Usage

Arguments

Value

References

Examples

Ratio test

Description

Usage

Arguments

Value

References

Examples