missing-data

I am trying to impute missing values in a time series with an ARIMA model in R. I tried this code but no success. x <- AirPassengers x[90:100] <- NA fit <- auto.arima(x) fitted(fit)[90:100] ## this is giving me NAs plot(x) lines(fitted(fit), col="red") The fitted model is not imputing the missing values. Any idea on how this is done? fitted gives in-sample one-step forecasts. The "right" way to do what you want is via a Kalman smoother. A rough approximation good enough for most purposes is obtained using the average of the forward and backward forecasts for the missing section. Like this: x <

The gnuplot command set datafile missing "nan" tells gnuplot to ignore nan data values in the data file. How to ignore both nan and -nan ? I tried the following in gnuplot, but then the effect of the first statement is overwritten by the next. gnuplot> set datafile missing "-nan" gnuplot> set datafile missing "nan" Is it possible to somewhow embed a grep -v nan in the gnuplot command, or even some kind of regexp to exclude any imaginable non-numerical data? Christoph It is not possible to use a regexp for set datafile missing , but you can use any program to filter you data before plotting,

I'm interested to specify types of missing values. I have data that have different types of missing and I am trying to code these values as missing in R, but I am looking for a solution were I can still distinguish between them. Say I have some data that looks like this, set.seed(667) df <- data.frame(a = sample(c("Don't know/Not sure","Unknown","Refused","Blue", "Red", "Green"), 20, rep=TRUE), b = sample(c(1, 2, 3, 77, 88, 99), 10, rep=TRUE), f = round(rnorm(n=10, mean=.90, sd=.08), digits = 2), g = sample(c("C","M","Y","K"), 10, rep=TRUE) ); df # a b f g # 1 Unknown 2 0.78 M # 2 Refused 2 0

I want to represent the structure of a data frame (or matrix, or data.table whatever) on a single plot with color-coding. I guess that could be very useful for many people handling various types of data, to visualize it in a single glance. Perhaps someone have already developed a package to do it, but I couldn't find one (just this ). So here is a rough mockup of my "vision", kind of a heatmap, showing in color codes: the NA locations, the class of variables (factors (how many levels?), numeric (with color gradient, zeros, outliers...), strings) dimensions etc..... So far I have just written a