Related
I'm working on the C++ version of Matt Zucker's Page dewarping. So far everything works fine, but I have a problem with optimization. In line 748 of Github repo Matt uses optimize function from Scipy. My C++ equivalent is find_min_bobyqa from dlib.net. The code is:
auto f = [&](const column_vector& ppts) { return objective( dstpoints, ppts, keypoint_index); };
dlib::find_min_bobyqa(f,
params,
2 * params.nr() + 1, // npt - number of interpolation points: x.size() + 2 <= npt && npt <= (x.size()+1)*(x.size()+2)/2
dlib::uniform_matrix<double>(params.nr(), 1, -2), // lower bound constraint
dlib::uniform_matrix<double>(params.nr(), 1, 2), // upper bound constraint
1, // initial trust region radius
1e-5, // stopping trust region radius
4000 // max number of objective function evaluations
);
In my concrete example params is a dlib::column_vector with double values and length = 189. Every element of params is less than 2.0 and greater than -2.0. Function objective() returns double value and "alone" it works properly because I get the same value as in the Python version. But after running fin_min_bobyqa function I usually get the message:
Terminate called after throwing an instance of 'dlib:bobyqa_failure', return from BOBYQA because the objective function has been called max_f_evals times.
I set max_f_evals to quite big value to see if it optimizes at all, but it doesn't. I did some tweaking with parameters but without good results. How should I set the parameters of find_min_bobyqa to get the right solution?
I am very interested in this issue as well. Zucker's work, with very minor tweaks, is ideal for straightening sheet music images, and I was looking for ways to implement it in a mobile platform when I came across your question.
My research so far suggests that BOBYQA is not the equivalent of Powell's method in scipy. BOBYQA is constrained, and the one in scipy is not.
See these links for more information, and a possible way to compile the right supporting library - I would try UOBYQA or NEWUOA.
https://github.com/jacobwilliams/PowellOpt
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#rdd2e1855725e-3
(See the Notes section)
EDIT: see C version here:
https://github.com/emmt/Algorithms/tree/master/newuoa
I wanted to post this as a comment, but I don't have enough points for that.
I am very interested in your progress. If you're willing, please keep me posted.
I finally solved this problem. I used PRAXIS library, because it doesn't need derivative information and is fast.
I modified the code a little to my needs and now it is faster around few seconds than original version written in Python.
I got this statement in Swift code which produces an error when executing in playground:
let colors: [String: [Float]] = ["skyBlue" : [240.0/255.0, 248.0/255.0, 255.0/255.0,1.0],
"cWhite" : [250.0/255.0, 250.0/255.0, 250.0/255.0, 1.0]]
The error is : expression was too complex to be solved in reasonable time; consider breaking up the expression into distinct sub-expressions
Then I changed the arrays element type to Double which just works fine.
However I am asking myself why this happens ?
As I said using Double it works just fine. So my guess is that Swift tries to guess the type and therefore Double works better in this example than Float.
Similar issues have been reported before, and (as I understand it) the problem is the automatic type inference for "complicated" expressions. You should file a bug report at Apple.
It compiles with a dictionary of one color, but not with two.
In this concrete case, you can work around it by converting each number in the array to a Float explicitly:
let colors = [
"skyBlue" : [Float(240.0/255.0), Float(248.0/255.0), Float(255.0/255.0),Float(1.0)],
"cWhite" : [Float(250.0/255.0), Float(250.0/255.0), Float(250.0/255.0), Float(1.0)]
]
Please have a look at the following OpenCV code
Mat *curent;
current = new Mat();
cv::Rect bRect = cv::boundingRect(Mat(*points).reshape(2));
Mat roi = *current(bRect);
Here, I am trying to get a ROI to the Mat called roi. But whenever I try to get execute the last line of the above code I get the error term does not evaluate to a function taking 1 arguments. I have followed the same technique of getting an ROI without pointers number of times before in C++ and they worked. I guess the issue is with pointer current ? current must be a pointer because local variable slowed the application in an unbelievable way.
So, how can I solve this issue and get the ROI ?
please, throw out those pointers!
you're going to wreck havoc on the internal Mat refcounts, produce undefined behaviour and memleaks
"local variable slowed the application in an unbelievable way."
really, you think, copying a 58 byte struct is the reason ? i just don't believe you.
well i'll give you a hint, anyway - the ( ) operator has a higher precedence than the * operator.
I get an error when using an R function that I wrote:
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: algorithm did not converge
What I have done:
Step through the function
Adding print to find out at what line the error occurs suggests two functions that should not use glm.fit. They are window() and save().
My general approaches include adding print and stop commands, and stepping through a function line by line until I can locate the exception.
However, it is not clear to me using those techniques where this error comes from in the code. I am not even certain which functions within the code depend on glm.fit. How do I go about diagnosing this problem?
I'd say that debugging is an art form, so there's no clear silver bullet. There are good strategies for debugging in any language, and they apply here too (e.g. read this nice article). For instance, the first thing is to reproduce the problem...if you can't do that, then you need to get more information (e.g. with logging). Once you can reproduce it, you need to reduce it down to the source.
Rather than a "trick", I would say that I have a favorite debugging routine:
When an error occurs, the first thing that I usually do is look at the stack trace by calling traceback(): that shows you where the error occurred, which is especially useful if you have several nested functions.
Next I will set options(error=recover); this immediately switches into browser mode where the error occurs, so you can browse the workspace from there.
If I still don't have enough information, I usually use the debug() function and step through the script line by line.
The best new trick in R 2.10 (when working with script files) is to use the findLineNum() and setBreakpoint() functions.
As a final comment: depending upon the error, it is also very helpful to set try() or tryCatch() statements around external function calls (especially when dealing with S4 classes). That will sometimes provide even more information, and it also gives you more control over how errors are handled at run time.
These related questions have a lot of suggestions:
Debugging tools for the R language
Debugging lapply/sapply calls
Getting the state of variables after an error occurs in R
R script line numbers at error?
The best walkthrough I've seen so far is:
http://www.biostat.jhsph.edu/%7Erpeng/docs/R-debug-tools.pdf
Anybody agree/disagree?
As was pointed out to me in another question, Rprof() and summaryRprof() are nice tools to find slow parts of your program that might benefit from speeding up or moving to a C/C++ implementation. This probably applies more if you're doing simulation work or other compute- or data-intensive activities. The profr package can help visualizing the results.
I'm on a bit of a learn-about-debugging kick, so another suggestion from another thread:
Set options(warn=2) to treat warnings like errors
You can also use options to drop you right into the heat of the action when an error or warning occurs, using your favorite debugging function of choice. For instance:
Set options(error=recover) to run recover() when an error occurs, as Shane noted (and as is documented in the R debugging guide. Or any other handy function you would find useful to have run.
And another two methods from one of #Shane's links:
Wrap an inner function call with try() to return more information on it.
For *apply functions, use .inform=TRUE (from the plyr package) as an option to the apply command
#JoshuaUlrich also pointed out a neat way of using the conditional abilities of the classic browser() command to turn on/off debugging:
Put inside the function you might want to debug browser(expr=isTRUE(getOption("myDebug")))
And set the global option by options(myDebug=TRUE)
You could even wrap the browser call: myBrowse <- browser(expr=isTRUE(getOption("myDebug"))) and then call with myBrowse() since it uses globals.
Then there are the new functions available in R 2.10:
findLineNum() takes a source file name and line number and returns the function and environment. This seems to be helpful when you source() a .R file and it returns an error at line #n, but you need to know what function is located at line #n.
setBreakpoint() takes a source file name and line number and sets a breakpoint there
The codetools package, and particularly its checkUsage function can be particularly helpful in quickly picking up syntax and stylistic errors that a compiler would typically report (unused locals, undefined global functions and variables, partial argument matching, and so forth).
setBreakpoint() is a more user-friendly front-end to trace(). Details on the internals of how this works are available in a recent R Journal article.
If you are trying to debug someone else's package, once you have located the problem you can over-write their functions with fixInNamespace and assignInNamespace, but do not use this in production code.
None of this should preclude the tried-and-true standard R debugging tools, some of which are above and others of which are not. In particular, the post-mortem debugging tools are handy when you have a time-consuming bunch of code that you'd rather not re-run.
Finally, for tricky problems which don't seem to throw an error message, you can use options(error=dump.frames) as detailed in this question:
Error without an error being thrown
At some point, glm.fit is being called. That means one of the functions you call or one of the functions called by those functions is using either glm, glm.fit.
Also, as I mention in my comment above, that is a warning not an error, which makes a big difference. You can't trigger any of R's debugging tools from a warning (with default options before someone tells me I am wrong ;-).
If we change the options to turn warnings into errors then we can start to use R's debugging tools. From ?options we have:
‘warn’: sets the handling of warning messages. If ‘warn’ is
negative all warnings are ignored. If ‘warn’ is zero (the
default) warnings are stored until the top-level function
returns. If fewer than 10 warnings were signalled they will
be printed otherwise a message saying how many (max 50) were
signalled. An object called ‘last.warning’ is created and
can be printed through the function ‘warnings’. If ‘warn’ is
one, warnings are printed as they occur. If ‘warn’ is two or
larger all warnings are turned into errors.
So if you run
options(warn = 2)
then run your code, R will throw an error. At which point, you could run
traceback()
to see the call stack. Here is an example.
> options(warn = 2)
> foo <- function(x) bar(x + 2)
> bar <- function(y) warning("don't want to use 'y'!")
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
> traceback()
7: doWithOneRestart(return(expr), restart)
6: withOneRestart(expr, restarts[[1L]])
5: withRestarts({
.Internal(.signalCondition(simpleWarning(msg, call), msg,
call))
.Internal(.dfltWarn(msg, call))
}, muffleWarning = function() NULL)
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x +
2)))
3: warning("don't want to use 'y'!")
2: bar(x + 2)
1: foo(1)
Here you can ignore the frames marked 4: and higher. We see that foo called bar and that bar generated the warning. That should show you which functions were calling glm.fit.
If you now want to debug this, we can turn to another option to tell R to enter the debugger when it encounters an error, and as we have made warnings errors we will get a debugger when the original warning is triggered. For that you should run:
options(error = recover)
Here is an example:
> options(error = recover)
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
Enter a frame number, or 0 to exit
1: foo(1)
2: bar(x + 2)
3: warning("don't want to use 'y'!")
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x + 2)))
5: withRestarts({
6: withOneRestart(expr, restarts[[1]])
7: doWithOneRestart(return(expr), restart)
Selection:
You can then step into any of those frames to see what was happening when the warning was thrown.
To reset the above options to their default, enter
options(error = NULL, warn = 0)
As for the specific warning you quote, it is highly likely that you need to allow more iterations in the code. Once you've found out what is calling glm.fit, work out how to pass it the control argument using glm.control - see ?glm.control.
So browser(), traceback() and debug() walk into a bar, but trace() waits outside and keeps the motor running.
By inserting browser somewhere in your function, the execution will halt and wait for your input. You can move forward using n (or Enter), run the entire chunk (iteration) with c, finish the current loop/function with f, or quit with Q; see ?browser.
With debug, you get the same effect as with browser, but this stops the execution of a function at its beginning. Same shortcuts apply. This function will be in a "debug" mode until you turn it off using undebug (that is, after debug(foo), running the function foo will enter "debug" mode every time until you run undebug(foo)).
A more transient alternative is debugonce, which will remove the "debug" mode from the function after the next time it's evaluated.
traceback will give you the flow of execution of functions all the way up to where something went wrong (an actual error).
You can insert code bits (i.e. custom functions) in functions using trace, for example browser. This is useful for functions from packages and you're too lazy to get the nicely folded source code.
My general strategy looks like:
Run traceback() to see look for obvious issues
Set options(warn=2) to treat warnings like errors
Set options(error=recover) to step into the call stack on error
After going through all the steps suggested here I just learned that setting .verbose = TRUE in foreach() also gives me tons of useful information. In particular foreach(.verbose=TRUE) shows exactly where an error occurs inside the foreach loop, while traceback() does not look inside the foreach loop.
Mark Bravington's debugger which is available as the package debug on CRAN is very good and pretty straight forward.
library(debug);
mtrace(myfunction);
myfunction(a,b);
#... debugging, can query objects, step, skip, run, breakpoints etc..
qqq(); # quit the debugger only
mtrace.off(); # turn off debugging
The code pops up in a highlighted Tk window so you can see what's going on and, of course you can call another mtrace() while in a different function.
HTH
I like Gavin's answer: I did not know about options(error = recover). I also like to use the 'debug' package that gives a visual way to step through your code.
require(debug)
mtrace(foo)
foo(1)
At this point it opens up a separate debug window showing your function, with a yellow line showing where you are in the code. In the main window the code enters debug mode, and you can keep hitting enter to step through the code (and there are other commands as well), and examine variable values, etc. The yellow line in the debug window keeps moving to show where you are in the code. When done with debugging, you can turn off tracing with:
mtrace.off()
Based on the answer I received here, you should definitely check out the options(error=recover) setting. When this is set, upon encountering an error, you'll see text on the console similar to the following (traceback output):
> source(<my filename>)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
Enter a frame number, or 0 to exit
1: source(<my filename>)
2: eval.with.vis(ei, envir)
3: eval.with.vis(expr, envir, enclos)
4: LinearParamSearch(data = dataset, y = data.frame(LGD = dataset$LGD10), data.names = data
5: LinearParamSearch.R#66: plot(x = x, y = y.data, xlab = names(y), ylab = data.names[i])
6: LinearParamSearch.R#66: plot.default(x = x, y = y.data, xlab = names(y), ylab = data.nam
7: LinearParamSearch.R#66: localWindow(xlim, ylim, log, asp, ...)
8: LinearParamSearch.R#66: plot.window(...)
Selection:
At which point you can choose which "frame" to enter. When you make a selection, you'll be placed into browser() mode:
Selection: 4
Called from: stop(gettextf("replacement has %d rows, data has %d", N, n),
domain = NA)
Browse[1]>
And you can examine the environment as it was at the time of the error. When you're done, type c to bring you back to the frame selection menu. When you're done, as it tells you, type 0 to exit.
I gave this answer to a more recent question, but am adding it here for completeness.
Personally I tend not to use functions to debug. I often find that this causes as much trouble as it solves. Also, coming from a Matlab background I like being able to do this in an integrated development environment (IDE) rather than doing this in the code. Using an IDE keeps your code clean and simple.
For R, I use an IDE called "RStudio" (http://www.rstudio.com), which is available for windows, mac, and linux and is pretty easy to use.
Versions of Rstudio since about October 2013 (0.98ish?) have the capability to add breakpoints in scripts and functions: to do this, just click on the left margin of the file to add a breakpoint. You can set a breakpoint and then step through from that point on. You also have access to all of the data in that environment, so you can try out commands.
See http://www.rstudio.com/ide/docs/debugging/overview for details. If you already have Rstudio installed, you may need to upgrade - this is a relatively new (late 2013) feature.
You may also find other IDEs that have similar functionality.
Admittedly, if it's a built-in function you may have to resort to some of the suggestions made by other people in this discussion. But, if it's your own code that needs fixing, an IDE-based solution might be just what you need.
To debug Reference Class methods without instance reference
ClassName$trace(methodName, browser)
I am beginning to think that not printing error line number - a most basic requirement - BY DEFAILT- is some kind of a joke in R/Rstudio. The only reliable method I have found to find where an error occurred is to make the additional effort of calloing traceback() and see the top line.
I get an error when using an R function that I wrote:
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: algorithm did not converge
What I have done:
Step through the function
Adding print to find out at what line the error occurs suggests two functions that should not use glm.fit. They are window() and save().
My general approaches include adding print and stop commands, and stepping through a function line by line until I can locate the exception.
However, it is not clear to me using those techniques where this error comes from in the code. I am not even certain which functions within the code depend on glm.fit. How do I go about diagnosing this problem?
I'd say that debugging is an art form, so there's no clear silver bullet. There are good strategies for debugging in any language, and they apply here too (e.g. read this nice article). For instance, the first thing is to reproduce the problem...if you can't do that, then you need to get more information (e.g. with logging). Once you can reproduce it, you need to reduce it down to the source.
Rather than a "trick", I would say that I have a favorite debugging routine:
When an error occurs, the first thing that I usually do is look at the stack trace by calling traceback(): that shows you where the error occurred, which is especially useful if you have several nested functions.
Next I will set options(error=recover); this immediately switches into browser mode where the error occurs, so you can browse the workspace from there.
If I still don't have enough information, I usually use the debug() function and step through the script line by line.
The best new trick in R 2.10 (when working with script files) is to use the findLineNum() and setBreakpoint() functions.
As a final comment: depending upon the error, it is also very helpful to set try() or tryCatch() statements around external function calls (especially when dealing with S4 classes). That will sometimes provide even more information, and it also gives you more control over how errors are handled at run time.
These related questions have a lot of suggestions:
Debugging tools for the R language
Debugging lapply/sapply calls
Getting the state of variables after an error occurs in R
R script line numbers at error?
The best walkthrough I've seen so far is:
http://www.biostat.jhsph.edu/%7Erpeng/docs/R-debug-tools.pdf
Anybody agree/disagree?
As was pointed out to me in another question, Rprof() and summaryRprof() are nice tools to find slow parts of your program that might benefit from speeding up or moving to a C/C++ implementation. This probably applies more if you're doing simulation work or other compute- or data-intensive activities. The profr package can help visualizing the results.
I'm on a bit of a learn-about-debugging kick, so another suggestion from another thread:
Set options(warn=2) to treat warnings like errors
You can also use options to drop you right into the heat of the action when an error or warning occurs, using your favorite debugging function of choice. For instance:
Set options(error=recover) to run recover() when an error occurs, as Shane noted (and as is documented in the R debugging guide. Or any other handy function you would find useful to have run.
And another two methods from one of #Shane's links:
Wrap an inner function call with try() to return more information on it.
For *apply functions, use .inform=TRUE (from the plyr package) as an option to the apply command
#JoshuaUlrich also pointed out a neat way of using the conditional abilities of the classic browser() command to turn on/off debugging:
Put inside the function you might want to debug browser(expr=isTRUE(getOption("myDebug")))
And set the global option by options(myDebug=TRUE)
You could even wrap the browser call: myBrowse <- browser(expr=isTRUE(getOption("myDebug"))) and then call with myBrowse() since it uses globals.
Then there are the new functions available in R 2.10:
findLineNum() takes a source file name and line number and returns the function and environment. This seems to be helpful when you source() a .R file and it returns an error at line #n, but you need to know what function is located at line #n.
setBreakpoint() takes a source file name and line number and sets a breakpoint there
The codetools package, and particularly its checkUsage function can be particularly helpful in quickly picking up syntax and stylistic errors that a compiler would typically report (unused locals, undefined global functions and variables, partial argument matching, and so forth).
setBreakpoint() is a more user-friendly front-end to trace(). Details on the internals of how this works are available in a recent R Journal article.
If you are trying to debug someone else's package, once you have located the problem you can over-write their functions with fixInNamespace and assignInNamespace, but do not use this in production code.
None of this should preclude the tried-and-true standard R debugging tools, some of which are above and others of which are not. In particular, the post-mortem debugging tools are handy when you have a time-consuming bunch of code that you'd rather not re-run.
Finally, for tricky problems which don't seem to throw an error message, you can use options(error=dump.frames) as detailed in this question:
Error without an error being thrown
At some point, glm.fit is being called. That means one of the functions you call or one of the functions called by those functions is using either glm, glm.fit.
Also, as I mention in my comment above, that is a warning not an error, which makes a big difference. You can't trigger any of R's debugging tools from a warning (with default options before someone tells me I am wrong ;-).
If we change the options to turn warnings into errors then we can start to use R's debugging tools. From ?options we have:
‘warn’: sets the handling of warning messages. If ‘warn’ is
negative all warnings are ignored. If ‘warn’ is zero (the
default) warnings are stored until the top-level function
returns. If fewer than 10 warnings were signalled they will
be printed otherwise a message saying how many (max 50) were
signalled. An object called ‘last.warning’ is created and
can be printed through the function ‘warnings’. If ‘warn’ is
one, warnings are printed as they occur. If ‘warn’ is two or
larger all warnings are turned into errors.
So if you run
options(warn = 2)
then run your code, R will throw an error. At which point, you could run
traceback()
to see the call stack. Here is an example.
> options(warn = 2)
> foo <- function(x) bar(x + 2)
> bar <- function(y) warning("don't want to use 'y'!")
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
> traceback()
7: doWithOneRestart(return(expr), restart)
6: withOneRestart(expr, restarts[[1L]])
5: withRestarts({
.Internal(.signalCondition(simpleWarning(msg, call), msg,
call))
.Internal(.dfltWarn(msg, call))
}, muffleWarning = function() NULL)
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x +
2)))
3: warning("don't want to use 'y'!")
2: bar(x + 2)
1: foo(1)
Here you can ignore the frames marked 4: and higher. We see that foo called bar and that bar generated the warning. That should show you which functions were calling glm.fit.
If you now want to debug this, we can turn to another option to tell R to enter the debugger when it encounters an error, and as we have made warnings errors we will get a debugger when the original warning is triggered. For that you should run:
options(error = recover)
Here is an example:
> options(error = recover)
> foo(1)
Error in bar(x + 2) : (converted from warning) don't want to use 'y'!
Enter a frame number, or 0 to exit
1: foo(1)
2: bar(x + 2)
3: warning("don't want to use 'y'!")
4: .signalSimpleWarning("don't want to use 'y'!", quote(bar(x + 2)))
5: withRestarts({
6: withOneRestart(expr, restarts[[1]])
7: doWithOneRestart(return(expr), restart)
Selection:
You can then step into any of those frames to see what was happening when the warning was thrown.
To reset the above options to their default, enter
options(error = NULL, warn = 0)
As for the specific warning you quote, it is highly likely that you need to allow more iterations in the code. Once you've found out what is calling glm.fit, work out how to pass it the control argument using glm.control - see ?glm.control.
So browser(), traceback() and debug() walk into a bar, but trace() waits outside and keeps the motor running.
By inserting browser somewhere in your function, the execution will halt and wait for your input. You can move forward using n (or Enter), run the entire chunk (iteration) with c, finish the current loop/function with f, or quit with Q; see ?browser.
With debug, you get the same effect as with browser, but this stops the execution of a function at its beginning. Same shortcuts apply. This function will be in a "debug" mode until you turn it off using undebug (that is, after debug(foo), running the function foo will enter "debug" mode every time until you run undebug(foo)).
A more transient alternative is debugonce, which will remove the "debug" mode from the function after the next time it's evaluated.
traceback will give you the flow of execution of functions all the way up to where something went wrong (an actual error).
You can insert code bits (i.e. custom functions) in functions using trace, for example browser. This is useful for functions from packages and you're too lazy to get the nicely folded source code.
My general strategy looks like:
Run traceback() to see look for obvious issues
Set options(warn=2) to treat warnings like errors
Set options(error=recover) to step into the call stack on error
After going through all the steps suggested here I just learned that setting .verbose = TRUE in foreach() also gives me tons of useful information. In particular foreach(.verbose=TRUE) shows exactly where an error occurs inside the foreach loop, while traceback() does not look inside the foreach loop.
Mark Bravington's debugger which is available as the package debug on CRAN is very good and pretty straight forward.
library(debug);
mtrace(myfunction);
myfunction(a,b);
#... debugging, can query objects, step, skip, run, breakpoints etc..
qqq(); # quit the debugger only
mtrace.off(); # turn off debugging
The code pops up in a highlighted Tk window so you can see what's going on and, of course you can call another mtrace() while in a different function.
HTH
I like Gavin's answer: I did not know about options(error = recover). I also like to use the 'debug' package that gives a visual way to step through your code.
require(debug)
mtrace(foo)
foo(1)
At this point it opens up a separate debug window showing your function, with a yellow line showing where you are in the code. In the main window the code enters debug mode, and you can keep hitting enter to step through the code (and there are other commands as well), and examine variable values, etc. The yellow line in the debug window keeps moving to show where you are in the code. When done with debugging, you can turn off tracing with:
mtrace.off()
Based on the answer I received here, you should definitely check out the options(error=recover) setting. When this is set, upon encountering an error, you'll see text on the console similar to the following (traceback output):
> source(<my filename>)
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(x) : no non-missing arguments to max; returning -Inf
Enter a frame number, or 0 to exit
1: source(<my filename>)
2: eval.with.vis(ei, envir)
3: eval.with.vis(expr, envir, enclos)
4: LinearParamSearch(data = dataset, y = data.frame(LGD = dataset$LGD10), data.names = data
5: LinearParamSearch.R#66: plot(x = x, y = y.data, xlab = names(y), ylab = data.names[i])
6: LinearParamSearch.R#66: plot.default(x = x, y = y.data, xlab = names(y), ylab = data.nam
7: LinearParamSearch.R#66: localWindow(xlim, ylim, log, asp, ...)
8: LinearParamSearch.R#66: plot.window(...)
Selection:
At which point you can choose which "frame" to enter. When you make a selection, you'll be placed into browser() mode:
Selection: 4
Called from: stop(gettextf("replacement has %d rows, data has %d", N, n),
domain = NA)
Browse[1]>
And you can examine the environment as it was at the time of the error. When you're done, type c to bring you back to the frame selection menu. When you're done, as it tells you, type 0 to exit.
I gave this answer to a more recent question, but am adding it here for completeness.
Personally I tend not to use functions to debug. I often find that this causes as much trouble as it solves. Also, coming from a Matlab background I like being able to do this in an integrated development environment (IDE) rather than doing this in the code. Using an IDE keeps your code clean and simple.
For R, I use an IDE called "RStudio" (http://www.rstudio.com), which is available for windows, mac, and linux and is pretty easy to use.
Versions of Rstudio since about October 2013 (0.98ish?) have the capability to add breakpoints in scripts and functions: to do this, just click on the left margin of the file to add a breakpoint. You can set a breakpoint and then step through from that point on. You also have access to all of the data in that environment, so you can try out commands.
See http://www.rstudio.com/ide/docs/debugging/overview for details. If you already have Rstudio installed, you may need to upgrade - this is a relatively new (late 2013) feature.
You may also find other IDEs that have similar functionality.
Admittedly, if it's a built-in function you may have to resort to some of the suggestions made by other people in this discussion. But, if it's your own code that needs fixing, an IDE-based solution might be just what you need.
To debug Reference Class methods without instance reference
ClassName$trace(methodName, browser)
I am beginning to think that not printing error line number - a most basic requirement - BY DEFAILT- is some kind of a joke in R/Rstudio. The only reliable method I have found to find where an error occurred is to make the additional effort of calloing traceback() and see the top line.