outrename: Rename homogen's output files
In climatol: Climate Tools (Series Homogenization and Derived Products)

Description Usage Arguments Details Value See Also Examples

This function inserts a suffix to the output file names of homogen, to prevent them from being rewritten by any further function run.

1	outrename(varcli, anyi, anyf, suffix, restore=FALSE)

`varcli`	Acronym of the name of the studied climatic variable, as in the data file name.
`anyi`	Initial year of the study period
`anyf`	Final year of the study period
`suffix`	Suffix to be inserted (or removed) in the output file names.
`restore`	Set this parameter to `TRUE` to remove the `suffix` previously inserted by this function.

The variable (or file base) name is separated from the appended suffix by a hyphen. The purpose of this function is to allow a new application of homogen to the same data with different parameters without overwriting the previous results.

This function does not return any value.

homogen

#Set a temporal working directory and write input files:
wd <- tempdir()
wd0 <- setwd(wd)
data(Ptest)
dim(dat) <- c(720,20)
dat[601:720,5] <- dat[601:720,5]*1.8
write(dat[481:720,1:5],'pcp_1991-2010.dat')
write.table(est.c[1:5,1:5],'pcp_1991-2010.est',row.names=FALSE,col.names=FALSE)
homogen('pcp',1991,2010,std=2) #homogenization
#Now run the example:
outrename('pcp',1991,2010,'ok')
#Return to user's working directory:
setwd(wd0)
#Input and output files can be found in directory:
print(wd)

Loading required package: maps
Loading required package: mapdata

HOMOGEN() APPLICATION OUTPUT  (From R's contributed package 'climatol' 3.1.1)

=========== Homogenization of pcp, 1991-2010. (Thu Jan 17 01:35:10 2019)

Parameters: varcli=pcp anyi=1991 anyf=2010 suf=NA nm=NA nref=10,10,4 std=2 swa=NA ndec=1 dz.max=5 dz.min=-5 wd=0,0,100 snht1=25 snht2=25 tol=0.02 maxdif=0.05 mxdif=0.05 maxite=999 force=FALSE wz=0.001 trf=0 mndat=NA gp=3 ini=NA na.strings=NA vmin=NA vmax=NA nclust=100 cutlev=NA grdcol=#666666 mapcol=#666666 hires=TRUE expl=FALSE metad=FALSE sufbrk=m tinc=NA tz=UTC cex=1.2 verb=TRUE

Read 1200 items
Data matrix: 240 data x 5 stations

-------------------------------------------
Stations in the 2 clusters:

$`1`
    Z Code        Name
1 183 S031 Station_031
4 129 S051 Station_051

$`2`
    Z Code        Name
2 125 S047 Station_047
3 100 S098 Station_098
5  79 S081 Station_081

---------------------------------------------
Computing inter-station distances:  1  2  3  4


========== STAGE 1 (SNHT on overlapping temporal windows) ===========

Computation of missing data with outlier removal
(Suggested data replacements are provisional)
  Station(rank) Date: Observed -> Suggested (Anomaly, in std. devs.)
S098(3) 1991-10-01: 298.4 -> 92.9 (5.56)
S081(5) 2002-10-01: 724.68 -> 390.7 (6.02)

Performing shift analysis on the 5 series...


========== STAGE 2 (SNHT on the whole series) =======================

Computation of missing data with outlier removal
(Suggested data replacements are provisional)
  Station(rank) Date: Observed -> Suggested (Anomaly, in std. devs.)
S098(3) 2008-09-01: 111.3 -> 290.8 (-5.09)

Performing shift analysis on the 5 series...

S081(5) breaks at 2000-12-01 (26.3)

Update number of series:  5 + 1 = 6 

Computation of missing data with outlier removal
(Suggested data replacements are provisional)
  Station(rank) Date: Observed -> Suggested (Anomaly, in std. devs.)
S081-2(6) 1993-10-01: 309 -> 111.2 (5.58)

Performing shift analysis on the 6 series...


========== STAGE 3 (Final computation of all missing data) ==========

Computing inter-station weights... (done)

Computation of missing data with outlier removal
(Suggested data replacements are provisional)

The following lines will have one of these formats:
  Station(rank) Date: Observed -> Suggested (Anomaly, in std. devs.)
  Iteration Max.data.difference (Station_code)
2 -5.569 (S081-2)
3 -3.255 (S081-2)
4 -2.049 (S081-2)
5 -1.308 (S081-2)
6 -0.843 (S081-2)
7 -0.547 (S081-2)
8 -0.356 (S081-2)
9 -0.232 (S081-2)
10 -0.152 (S081-2)
11 -0.099 (S081-2)
12 -0.065 (S081-2)
13 -0.043 (S081-2)

Last series readjustment (please, be patient...)

======== End of the homogenization process, after 2.67 secs 

----------- Final computations:

ACmx: Station maximum absolute autocorrelations of anomalies
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2000  0.2275  0.2900  0.2733  0.3000  0.3500 

SNHT: Standard normal homogeneity test (on anomaly series)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.200   2.300   2.400   2.583   2.575   4.600 

RMSE: Root mean squared error of the estimated data
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  30.49   31.56   32.73   36.98   38.67   54.11 

POD: Percentage of original data
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  49.00   57.50   89.50   78.67   96.75   98.00 

  ACmx SNHT RMSE POD Code   Name         
1 0.30 2.6  40.5 98  S031   Station_031  
2 0.20 2.3  31.3 96  S047   Station_047  
3 0.21 2.3  32.2 97  S098   Station_098  
4 0.28 2.5  33.3 83  S051   Station_051  
5 0.35 1.2  54.1 49  S081   Station_081  
6 0.30 4.6  30.5 49  S081-2 Station_081-2

----------- Generated output files: -------------------------

pcp_1991-2010.txt :  This text output 
pcp_1991-2010_out.csv :  List of corrected outliers 
pcp_1991-2010_brk.csv :  List of corrected breaks 
pcp_1991-2010.pdf :  Diagnostic graphics 
pcp_1991-2010.rda :  Homogenization results. Postprocess with (examples):
   dahstat('pcp',1991,2010) #get averages in file pcp_1991-2010-me.csv 
   dahstat('pcp',1991,2010,stat='tnd') #get OLS trends and their p-values 
   dahgrid('pcp',1991,2010,grid=YOURGRID) #get homogenized grids 
   ... (See other options in the package documentation)

[1] "/work/tmp/tmp/RtmpDj8rfR"