I have a data.table like this:
testDT <- data.table(SFf = as.character(c("C1H1", "C3H4Cl")),
Mult = as.integer(c(3,5))
)
now I am trying to loop over the rows of this table and apply a function (multiform() from the package enviPat) to it like this:
mapply(multiform,testDT$SFf,testDT$Mult)
the output of it should be two strings, namely "C3H3" and "C15H20Cl5". However the function simply never stops calculating and seems to get caught up in an endless loop.
When I apply the function like
multiform("C1H1",3)
it works and I get "C3H3" as output. What am I doing wrong?
Yasel
I got it. The problem was that the function multiform() does not allow elements without numbers behind them.
So it wont take "C3H4Cl" but "C3H4Cl1".
I don't think that this is intended and will try to contact the authors of the package.
Related
I have a command with six lines that I want to use several times. Therfore, I want to assign a name to this command and use it as a procedure instead of writing the whole command lines over and over.
In this case it is a <-rbind() command, but the issue is also more general.
modelcoeff<-rbind(modelcoeff,c(as.character((summary(mymodel)$terms[[2]])[[3]]),
as.character((((((summary(mymodel)$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summary(mymodel)$coefficients[2,1],
summary(mymodel)$coefficients[2,4],
summary(mymodel)$coefficients[2,2],
summary(mymodel)$r.squared*100))
I would like to call something like rbindmodelcoeff and execute these command lines. How can I achieve this?
I tried to write a function, but it didn't seem to be the right approach.
A literal wrapping of your code into a function:
rbindmodelcoeff <- function(modelcoeff, mymodel) {
rbind(modelcoeff,
c(as.character((summary(mymodel)$terms[[2]])[[3]]),
as.character((((((summary(mymodel)$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summary(mymodel)$coefficients[2,1],
summary(mymodel)$coefficients[2,4],
summary(mymodel)$coefficients[2,2],
summary(mymodel)$r.squared*100))
}
However, there are a couple changes I recommend:
call summary(mymodel) once, then re-use the results
you are using as.character on some of the objects but not all within the enclosing c(.), so everything is being converted to a character; to see what I mean, try c(as.character(1), 2); we can use a list instead to preserve string-vs-number
rbindmodelcoeff <- function(modelcoeff, mymodel) {
summ <- summary(mymodel)
rbind(modelcoeff,
list(as.character((summ$terms[[2]])[[3]]),
as.character((((((summ$terms[[2]])[[2]])[[3]])[[3]])[[2]])[[3]]),
summ$coefficients[2,1],
summ$coefficients[2,4],
summ$coefficients[2,2],
summ$r.squared*100))
}
But there are still some problems with this. I can't get it to work at the moment since I don't know the model parameters you're using, so as.character((summ$terms[[2]])[[3]]) for me will fail. With that, I'm always hesitant to hard-code so many brackets without a firm understanding of what is being used. It's out of scope for this question (which is being converting your basic code into a function), but you might want to find out how to generalize that portion a bit.
I'm working on a non-linear optimization, with the constrOptim.nl from the alabama package. However, my problem is more related to passing arguments (and the dot-dot-dot (ellipis/"...") and maybe do.call)- so I give first a general example and later refer to the constrOptim.nl function.
Suppose, I have the following functions - from which I only can edit the second and third but not the first.
first<-function (abc, second, third, ...){
second(abc,...)
third(abc,...)
}
second<- function(abc, ttt='nothing special'){
print(abc)
print(ttt)
}
third<- function(abc, zzz="default"){
print(abc)
print(zzz)
}
The output I want is the same I would get when I just run
second("test", ttt='something special')
third("test", zzz="non-default")
This is
"test"
"something special"
"test"
"non-default"
However, the code below doesn't work to do this.
first("test",second=second, third=third, ttt='something special',zzz="non-default")
How can I change the call or the second and third function to make it work?
http://www.r-bloggers.com/r-three-dots-ellipsis/
here I found some advice that do.call could help me but at the moment I'm not capable of understanding how it should work.
I cannot change the first function since this is the constrOptim.nl in my particular problem - and it is designed to be capable of passing more arguments to different functions. However, I can change the second and third function - as they are the restrictions and the function that I want to minimize. Obviously I can also change the call of the function.
So to be more particular, here is my specific problem:
I perform a maximum likelihood estimation with non-linear restrictions:
minimize <- function(Param,VARresiduals){
#Blahblah
for (index in 1:nrow(VARreisduals)){
#Likelihood Blahbla
}
return(LogL)
}
heq<-function(Param,W){
B<-Param[1:16]
restriction[1]<-Lrestriction%*%(diag(4)%x%(solve(W))%*%as.vector(B))
restriction[2:6]<-#BlablaMoreRestrictions
return(restriction)
}
Now I call the constrOptim.nl...
constrOptim.nl(par=rnorm(20), fn=minimize,hin=NULL heq=heq,VARresiduals,W)
...but get the same error, as I receive when I call the first function above - something like: "Error in second(abc, ...) : unused argument (zzz = "non-default")".
How can I change minimize and heq or the call? :) Thanks in Advance
Update after the post got marked as a duplicate:
The answer to the related post changes the first function in my example - as it implements a do.call there, that calls the other functions. However, I cannot change the first function in my example as I want to keep the constrOptim.nl working a variety of different functions. Is there another way?
The solution I came up with is not very elegant but it works.
second_2<- function(abc, extras){
a<-extras[[1]]
print(abc)
print(a)
}
third_2<- function(abc, extras){
a<-extras[[2]]
print(abc)
print(a)
}
extras<-list()
extras[[1]]<-'something special'
extras[[2]]<-"non-default"
extras
first("test",second=second_2, third=third_2, extras)
surprisingly also the following code works, but with a slightly different outcome
first("test",second=second, third=third, extras)
after all, setting default values is now a little clumsy but not infeasible.
I'm trying to package some code I use for data analysis so that other workers can use it. Currently, I'm stuck trying to write a simple function that imports data from a specific file type generated by a datalogger and trims it for use by other functions. Here's the code:
import<-function(filename,type="campbell",nprobes){
if (filename==TRUE){
if (type=="campbell"){
message("File import type is from Campbell CR1000")
flux.data<<-read.table(filename,sep=",",header=T,skip=1)
flux.data<<-flux.data[,-c(1,2)];flux.data<<-flux.data[-c(1,2),]
if (nprobes=="missing"){
nprobes<-32
}
flux.data<<-flux.data[,c(1:nprobes)]
flux.data.names<<-colnames(flux.data) #Saves column names
}
}
}
Ideally, the result would be a dataframe/matrix flux.data and a concomittant vector/list of the preserved column headers flux.data.names. The code runs and the function executes without errors, but the outputs aren't preserved. I usually use <<- to get around the function enclosure but its not working in this case - any suggestions?
I think the real problem is that I don't quite understand how enclosures work, despite a lot of reading... should I be using environment to assign environments within the function?
User joran answered my question in the comments above:
The critical issue was just in how the function was written: the conditional at the start (if filename==TRUE) was intended to see if filename was specified, and instead was checking to see if it literally equaled TRUE. The result was the conditional never being met, and no function output. Here's what fixed it:
import<-function(filename,type="campbell",nprobes){
if (exists(filename){
if (type=="campbell"){
#etc....
Another cool thing he pointed out was that I didn't need the <<- operator to utilize the function output and instead could write return(flux.data). This is a much more flexible approach, and helped me understand function enclosures a lot better.
Just started using Scilab, looks like a lot to learn but I am stuck at very first basic program. I need to display 1 to 10 numbers without using loop.
I know that using loop we can use this code to display numbers from 1 to 10:
for i = 1:10
disp(i)
end
But i need to display them without using any loop. In C programming it's a bit easy using recursive function but here i have tried alot and failed.
Please can any one help me out via code snippet.
I think a recursive call is still kind of a loop, but it could be done like this:
function recursivePrint(i, maxNumber)
if( i <= maxNumber )
disp(i)
recursivePrint(i+1, maxNumber)
end
endfunction
recursivePrint(1, 10);
Usually when people ask to write a function without a loop they mean something like:
disp(1:10)
disp(1)
disp(2)
disp(3)
disp(4)
disp(5)
disp(6)
disp(7)
disp(8)
disp(9)
disp(10)
Curious what you tried..
I am running the following code:
disc<-for (i in 1:33) {
m=n[i]
xbar<-sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
Sx
i=i+1}
Running it:
>disc
NULL
Why is it giving me NULL?
This is from the documentation for for, accessible via ?`for`:
‘for’, ‘while’ and ‘repeat’ return ‘NULL’ invisibly.
Perhaps you are looking for something along the following lines:
library(plyr)
disc <- llply(1:33, function(i) {
m=n[i]
xbar<-sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
Sx
})
Other variants exists -- the ll in llply stands for "list in, list out". Perhaps your intended final result is a data frame or an array -- appropriate functions exist.
The code above is a plain transformation of your example. We might be able to do better by splitting data right away and forgetting the otherwise useless count variable i (untested, as you have provided no data):
disc <- daply(cbind(data, n=n), .(), function(data.i) {
m=data.i$n
xbar<-sum(data.i,na.rm=TRUE)/m
sqrt(sum((data.i-xbar)^2,na.rm=TRUE)/(m-1))
})
See also the plyr website for more information.
Related (if not a duplicate): R - How to turn a loop to a function in R
krlmlr's answer shows you how to fix your code, but to explain your original problem in more abstract terms: A for loop allows you to run the same piece of code multiple times, but it doesn't store the results of running that code for you- you have to do that yourself.
Your current code only really assigns a single value, Sx, for each run of the for loop. On the next run, a new value is put into the Sx variable, so you lose all the previous values. At the end, you'll just end up with whatever the value of Sx was on the last run through the loop.
To save the results of a for loop, you generally need to add them to a vector as you go through, e.g.
# Create the empty results vector outside the loop
results = numeric(0)
for (i in 1:10) {
current_result = 3 + i
results = c(results, current_result)
}
In R for can't return a value. The unique manner to return a value is within a function. So the solution here, is to wrap your loop within a function. For example:
getSx <- function(){
Sx <- 0
disc <- for (i in 1:33) {
m=n[i]
xbar <- sum(data[i,],na.rm=TRUE)/m
Sx <- sqrt(sum((data[i,]-xbar)^2,na.rm=TRUE)/(m-1))
}
Sx
}
Then you call it:
getSx()
Of course you can avoid the side effect of using a for by lapply or by giving a vectorized But this is another problem: You should maybe give a reproducible example and explain a little bit what do you try to compute.