Mean and Timeseries modelling
Hi:
On Fri, Jul 22, 2011 at 2:28 AM, Marko <markho1984 at googlemail.com> wrote:
Hello, i have following problem and I hope you can help me a little bit My dataframe looks like: df a ? ? ? m ?d ? typ ? ?value 1950 ?1 ?1 ? ?5 ? ? ?-4.1 1950 ?1 ?2 ? ?9 ? ? ? 2.7 1950 ?1 ?3 ? ?3 ? ? ?-1.3 1950 ?1 ?4 ? ?5 ? ? ?-1.9 1950 ?1 ?5 ? ?2 ? ? ? 0.2 1950 ?1 ?6 ? ?8 ? ? ? 0.5 1951 ?1 ?1 ? ?4 ? ? ? 1.3 .... It consists by daily observations from 1950- 2009. Now, I get with.... for (i in df$V5) neu <- tapply(df[,5],list(df$V4,df$V1),mean) the yearly means of the types (1-18) for every year.
Other ways to do this, which output data frames rather than matrices, would include # the formula interface below works with R-2.11.0 + aggregate(value ~ a + typ, data = df, FUN = mean) library(plyr) ddply(df, .(a, typ), summarise, m = mean(value)) Both would give you the 'long form' of the data. One could use the cast() function in the reshape[2] package or the reshape() function from the base package to convert it to 'wide' form.
The new df looks like: new typ ? ? ? 1950 ? ? ? 1951 ? ? ? 1952 ? ? ? ?1953 ? ? ? ?1954 ? ? ? ?1955 1956 ? ? ? ? ? ?... ? ? ? 2009 1 ? 0.40588235 -0.1714286 -1.8111111 ? 5.4000000 ?-0.9555556 ?2.65833333 2 ?-3.17777778 ?1.4130435 -0.9166667 ?-4.9000000 ? 0.2900000 ?3.54285714 3 ? 0.08888889 -2.0000000 -2.9666667 ? 2.2000000 ?-1.8600000 -0.50000000 ... 18 Now, i would like to generate a timeseries with the means, according to the different types. For example: For all days in the year 1950 with the typ 1, I would like to write the mean value for Typ 1 in year 1950. In year 1951 I would like to write the mean value for typ 1 in 1951 etc. (for all 18 types) The output should look like as following: erg a ? ? ? m ?d ? typ ? ?value ? mean_typ_year 1950 ?1 ?1 ? ?1 ? ? ?-4.1 ? ? ?0,4 1950 ?1 ?2 ? ?2 ? ? ? 2.7 ? ? ?Mean (Typ2 1950) 1950 ?1 ?3 ? ?1 ? ? ?-1.3 ? ? ?0,4 1950 ?1 ?4 ? ?5 ? ? ?-1.9 ? ? ?Mean (Typ5 1950) 1950 ?1 ?5 ? ?2 ? ? ? 0.2 ? ? ?... 1950 ?1 ?6 ? ?8 ? ? ? 0.5 ? ? ?... 1951 ?1 ?1 ? ?1 ? ? ? 1.3 ? ? ?-0,17 1951 ?1 ?2 ? ?2 ? ? ? 2.1 ? ? ?Mean (Typ2 1951) .... I hope you can help me by solving this problem
It sounds like you want something like ave(). One approach (untested)
might be as follows:
ddply(df, .(a, typ), transform, mean_typ_year = mean(value))
You may want to re-sort the data afterward because ddply() will sort
by a x typ combinations rather than a x m x d. You could also use the
transform() function, perhaps something like
transform(df, mean_typ_year = ave(value, list(a, typ), FUN = mean))
##------
Here's a toy example to illustrate:
df <- data.frame(a = factor(rep(LETTERS[1:3], each = 6)),
b = factor(rep(letters[1:3], each = 2)),
d = rep(1:6, 3),
val = rnorm(18, m = 40))
library('plyr')
ddply(df, .(a, b), transform, m = mean(val))
transform(df, m = ave(val, list(a, b), FUN = mean))
HTH,
Dennis
Best regards, Marko -- View this message in context: http://r.789695.n4.nabble.com/Mean-and-Timeseries-modelling-tp3686326p3686326.html Sent from the R help mailing list archive at Nabble.com.
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