Nothing
#' Aphid attraction at different light intensities
#'
#' The light intensity (mumol/m^2s) of green LED light should be found, which attracts Aphis fabae best. At each of 4 replicates 20 aphids were put in a lightproof box with only one green LED at one end. All aphids that fly to the green light are caught and counted after a period of 5h. This procedure was replicated for 9 increasing light intensities.
#'
#' @format A data frame with 36 observations on the following 3 variables.
#' \describe{
#' \item{\code{light}}{a numeric vector denoting the concentration levels}
#' \item{\code{black}}{a numeric vector with the number of aphids remaining in the box.}
#' \item{\code{green}}{a numeric vector with the number of attracted aphids}
#' }
#'
#' @references Akyazi, G (2009): Zum Einfluss auf Lichtintensitaet und Lichtqualitaet (Hochleistungs-LEDs) auf das Verhalten von Aphis fabae. IPP MSc 19.
"aphidlight"
#'Cell transformation assay dataset
#'
#' Balb//c 3T3 cells are treated with different concentrations of a carcinogen. Cells treated with a carcinogen do not stop proliferation. Number of foci (cell accumulations) are counted for 10 replicates per concentration level.
#'
#' @format A data frame with 80 observations on the following 2 variables.
#' \describe{
#' \item{\code{conc}}{a numeric vector denoting the concentration levels}
#' \item{\code{foci}}{a numeric vector with the number of foci}
#' }
#'
#' @references Thomas C (2008): ECVAM data
"cta"
#' Identifying the lethal dose of a crop protection product.
#'
#' Increasing dose levels of a toxin, used as a pesticide for crop protection, is applied to non-target species. The lethal dose should be identified in this experiment. The dataset represents simulated data based on a real experiment.
#'
#' @format A data frame with 6 observations on the following 3 variables.
#' \describe{
#' \item{\code{dose}}{a numeric vector denoting the toxin concentration levels}
#' \item{\code{dead}}{a numeric vector with the number of dead insects.}
#' \item{\code{alive}}{a numeric vector with the number of surviving insects.}
#' }
#'
#' @examples
#' str(toxinLD)
#'
#' ###############################################
#' # logistic regression on the logarithmic dose #
#' ###############################################
#'
#' toxinLD$logdose <- log(toxinLD$dose)
#' fm <- glm(cbind(dead, alive) ~ logdose, data=toxinLD, family=binomial(link="logit"))
#'
#' #############
#' # profiling #
#' #############
#'
#' # contrast matrix
#' pdose <- seq(-1,2.3, length=7)
#' CM <- model.matrix(~ pdose)
#'
#' # user defined grid to construct profiles
#' mcpgrid <- matrix(seq(-11,8,length=15), nrow=15, ncol=nrow(CM))
#' mc <- mcprofile(fm, CM, grid=mcpgrid)
#'
#' ####################################
#' ## confidence interval calculation #
#' ####################################
#'
#' # srdp profile
#' ci <- confint(mc)
#' ppdat <- data.frame(logdose=pdose)
#' ppdat$estimate <- fm$family$linkinv(ci$estimate$Estimate)
#' ppdat$lower <- fm$family$linkinv(ci$confint$lower)
#' ppdat$upper <- fm$family$linkinv(ci$confint$upper)
#' ppdat$method <- "profile"
#'
#' # wald profile
#' wci <- confint(wald(mc))
#' wpdat <- ppdat
#' wpdat$estimate <- fm$family$linkinv(wci$estimate$Estimate)
#' wpdat$lower <- fm$family$linkinv(wci$confint$lower)
#' wpdat$upper <- fm$family$linkinv(wci$confint$upper)
#' wpdat$method <- "wald"
#'
#' # higher order approximation
#' hci <- confint(hoa(mc))
#' hpdat <- ppdat
#' hpdat$estimate <- fm$family$linkinv(hci$estimate$Estimate)
#' hpdat$lower <- fm$family$linkinv(hci$confint$lower)
#' hpdat$upper <- fm$family$linkinv(hci$confint$upper)
#' hpdat$method <- "hoa"
#'
#' # combine results
#' pdat <- rbind(ppdat, wpdat, hpdat)
#'
#'
#' #####################################
#' # estimating the lethal dose LD(25) #
#' #####################################
#'
#' ld <- 0.25
#' pspf <- splinefun(ppdat$upper, pdose)
#' pll <- pspf(ld)
#' wspf <- splinefun(wpdat$upper, pdose)
#' wll <- wspf(ld)
#' hspf <- splinefun(hpdat$upper, pdose)
#' hll <- hspf(ld)
#'
#' ldest <- data.frame(limit=c(pll, wll, hll), method=c("profile","wald", "hoa"))
#'
#' ################################
#' # plot of intervals and LD(25) #
#' ################################
#'
#' ggplot(toxinLD, aes(x=logdose, y=dead/(dead+alive))) +
#' geom_ribbon(data=pdat, aes(y=estimate, ymin=lower, ymax=upper,
#' fill=method, colour=method, linetype=method),
#' alpha=0.1, size=0.95) +
#' geom_line(data=pdat, aes(y=estimate, linetype=method), size=0.95) +
#' geom_point(size=3) +
#' geom_hline(yintercept=ld, linetype=2) +
#' geom_segment(data=ldest, aes(x=limit, xend=limit, y=0.25, yend=-0.05,
#' linetype=method), size=0.6, colour="grey2") +
#' ylab("Mortality rate")
"toxinLD"
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.