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)

Arguments

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

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)

Arguments

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

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)

Arguments

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

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_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
)

Arguments

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

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_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'

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

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

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

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

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)

Arguments

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

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

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