I am trying to run a power analysis using a MonteCarlo approach in R.
I have created a function of two parameters that does output a boolean (tested manually for all relevant values of the parameters). I also have run baby-examples of the MonteCarlo function to make sure that I understand it and that it works well.
Yet when I try to run the real thing, I get the following error message:
Error in parse(text = all_funcs_found[i]) : <text>:1:1: unexpected '::'
1: ::
I read through the source code of the MonteCarlo function (which I found here) and found
#loop through non-primitive functions used in func and check from which package they are
for(i in 1:length(all_funcs_found)){
if(environmentName(environment(eval(parse(text=all_funcs_found[i]))))%in%env_names){
packages<-c(packages,env_names[which(env_names==environmentName(environment(eval(parse(text=all_funcs_found[i])))))])
}
}
which doesn't really make sense to me - why should there be a problem there?
Thank you for any ideas.
I found the answer: the function I wrote was calling a function from a specific library in the form libraryname::functionname.
This works OK if you use the function once manually, but makes MonteCarlo break.
I solved the problem by first loading the relevant library, then removing the 'libraryname::' part from the definition of the main function. MonteCarlo then runs just fine.
Related
can someone please help me understand this:
I encountered an error when calling a function from a library, specifically "steinertree" from the "SteinerNet" package. When stepping into the function with debug(steinertree), I see that the error occurs, when the function in turn calls "steinertree3". When I try debug(steinertree3), I get "object 'steinertree3' not found". Similarly, I can get the code for 'steinertree' by typing it in the terminal, but not for 'steinertree3'.
So it seems to me that there are some "higher-level" functions and "hidden" functions in packages. I did eventually find the error by finding a file "steinertree.R" in the package at CRAN, which contains both 'steinertree' and 'steinertree3', but I`m wondering how to properly go about debugging such "hidden" functions.
Here is a simple example:
library(igraph)
library(SteinerNet)
set.seed(1)
g= erdos.renyi.game(n=10,p.or.m=0.2)
plot(g)
steinertree(type= 'KB', terminals= c(1,3), graph= g)
Thank you!
Use triple colon ::: to execute a function that is not exported by the package/namespace:
package:::hidden_function()
I stumbled on the function is.single() and do not understand what is does. The description says:
is.single reports an error. There are no single precision values in R.
And indeed, if I try to test different things inside the function I get an error. Example:
is.single(1)
Error in is.single(1) : type "single" unimplemented in R
I see no sense in a function that seemingly always gives an error. What is the function for?
I am trying to understand the way the YourCast R package works and make it work with my data.
For example, if a function produces errors, I
get the source code of that function using YourCast:::bad.fn
add outputs of critical
values at critical stages
use reassignInPackage(name="original.fn", package="YourCast", value="my.fn")
Once I find the cause of the error, I fix it in the function and reassign it in the package.
However, for some strange reason this does not work for non-hidden functions.
For example:
install.packages("YourCast")
Library(YourCast)
YourCast:::check.depvar
This will print the hidden function check.depvar. One line if (all(ix == 1:3)) will produce an error message if any of the x is missing.
Thus, I change the whole function to the following and replace the original formula:
mzuba.check.depvar <- function(formula)
{
return (grepl("log[(]",as.character(formula)[2]))
}
reassignInPackage("check.depvar",
pkgName="YourCast",
mzuba.check.depvar)
rm(mzuba.check.depvar)
Now YourCast:::check.depvar will print my version of that function, and everything is fine.
However
YourCast::yourcast or YourCast:::yourcast or simply yourcast will print the non-hidden function yourcast. Suppose I want to change that function as well.
reassignInPackage(name="yourcast",
pkgName="YourCast",
value=test)
Now, YourCast::yourcast and YourCast:::yourcast will print the new, modified version but yourcast still gives the old version!
That might not a problem if I could simply call YourCast::yourcast instead of yourcast, but that produces some kind of error that I can't trace back because suddenly R-Studio does not print error messages at all anymore!, although it still does something if it is capable to:
> Uagh! do something!
> 1 + 1
[1] 2
> Why no error msg?
>
Restarting the R-session will solve the error-msg problem, though.
So my question is: How do I reassign non-hidden functions in packages?
Furthermore (this would faciliate testing a lot), is there a way to make all hidden functions available without using the ::: operator? I.e., How to export all functions from a package?
I'm trying to call the checkm function from within another function that accepts a formula as a parameter. I'm getting an object not found error. Here is the minimal implementation and error.
library(lrmest)
data(pcd)
form<-formula(Y~X1+X2+X3+X4)
checkm(form,data=pcd)
Wrap<-function(f){
checkm(f,data=pcd)
}
Wrap(form)
The error is:
Error in model.frame(formula = f, data = pcd, NULL) :
object 'f' not found
Called from: eval(expr, envir, enclos)
My guess from reading around is this has to do with my not understanding environments or promises but given that I don't understand them, I'm probably wrong.
Any quick fixes?
One quick fix is to change the name of your formula argument. It happens to conflict with the eval(cal) call within checkm. I suspect #joran is right that this isn't your fault. This works:
library(lrmest)
data(pcd)
form<-Y~X1+X2+X3+X4
checkm(form,data=pcd)
Wrap<-function(formula){
checkm(formula,data=pcd)
}
Wrap(form)
As #joran pointed out, there is a bug/error in the function caused from not using the correct frame to evaluate the command. If you swap out checkm for lm you'll see it runs just fine. You can create your own function that changes just that one line of code with
checkm2<-checkm
body(checkm2)[[6]]<-quote(cal <- eval(cal, parent.frame()))
And then run
library(lrmest)
data(pcd)
form<-formula(Y~X1+X2+X3+X4)
checkm2(form,data=pcd)
Wrap<-function(f){
checkm2(f,data=pcd)
}
Wrap(form)
and everything seems to run properly. So that just appears to be the fault of the people who wrote the code. You might consider contacting them to file a bug report.
I am fairly new to R, so my apologies if this question is a bit silly.
I am calling a function in an external package ('mmlcr', although I don't think that is directly relevant to my problem), and one of the required inputs (data) is a data.frame. I compose the data.frame from various data using the following approach (simplified for illustration):
#id, Time, and value are vectors created elsewhere in the code.
myData = data.frame(a=id, b=Time, c=value)
out <- mmlcr( input1, input2, data=myData, input4)
Which throws the error:
Error in is.data.frame(data) : object 'myData' not found
The debugger indicates that this error is thrown during the mmlcr() call.
I then added a print(ls()) immediately prior to the mmlcr() call, and the output confirmed that "myData" was in my function workspace; further is.data.frame(myData) returned TRUE. So it seems that "myData" is successfully being created, but for some reason it is not passing into the mmlcr() function properly. (Commenting this line causes no error to be thrown, so I'm pretty sure this is the problematic line).
However, when I put the exact same code in a script (i.e., not within a function block), no such error is thrown and the output is as expected. Thus, I assume there is some scoping issue that arises.
I have tried both assignment approaches:
myData = data.frame(a=id, b=Time, c=value)
myData <- data.frame(a=id, b=Time, c=value)
and both give me the same error. I admit that I don't fully understand the scope model in R (I've read about the differences between = and <- and I think I get it, but I'm not sure).
Any advice you can offer would be appreciated.
MMLCR is now deprecated and you should search for some alternatives. Without looking too much into it, I sleuthed through an old repo and found the culprit:
m <- eval(m, data)
in the function mmlcr.default. There are a lot of reasons why this is bad, but scoping is the big one. R has this issue with the subset.data.frame function, see my old SO question. Rather than modify the source code, I would find a way to do your function with a subroutine using a for, repeat, or while loop.