> cstruct<-function(int, bool){
+
+ myint<- int*2;
+
+ mybool<-!bool;
+ myvec<-rep(mybool,10)
+
+ mymat<-matrix(myint*10,nrow=3,ncol=3)
+ myframe<-data.frame(int=rep(myint,5),bool=rep(bool,5))
+ returnlist<-list(myint=myint,mybool=mybool,myvec=myvec,mymat=mymat,myframe
+ =myframe)
+ return(returnlist)
+
+
+
+ }
>
> test<-cstruct(3,T)
> test
$myint
[1] 6
$mybool
[1] FALSE
$myvec
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
$mymat
[,1] [,2] [,3]
[1,] 60 60 60
[2,] 60 60 60
[3,] 60 60 60
$myframe
int bool
1 6 TRUE
2 6 TRUE
3 6 TRUE
4 6 TRUE
5 6 TRUE
> test$myint
[1] 6
> test$mybool
[1] FALSE
> test$myvec
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
> test$myvec[2]
[1] FALSE
> test$mymat
[,1] [,2] [,3]
[1,] 60 60 60
[2,] 60 60 60
[3,] 60 60 60
> test$mymat[2,2]
[1] 60
> test$mymat[,2]
[1] 60 60 60
> test$myframe
int bool
1 6 TRUE
2 6 TRUE
3 6 TRUE
4 6 TRUE
5 6 TRUE
> test$myframe$int
[1] 6 6 6 6 6
> test$myframe$bool
[1] TRUE TRUE TRUE TRUE TRUE
> test$myframe$int[2]
[1] 6
> test$myframe$bool[3]
[1] TRUE
>
> listoftest<-list(cstruct(3,T),cstruct(4,F),cstruct(5,T))
> listoftest[1]
[[1]]
[[1]]$myint
[1] 6
[[1]]$mybool
[1] FALSE
[[1]]$myvec
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[[1]]$mymat
[,1] [,2] [,3]
[1,] 60 60 60
[2,] 60 60 60
[3,] 60 60 60
[[1]]$myframe
int bool
1 6 TRUE
2 6 TRUE
3 6 TRUE
4 6 TRUE
5 6 TRUE
> listoftest[1]$myframe$int[3]
NULL
> listoftest[1]$myframe$int
NULL
> listoftest[1]$myframe
NULL
> listoftest[[1]]$myframe
int bool
1 6 TRUE
2 6 TRUE
3 6 TRUE
4 6 TRUE
5 6 TRUE
> listoftest[[1]]$myframe$int[1]
[1] 6
> listoftest2<-list(flist=cstruct(65,T),slist=cstruct(12,F))
> listoftest2$flist$myframe$int[3]
[1] 130
TADA!
On Sat, May 1, 2010 at 12:42 PM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On May 1, 2010, at 3:14 PM, steven mosher wrote:
>
> maybe I can illustrate the problem by showing how a c programmer might
>> think
>> about the problem and the kinds of mistakes 'we' ( I) make when trying to
>> do
>> this in R
>>
>> cstruct<-function(int, bool){
>> +
>> + myint<- int*2;
>> +
>> + mybool<-!bool;
>> + myvec<-rep(mybool,10)
>> + mymat<-matrix(myint*10,nrow=3,ncol=3)
>> + myframe<-data.frame(rep(myint,5),rep(bool,5))
>> + returnlist<-list(myint,mybool,myvec,mymat,myframe)
>> + return(returnlist)
>> +
>> +
>> +
>> + }
>>
>> # so I have a function that returns a list of hetergenous variables.
>> # an int, a bool, a vector of bools, a matrix of ints, a dataframe of ints
>> and bools
>>
>> test<-cstruct(3,T)
>>>
>>
>>
>> test
>>>
>> [[1]]
>> [1] 6
>>
>> [[2]]
>> [1] FALSE
>>
>> [[3]]
>> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
>>
>> [[4]]
>> [,1] [,2] [,3]
>> [1,] 60 60 60
>> [2,] 60 60 60
>> [3,] 60 60 60
>>
>> [[5]]
>> rep.myint..5. rep.bool..5.
>> 1 6 TRUE
>> 2 6 TRUE
>> 3 6 TRUE
>> 4 6 TRUE
>> 5 6 TRUE
>>
>> # Now I want to access the first element of my list which is an "an int"
>> # first mistake I always make is I just revert to thinking in the
>> # 'dot' structure of a c struct.
>>
>> test.myint
>>>
>> Error: object 'test.myint' not found
>>
>
> There is no dot "." accessor function. If the first element were named
> (which ist is not) then you could have used test$myint.
>
> If you wnated to access the elements of htat list with names you need to
> assing to names at the time it is created, eg.:
>
> returnlist<-list(myint=myint, mybool=mybool, myvec-myvec, mymat=mymat,
> myframe=myframe)
>
> As it is you need to do this to get what you later indicate you want, an
> atomic object:
>
> test[[1]]
>
> Double-brackets yield the thing itself, whereas single brackets yield a
> "sub-list".
>
> > test[4]
> [[1]]
>
> [,1] [,2] [,3]
> [1,] 60 60 60
> [2,] 60 60 60
> [3,] 60 60 60
>
> > test[[4]]
>
> [,1] [,2] [,3]
> [1,] 60 60 60
> [2,] 60 60 60
> [3,] 60 60 60
> > class(test[4])
> [1] "list"
> > class(test[[4]])
> [1] "matrix"
>
>
>
>> # Then I think its stored like a var in a dataframe, accessed by the $
>>
>>> test$myint
>>>
>> NULL
>>
>> # then I try to access the first element of the list
>>
>>> test[1]
>>>
>> [[1]]
>> [1] 6
>>
>> # That works.. but the [[1]] confuses me when I eval test[1] I want 6 back
>> # again thinking in C.
>> # so I try the third element
>>
>> test[3]
>>>
>> [[1]]
>> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
>>
>> # ok I get my vect of bools back. Now I want the first element
>> # of that thing
>> # well test[3] is that thing.. and I want element 1 of test[3]
>>
>> test[3][1]
>>>
>> [[1]]
>> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
>>
>> #hmm thats not what I expect. I wanted F back.
>> # frustrated I try this which i know is wrong
>>
>> test[3,1]
>>>
>> Error in test[3, 1] : incorrect number of dimensions
>>
>> # crap.. maybe the $ is supposed to be used
>>
>>> test$V3
>>>
>> NULL
>>
>> # arrg.. how about 'dot"
>>
>>> test.myvec
>>>
>> Error: object 'test.myvec' not found
>>
>>
>> Anyways, That's the kind of frustration. I have a list, third element is a
>> matrix
>> how do I referernce the 2 row 2 colum of the matrix in my list.. for
>> example.
>>
>
>
> > str(test)
> List of 5
> $ : num 6
> $ : logi FALSE
> $ : logi [1:10] FALSE FALSE FALSE FALSE FALSE FALSE ...
> $ : num [1:3, 1:3] 60 60 60 60 60 60 60 60 60
> $ :'data.frame': 5 obs. of 2 variables:
> ..$ rep.myint..5.: num [1:5] 6 6 6 6 6
> ..$ rep.bool..5. : logi [1:5] TRUE TRUE TRUE TRUE TRUE
>
> > test[[4]][2,2]
> [1] 60
>
>
>
> and so forth..
>>
>> On Sat, May 1, 2010 at 10:56 AM, Ted Harding
>> <ted.hard...@manchester.ac.uk>wrote:
>>
>> On 01-May-10 16:58:49, Giovanni Azua wrote:
>>>
>>>>
>>>> On May 1, 2010, at 6:48 PM, steven mosher wrote:
>>>>
>>>>> I was talking with another guy on the list about this very topic.
>>>>>
>>>>> A simple example would help.
>>>>>
>>>>> first a sample C struct, and then how one would do the equivalent in
>>>>> R.
>>>>>
>>>>> In the end i suppose one want to do a an 'array' of these structs, or
>>>>> list
>>>>> of the structs.
>>>>>
>>>>
>>>> Or like in my use-case ... I needed a c-like struct to define the type
>>>> for aggregating the data to return from a function.
>>>>
>>>> Best regards,
>>>> Giovanni
>>>>
>>>
>>> Assuming that I understand what you want, this is straightforward
>>> and can be found throughout the many functions available in R.
>>> The general form is:
>>>
>>> myfunction <- function(...){
>>> <code to compute objects A1, A2, ... , An>
>>> list(valA1=A1, valA2=A2, ... , valAn=An)
>>> }
>>>
>>> and then a call like
>>>
>>> myresults <- myfunction(...)
>>>
>>> will create a list "myresults" with compnents "valA1", ... ,"valAn"
>>> which you can access as desired on the lines of
>>>
>>> myresults$valA5
>>>
>>> As a simple example, the following is a function which explores
>>> by simulation the power of the Fisher Exact Test for comparing
>>> two proportions in a 2x2 table:
>>>
>>> power.fisher.test <- function(p1,p2,n1,n2,alpha=0.05,nsim=100){
>>> y1 <- rbinom(nsim,size=n1,prob=p1)
>>> y2 <- rbinom(nsim,size=n2,prob=p2)
>>> y <- cbind(y1,n1-y1,y2,n2-y2)
>>> p.value <- rep(0,nsim)
>>> for (i in 1:nsim)
>>> p.value[i] <- fisher.test(matrix(y[i,],2,2))$p.value
>>> list(Pwr=mean(p.value < alpha),SE.Pwr=sd(p.value < alpha)/sqrt(nsim))
>>> }
>>>
>>> So, given two binomials B(n1,p1) and B(n2,p2), what would be the
>>> power of the Fisher test to detect that p1 was different from p2,
>>> at given significance level alpha? This is investigated by repeating,
>>> nsim times:
>>> sample from Bin(n1,p1), sample from Bin(n2.p2)
>>> do a Fisher test and get its P-value; store it
>>> in a vector p.value of length nsim
>>> and then finally:
>>> estimate the power as the proportion Pwr of the nsim cases
>>> in which the P-value was less than alpha
>>> get the SE of this estimate
>>> return these two values as components Pwr and SE.Pwr of a list
>>>
>>> As it happens, here each component of the resulting list is of
>>> the same type (a single number); but in a different computation
>>> each component (and of course there could be more than two)
>>> could be anything -- even another list. So you can have lists
>>> of lists ... !
>>>
>>> Thus, instead of the simple returned list above:
>>>
>>> list(Pwr=mean(p.value < alpha),
>>> SE.Pwr=sd(p.value < alpha)/sqrt(nsim))
>>>
>>> you could have
>>>
>>> list(Binoms=list(Bin1=list(size=n1,prob=p1),
>>> Bin2=list(size=n2,prob=p2))
>>> Pwr=mean(p.value < alpha),
>>> SE.Pwr=sd(p.value < alpha)/sqrt(nsim))
>>>
>>> thus also returning the details of the Binomials for which the
>>> simulation was carried out. You could access these all together as:
>>>
>>> power.fisher.test(...)$Binoms
>>>
>>> or separately as
>>>
>>> power.fisher.test(...)$Binoms$Bin1
>>> or
>>> power.fisher.test(...)$Binoms$Bin2
>>>
>>> or even
>>> power.fisher.test(...)$Binoms$Bin1$size
>>> power.fisher.test(...)$Binoms$Bin1$prob
>>> etc.
>>>
>>> Ted.
>>>
>>> --------------------------------------------------------------------
>>> E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk>
>>> Fax-to-email: +44 (0)870 094 0861
>>> Date: 01-May-10 Time: 18:56:50
>>> ------------------------------ XFMail ------------------------------
>>>
>>> ______________________________________________
>>> R-help@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.
>>>
>>>
>> [[alternative HTML version deleted]]
>>
>>
>> ______________________________________________
>> R-help@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.
>>
>
> David Winsemius, MD
> West Hartford, CT
>
>
[[alternative HTML version deleted]]
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