Gabor,

Thank you for your help -- it did help me a lot. However, with my data:

   lead_time cycle    r_squared  fcst_date
1          6     0 5.405095e-02 07/31/2010
2         12     0 5.521620e-06 07/31/2010
3         18     0 1.565910e-04 07/31/2010
4         24     0 8.646822e-02 07/31/2010
5         30     0 1.719604e-02 07/31/2010
6         36     0 5.768113e-04 07/31/2010
7         42     0 2.501269e-06 07/31/2010
8         48     0 6.451727e-02 07/31/2010
9          6    12 2.857931e-01 07/31/2010
10        12    12 1.138635e-01 07/31/2010
11        18    12 2.225503e-02 07/31/2010
12        24    12 1.182031e-03 07/31/2010
13        30    12 8.841142e-04 07/31/2010
14        36    12 1.082490e-01 07/31/2010
15        42    12 1.502887e-05 07/31/2010
17         6     0 8.689588e-02 08/01/2010
18        12     0 5.884336e-04 08/01/2010
19        18     0 2.219316e-07 08/01/2010
20        24     0 3.960752e-02 08/01/2010
21        30     0 1.087413e-04 08/01/2010
23        42     0 3.583030e-07 08/01/2010
24        48     0 2.907109e-05 08/01/2010
25         6    12 8.693451e-02 08/01/2010
26        12    12 3.208215e-02 08/01/2010
27        18    12 0.000000e+00 08/01/2010
28         6     0 2.962669e-02 08/02/2010
29         6    12 2.363506e-05 08/02/2010
30        12    12 9.050178e-03 08/02/2010

from:

> z <- read.zoo(q,index = 4, FUN = as.yearmon, format =
"%m/%d/%Y",aggregate = mean)

I get:
> z
         lead_time    cycle  r_squared
Jul 2010  25.60000 5.600000 0.05034771
Aug 2010  18.46154 4.615385 0.02191903

what I need is to NOT have the lead_time and cycle averaged, but only have
the r_squared values averaged by lead_time and cycle. I can not seem to
figure out the correct syntax to do this. I assume I use something like:

q_agg<-aggregate(q,by=list(q$lead_time,q$cycle),index = 4, FUN =
as.yearmon, format = "%m/%d/%Y")

but I get errors or nonsense when I follow with...

z <- read.zoo(q_agg,index = 4, FUN = as.yearmon, format =
"%m/%d/%Y",aggregate = mean)

or some variation of this.

Regards,
Tom


On Sat, Mar 24, 2012 at 10:58 PM, Gabor Grothendieck <
ggrothendieck@gmail.com> wrote:

> On Sat, Mar 24, 2012 at 10:44 PM, Thomas Adams <thomas.adams@noaa.gov>
> wrote:
> > All:
> >
> > I have a SQlite database where I have stored some verification data by
> date
> > & time (cycle Z/UTC), lead_time as well as type, duration, etc. I would
> > like to analyze & plot the data as monthly averages. I have looked at a
> > bunch of examples which use some combination of zoo and aggregate, but I
> > have not been able to successfully apply bits and pieces from the
> examples
> > I have found. Any help is appreciated. BTW, I calculate mae (mean
> absolute
> > error), mse (mean squared error), me (mean error), and other measures
> > obtained by using the R verification package.
> >
> > The example below is limited to 20 records and shows lead_time,
> r_squared,
> > (forecast) cycle, fcst_date (forecast date) -- the full data set is just
> > over 2 years of daily data with 3 forecast cycles (00Z, 12Z, and 18Z)
> daily.
> >
> > >From my query, below) how do I construct an appropriate data structure
> to
> > analyze & plot the data as monthly averages?
> >
> > Regards,
> > Tom
> >
> >> q<-dbGetQuery(con,"select lead_time,r_squared,cycle,fcst_date from
> > verify_table where duration=6 limit 20")
> >> q
> >   lead_time    r_squared cycle  fcst_date
> > 1          6 5.405095e-02    00 07/31/2010
> > 2         12 5.521620e-06    00 07/31/2010
> > 3         18 1.565910e-04    00 07/31/2010
> > 4         24 8.646822e-02    00 07/31/2010
> > 5         30 1.719604e-02    00 07/31/2010
> > 6         36 5.768113e-04    00 07/31/2010
> > 7         42 2.501269e-06    00 07/31/2010
> > 8         48 6.451727e-02    00 07/31/2010
> > 9          6 2.857931e-01    12 07/31/2010
> > 10        12 1.138635e-01    12 07/31/2010
> > 11        18 2.225503e-02    12 07/31/2010
> > 12        24 1.182031e-03    12 07/31/2010
> > 13        30 8.841142e-04    12 07/31/2010
> > 14        36 1.082490e-01    12 07/31/2010
> > 15        42 1.502887e-05    12 07/31/2010
> > 16        48           NA    12 07/31/2010
> > 17         6 8.689588e-02    00 08/01/2010
> > 18        12 5.884336e-04    00 08/01/2010
> > 19        18 2.219316e-07    00 08/01/2010
> > 20        24 3.960752e-02    00 08/01/2010
> >
>
> Try this:
>
> Lines <- "lead_time    r_squared cycle  fcst_date
> 1          6 5.405095e-02    00 07/31/2010
> 2         12 5.521620e-06    00 07/31/2010
> 3         18 1.565910e-04    00 07/31/2010
> 4         24 8.646822e-02    00 07/31/2010
> 5         30 1.719604e-02    00 07/31/2010
> 6         36 5.768113e-04    00 07/31/2010
> 7         42 2.501269e-06    00 07/31/2010
> 8         48 6.451727e-02    00 07/31/2010
> 9          6 2.857931e-01    12 07/31/2010
> 10        12 1.138635e-01    12 07/31/2010
> 11        18 2.225503e-02    12 07/31/2010
> 12        24 1.182031e-03    12 07/31/2010
> 13        30 8.841142e-04    12 07/31/2010
> 14        36 1.082490e-01    12 07/31/2010"
>
> library(zoo)
> q <- read.table(text = Lines)
>
> z <- read.zoo(q, index = 4, FUN = as.yearmon, format = "%m/%d/%Y",
> aggregate = mean)
> plot(z)
>
> See the 5 vignettes that come with zoo as well as ?read.zoo, ?plot.zoo
>  and ?xyplot.zoo
>
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com
>



-- 

Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177

EMAIL:	thomas.adams@noaa.gov
VOICE:	937-383-0528
FAX:	937-383-0033

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