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 <
ggrothendi...@gmail.com> wrote:
> On Sat, Mar 24, 2012 at 10:44 PM, Thomas Adams <thomas.ad...@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.ad...@noaa.gov
VOICE: 937-383-0528
FAX: 937-383-0033
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