I scripted a simple for-loop to iterate over each row of a data set to calculate the distance between two coordinates. The code uses the 'geosphere' package and the 'distm' function which takes two sets of coordinates and returns the distance in meters (which I convert to miles by multiplying by 0.00062137).
Here is my loop:
##For loop to find distance in miles for each coordinate pair
miles <- 0
for (i in i:3303) {
miles[i] <- distm(x = c(clean.zips[i,4], clean.zips[i,3]), y = c(clean.zips[i,7], clean.zips[i,6]))[,1] * 0.00062137
}
However, when I run it I receive an error:
Error: object 'i' not found
The thing is, I've run this code before and it worked. Other times, I get this error. I'm not changing any code, it just seems to randomly work only some of the times. I feel the loop must be constructed correctly if it does what I want on occasion, but why would it only work sometimes?
OK, I'm not certain what justifies the down votes on this, but guess I apologize to whomever thought that necessary.
The issue seems to have just been starting the indexing with an actual numeric value like Zheyuan suggested (i.e. using '1:3303' rather than 'i:3303'). I feel like I've created loops before using 'i in i:xxx' without first defining 'i' but maybe not. Anyway, it's solved and thank you!
Related
I'm trying to figure out how to get a for loop setup in R when I want it to run two or more parameters at once. Below I have posted a sample code where I am able to get the code to run and fill a matrix table with two values. In the 2nd line of the for loop I have
R<-ARMA.var(length(x_global_sample),ar=c(tt[i],-.7))
And what I would like to do is replace the -.7 with another tt[i], example below, so that my for loop would run through the values starting at (-1,-1), then it would be as follows (-1,-.99),
(-1,-.98),...,(1,.98),(1,.99),(1,1) where the result matrix would then be populated by the output of Q and sigma.
R<-ARMA.var(length(x_global_sample),ar=c(tt[i],tt[i]))
or something similar to
R<-ARMA.var(length(x_global_sample),ar=c(tt[i],ss[i]))
It may be very possible that this would be better handled by two for loops however I'm not 100% sure on how I would set that up so the first parameter would be fixed and the code would run through the sequence of the second parameter, once that would get finished the first parameter would now increase by one and fix itself at that increase until the second parameter does another run through.
I've posted some sample code down below where the ARMA.var function just comes from the ts.extend package. However, any insight into this would be great.
Thank you
tt<-seq(-1,1,0.01)
Result<-matrix(NA, nrow=length(tt)*length(tt), ncol=2)
for (i in seq_along(tt)){
R<-ARMA.var(length(x_global_sample),ar=c(tt[i],-.7))
Q<-t((y-X%*%beta_est_d))%*%solve(R)%*%(y-X%*%beta_est_d)+
lam*t(beta_est_d)%*%D%*%beta_est_d
RSS<-sum((y-X%*%solve(t(X)%*%solve(R)%*%X+lam*D)%*%t(X)%*%solve(R)%*%y)^2)
Denom<-n-sum(diag(X%*%solve(t(X)%*%solve(R)%*%X+lam*D)%*%t(X)%*%solve(R)))
sigma<-RSS/Denom
Result[i,1]<-Q
Result[i,2]<-sigma
rm(Q)
rm(R)
rm(sigma)
}
Edit: I realize that what I have posted above is quite unclear so to simplify things consider the following code,
x<-seq(1,20,1)
y<-seq(1,20,2)
Result<-matrix(NA, nrow=length(x)*length(y), ncol=2)
for(i in seq_along(x)){
z1<-x[i]+y[i]
z2<-z1+y[i]
Result[i,1]<-z1
Result[i,2]<-z2
}
So the results table would appear as follow as the following rows,
Row1: 1+1=2, 2+1=3
Row2: 1+3=4, 4+3=7
Row3: 1+5=6, 6+5=11
Row4: 1+7=8, 8+7=15
And this pattern would continue with x staying fixed until the last value of y is reached, then x would start at 2 and cycle through the calculations of y to the point where my last row is as,
RowN: 20+19=39, 39+19=58.
So I just want to know if is there a way to do it in one loop or if is it easier to run it as 2 loops.
I hope this is clearer as to what my question was asking, and I realize this is not the optimal way to do this, however for now it is just for testing purposes to see how long my initial process takes so that it can be streamlined down the road.
Thank you
I'm on a project in remote sensing running on R. I've got a RasterBrick(x) with the raster for all the dates I'm interested in, a Time Serie with the dates corresponding (called time in the function), and a function which works as I want it when processed manually (z is the pixel I want) :
function(x,z)
{
d<-bfastts(as.vector(x[as.numeric(z)]),time,type="16-day")
n<-bfast(d, h=0.15, season="harmonic", max.iter = 1)
l[[z]]<-list(n$output[[1]]$Tt)
}
The bfastts function is used to create a ts object containing the values of one pixel along the time serie, the bfast is another processing some statisticals of which I only want one result (this is the third line)? None of this two functions are mine, and they are stable and foundable in the R package repository.
So, I would like to add "another level" of function (sorry for my vocabulary which may not be very precise) which would allow to run this function automatically. My expected result would be a list of the result of the function above, so in other words a list of each pixel's time series.
I've tried this (x is still the RasterBrick) :
function(x)
{
z<-nrow(x)*ncol(x)
j<-last(z[[1]])
l<-vector('list',length = j)
index<-function(x)
{
d<-bfastts(as.vector(x[as.numeric(z)]),time,type="16-day")
n<-bfast(d, h=0.15, season="harmonic", max.iter = 1)
l[[z]]<-list(n$output[[1]]$Tt) # this is to add the newly created element to the list
}
lapply(x, FUN='index')
}
but I'm getting an answer that it is not possible to coerce a S4 object to a vector, I guess the problem is in lapply who doesn't like the RasterBrick class... Furthermore I want a list of list in output, and not a list of RasterBrick (I think I understood lapply returns a list of object with the same class as x).
I've tried different workaround, none succesfully, which is not surprising giving my low level in programming, and this one seems to me the closest to what I need. I don't think I fully understand neither how lapply works nor the use of a function in a function.
Thank you very much if you can help me.
Cheers
Guillaume
So, in case it could be useful to someone, here is how I solved this problem (it seems rather very simple finally), the "brick" object is the RasterBrick:
pixelts<- as.list(as.data.frame(t(as.data.frame(brick))))
I am totally new to R. Hopefully you can help. I am trying to simulate from a Hawkes process using R. The main idea is that-first of all I simulated some events from a homogeneous Poisson process. Then each of these events will create their own children using a non homogeneous Poisson process. The code is like as below:
SimulateHawkesprocess<-function(n,tmax,lambda,lambda2){
times<-Simulatehomogeneousprocess(n,lambda)
count<-1
while(count<n){
newevent<-times[count] + Simulateinhomogeneousprocess(lambda2,tmax,lambdamax=NA)
times<-c(times,newevent)
count<-count+1
n<-length(times)
}
return(times)
}
But the r code is producing this infinite loop(probably because of the last line: (n<-length(times))). How can I overcome this problem? How can I put a stopping condition?
This is not a R specific problem. You need to get your algorithm working correctly first. Compare the code you have written against what you want to do. If you need help with the algorithm then tag the question as such. Moreover the function call to Simulateinhomogeneousprocess is very inconsistent. Some insight into that function would help. What is that function returning, a number or a vector?
Within the loop you are increasing the value of n by at least 1 each time so you never reach the end.
newevent<-times[count] + Simulateinhomogeneousprocess(lambda2,tmax,lambdamax=NA)
This creates a non empty variable
times<-c(times,newevent)
Increases the "times" vector by at least 1 (since newevent is non-empty)
count<-count+1
n<-length(times)
You increase the count by 1 but also increase the n value by atleast 1 thus creating a never ending loop. One of these things has to change for the loop to stop.
I wrote a function in R - called "filtre": it takes a dataframe, and for each line it says whether it should go in say bin 1 or 2. At the end, we have two data frames that sum up to the original input, and corresponding respectively to all lines thrown in either bin 1 or 2. These two sets of bin 1 and 2 are referred to as filtre1 and filtre2. For convenience the values of filtre1 and filtre2 are calculated but not returned, because it is an intermediary thing in a bigger process (plus they are quite big data frame). I have the following issue:
(i) When I later on want to use filtre1 (or filtre2), they simply don't show up... like if their value was stuck within the function, and would not be recognised elsewhere - which would oblige me to copy the whole function every time I feel like using it - quite painful and heavy.
I suspect this is a rather simple thing, but I did search on the web and did not find the answer really (I was not sure of best key words). Sorry for any inconvenience.
Thxs / g.
It's pretty hard to know the optimum way of achieve what you want as you do not provide proper example, but I'll give it a try. If your variables filtre1 and filtre2 are defined inside of your function and you do not return them, of course they do not show up on your environment. But you could just return the classification and make filtre1 and filtre2 afterwards:
#example data
df<-data.frame(id=1:20,x=sample(1:20,20,replace=TRUE))
filtre<-function(df){
#example function, this could of course be done by bins<-df$x<10
bins<-numeric(nrow(df))
for(i in 1:nrow(df))
if(df$x<10)
bins[i]<-1
return(bins)
}
bins<-filtre(df)
filtre1<-df[bins==1,]
filtre2<-df[bins==0,]
I'm trying to subset a dataframe within a function using a mixture of fixed variables and some variables which are created within the function (I only know the variable names, but cannot vectorise them beforehand). Here is a simplified example:
a<-c(1,2,3,4)
b<-c(2,2,3,5)
c<-c(1,1,2,2)
D<-data.frame(a,b,c)
subbing<-function(Data,GroupVar,condition){
g=Data$c+3
h=Data$c+1
NewD<-data.frame(a,b,g,h)
subset(NewD,select=c(a,b,GroupVar),GroupVar%in%condition)
}
Keep in mind that in my application I cannot compute g and h outside of the function. Sometimes I'll want to make a selection according to the values of h (as above) and other times I'll want to use g. There's also the possibility I may want to use both, but even just being able to subset using 1 would be great.
subbing(D,GroupVar=h,condition=5)
This returns an error saying that the object h cannot be found. I've tried to amend subset using as.formula and all sorts of things but I've failed every single time.
Besides the ease of the function there is a further reason why I'd like to use subset.
In the function I'm actually working on I use subset twice. The first time it's the simple subset function. It's just been pointed out below that another blog explored how it's probably best to use the good old data[colnames()=="g",]. Thanks for the suggestion, I'll have a go.
There is however another issue. I also use subset (or rather a variation) in my function because I'm dealing with several complex design surveys (see package survey), so subset.survey.design allows you to get the right variance estimation for subgroups. If I selected my group using [] I would get the wrong s.e. for my parameters, so I guess this is quite an important issue.
Thank you
It's happening right as the function is trying to define GroupVar in the beginning. R is looking for the object h by itself (not within the dataframe).
The best thing to do is refer to the column names in quotes in the subset function. But of course, then you'd have to sidestep the condition part:
subbing <- function(Data, GroupVar, condition) {
....
DF <- subset(Data, select=c("a","b", GroupVar))
DF <- DF[DF[,3] %in% condition,]
}
That will do the trick, although it can be annoying to have one data frame indexing inside another.