[R] Running something without a loop when the result from the previous iteration is require for the current iteration
Petr PIKAL
petr.pikal at precheza.cz
Fri Aug 13 09:29:27 CEST 2010
Hi
wootten.adrienne at gmail.com napsal dne 12.08.2010 14:15:30:
> Not quite what I was trying to say. The process generates a random
uniform
> number between 0 and 1 and compares to a specific conditional
probability. It
> is looking for this in particular:
>
> random number < Pr( rain(station=i,day=d)=1 | rain(station=i,day=d-1)=0
& rain
> (station=j,day=d)=0 & rain(station=k,day=d)=0)
>
> In this particular example, if the random number is less than the
probability
> the value for station i and day d will be given as 1, otherwise it will
be zero.
>
> There are 8 possible combinations. i is the station to be generated, j
and k
> are the two stations most strongly correlated with station i. Stations
j and
> k have already been generated in the example I gave previously. So I
want to
> know given what is going on at stations j and k during day d and at
station i
> for day d-1 if the value for station i day d will be 1 or 0.
But AFAIK that is what I said. Did you try anything from what I suggested?
You have 4 possible combinations from 2 stations (I named them col1 col2,
but you can name them differently - station j and k). So vector named cols
results in numbers 1:4 based on values col1 and col2 and you can do it
outside of loop.
If col1 and col2 are 0/1 vectors you can get it e.g. by
cols <- (col1+(col2+1)*2)-1
and you get vector based on 0/1 value combination of your 2 vectors
(please try it:-)
You have 8 combinations of probabilities outcome (you call it specific
probability) and I presume it is a vector of 8 values (you still does not
provide small ***reproducible*** example)
So you can generate vector of random numbers with outside of loop and
compute combination of all possible outcomes with
> ran<-runif(20)
> p<-runif(8)
> comparison <- outer(ran,p, "<")
You will get comparison matrix e.g. for one month and you can just choose
appropriate row/column value in cycle based on cols value and previous day
value in station i.
>
> Hope this provides some clarification.
> A
If you do not provide some data with input and required specific outcome
you won't get specific answer. Instead of trying to explain it by
elaborated text use dput for exporting objects needed for computation and
if possible also the outcome.
Regards
Petr
> On Thu, Aug 12, 2010 at 3:21 AM, Petr PIKAL <petr.pikal at precheza.cz>
wrote:
> Hi
>
> without toy example it is rather complicated to check your function. So
> only few remarks:
>
> Instead of generating 1 random number inside a loop generate whole
vector
> of random numbers outside a loop and choose a number
>
> Do not mix ifelse with if. ifelse is intended to work with whole vector.
>
> Work with matrices instead of data frames whenever possible if speed is
an
> issue.
>
> If I understand correctly you want to put 1 or 0 into one column based
on:
>
> previous value in the same column
> comparison of some random number with predefined probabilities in vector
> of 6 values
>
> So here is vectorised version of your 4 ifs based on assumption
>
> 0 in col1 0 in col 2 = 5
> 0 in col1 1 in col 2 = 9
> 1 in col1 0 in col 2 = 6
> 1 in col1 1 in col 2 =10
>
>
> col1<-sample(1:2, 20, replace=T)
> col2<-sample(c(4,8), 20, replace=T)
>
> col1+col2
> [1] 5 6 9 6 6 5 9 10 9 9 6 9 10 6 10 9 10 9 5 5
> cols<-as.numeric(as.factor(col1+col2))
>
> cols
> [1] 1 2 3 2 2 1 3 4 3 3 2 3 4 2 4 3 4 3 1 1
>
>
> And here is computed comparison of six values p (ortho obs used) with 20
> generated random values
>
> ran<-runif(20)
> p<-runif(8)
> comparison <- outer(ran,p, "<")
> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
> [1,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [2,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [3,] FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE
> [4,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [5,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [6,] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
> [7,] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
> [8,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [9,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [10,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [11,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [12,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [13,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
> [14,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [15,] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> [16,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [17,] FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
> [18,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [19,] FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
> [20,] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
>
>
> Now the only what you need to put in loop is to select appropriate
column
> from matrix comparison based on value on cols vector and 0 or 1 in
> previous row of station column.
>
> Something like (untested)
>
> gen.log<-rep(NA, nrow(genmat)-1)
>
> for (i in 2:nrow(genmat)) {
>
> gen.log[i] <- if( genmat[i-1, num] ==0) comparison[i, cols[i]] else
> comparison[i,cols[i+5]]
>
> }
>
> genmat[2:nrow(genmat), num] <- gen.log*1
>
> Regards
> Petr
>
>
> r-help-bounces at r-project.org napsal dne 11.08.2010 18:35:37:
>
> > Hello Everyone!
> >
> > Here's what I'm trying to do. I'm working on generating occurrences
of
> > precipitation based upon precipitation occurrence for a station during
> the
> > previous day and two stations that have already been generated by
joint
> > probablities and 1st order Markov chains or by the same generation
> process.
> > This has to be done for each remaining stations for each month.
> >
> > > genmat # 7 stations in this example, line_before is the climatology
of
> the
> > last day of the previous month. Stations 4 and 6 have been generated
> already
> > in this example
> > [,1] [,2] [,3] [,4] [,5] [,6] [,7]
> > line_before 1 1 1 0 1 1 1
> > NA NA NA 1 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 1 NA 0 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 0 NA 0 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 1 NA 1 NA
> > NA NA NA 0 NA 0 NA
> > > num # station to generate
> > [1] 2
> > > use1 # 1st station to use in generation
> > [1] 6
> > > use2 # 2nd station to use in generation
> > [1] 4
> >
> > > genmat = event.gen2(genmat,use1,use2,num,ortho_obs_used) #
Generation
> > function shown below
> > > genmat # genmat - after it has gone through station 2
> > [,1] [,2] [,3] [,4] [,5] [,6] [,7]
> > line_before 1 1 1 0 1 1 1
> > NA 0 NA 1 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 1 NA 0 NA
> > NA 1 NA 1 NA 1 NA
> > NA 1 NA 1 NA 1 NA
> > NA 1 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 1 NA 1 NA 1 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 1 NA 1 NA 1 NA
> > NA 0 NA 0 NA 0 NA
> > NA 1 NA 1 NA 1 NA
> > NA 0 NA 1 NA 1 NA
> > NA 1 NA 1 NA 1 NA
> > NA 0 NA 0 NA 0 NA
> > NA 1 NA 0 NA 1 NA
> > NA 0 NA 0 NA 0 NA
> > NA 0 NA 0 NA 0 NA
> > NA 1 NA 1 NA 1 NA
> > NA 1 NA 1 NA 1 NA
> > NA 1 NA 1 NA 1 NA
> > NA 0 NA 0 NA 0 NA
> >
> > Where event.gen2 is this function:
> >
> > event.gen2 = function(genmat,use1,use2,num,ortho_obs_used){
> >
> > for(r in 2:nrow(genmat)){
> >
> > ran = runif(1,0,1)
> >
> > if(genmat[r,use1]==0 & genmat[r,use2]==0){
> >
>
genmat[r,num]<-ifelse(genmat[r-1,num]==0,ifelse(ran<ortho_obs_used$Pr[1],1,
> > 0),ifelse(ran<ortho_obs_used$Pr[4],1,0))
> > }
> >
> > if(genmat[r,use1]==0 & genmat[r,use2]==1){
> >
>
genmat[r,num]<-ifelse(genmat[r-1,num]==0,ifelse(ran<ortho_obs_used$Pr[2],1,
> > 0),ifelse(ran<ortho_obs_used$Pr[5],1,0))
> > }
> >
> > if(genmat[r,use1]==1 & genmat[r,use2]==0){
> >
>
genmat[r,num]<-ifelse(genmat[r-1,num]==0,ifelse(ran<ortho_obs_used$Pr[3],1,
> > 0),ifelse(ran<ortho_obs_used$Pr[7],1,0))
> > }
> >
> > if(genmat[r,use1]==1 & genmat[r,use2]==1){
> >
>
genmat[r,num]<-ifelse(genmat[r-1,num]==0,ifelse(ran<ortho_obs_used$Pr[6],1,
> > 0),ifelse(ran<ortho_obs_used$Pr[8],1,0))
> > }
> >
> > gc()
> > }
> >
> > genmat
> >
> > }
> >
> > ####
> >
> > ortho_obs_used is a data frame that contains the probablity of
> precipitation
> > occurring on a given day for a specific set of condtions.
> > For instance ortho_obs_used$Pr[1] is the probablity of rain at station
s
> for
> > day d, given that there was no rain at station s for day d-1 and no
rain
> at
> > either of the other two stations for day d.
> >
> > The event.gen2 function handles the generation, and it runs quickly
for
> the
> > 5 remaining stations and one month, but I have to run this for 317
> stations
> > over 48 months or more, and it becomes a really bad bottleneck. So
what
> I'd
> > like to know is if there is anyway that I can re-write this function
to
> work
> > without a loop. I couldn't find anything from previous posts about
> getting
> > out of loops where the previous iteration is required to determine the
> next
> > calculation.
> >
> > Sorry for the length of the post, but I thought it best to try to
> explain
> > what I was doing first, before diving into my question
> >
> > Thanks in advance!
> >
> > Adrienne Wootten
> > Graduate Research Assistant/Environmental Meteorologist
> > M.S. Atmospheric Science
> > NC State University
> > State Climate Office of North Carolina
> > Raleigh, NC 27695
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
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