I am minimizing this function below using the optim function, which works really well. My only problem is that I can't save the W matrix, I am computing inside the function when minimizing. Is there a way to save the W matrix somehow?
W<-c()
GMM_1_stage <- function(beta) {for (i in 1:(nrow(gmm_i))){
gmm_i[i,]=g_beta(i,beta)}
gmm_N=t(colSums(gmm_i))%*%colSums(gmm_i)
W<-solve((1/(nrow(A)/5))*t(gmm_i)%*%gmm_i)
return(gmm_N)
}
GMM_1<-optim(beta_MLE,GMM_1_stage)
Best regards
Here is a safer version of #mrip's answer that uses a temporary environment rather than <<-:
tempenv <- new.env()
tempenv$xx <- c()
fun<-function(x){
tempenv$xx[ length(tempenv$xx) + 1 ] <- x
x^2
}
optimize(fun,c(-1,1))
tempenv$xx
By using the temporary environment you don't need to worry about accidentally writing over an object in the global environment or <<- assigning in an unexpected place.
You can assign to an object in the global environment (or in a the closest ancestor environment where the variable is defined) using <<-. So, for example, if I wanted to keep track of every value of x during a simple optimization I could do this.
xx<-c()
fun<-function(x){
xx[length(xx)+1]<<-x
x^2
}
optimize(fun,c(-1,1))
xx
## [1] -2.360680e-01 2.360680e-01 5.278640e-01 -2.775558e-17 4.069010e-05
## [6] -4.069010e-05 -2.775558e-17
In your case, if you only want the last value of W you can replace that line in your code with:
W<<-solve((1/(nrow(A)/5))*t(gmm_i)%*%gmm_i)
If you want them all, then first set Wlist<-list(), and then in your function set
Wlist[[length(Wlist)+1]]<<-solve((1/(nrow(A)/5))*t(gmm_i)%*%gmm_i)
Related
I want to add an environment to a search path and modify the values of variables within that environment, in a limited chunk of code, without having to specify the name of the environment every time I refer to a variable: for example, given the environment
ee <- list2env(list(x=1,y=2))
Now I would like to do stuff like
ee$x <- ee$x+1
ee$y <- ee$y*2
ee$z <- 6
but without appending ee$ to everything (or using assign("x", ee$x+1, ee) ... etc.): something like
in_environment(ee, {
x <- x+1
y <- y+2
z <- 6
})
Most of the solutions I can think of are explicitly designed not to modify the environment, e.g.
?attach: "The database is not actually attached. Rather, a new environment
is created on the search path ..."
within(): takes lists or data frames (not environments) "... and makes the corresponding modifications to a copy of ‘data’"
There are two problems with <<-: (1) using it will cause NOTEs in CRAN checks (I think? can't find direct evidence of this, but e.g. see here — maybe this only happens because of the appearance of assigning to a locally undefined symbol? I guess I could put this in a package and test it with --as-cran to confirm ...); (2) it will try to assign in the parent environment, which in a package context [which this is] will be locked ...
I suppose I could use a closure as described in section 10.7 of the Introduction to R by doing
clfun <- function() {
x <- 1
y <- 2
function(...) {
x <<- x + 1
y <<- y * 2
}
}
myfun <- clfun()
This seems convoluted (but I guess not too bad?) but:
will still incur problem #1 (CRAN check?).
I think (??) it won't work with variables that don't already exist in the environment (would need an explicit assign() for that ...)
doesn't allow a choice of which environment to operate in - it's necessarily going to work in the enclosing environment, not with arbitrary environment ee
Am I missing something obvious and idiomatic?
Thanks to #Nuclear03020704 ! I think with() was what I wanted all along; I was incorrectly assuming that it would also create a local copy of the environment, but it only does this if the data argument is not already an environment.
ee <- list2env(list(x=1,y=2))
with(ee, {
x <- x+1
y <- y+2
z <- 6
})
does exactly that I want.
Just had another idea, which also seems to have some drawbacks: using a big eval clause. Rather than make my question a long laundry list of unsatisfactory solutions, I'll add it here.
myfun <- function() {
eval(quote( {
x <- x+1
y <- y*2
z <- 3
}), envir=ee)
}
This does seem to work, but also seems very weird/mysterious! I hate to think about explaining it to someone who's being using R for less than 10 years ... I suppose I could write an in_environment() based on this, but I'd have to be very careful to capture the expression properly without evaluating it ...
What about with()? From here,
with(data, expr)
data is the data to use for constructing an environment. For the default with method this may be an environment, a list, a data frame, or an integer.
expr is the expression to evaluate.
with is a generic function that evaluates expr in a local environment constructed from data. The environment has the caller's environment as its parent. This is useful for simplifying calls to modeling functions. (Note: if data is already an environment then this is used with its existing parent.)
Note that assignments within expr take place in the constructed environment and not in the user's workspace.
with() returns value of the evaluated expr.
ee <- list2env(list(x=1,y=2))
with(ee, {
x <- x+1
y <- y+2
z <- 6
})
I just finished reading about scoping in the R intro, and am very curious about the <<- assignment.
The manual showed one (very interesting) example for <<-, which I feel I understood. What I am still missing is the context of when this can be useful.
So what I would love to read from you are examples (or links to examples) on when the use of <<- can be interesting/useful. What might be the dangers of using it (it looks easy to loose track of), and any tips you might feel like sharing.
<<- is most useful in conjunction with closures to maintain state. Here's a section from a recent paper of mine:
A closure is a function written by another function. Closures are
so-called because they enclose the environment of the parent
function, and can access all variables and parameters in that
function. This is useful because it allows us to have two levels of
parameters. One level of parameters (the parent) controls how the
function works. The other level (the child) does the work. The
following example shows how can use this idea to generate a family of
power functions. The parent function (power) creates child functions
(square and cube) that actually do the hard work.
power <- function(exponent) {
function(x) x ^ exponent
}
square <- power(2)
square(2) # -> [1] 4
square(4) # -> [1] 16
cube <- power(3)
cube(2) # -> [1] 8
cube(4) # -> [1] 64
The ability to manage variables at two levels also makes it possible to maintain the state across function invocations by allowing a function to modify variables in the environment of its parent. The key to managing variables at different levels is the double arrow assignment operator <<-. Unlike the usual single arrow assignment (<-) that always works on the current level, the double arrow operator can modify variables in parent levels.
This makes it possible to maintain a counter that records how many times a function has been called, as the following example shows. Each time new_counter is run, it creates an environment, initialises the counter i in this environment, and then creates a new function.
new_counter <- function() {
i <- 0
function() {
# do something useful, then ...
i <<- i + 1
i
}
}
The new function is a closure, and its environment is the enclosing environment. When the closures counter_one and counter_two are run, each one modifies the counter in its enclosing environment and then returns the current count.
counter_one <- new_counter()
counter_two <- new_counter()
counter_one() # -> [1] 1
counter_one() # -> [1] 2
counter_two() # -> [1] 1
It helps to think of <<- as equivalent to assign (if you set the inherits parameter in that function to TRUE). The benefit of assign is that it allows you to specify more parameters (e.g. the environment), so I prefer to use assign over <<- in most cases.
Using <<- and assign(x, value, inherits=TRUE) means that "enclosing environments of the supplied environment are searched until the variable 'x' is encountered." In other words, it will keep going through the environments in order until it finds a variable with that name, and it will assign it to that. This can be within the scope of a function, or in the global environment.
In order to understand what these functions do, you need to also understand R environments (e.g. using search).
I regularly use these functions when I'm running a large simulation and I want to save intermediate results. This allows you to create the object outside the scope of the given function or apply loop. That's very helpful, especially if you have any concern about a large loop ending unexpectedly (e.g. a database disconnection), in which case you could lose everything in the process. This would be equivalent to writing your results out to a database or file during a long running process, except that it's storing the results within the R environment instead.
My primary warning with this: be careful because you're now working with global variables, especially when using <<-. That means that you can end up with situations where a function is using an object value from the environment, when you expected it to be using one that was supplied as a parameter. This is one of the main things that functional programming tries to avoid (see side effects). I avoid this problem by assigning my values to a unique variable names (using paste with a set or unique parameters) that are never used within the function, but just used for caching and in case I need to recover later on (or do some meta-analysis on the intermediate results).
One place where I used <<- was in simple GUIs using tcl/tk. Some of the initial examples have it -- as you need to make a distinction between local and global variables for statefullness. See for example
library(tcltk)
demo(tkdensity)
which uses <<-. Otherwise I concur with Marek :) -- a Google search can help.
On this subject I'd like to point out that the <<- operator will behave strangely when applied (incorrectly) within a for loop (there may be other cases too). Given the following code:
fortest <- function() {
mySum <- 0
for (i in c(1, 2, 3)) {
mySum <<- mySum + i
}
mySum
}
you might expect that the function would return the expected sum, 6, but instead it returns 0, with a global variable mySum being created and assigned the value 3. I can't fully explain what is going on here but certainly the body of a for loop is not a new scope 'level'. Instead, it seems that R looks outside of the fortest function, can't find a mySum variable to assign to, so creates one and assigns the value 1, the first time through the loop. On subsequent iterations, the RHS in the assignment must be referring to the (unchanged) inner mySum variable whereas the LHS refers to the global variable. Therefore each iteration overwrites the value of the global variable to that iteration's value of i, hence it has the value 3 on exit from the function.
Hope this helps someone - this stumped me for a couple of hours today! (BTW, just replace <<- with <- and the function works as expected).
f <- function(n, x0) {x <- x0; replicate(n, (function(){x <<- x+rnorm(1)})())}
plot(f(1000,0),typ="l")
The <<- operator can also be useful for Reference Classes when writing Reference Methods. For example:
myRFclass <- setRefClass(Class = "RF",
fields = list(A = "numeric",
B = "numeric",
C = function() A + B))
myRFclass$methods(show = function() cat("A =", A, "B =", B, "C =",C))
myRFclass$methods(changeA = function() A <<- A*B) # note the <<-
obj1 <- myRFclass(A = 2, B = 3)
obj1
# A = 2 B = 3 C = 5
obj1$changeA()
obj1
# A = 6 B = 3 C = 9
I use it in order to change inside map() an object in the global environment.
a = c(1,0,0,1,0,0,0,0)
Say I want to obtain a vector which is c(1,2,3,1,2,3,4,5), that is if there is a 1, let it 1, otherwise add 1 until the next 1.
map(
.x = seq(1,(length(a))),
.f = function(x) {
a[x] <<- ifelse(a[x]==1, a[x], a[x-1]+1)
})
a
[1] 1 2 3 1 2 3 4 5
This question already has answers here:
Closed 10 years ago.
Possible Duplicate:
General suggestions for debugging R?
When debugging, I would often like to know the value of a variable used in a function that has completed executing. How can that be achieved?
Example:
I have function:
MyFunction <- function() {
x <- rnorm(10) # assign 10 random normal numbers to x
return(mean(x))
}
I would like to know the values stored in x which are not available to me after the function is done executing and the function's environment is cleaned up.
You mentioned debugging, so I assume the values are not needed later in the script, you just want to check what is happening. In that case, what I always do is use browser:
MyFunction <- function() {
browser()
x <- rnorm(10) # assign 10 random normal numbers to x
return(mean(x))
}
This drops you into an interactive console inside the scope of the function, allowing you to inspect what is happening inside.
For general information about debugging in R I suggest this SO post.
MyFunction <- function() {
x <- rnorm(10) # assign 10 random normal numbers to x
return(list(x,mean(x)))
}
This will return a list where the first element is x and the second is its mean
You have many options here. The easiest is to use the <<- operator when you assign to x. It's also the most likely to get you into trouble.
> test <- function() x <- runif(1)
> x <- NA
> test()
> x
[1] NA
> test <- function() x <<- runif(1)
> test()
> x
[1] 0.7753325
Edit
#PaulHeimstra points out that you'd like this for debugging. Here's a pointer to some general tricks:
General suggestions for debugging in R
I'd recommend either setting options(error=recover) or using trace() in combination with browser().
There are already some good solutions, I'd like to add one possibility. I emphasize on the fact that you want to know the value of a variable used in a function that has completed executing. So there is maybe no need to assign those values, and you don't want (a priori) to stop execution. The solution is to simply use print. So it is not used by default but only when you want to debug, the option to print or not can be passed as a function argument:
MyFunction <- function(x, y, verbose = FALSE) {
a <- x * y
if (verbose) print(a)
b <- x - y
if (verbose) print(b)
return(a * b)
}
In general, you would run your function like this: MyFunction(10, 4) but when you want to see those intermediate results, do MyFunction(10, 4, verbose = TRUE).
I have a function that needs to access a variable in its parent environment (scope from which the function is called). The variable is large in terms of memory so I would prefer not to pass it to by value to the function being called. Is there a standard way of doing this other than declaring the variable in the global scope? For example:
g <- function (a, b) { #do stuff}
f <- function(x) {
y <- 3 #but in my program y is very large
g(x, y)
}
I would like to access y in g(). So something like this:
g <- function (a) { a+y }
f <- function(x) {
y <- 3 #but in my program y is very large
g(x)
}
Is this possible?
Thanks
There is no advantage to "declaring the variable in the global scope" and it may not even be possible in R depending on what you mean by that. You certainly could use the second form. The action that causes duplicate or even triplicate copies of an object is assignment. You will need to describe in more detail what you are trying to illustrate by the code: y <- 3. That would not normally be needed inside a function that merely accessed an object named "y" that was located in an enclosing frame.
Storing variables in a declared environment will sometimes improve efficiency of access, but my understanding is that the efficiency is in terms of improved speed because a hash table is used. One accesses items in an environment in the same manner as one accesses list elements:
> evn <- new.env()
> evn$a <- rnorm(100000)
> ls(evn)
[1] "a"
> length(evn$a)
[1] 100000
The BigMemory project may offer facilities for this:
http://www.bigmemory.org/ .
It and Lumley's biglm may help with the large dataset mentioned in the comments.
The normal approach to writing functions in R (as I understand) is to avoid side-effects and return a value from a function.
contained <- function(x) {
x_squared <- x^2
return(x_squared)
}
In this case, the value computed from the input into the function is returned. But the variable x_squared is not available.
But if you need to violate this basic functional programming tenet (and I'm not sure how serious R is about this issue) and return an object from a function, you have two choices.
escape <- function(x){
x_squared <<- x^2
assign("x_times_x", x*x, envir = .GlobalEnv)
}
Both objects x_squared and x_times_x are returned. Is one method preferable to the other and why so?
Thomas Lumley answers this in a superb post on r-help the other day. <<- is about the enclosing environment so you can do thing like this (and again, I quote his post from April 22 in this thread):
make.accumulator<-function(){
a <- 0
function(x) {
a <<- a + x
a
}
}
> f<-make.accumulator()
> f(1)
[1] 1
> f(1)
[1] 2
> f(11)
[1] 13
> f(11)
[1] 24
This is a legitimate use of <<- as "super-assignment" with lexical scope. And not simply to assign in the global environment. For that, Thomas has these choice words:
The Evil and Wrong use is to modify
variables in the global environment.
Very good advice.
According to the manual page here,
The operators <<- and ->> cause a search to made through the environment for an existing definition of the variable being assigned.
I've never had to do this in practice, but to my mind, assign wins a lot of points for specifying the environment exactly, without even having to think about R's scoping rules. The <<- performs a search through environments and is therefore a little bit harder to interpret.
EDIT: In deference to #Dirk and #Hadley, it sounds like assign is the appropriate way to actually assign to the global environment (when that's what you know you want), while <<- is the appropriate way to "bump up" to a broader scope.
As pointed out by #John in his answer, assign lets you specify the environment specifically. A specific application would be in the following:
testfn <- function(x){
x_squared <- NULL
escape <- function(x){
x_squared <<- x^2
assign("x_times_x", x*x, envir = parent.frame(n = 1))
}
escape(x)
print(x_squared)
print(x_times_x)
}
where we use both <<- and assign. Notice that if you want to use <<- to assign to the environment of the top level function, you need to declare/initialise the variable. However, with assign you can use parent.frame(1) to specify the encapsulating environment.