# data-Beetle: Flour beetle mortality data In MuMIn: Multi-Model Inference

 Beetle R Documentation

## Flour beetle mortality data

### Description

Mortality of flour beetles (Tribolium confusum) due to exposure to gaseous carbon disulfide CS2, from Bliss (1935).

### Usage

```Beetle
```

### Format

`Beetle` is a data frame with 5 elements.

Prop

a matrix with two columns named nkilled and nsurvived

mortality

observed mortality rate

dose

the dose of CS2 in mg/L

n.tested

number of beetles tested

n.killed

number of beetles killed.

### Source

Bliss C. I. (1935) The calculation of the dosage-mortality curve. Annals of Applied Biology, 22: 134-167.

### References

Burnham, K. P. and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, Springer-Verlag.

### Examples

```
# "Logistic regression example"
# from Burnham & Anderson (2002) chapter 4.11
# Fit a global model with all the considered variables

globmod <- glm(Prop ~ dose + I(dose^2) + log(dose) + I(log(dose)^2),
data = Beetle, family = binomial, na.action = na.fail)
# A logical expression defining the subset of models to use:
# * either log(dose) or dose
# * the quadratic terms can appear only together with linear terms
msubset <- expression(xor(dose, `log(dose)`) &
dc(dose, `I(dose^2)`) &
dc(`log(dose)`, `I(log(dose)^2)`))

# Table 4.6
# Use 'varying' argument to fit models with different link functions
# Note the use of 'alist' rather than 'list' in order to keep the
# 'family' objects unevaluated
logit = binomial("logit"),
probit = binomial("probit"),
cloglog = binomial("cloglog")
))

(ms12 <- dredge(globmod, subset = msubset, varying = varying.link,
rank = AIC))

# Table 4.7 "models justifiable a priori"
(ms3 <- subset(ms12, has(dose, !`I(dose^2)`)))
# The same result, but would fit the models again:
# ms3 <- update(ms12, update(globmod, . ~ dose), subset =,
#    fixed = ~dose)
mod3 <- get.models(ms3, 1:3)
# Table 4.8. Predicted mortality probability at dose 40.
# calculate confidence intervals on logit scale
logit.ci <- function(p, se, quantile = 2) {
C. <- exp(quantile * se / (p * (1 - p)))
p /(p + (1 - p) * c(C., 1/C.))
}

mavg3 <- model.avg(mod3, revised.var = FALSE)
# get predictions both from component and averaged models
pred <- lapply(c(component = mod3, list(averaged = mavg3)), predict,
newdata = list(dose = 40), type = "response", se.fit = TRUE)
# reshape predicted values
pred <- t(sapply(pred, function(x) unlist(x)[1:2]))
colnames(pred) <- c("fit", "se.fit")

# build the table
tab <- cbind(
c(Weights(ms3), NA),
pred,
matrix(logit.ci(pred[,"fit"], pred[,"se.fit"],
quantile = c(rep(1.96, 3), 2)), ncol = 2)
)
colnames(tab) <- c("Akaike weight", "Predicted(40)", "SE", "Lower CI",
"Upper CI")
rownames(tab) <- c(as.character(ms3\$family), "model-averaged")
print(tab, digits = 3, na.print = "")
# Figure 4.3
newdata <- list(dose = seq(min(Beetle\$dose), max(Beetle\$dose), length.out = 25))

# add model-averaged prediction with CI, using the same method as above
avpred <- predict(mavg3, newdata, se.fit = TRUE, type = "response")

avci <- matrix(logit.ci(avpred\$fit, avpred\$se.fit, quantile = 2), ncol = 2)

matplot(newdata\$dose, sapply(mod3, predict, newdata, type = "response"),
type = "l", xlab = quote(list("Dose of" ~ CS,(mg/L))),
ylab = "Mortality", col = 2:4, lty = 3, lwd = 1
)
matplot(newdata\$dose, cbind(avpred\$fit, avci), type = "l", add = TRUE,
lwd = 1, lty = c(1, 2, 2), col = 1)

legend("topleft", NULL, c(as.character(ms3\$family), expression(`averaged`
%+-% CI)), lty = c(3, 3, 3, 1), col = c(2:4, 1))

```

MuMIn documentation built on March 18, 2022, 5:28 p.m.