R Dataset / Package robustbase / ambientNOxCH
| Attachment | Size |
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| dataset-76142.csv | 78.34 KB |
| Documentation |
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On this Picostat.com statistics page, you will find information about the ambientNOxCH data set which pertains to Daily Means of NOx (mono-nitrogen oxides) in air. The ambientNOxCH data set is found in the robustbase R package. Try to load the ambientNOxCH data set in R by issuing the following command at the console data("ambientNOxCH"). This may load the data into a variable called ambientNOxCH. If R says the ambientNOxCH data set is not found, you can try installing the package by issuing this command install.packages("robustbase") and then attempt to reload the data with library("robustbase") followed by data("ambientNOxCH"). 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 ambientNOxCH 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 ambientNOxCH R data set. The size of this file is about 80,224 bytes. Daily Means of NOx (mono-nitrogen oxides) in airDescriptionThis dataset contains daily means (from midnight to midnight) of NOx, i.e., mono-nitrogen oxides, in [ppb] at 13 sites in central Switzerland and Aarau for the year 2004. Usagedata(ambientNOxCH) FormatA data frame with 366 observations on the following 14 variables.
DetailsThe 13 sites are part of one of the three air quality monitoring networks:
inLuft (regional authorities of central Switzerland and canton Aargau)
When the measuring sites are exposed to the same atmospheric condition
and when there is no singular emission event at any site,
Sourcehttp://www.in-luft.ch/ See Alsoanother NOx dataset, Examples
data(ambientNOxCH)
str (ambientNOxCH)yearly <- log(colMeans(ambientNOxCH[,-1], na.rm=TRUE))
xlim <- range(yearly)
lNOx <- log(ambientNOxCH[, -1])
days <- ambientNOxCH[, "date"]## Subset of 9 days starting at April 4:
idays <- seq(which(ambientNOxCH$date=="2004-12-04"), length=9)
ylim <- range(lNOx[idays,],na.rm=TRUE)
op <- par(mfrow=c(3,3),mar=rep(1,4), oma = c(0,0,2,0))for (id in idays) {
daily <- unlist(lNOx[id,])
plot(NA, xlim=xlim,ylim=ylim, ann=FALSE, type = "n")
abline(0:1, col="light gray")
abline(lmrob(daily~yearly, na.action=na.exclude),
col="red", lwd=2)
text(yearly, daily, names(yearly), col="blue")
mtext(days[id], side=1, line=-1.2, cex=.75, adj=.98)
}
mtext("Daily ~ Yearly log( NOx mean values ) at 13 Swiss locations",
outer=TRUE)
par(op)## do all 366 regressions: Least Squares and Robust:
LS <- lapply(1:nrow(ambientNOxCH), function(id)
lm(unlist(lNOx[id,]) ~ yearly,
na.action = na.exclude))
R <- lapply(1:nrow(ambientNOxCH),
function(id) lmrob(unlist(lNOx[id,]) ~ yearly,
na.action = na.exclude))
## currently 4 warnings about non-convergence;
## which ones?
days[notOk <- ! sapply(R, `[[`, "converged") ]
## "2004-01-10" "2004-05-12" "2004-05-16" "2004-11-16"## first problematic case:
daily <- unlist(lNOx[which(notOk)[1],])
plot(daily ~ yearly,
main = paste("lmrob() non-convergent:",days[notOk[1]]))
rr <- lmrob(daily ~ yearly, na.action = na.exclude,
control = lmrob.control(trace=3, max.it = 100))
##-> 53 iter.## Look at all coefficients:
R.cf <- t(sapply(R, coef))
C.cf <- t(sapply(LS, coef))
plot(C.cf, xlim=range(C.cf[,1],R.cf[,1]),
ylim=range(C.cf[,2],R.cf[,2]))
mD1 <- rowMeans(abs(C.cf - R.cf))
lrg <- mD1 > quantile(mD1, 0.80)
arrows(C.cf[lrg,1], C.cf[lrg,2],
R.cf[lrg,1], R.cf[lrg,2], length=.1, col="light gray")
points(R.cf, col=2)## All robustness weights
aW <- t(sapply(R, weights, type="robustness"))
colnames(aW) <- names(yearly)
summary(aW)
sort(colSums(aW < 0.05, na.rm = TRUE)) # how often "clear outlier":
# lu st zg ba se sz su si re la ef ad ri
# 0 0 0 1 1 1 2 3 4 10 14 17 48lattice::levelplot(aW, asp=1/2, main="Robustness weights",
xlab= "day", ylab= "site")
-- Dataset imported from https://www.r-project.org. |
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