R Dataset / Package MASS / OME
| Attachment | Size |
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| dataset-52585.csv | 33.22 KB |
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On this Picostat.com statistics page, you will find information about the OME data set which pertains to Tests of Auditory Perception in Children with OME. The OME data set is found in the MASS R package. Try to load the OME data set in R by issuing the following command at the console data("OME"). This may load the data into a variable called OME. If R says the OME data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data with library("MASS") followed by data("OME"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing OME at the command-line which should display the entire dataset. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the OME R data set. The size of this file is about 34,018 bytes. Tests of Auditory Perception in Children with OMEDescriptionExperiments were performed on children on their ability to differentiate a signal in broad-band noise. The noise was played from a pair of speakers and a signal was added to just one channel; the subject had to turn his/her head to the channel with the added signal. The signal was either coherent (the amplitude of the noise was increased for a period) or incoherent (independent noise was added for the same period to form the same increase in power). The threshold used in the original analysis was the stimulus loudness needs to get 75% correct responses. Some of the children had suffered from otitis media with effusion (OME). UsageOME FormatThe
BackgroundThe experiment was to study otitis media with effusion (OME), a very common childhood condition where the middle ear space, which is normally air-filled, becomes congested by a fluid. There is a concomitant fluctuating, conductive hearing loss which can result in various language, cognitive and social deficits. The term ‘binaural hearing’ is used to describe the listening conditions in which the brain is processing information from both ears at the same time. The brain computes differences in the intensity and/or timing of signals arriving at each ear which contributes to sound localisation and also to our ability to hear in background noise. Some years ago, it was found that children of 7–8 years with a history of significant OME had significantly worse binaural hearing than children without such a history, despite having equivalent sensitivity. The question remained as to whether it was the timing, the duration, or the degree of severity of the otitis media episodes during critical periods, which affected later binaural hearing. In an attempt to begin to answer this question, 95 children were monitored for the presence of effusion every month since birth. On the basis of OME experience in their first two years, the test population was split into one group of high OME prevalence and one of low prevalence. SourceSarah Hogan, Dept of Physiology, University of Oxford, via Dept of Statistics Consulting Service Examples
# Fit logistic curve from p = 0.5 to p = 1.0
fp1 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/scal)),
c("L75", "scal"),
function(x,L75,scal)NULL)
nls(Correct/Trials ~ fp1(Loud, L75, scal), data = OME,
start = c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start=c(L75=45, scal=3))
nls(Correct/Trials ~ fp1(Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))# individual fits for each experimentaa <- factor(OME$Age)
ab <- 10*OME$ID + unclass(aa)
ac <- unclass(factor(ab))
OME$UID <- as.vector(ac)
OME$UIDn <- OME$UID + 0.1*(OME$Noise == "incoherent")
rm(aa, ab, ac)
OMEi <- OMElibrary(nlme)
fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/2)),
"L75", function(x,L75) NULL)
dec <- getOption("OutDec")
options(show.error.messages = FALSE, OutDec=".")
OMEi.nls <- nlsList(Correct/Trials ~ fp2(Loud, L75) | UIDn,
data = OMEi, start = list(L75=45), control = list(maxiter=100))
options(show.error.messages = TRUE, OutDec=dec)
tmp <- sapply(OMEi.nls, function(X)
{if(is.null(X)) NA else as.vector(coef(X))})
OMEif <- data.frame(UID = round(as.numeric((names(tmp)))),
Noise = rep(c("coherent", "incoherent"), 110),
L75 = as.vector(tmp), stringsAsFactors = TRUE)
OMEif$Age <- OME$Age[match(OMEif$UID, OME$UID)]
OMEif$OME <- OME$OME[match(OMEif$UID, OME$UID)]
OMEif <- OMEif[OMEif$L75 > 30,]
summary(lm(L75 ~ Noise/Age, data = OMEif, na.action = na.omit))
summary(lm(L75 ~ Noise/(Age + OME), data = OMEif,
subset = (Age >= 30 & Age <= 60),
na.action = na.omit), cor = FALSE)# Or fit by weighted least squares
fpl75 <- deriv(~ sqrt(n)*(r/n - 0.5 - 0.5/(1 + exp(-(x-L75)/scal))),
c("L75", "scal"),
function(r,n,x,L75,scal) NULL)
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, scal=3))
nls(0 ~ fpl75(Correct, Trials, Loud, L75, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, scal=3))# Test to see if the curves shift with age
fpl75age <- deriv(~sqrt(n)*(r/n - 0.5 - 0.5/(1 +
exp(-(x-L75-slope*age)/scal))),
c("L75", "slope", "scal"),
function(r,n,x,age,L75,slope,scal) NULL)
OME.nls1 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "coherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls1)))OME.nls2 <-
nls(0 ~ fpl75age(Correct, Trials, Loud, Age, L75, slope, scal),
data = OME[OME$Noise == "incoherent",],
start = c(L75=45, slope=0, scal=2))
sqrt(diag(vcov(OME.nls2)))# Now allow random effects by using NLME
OMEf <- OME[rep(1:nrow(OME), OME$Trials),]
OMEf$Resp <- with(OME, rep(rep(c(1,0), length(Trials)),
t(cbind(Correct, Trials-Correct))))
OMEf <- OMEf[, -match(c("Correct", "Trials"), names(OMEf))]## Not run: ## these fail in R on most platforms
fp2 <- deriv(~ 0.5 + 0.5/(1 + exp(-(x-L75)/exp(lsc))),
c("L75", "lsc"),
function(x, L75, lsc) NULL)
try(summary(nlme(Resp ~ fp2(Loud, L75, lsc),
fixed = list(L75 ~ Age, lsc ~ 1),
random = L75 + lsc ~ 1 | UID,
data = OMEf[OMEf$Noise == "coherent",], method = "ML",
start = list(fixed=c(L75=c(48.7, -0.03), lsc=0.24)), verbose = TRUE)))try(summary(nlme(Resp ~ fp2(Loud, L75, lsc),
fixed = list(L75 ~ Age, lsc ~ 1),
random = L75 + lsc ~ 1 | UID,
data = OMEf[OMEf$Noise == "incoherent",], method = "ML",
start = list(fixed=c(L75=c(41.5, -0.1), lsc=0)), verbose = TRUE)))## End(Not run)
-- Dataset imported from https://www.r-project.org. |
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