welchADF (version 0.3.2)

miceData: Number of visits and time spent in different tunnels of laboratory mice

Description

Wild strain house mice were, at birth, cross fostered onto house mouse (Mus), deer mouse (Peromyscus) or rat (Rattus) nursing mothers. Ten days after weaning, each subject was tested in an apparatus that allowed it to enter four different tunnels: one scented with clean pine shavings, and the other three tunnels with shavings bearing the scent of Mus, Peromyscus, or Rattus respectively. Three variables were measured for each tunnel: the number of visits to the tunnel during a twenty minute test, the time spent by each subject in each of the four tunnels and the latency to first visit of each tunnel.

Usage

miceData

Format

A data frame with 144 rows and 6 variables:

Subject

an ID that identifies the mouse to which the measurements of the row correspond.

nurs

type of nursing mother this mouse had at birth.

tunnel

type of tunnel to which the measurements of this row correspond.

visits

number of visits to the aforementioned tunnel.

time

total time spent by the mouse at this tunnel.

latency

seconds passed before the mouse first visited this tunnel.

References

K. L.Wuensch. Fostering house mice onto rats and deer mice: Effects on response to species odors. Animal Learning & Behavior, 20(3):253 - 258, 1992

Examples

# NOT RUN {
omnibus_LSM <- welchADF.test(miceData, response = c("visits", "time", "latency"),
  between.s = "nurs", within.s = "tunnel", subject = "Subject", contrast = "omnibus")
# Formula interface, using cbind() to specify a multivariate response.
omnibus_LSM_formula <- welchADF.test(
  cbind(visits, time, latency) ~ nurs*tunnel + (tunnel|Subject), data = miceData) 
pairwise_LSM_nurs <- welchADF.test(miceData, response = c("visits", "time",
  "latency"), between.s = "nurs", within.s = "tunnel", subject = "Subject",
  effect = "nurs", contrast = "all.pairwise")
pairwise_LSM_tunnel <- update(pairwise_LSM_nurs, effect = "tunnel")
# }
# NOT RUN {
  pairwise_nurs_trimmed_boot <- update(pairwise_LSM_nurs, trimming = TRUE, bootstrap = TRUE)
  pairwise_tunnel_trimmed_boot <- update(pairwise_nurs_trimmed_boot, effect = "tunnel")
  summary(pairwise_nurs_trimmed_boot)
# }
# NOT RUN {
summary(omnibus_LSM)
summary(pairwise_LSM_nurs)
summary(pairwise_LSM_tunnel)
# }