R error in inheritsx data.frame

r error in inheritsx data.frame

Communicating these problems to the user is the job of conditions: errors, to file last.dump.rda dump.frames(to.file = TRUE) # Quit R with error status. CRAN packages with unused arguments error That is DriveML SmartEDA anomalize bayestestR broom effectsize heemod isoreader mlrCPO panelr. If X is not an array but an object of a class with a non-null dim value (such as a data frame), apply attempts to coerce it to an array via as.matrix if it. r error in inheritsx data.frame

Have hit: R error in inheritsx data.frame

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Creating functions

Once you are past the basics, you should be looking at creating functions for your common tasks. Why create functions?

  • Ensure scoping of variables in controlled
  • More efficient memory management
  • Good names
  • Clear, concise code
  • Reproducability
  • First step to putting them in a package for documentation

A good rule of thumb is if you are performing the same task more than twice, look to create a function to do it.

A good name

The “good names” point is more powerful than it sounds - you will start to create building blocks for yourself that can in themselves be built upon.

For example, here is an example of using some functions you may create to download Google Analytics data and upload it asus chassis intrusion fatal error your own private database:

With good naming, commenting code becomes (almost) unnecessary.

You may later then decide to generalise error while loading shared libraries ./libsdl-1.2.so.0 r error in inheritsx data.frame for any viewId:

Now using you can download and email several Google Analytics downloads with a couple of lines of R:

As you abstract away inner functions, higher level thinking is encouraged, building on your past successes.

Starting with functions

As ever, Hadley Wickham’s Advanced R book is my prime reference for R concepts, in particular its chapter on functions.

When writing a function, you are actually asigning a new object type to a variable, just as you would for say for other data. However, once assigned a function, you can then get that function to operate on other objects by using afterwards:

R has some shortcuts that lets you not need to specify the name of the argument if r error in inheritsx data.frame the first one positionally, so the above call can also be written:

You can also define functions with no arguments:

Scoping

R has some unusual scoping behaviour compared to other languages. Variables you declare outside of functions can effect interior functions if left to defaults. This can cause confusing errors so its worth highlighting:

This is linked to the environments concept with R, and lexical scoping. This is an advanced topic, r error in inheritsx data.frame, but briefly its important to know that R will evaluate in the context of the function, but if it doesn’t find a particular object it will look in the parent frame all the way up to a global. As such you should keep an eye on what is in your global environment vs within a function, as you may forget to define a variable in a function that R then looks for elsewhere, which can be very confusing when debugging.

Ellipses …

Often functions are calling other functions within them. If you have a inner function that relies on arguments from the function above, you could laboriously copy all the argumets down to the function that needs them:

…or, you can use the construct which will copy the arguments for you:

Exercise - creating a simple function

Create a function that takes a numeric vector and prints out the max, min, mean and median. You can use to print out to the console. Here is a starting template:

Getting some inspiration

Once you have the basics, the best way to learn is to examine what others are doing.

Every function you use in R has its code available if you issue the function name with no brackets e.g.

However, some are easier than others. Some functions, including many that are fundamental to R in its base package, are written in C and are called and the R function simply calls that underlying code. These functions won’t return much of use:

But a lot of R functions are available on GitHub which is rapidly becoming an R standard practice. All of Hadley Wickham’s packages have their functions available for example - find them on GitHub and look within the folder (we look more into navigating R package structure later)

R Methods

Now you are looking at R code, once thing worth mentioning are R. If you have some programming expereince you may be familiar with the concept of object orientated programming. R has several implementations of this, but the most popular is the method that we touch on briefly today.

In brief you may see some references to in some code, r error in inheritsx data.frame. This acts as a signpost that decides what actual code to run against the passed in object judged on its (remember those?). For instance, the same function could act different if you pass it an object of class or of class .

The is r error in inheritsx data.frame signpost, but where is the destination? looks for functions that have the same name as the original function, but with a suffix.

e.g.

This offers several advantages for clean code, especially if you are used to this style of programming in other languages, but won’t be covered today. See here for more details if you are keen.

Defensive programming

There are some good habits I have developed over time that mitigate against too many bugs in your code.

The basic premise is you want to know as soon as possible what is wrong, and print an informative error message so you know whats up. A somewhat valid criticisim of R is that the R error messages are obscure. By creating your own, you can do something to help yourself and users of your functions r error in inheritsx data.frame whats wrong.

Try to avoid names that R already uses

One big gotcha with R is you can assign names to r error in inheritsx data.frame, including names already used by base functions. This means you can do evil things like this:

Whilst an extreme example, more common are variable such as or which will happily accept your assignment, then throw an obscure error when you forget to assign them later on. Best is to avoid using these names altogetehr unless you really mean to.

stop() - errors are good! (if you control them)

A key element for this is the user of the function. This as it says stops the function and will print out an error message of your choosing:

A short cut for this common task () is the function, although you can’t set a custom error message:

As always, Hadley has an alternative that gives better error messages than -

You can also write your own checks with error messages, like these examples:

Errors that fail as soon as something is wrong means you will be closer to where the problem occured when debugging.

try() and tryCatch()

Sometimes you don’t want to stop the program, but rather do something else if an error is detected.

A good use case for this is when fetching from an API, as you can’t always guarantee the API will return what you expect. Wrapping php $this view error call in a command means that instead of an error you will get an object of class. You can then test for this and react accordingly.

also adds some missing checks that can be useful, such as that you can use with aotherwise you can use the more verbose in base R

Checking arguments

Armed with the above, good habits will be to always check that the inputs (and outputs if you like) are exactly what you expect. Since the majority of R errors are caused by unexpected types, this should help you mitigate against weird bugs.

As standard now, I always look to check the inputs at the beginning of a created function, and give an error or warning if its not expected.

Another tool for this is the function which lets you limit the choices an argument can have to a vector of error only one processor found you provide. An example on how it is used it below:

Debugging tips

With good defensive programming techniques, you an be more sure that the functions are getting the data you expect, but you will still probably need to debug as you go. Getting a quick, iterative process to this is key as unfortunetly the time split is usually 90% of the code programmed in 20% of the time (this is the fun bit) with the remaining 80% of the time debugging 10% of your code.

For speed of delivery of useful programs, getting this debugging time down is key.

Below are some tips to help with this:

  • Use version control such as Github (so you can check what changed) - when you have a working version, commit.
  • Use to examine the state of a function where its going wrong - use RStudio’s breakpoints or insert the line where you want the program to stop. You can then check variables in the environment of the function using RStudio’s Environment pane, try executing lines to replicate errors, etc.
  • Insert or commands to print out what arguments are, to see if they are as you expect. Comment them out again afterwards as needed, although sometimes its nice to leave dbntsrv.exe error shutting down service in for user feedback.

Exercise in writing good errors

Rewrite the function below so it also gives custom errors:

  • if the is not in the :
  • if the is not in the :

Compare with base R, r error in inheritsx data.frame, where you instead get three different classes of results (an error, s, and )

Debugging, condition handling, and defensive programming

You’re reading the first edition of Advanced R; for the latest on this topic, see the Conditions and Debugging chapters in the second edition.

What happens when something goes wrong with your R code? What do you do? What tools do you have to address the problem? This chapter will teach you how to fix unanticipated problems (debugging), show you how functions can communicate problems and how you can take action based on those communications (condition handling), and teach you how to avoid common problems before they occur (defensive programming).

Debugging is the art and science of fixing unexpected problems in your code. In this section you’ll learn the tools and techniques that help you get to the root cause of an error. You’ll learn general strategies for debugging, useful R functions like andand interactive tools in RStudio.

Not all problems are unexpected. When writing a function, you can often anticipate potential problems (like a non-existent file or the wrong type of input). Communicating these problems to the user is the job of conditions: errors, warnings, and messages.

  • Fatal errors are raised by and force all execution to terminate. Errors are used when there is no way for a function to continue.

  • Warnings are generated by and are used to display potential problems, such as when some elements of a vectorised input are invalid, like .

  • Messages are generated by and are used to give informative output in a way that can easily be suppressed by the user (). I often use messages to let the user know what value the function has chosen for an important missing argument.

Conditions are usually displayed prominently, in a bold font or coloured red depending on your R interface. You can tell them apart because errors always start with “Error” and warnings with “Warning message”. Function authors can also communicate with their users with orr error in inheritsx data.frame, but I think that’s a bad idea because it’s hard to capture and selectively ignore this sort of output. Printed output is not a condition, so you can’t use any of the useful condition handling tools you’ll learn about below.

Condition handling tools, like, and allow you to take specific actions when a condition occurs. For example, if you’re fitting many models, you might want to continue fitting the others even if one fails to converge. R offers an exceptionally powerful condition handling system based on ideas from Common Lisp, but it’s currently not very well documented or often used. This chapter will introduce you to the most important basics, but if you want to learn more, I recommend the following two sources:

The chapter concludes with a discussion of “defensive” programming: ways to avoid common errors before they occur. In the short run you’ll spend more time writing r error in inheritsx data.frame, but in the long run you’ll save time because error messages will be more informative and will let you narrow in on the root cause more quickly. The basic principle of defensive programming is to “fail fast”, to raise an error as soon as something goes wrong. In R, this takes three particular forms: checking that inputs are correct, avoiding non-standard evaluation, and avoiding functions that can return different types of output.

Quiz

Want to skip this chapter? Go for it, if you can answer the questions below. Find the answers at the end of the chapter in answers.

  1. How can you find out where an error occurred?

  2. What does do? List the five useful single-key commands that you can use inside of a environment.

  3. What function do you use to ignore errors in block of code?

  4. Why might you want to create an error with a custom S3 class?

Outline
  1. Debugging techniques outlines a general approach for finding and resolving bugs.

  2. Debugging tools introduces you to the R functions and RStudio features that help you locate exactly where an error scp lost connection error handling shows you how you can catch conditions (errors, warnings, and messages) in your own code. This allows you to create code that’s both more robust and more informative in the presence of errors.

  3. Defensive programming introduces you to some important techniques for defensive programming, techniques that help prevent redefinition class error from occurring in the first place.

Debugging techniques

“Finding your bug is a process of confirming the many things that you believe are true — until you find one which is not true.”

—Norm Matloff

Debugging code is challenging. Many bugs are subtle and hard to find. Indeed, if a bug was obvious, you probably would’ve been able to avoid it in the first place, r error in inheritsx data.frame. While it’s true that with a good technique, you can productively debug a android torrent error with justthere are times when additional help would be welcome. In this section, we’ll discuss some useful tools, which R and RStudio provide, and outline a r error in inheritsx data.frame procedure for debugging.

While the procedure below is by no means foolproof, r error in inheritsx data.frame, it will hopefully help you to organise your thoughts when debugging. There are four steps:

  1. Realise that you have a bug

    If you’re reading this chapter, you’ve probably already completed this step. It is a surprisingly r error in inheritsx data.frame one: you can’t fix a bug until you know it exists. This is one reason why automated test suites are important when producing high-quality code. Unfortunately, automated testing is outside the scope of this book, but you can read more about it at http://r-pkgs.had.co.nz/tests.html.

  2. Make it repeatable

    Once you’ve determined you have a bug, you need to be able to reproduce it on command. Without this, it becomes extremely difficult to isolate its cause and to confirm that you’ve successfully fixed it.

    Generally, you will start with a big block of code that you unable to install kb957789 + error 57e causes the error and then slowly whittle it down to get to the smallest possible snippet that still causes the error, r error in inheritsx data.frame. Binary search is particularly useful for this. To do a binary search, you repeatedly remove half of r error in inheritsx data.frame code until you find the bug. This is fast because, with each step, you reduce the amount r error in inheritsx data.frame code to look through by half.

    If it takes a long time to generate the bug, it’s also worthwhile to figure out how to generate it faster. The quicker you can do this, the quicker you can figure out the cause.

    As you work on creating a minimal example, you’ll also discover similar inputs that don’t trigger the bug. Make note of them: they will be helpful when diagnosing the cause of the bug.

    If you’re using automated testing, this is also a good time to create an automated test case. If your existing test coverage is low, take the opportunity to add some nearby tests to ensure that existing good behaviour is preserved. This reduces the chances of creating a new bug.

  3. Figure out where it is

    If you’re lucky, one of the tools in the following section will help you to quickly identify the line of code that’s causing the bug. Usually, however, you’ll have to think a bit more about the problem. It’s a great idea to adopt the scientific method. Generate hypotheses, design experiments to test them, and record your results. This may seem like a lot of work, but a systematic approach will end up saving you time. I often waste a lot of time relying on my intuition to solve a bug (“oh, it must be an off-by-one error, so I’ll just subtract 1 here”), when I would have been better off taking a systematic approach.

  4. Fix it and test it

    Once you’ve found the bug, you need to figure out how to fix it and to check that the fix actually worked. Again, it’s very useful to have automated tests in place. Not only does this help to ensure that you’ve actually fixed the bug, it also helps to ensure you haven’t introduced any new bugs in the process. In the absence of automated tests, make sure to carefully record the correct output, and check against the inputs that previously failed.

Debugging tools

To implement a strategy of debugging, you’ll need tools. In this section, you’ll learn about the tools provided by R and the RStudio IDE. RStudio’s integrated debugging support makes life easier by exposing existing R tools in a user friendly way. I’ll show you both the R and RStudio ways so that you can work with whatever environment you use. You may also want to refer to the official RStudio debugging documentation which always reflects the tools in the latest version of RStudio.

There are three key debugging tools:

  • RStudio’s error inspector and which list the sequence of calls that lead to the error.

  • RStudio’s “Rerun with Debug” tool and which open an interactive session where the error occurred.

  • RStudio’s breakpoints and which open an interactive session at an arbitrary location in the code.

I’ll explain each tool in more detail below.

You shouldn’t need to use these tools when writing new functions. If you find yourself using them frequently with new code, you may want to reconsider your approach. Instead of trying to write one big function all at once, work interactively on small pieces. If you start small, r error in inheritsx data.frame, you can quickly identify why something doesn’t work. But if you start large, you may end up struggling to identify the source of the problem.

Determining the sequence of calls

The first tool is the call stack, the sequence of calls that lead up to an error. Here’s a simple example: you can see that calls calls calls which adds together a number and a string creating a error:

When we run this code in RStudio we see:

Two options appear to the right of the error message: “Show Traceback” and “Rerun with Debug”. If you click “Show traceback” you see:

If you’re not using RStudio, you can use to get the same information:

Read the call stack from bottom to top: the initial call iswhich callsthenthenwhich triggers the error. If you’re calling code that you d into R, the traceback will also display the location of the function, in the form. These are clickable in RStudio, and will r error in inheritsx data.frame you to the corresponding line of code in the editor.

Sometimes this is enough information to let you track down the error and fix it. However, it’s usually not. shows you where the error occurred, but not why. The next useful tool is the interactive debugger, which allows you to pause execution of a function and interactively explore its state.

Browsing on error

The easiest way to enter the interactive debugger is through RStudio’s “Rerun with Debug” tool. This reruns the command that created the error, pausing execution where the error occurred. You’re now in an interactive state inside the function, and you can interact with any object defined there. You’ll see the corresponding code in the editor (with the statement that will be run next highlighted), objects in the current environment in the “Environment” pane, the call stack in a “Traceback” pane, and you can run arbitrary R code in the console.

As well as any regular R function, there are a few special commands you can use in debug mode. You can access them either with the RStudio toolbar () or with the keyboard:

  • Next, : executes the next step in the function. Be careful if you have a variable named ; to print it you’ll need to do .

  • Step into, or : works like next, but if the next step is a function, it will step into that function so you can work through each line.

  • Finish, or : finishes execution of the current loop or function.

  • Continue, : leaves interactive debugging and continues regular execution of the function. This is r error in inheritsx data.frame if you’ve fixed the bad state and want to check that the function proceeds correctly.

  • Stop, : stops debugging, r error in inheritsx data.frame, terminates the function, and returns to the global workspace. Use this once you’ve figured out where the problem is, and you’re ready to fix it and reload the code.

There are two other slightly less useful commands that aren’t available in the toolbar:

  • Enter: repeats the previous command. I find this too easy to activate accidentally, so I turn it off using .

  • : prints stack trace of active calls (the interactive equivalent of ).

To enter this style of debugging outside of RStudio, you can use the option which specifies a function to run when an remote insight board interface error hp occurs. The function most similar to Rstudio’s debug is : this will start an interactive console in the environment where the error occurred. Use to turn it on, re-run the previous command, then use to return to the default error behaviour. You could automate this with the function as defined below:

(You’ll learn more about functions that return functions in Functional programming.)

There are two other useful functions that you can use with the option:

  • is a step up fromas it allows you to enter the environment of any of the calls in the call stack. This is useful because often the root cause of the error is a number of calls back.

  • is an equivalent to for non-interactive code. It creates a file in the current working directory. Then, r error in inheritsx data.frame, in a r error in inheritsx data.frame interactive R session, you load that file, and use to enter an r error in inheritsx data.frame debugger r error in inheritsx data.frame the same interface as. This allows interactive debugging of batch code.

To reset error behaviour to the default, use. Then errors will print a message and abort function execution.

Browsing arbitrary code

As well as entering an interactive console on error, you can enter it at an arbitrary code location by using either an Rstudio breakpoint or. You can set a breakpoint in Rstudio by clicking to the left of the line number, or pressing. Equivalently, add where you want execution to pause. Breakpoints behave similarly to but they are easier to set (one click instead of nine key presses), and you don’t run the risk of accidentally including a statement in your source code. There are two small downsides to breakpoints:

  • There are a few unusual situations in which breakpoints will not work: read breakpoint troubleshooting for more details.

  • RStudio currently does not support conditional breakpoints, whereas you can always put inside an statement.

As well as adding yourself, there are two other functions that will add it to code:

  • inserts a browser statement in the first line of the specified function. removes it. Alternatively, you can use to browse only on the next run.

  • works similarly, but instead of taking a function name, it takes a file name and line number and finds the appropriate function for you.

These two functions are both special cases ofwhich inserts arbitrary code at any position in an existing function. is occasionally useful when you’re debugging code that you don’t have the source for. To remove tracing from a error in /sdcard/update.zipstatus 6, use. You can only perform one trace per function, but that one trace can call multiple functions.

The call stack:, r error in inheritsx data.frame, and

Unfortunately the call stacks printed by +r error in inheritsx data.frame, and are not consistent. The following table shows how the call stacks from a simple nested set of calls are displayed by the three tools.

Note that numbering is different between andand that displays calls in the opposite order, and omits the call to. RStudio displays calls in the same order as but omits the numbers.

Other types of failure

There are other ways for a function to fail apart from throwing an error or returning an incorrect result.

  • A function may generate an unexpected warning. The easiest way to track down warnings is to convert them into errors with and use the regular debugging tools, r error in inheritsx data.frame. When you do this you’ll see some extra calls in the call stack, like,and. Ignore these: they are internal functions used to turn warnings into errors.

  • A function may generate an unexpected message. There’s no built-in tool to help solve this problem, but it’s possible to create one:

    As with warnings, you’ll need to ignore some of the calls on the traceback (i.e., the first two and the last seven).

  • A function might never return. This is particularly hard to debug automatically, but sometimes terminating the function and looking at the call stack is informative. Otherwise, use the basic debugging strategies described above.

  • The worst scenario is that your code might crash R completely, leaving you with no way to interactively debug your code. This indicates r error in inheritsx data.frame bug in the underlying C code. This is hard to debug, r error in inheritsx data.frame. Sometimes an interactive debugger, likecan be useful, but describing how to use it is beyond the scope of this book.

    If the crash is caused by base R code, post a reproducible example to R-help. If sub process /usr/bin/dpkg returned an error code in a package, contact the package maintainer. If it’s your own C or C++ code, you’ll need to use numerous statements to narrow down the location of the bug, and then you’ll need to use many more print statements to figure out which data structure doesn’t have the properties that you expect.

Condition handling

Unexpected errors require interactive debugging to figure out what went wrong. Some errors, however, are fatal error uncaught exception smartycompilerexception, and you want to handle them automatically. In R, expected errors crop up most frequently when you’re fitting many models to different datasets, such as bootstrap replicates. Sometimes the model might fail to fit and throw an error, but you don’t want to stop everything. Instead, you want to fit pelicula completa de terror many models as possible and then perform diagnostics after the fact.

In R, there are three tools for handling conditions (including errors) programmatically:

  • gives you the ability to continue execution even when an error occurs.

  • lets you specify handler functions that control what happens when a condition is signalled.

  • is a variant of that establishes local handlers, whereas registers exiting handlers. Local handlers are called in the same context as where the condition is signalled, without interrupting the execution of the function. When a exiting handler from is called, the execution of the function is interrupted and the handler is called. is rarely needed, but is useful to be aware of.

The following sections describe these tools in more detail.

Ignore errors with try

allows execution to continue even after an error has occurred. For example, normally if you run a function that throws an error, it terminates immediately and doesn’t return a value:

However, if you wrap the statement that creates the error inthe error message will be printed but execution will continue:

You can suppress the message with .

To pass larger blocks of code towrap them in :

You can also capture the output of the function. If successful, it will be the last result evaluated in r error in inheritsx data.frame block (just like a function). If unsuccessful it will be an (invisible) object of r error in inheritsx data.frame “try-error”:

is particularly useful when you’re applying a function to multiple elements in a list:

There isn’t a built-in function to test for the try-error class, so we’ll define one. Then you can easily find the locations of errors with (as discussed in Functionals), and extract the successes or look at the inputs that lead to failures.

Another useful idiom is using a default value if an expression fails. Simply assign python importerror no module named pycurl default value outside the try block, and then run the risky code:

There is alsowhich makes this strategy even easier to implement. See Function Operators for more details.

Handle conditions with

is a general tool for handling conditions: in addition to errors, you can take different actions for warnings, messages, and interrupts. You’ve seen errors (made by ), warnings () and messages () before, but interrupts are new. They can’t be generated directly by the programmer, but are raised when the user attempts to terminate execution by pressing Ctrl + Break, Escape, or Ctrl + C (depending on the platform).

With you map conditions to handlers, named functions that are called with the condition as an input. If a condition is signalled, will call the first handler whose name matches one of the classes of the condition. The only useful built-in names are, and the catch-all. A handler function can do anything, but typically it will either return a value or create a more informative error message. For example, the function below sets up handlers that return the type of condition signalled:

You can use to implement. A simple implementation is shown below. is more complicated in order to make the error message look more like what r error in inheritsx data.frame see if wasn’t used. Note the use of to extract the message associated with the original error.

As well as returning default values when a condition is signalled, handlers can be used to make more informative error messages. For example, by modifying the message stored in the error condition object, the following function wraps to add the file name to any errors:

Catching interrupts can be useful if you want to take special action when the user tries to abort running code. But be careful, it’s easy to create a loop that you can never escape (unless you kill R)!

has one other argument:. It specifies a block of code (not a function) to run regardless of whether the initial expression succeeds or fails. This can be useful for clean up (e.g., deleting files, closing connections). This is functionally equivalent to using but it can wrap smaller chunks of code than an entire function.

An alternative to is. The difference between the two is that the former establishes exiting handlers while the latter registers local handlers. Here the main differences between the two kind of handlers:

  • The handlers in are called in the context of the call that generated the condition whereas r error in inheritsx data.frame handlers in are called in the context canon e00007 error. This is shown here withwhich is the run-time equivalent of — it lists all calls r error in inheritsx data.frame to the current function.

    This also affects the order in which is called.

  • A related difference is that withthe flow of execution is interrupted when a handler is called, while withexecution continues normally when the handler returns. This includes the signalling function which continues its course after having called the handler (e.g., will continue stopping the program and or will continue signalling a message/warning). This is why it is often better to handle a message with rather thansince the latter will stop the program:

  • The return value of a handler is returned bywhereas it is ignored with :

These subtle differences are rarely useful, except when you’re trying to capture exactly what went wrong and pass it on to another function. For most purposes, you should never need to use .

Custom signal classes

One of the challenges of error handling in R is that most functions just call with a string. That means if you want to figure out if a particular error occurred, you have to look at the text of the error message. This is error prone, not only because the text of the error might change r error in inheritsx data.frame time, but also because many error messages are translated, so the message might be completely different to what you expect.

R has a little known and little used feature to solve this problem. Conditions are S3 classes, so you can define your own classes if you want to distinguish different types of error. Each condition signalling function,r error in inheritsx data.frame,andcan be given either a list of strings, or a custom S3 condition object. Custom condition objects are not used very often, but are very useful because they make it possible for the user to respond to different errors in different r error in inheritsx data.frame. For example, “expected” errors (like a model failing to converge for some input datasets) can be silently ignored, while unexpected errors (like no disk space available) can be propagated to the user.

R doesn’t come with a built-in constructor function for conditions, but we can easily add one. Conditions must contain and components, and may contain other useful components. When creating a new condition, it should always oki 134 fatal error from and should in most cases inherit from one of, or .

You can signal an arbitrary condition withbut nothing will happen unless you’ve instantiated a custom signal handler (with r error in inheritsx data.frame or ). Instead, pass this condition to, or as appropriate to trigger the usual handling. R won’t complain if the class of your condition doesn’t match the function, but in real code you should pass a condition that inherits from the appropriate class: for forand for.

You can then use to take different actions for different types of errors. In this example we make a convenient function that allows us to signal error conditions with arbitrary classes. In a real application, it would be better to have individual S3 constructor functions that you could document, describing the error classes in more detail.

Note that when using with multiple handlers and custom classes, the first handler to match any class in the signal’s class hierarchy is called, not the best match. For this reason, you need to make sure to put the most specific handlers first:

Exercises

  • Compare the following two implementations of. What is the main advantage of in this scenario? (Hint: r error in inheritsx data.frame carefully at the traceback.)

Defensive programming

Defensive programming is the art of making code fail in a well-defined manner even when something unexpected occurs. A key principle of defensive programming is to “fail fast”: as soon as something wrong is discovered, signal an error. This is more work for the author of the function (you!), but it makes debugging easier for users because they get errors earlier rather than later, after unexpected input has passed through several functions.

In R, the “fail fast” principle is implemented in three ways:

  • Be strict about what you accept. For example, if your function is r error in inheritsx data.frame vectorised in its inputs, but uses functions that are, make sure to check that the inputs are scalars. You can usethe assertthat package, or simple statements and .

  • Avoid functions that use non-standard evaluation, like, and. These functions save time when used interactively, but because they make assumptions to reduce typing, when they fail, they often fail with uninformative error messages. You can learn more about non-standard evaluation r error in inheritsx data.frame non-standard evaluation.

  • Avoid functions that return different types of output depending on their input. The two biggest offenders are and. Whenever subsetting a data frame in a function, you should always useotherwise you will accidentally convert 1-column data frames into vectors. Similarly, never use inside a function: always use the stricter which will throw an error if the inputs are incorrect types and return the correct type of output even for zero-length inputs.

There is a tension between interactive analysis and programming. When you’re working interactively, you want R to do what you mean. If it guesses wrong, r error in inheritsx data.frame, you want to discover that right away so you can fix it. When you’re programming, you want functions that signal errors if anything is even slightly wrong or underspecified. Keep this tension in mind when writing functions. If you’re writing functions to facilitate interactive data analysis, feel free to guess what the analyst wants and recover from minor misspecifications automatically. If you’re writing functions for programming, be strict. Never try to guess what the caller wants.

Exercises

  • The goal of the function defined below is to compute the means of all numeric columns in a data frame.

    However, the function is not robust to unusual inputs, r error in inheritsx data.frame. Look at the following results, decide which ones are incorrect, and modify to be more robust. (Hint: there are two function calls in that are particularly prone to problems.)

  • The following function “lags” a vector, returning a version of that is values behind the original, r error in inheritsx data.frame. Improve the function so that it (1) returns a useful error message if is not a vector, and (2) has reasonable behaviour when is 0 or longer than .

Quiz answers

  1. The most useful tool to determine where a error occurred is. Or use RStudio, which displays it automatically where an error occurs.

  2. pauses execution at the specified line and allows you to enter an interactive environment. In that environment, there are five useful commands:blue screen error codes on windows 7 the next command;step into the next function;finish the current loop or function;continue execution normally;stop failed to run getlasterror is 0*0 function and return to the console.

  3. You could use or .

  4. Because you can then capture specific types of error withrather than relying on the comparison of error strings, which is risky, especially when messages are translated.

Re: [R] Error in inherits(x, "data.frame") : subscript out of bounds

On 13.03.2010 17:25, [email protected] wrote:
Lainaus "Uwe Ligges" <[email protected]>:
On 05.03.2010 15:24, [email protected] wrote:
Hi, I have a list p with different size dataframes and length of over 8000. I'm trying to calculate correlations between the rows of dataframes of this list and columns of another dataset (type data.frame also) so that first column is correlated with all the rows in the list dataframe. Some information from r error in inheritsx data.frame dataset is also included to the final output (all.corrs). This worked a couple of weeks ago when I wrote it but now it seems not to, and gives an error message: Error in inherits(x, "data.frame") : subscript out of bounds In addition: There were 50 or more warnings (use warnings() to see the first 50) warnings() Warning messages: 1: In corrs[j] <- cbind(expressions[j, 1:5], SNP.expr.cor) : number of items to replace is not a multiple of replacement length error function declaration isnt a prototype which indicates that the problem is with getting correlation and other information into corrs. cbind(expressions[j,1:5], r error in inheritsx data.frame, SNP.expr.cor) is type data.frame. Changing corrs into matrix, dataframe, list squid error no running copy redhat any other type has not helped. I've updated R from 2.9.0 to the recent version in between. Would anyone have a solution for this problem? I very much appreciate all help. SNP.expr.cor<-NULL all.corrs<-NULL corrs<-NULL for(i in 1:length(p)){ dim.exp<-dim(p[[i]]) expressions<-p[[i]] expressions.m<-as.matrix(expressions[,6:48]) for(j in 1:dim.exp[1]){ SNP.expr.cor<-cor(genotypes[,i],expressions.m[j,],use="na.or.complete") corrs[j]<-cbind(expressions[j,1:5], SNP.expr.cor) } all.corrs[i]<-list(cbind(map[i,1:6], corrs)) } BR Katja
Your example is not reproducible, since we do not have the data. What I guess is that you raid error press a key to reboot to make your objects lists in advance. I cannot try out, but maybe the following works better right away all.corrs <- vector(mode = "list", length = length(p)) for(i in seq(along = p)){ dim.exp <- dim(p[[i]]) corrs <- vector(mode = "list", length = dim.exp[1]) expressions <- p[[i]] expressions.m <- as.matrix(expressions[,6:48]) for(j in 1:dim.exp[1]){ SNP.expr.cor <- cor(genotypes[, i], expressions.m[j, ], use = "na.or.complete") corrs[[j]] <- cbind(expressions[j, 1:5], SNP.expr.cor) } all.corrs[[i]] <- list(cbind(map[i, 1:6]), corrs) } Best, r error in inheritsx data.frame, Uwe Ligges
Hi, sorry about that, here's a new try: corrs<-NULL #I've tried everything from vector to matrix to data.frame to list a<-rnorm(30) b<-cbind(a,a,a,a,a) e<-as.matrix(t(b)) c<-c("one", "two", "three", "four", "five") d<-c("one", "two", "three", "four", "five") c<-rbind(c,c,c,c,c) k<-dim(dat)
Well, r error in inheritsx data.frame, dat is still unknown to us.
for(j in 1:k[1]){ dat.cor<-cor(b[,i],e[j,],use="na.or.complete")
corrs[j]<-cbind(c[j,1:5], dat.cor)
corrs[j,] <- cbind(c[j,1:5], dat.cor) Best, Uwe Ligges
} The trouble seems to be in the inner loop. I'd love to have it as data.frame or matrix, r error in inheritsx data.frame as a list. I think there has been similar problems presented here before but I haven't been able to find a working solution yet. BR Katja Löytynoja
______________________________________________ [email protected] 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.
nameerror name admin is not defined django Group, I trying to use samr. I have read a previous post about the ease of use of siggenes vs. samr. It is so true! I used siggenes originally, but that doesn't help me with the problem I am having. I still need to use samr because I want to assess sample size using sam.assess.samplesize. To assess sample size using sam, I need to supply runtime error program with a "data" vector, r error in inheritsx data.frame. I can't understand how to form this vector - perhaps a manual would help, but the manual is not on the SAM website as the R-help files claim. I am using a PowerMac G5 with R Version 2.2.1 (2005-12-20 r36812) installed. I would like to use samr to assess the sample size r error in inheritsx data.frame for an experiment I am planning. I have some training data, which is the drosophila spike-in experiment data given in Choe, S. E., Boutros, M., r error in inheritsx data.frame, Michelson, A. M., r error in inheritsx data.frame, Church, G. M., & Halfon, M. S. (2005). Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biology, 6, R16. Here is what I have done: gsbatch = ReadAffy() # the experiment consists of 3 technical replicates from "control" chips # and 3 technical replicates from Spike-in chips on th DrosGenome1 chip > gs.rma = rma(gsbatch) # get expression values ## get the exprSet into a format that samr can manage: > gs.rma.fr = as.data.frame.exprSet(gs.rma) > gs.mat = matrixgs.rma.fr$exprs,nrow=14010,ncol=6) > gs.mat.con = gs.mat[,1:3] > gs.mat.si = gs.mat[,4:6] > gs.mat.sam = rbind(gs.mat.con,gs.mat.si) ## this is a matrix with dim 28020 by 3, control arrays on top, spike-ins on bottom ## grouping vector > y = c(rep(1,14010),rep(2,14010)) > geneid = as.character(1:nrow(gs.mat.sam)) > genenames = gs.rma.fr$genenames[1:14010] > data = list(x=gs.mat.sam, y =ygeneid = geneid, genenames = rep(genenames,2),logged2=TRUE) > samr(data,resp.type="Two class unpaired",nperms=20) Error in inherits(x, "data.frame") : (subscript) logical subscript too long I also tried deleting the geneid & genenames vectors from the "data" list, but still received the same error. I can't figure this out. I am sure the problem is in the way that I defined the "data" list, but, without a manual, I really don't understand what I did wrong. Thank you for your help, Monnie Monnie McGee, Ph.D. Assistant Professor Department of Statistical Science Southern Methodist University Ph: 214-768-2462 Fax: 214-768-4035 -----Original Message----- From: [email protected] on behalf of [email protected] Sent: Thu 2/16/2006 5:00 AM To: bioconductor at stat.math.ethz.ch Subject: Bioconductor Digest, Vol 36, Issue 14 Send Bioconductor mailing list submissions to bioconductor at stat.math.ethz.ch To subscribe or unsubscribe via r error in inheritsx data.frame World Wide Web, visit https://stat.ethz.ch/mailman/listinfo/bioconductor or, via email, send a message with subject or body 'help' to bioconductor-request at stat.math.ethz.ch You can reach the person managing the list at bioconductor-owner at stat.math.ethz.ch When replying, please edit your Subject line so it is more specific than "Re: Contents of Bioconductor digest." Today's Topics: dsl rx total error counts 1. Affy Package - MAplot Function help needed. (Joern Wessels) 2. Re: Affy Package - MAplot Function help needed. (James W. MacDonald) 3. Re: RMA normalization when using subsets of samples (Ron Ophir) 4. LPE (Nicolas Servant) freetv internet service connection error 5. Re: Timeseries loop design analysis using Limma or Maanova? (Pete) 6. Re: Timeseries loop design analysis using Limma or Maanova? (James W. MacDonald) 7. interpretation of vsn normalized data (Maurice Melancon) 8. interpretation of vsn normalized data (Maurice Melancon) 9. Fold Change values after RMA (kfbargad at ehu.es) 10. Re: interpretation of vsn normalized data (Wolfgang Huber) 11. Re: Fold Change values after RMA (James W. MacDonald) 12. ANN: BioC2006 Conference Scheduled for August in Seattle (Nianhua Li) 13. Run GOHyperG without specifying a chip (knaxerov at ix.urz.uni-heidelberg.de) 14. Data Frame Error in Affycomp (John.Brozek at it-omics.com) 15. Re: Fold Change values after RMA (Ben Bolstad) 16. Differences: mas5/mas5calls vs. call.expr/pairwise.comparison (Benjamin Otto) 17. comparing correlation coefficients (fwd) (Ilhem Diboun) 18. Re: Differences: mas5/mas5calls vs. call.expr/pairwise.comparison (Benjamin Otto) 19. GeneR (Antoine Lucas) ---------------------------------------------------------------------- Message: 1 Date: Wed, 15 Feb 2006 13:09:19 +0100 From: "Joern Wessels" <[email protected]> Subject: [BioC] Affy Package - MAplot Function help needed. To: <bioconductor at="" stat.math.ethz.ch=""> Message-ID: <000101c63228$a93548c0$b8acf889 at pc17368> Content-Type: text/plain; charset="iso-8859-1" Hi everybody, this is my very first post to this mailing list :-) I have got a problem Microsoft visual basic run-time error 13 excel could not solve via manual or google: When I swfupload error 302 the Maplot function of the affy package on more than three array data sets, the text in the fields showing Median and IQR is to big to be shown correctly. I tried to use the "cex.main, r error in inheritsx data.frame, cex.lab, cex.axis, cex.sub but aside from changing axis text I could not change anything. Using ?MAplot did not produce any useable help (for me as a beginner at least). I used the following line to get my graphs: MAplot(Data, pairs = TRUE) I sombody could help me with that I would be one happy R rookie user. Thanks, J?rn ________________ J?rn We?els Diplom-Biologe Philipps-Universit?t Marburg Fachbereich Biologie - Tierphysiologie Karl-von-Frisch-Str. 8 35032 Marburg an der Lahn Tel.: +49 (0) 6421 28 23547 Fax: +49 (0) 6421 28 28937 Mobil: +49 (0) 170 9346198 E-Mail: wessels at staff.uni-marburg.de Webseite: http://cgi-host.uni- marburg.de/~omtierph/stoff/member.php?mem=wessels&lang=d e ------------------------------ Message: 2 Date: Wed, 15 Feb 2006 09:36:39 -0500 From: "James W. MacDonald" <[email protected]> Subject: Re: [BioC] Affy Package - MAplot Function help needed. To: Joern Wessels <wessels at="" staff.uni-marburg.de=""> Cc: bioconductor at stat.math.ethz.ch Message-ID: <43F33C77.8070705 at med.umich.edu> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Hi Joern, Joern Wessels wrote: > Hi everybody, > this is my very first post to this mailing list :-) I have got a problem I > could not solve via manual or google: > When I use the Maplot function of the affy package on more than three array > data sets, the text in the fields showing Median and IQR is to big to be > shown correctly. > I tried to use the "cex.main, cex.lab, cex.axis, cex.sub but aside from > changing axis text I could not change anything. Using ?MAplot did not > produce any useable help (for me as a beginner at least). > > I used the following line to get my graphs: > > MAplot(Data, pairs = TRUE) > > I sombody could help me with that I would be one happy R rookie user. Well, r error in inheritsx data.frame, unfortunately the cex arguments for the text is hard coded, so no combination of cex.xxx = ? is going to change things. I will probably fix this so you can pass the cex argument to the function, r error in inheritsx data.frame, but that will take a few days to propagate to the devel download repository and would require that you use the devel version of R. Neither of these things is likely to be a good thing for a rookie, so the best fix in this case is for you to make a modified function that will do what you want (which has the added benefit of helping you to learn R). When you call MAplot() with pairs = TRUE, this function calls another function called mva.pairs(). If you type mva.pairs at an R prompt, it will be printed to the screen: > mva.pairs function (x, labels = colnames(x), log.it = TRUE, span = 2/3, family.loess = "gaussian", digits = 3, line.col = 2, r error in inheritsx data.frame, main = "MVA plot". .) { if log.it) r error in inheritsx data.frame x <- log2(x) sespider db connect error J <- dim(x)[2] frame() old.par <- par(no.readonly = TRUE) on.exit(par(old.par)) par(mfrow = c(J, J), mgp = c(0, 0.2, 0), mar = c(1, 1, 1, 1), oma = c(1, 1.4, 2, 1)) for (j in 1:(J - 1)) { par(mfg = usage show errors function procedure package oracle, j)) attempt to serve directory apache error plot(1, 1, type = "n", xaxt = "n", yaxt = "n", xlab = "", ylab = "") r error in inheritsx data.frame text(1, 1, labels[j], cex = 2) for (k in (j + 1):J) { par(mfg = c(j, k)) yy <- x[, j] - x[, k] xx <- (x[, j] + x[, k])/2 sigma <- IQR(yy) mean <- median(yy) ma.plot(xx, yy, tck = 0, show.statistics = FALSE, pch = ".", xlab = "", r error in inheritsx data.frame, ylab = "", tck = 0, span = span, .) par(mfg = c(k, j)) txt <- format(sigma, digits = digits) txt2 <- format(mean, digits = digits) plot(c(0, 1), c(0, 1), type = "n", ylab = "", xlab = "", xaxt = "n", yaxt = "n") text(0.5, 0.5, paste(paste("Median:", txt2), paste("IQR:", txt), sep = "\n"), cex = 2) } } par(mfg = c(J, J)) plot(1, 1, type = "n", xaxt = "n", yaxt r error in inheritsx data.frame "n", xlab = "", r error in inheritsx data.frame, ylab = "") text(1, 1, labels[J], cex = 2) mtext("A", 1, outer = TRUE, cex = 1.5) mtext("M", 2, outer = TRUE, cex = 1.5, las = 1) mtext(main, 3, outer = TRUE, cex = 1.5) invisible() } You can then copy this output and paste it into your favorite text editor. Fix the first line to say something like this: my.mva.pairs <- function (x, labels = colnames(x), log.it = TRUE, span = 2/3, family.loess = "gaussian", digits = 3, line.col = 2, main = "MVA plot", cex.text = 2. .) Note the change in the function name (along with the <- ), and the addition of cex.text = 2. Now change the line that reads text(0.5, 0.5, paste(paste("Median:", txt2), paste("IQR:", txt), sep = "\n"), cex = 2) to say text(0.5, 0.5, paste(paste("Median:", txt2), paste("IQR:", r error in inheritsx data.frame, txt), sep = "\n"), cex = cex.text) Now you can either copy/paste this function back into your R session, or save it as something like my.mva.pairs.R and source() it into your R session. You are almost there - MAplot does some pre-processing of the data first that you will have to do by hand. You need to extract the raw intensity data from your AffyBatch and pass that to your new function: pms <- unlist(indexProbes(Data, "both")) x <- intensity(Data)[pms, ] my.mva.pairs(x, cex.text = 1) Poking around in other people's code and seeing what it does/changing it to do slightly different things is one of the best ways IMO to learn R. HTH, Jim > > Thanks, > J?rn > > > ________________ > J?rn We?els > Diplom-Biologe > > Philipps-Universit?t Marburg > Fachbereich Biologie - Tierphysiologie > Karl-von-Frisch-Str. 8 > 35032 Marburg an der Lahn > > Tel.: +49 (0) 6421 28 23547 > Fax: +49 (0) 6421 28 28937 > Mobil: +49 (0) 170 9346198 > E-Mail: wessels at staff.uni-marburg.de > Webseite: > http://cgi-host.uni- marburg.de/~omtierph/stoff/member.php?mem=wessels&lang=d > e > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 ------------------------------ Message: 3 Date: Wed, 15 Feb 2006 17:05:13 +0200 From: "Ron Ophir" <[email protected]> Subject: Re: [BioC] RMA normalization when using subsets of samples To: <bioconductor at="" stat.math.ethz.ch=""> Message-ID: <s3f35f58.078 at="" wisemail.weizmann.ac.il=""> Content-Type: text/plain; charset=US-ASCII Dear all, r error in inheritsx data.frame, I think that D.R. Godstein has tried to answer Sylvia's question in http://ludwig-sun2.unil.ch/~darlene/ms/prRMA.pdf Ron >>> <larry.lapointe at="" csiro.au=""> 02/15/06 11:55 AM >>> Dear Martin, We have run up to 550 chips achieving a reasonable processing time -- not more than an hour or so. The practical limits seem to be more related to machine RAM and R memory management. RMA normalization of 550 chips occupies about 12 GB or so on our quad processor Opteron- based system. Larry Lawrence LaPointe CSIRO Bioinformatics for Human Health Sydney, Australia -----Original Message----- From: bioconductor-bounces at stat.math.ethz.ch on behalf of martin.schumacher at novartis.com Sent: Wed 2/15/2006 7:43 PM To: bioconductor at stat.math.ethz.ch Cc: Subject: Re: [BioC] RMA normalization when using subsets of samples Dear Colleagues, Greetings from Switzerland ! I agree with the statements of Wolfgang and Adai. Using all chips will certainly put you on the safe side. I wonder what you feel would be the minimal number of chips for a "stable" (meaning that a larger set would give essentially the same results) RMA processing? People from GeneLogic told me that about 20 chips are sufficient. Is it possible to run RMA using Bioconductor with 200 chips and get the results back within a reasonable time? Best regards, Martin Adaikalavan Ramasamy <ramasamy at="" cancer.org.uk=""> Sent by: bioconductor-bounces at stat.math.ethz.ch 15.02.2006 01:01 Please respond to ramasamy To: Wolfgang Huber <huber at="" ebi.ac.uk=""> cc: Sylvia.Merk at ukmuenster.de, bioconductor at stat.math.ethz.ch, (bcc: Martin Schumacher/PH/Novartis) Subject: Re: r error in inheritsx data.frame RMA normalization when using subsets of samples Category: This would be a problem if one or more of the resulting subsets is small and contains outliers. My preference is to preprocess all arrays together. My reasoning is that doing this will give RMA median polish (and to a lesser extent with the quantile normalisation) steps much more information to work with. Regards, Adai On Wed, 2006-02-15 at 17:16 +0000, Wolfgang Huber wrote: > Dear Sylvia, > > this might not be the answer that you want to hear, but for the end > result it should not matter (substantially) which of the two > possibilities you take, and I would be worried if it did. > > Best wishes > Wolfgang > > ------------------------------------- > Wolfgang Huber > European Bioinformatics Institute > European Molecular Biology Laboratory > Cambridge CB10 1SD > England > Phone: +44 1223 494642 > Fax: miles error mss dll +44 1223 494486 > Http: www.ebi.ac.uk/huber > ------------------------------------- > > Sylvia.Merk at ukmuenster.de wrote: > > Dear bioconductor list, > > > > I have a question concerning RMA-normalization: > > > > There are for example 200 CEL-Files and the clinicians have several > > research questions - each concernig only a subset of the 200 samples > > including the possibility that some samples are included in more than > > one question. > > > > There are two possibilities to normalize the CEL-Files: > > > > 1.: Read all 200 chips in an affybatch-object and normalize all 200 > > chips together and further analyze the required subset. > > > > 2.: Read only the required chips in an affybatch-object, normalize these > > chips and then perform further analysis > > I think that this approach is the better one but it has the disadvantage > > that some samples are included in several normalizations ending in > > different gene expression levels for a single sample. > > > > What is (from a statisticians view) the appropriate approach to > > normalize CEL-Files in this case? > > > > Thank you in advance > > Sylvia > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor ------------------------------ Message: 4 Date: Wed, 15 Feb 2006 16:07:16 +0100 From: Nicolas Servant <[email protected]> Subject: [BioC] LPE To: bioconductor at stat.math.ethz.ch Message-ID: <43F343A4.9030500 at curie.fr> Content-Type: text/plain; charset=ISO-8859-1; format=flowed -- Nicolas Servant Equipe Bioinformatique Institut Curie 26, rue d'Ulm - 75248 Paris Cedex 05 - FRANCE Email: Nicolas.Servant at curie.fr Tel: 01 44 32 42 75 ------------------------------ Message: 5 Date: Wed, 15 Feb 2006 15:38:57 -0000 From: "Pete" <[email protected]> Subject: Re: [BioC] Timeseries loop design analysis using Limma or Maanova? To: <bioconductor at="" stat.math.ethz.ch="">, "Jenny Drnevich" <drnevich at="" uiuc.edu=""> Message-ID: <03d001c63245$ef9799e0$a2f1f682 at mrc153> Content-Type: text/plain; format=flowed; charset="iso-8859-1"; reply-type=original Thanks for your response, >>Hello all, >> >>I have been asked to analyse a set of timecourse data with an unusual >>incomplete loop design. This is the design of this type I have looked at >>and I'm not entirely sure how to treat it. >> >>The initial (and fairly easy) question asked of the data is, what are the >>differences between the mutant and the control animals at each timepoint? > > I am interested in how you are going to analyze the differences between > mutants and controls at each time point given that there is no replication > of the control animals (only r error in inheritsx data.frame control pool). I just advised a researcher > against this kind of experimental design because I could not think offhand > of a way to analyze it statistically. If there is a statistically valid > method, I would like to know about it. > I'm not quite sure I understand your point here? I was going to treat this as a simple dye swap experiment, ignoring time and comparing mutant to WT. Is this not a statistically valid approach? There are 3 independ mutant samples compared in dyeswaps to the WT pool. I understand that there is no biological replicate for the WT pool, however it is technically replicated at the dyeswap level and cDNA synthesis level. The biological variation of the WT population is not r error in inheritsx data.frame immediate interest in this case, hence a pool was used. Individual mutant samples were used instead of a pool, because only a limited r error in inheritsx data.frame of mutants were available. > >>The second question is how the mutant changes across the timeseries. The >>authors >>wish to use a bayesian timeseries clustering algorithmn to analyse this, >>but >>this r error in inheritsx data.frame a standardised measure for the mutant at each timepoint, r error in inheritsx data.frame. > > How are you going to implement this bayesian timeseries clustering? My > interpretation of clustering algorithms in general is that they should not > be used to determine which genes are "differentially" expressed, but > rather > one should first use a statistical model to determine differential > expression, then only cluster the genes that show a significant difference > somewhere along the time series to find groups of genes that show a > similar > expression pattern. My approach to this situation would be something along > the lines of a single-channel analysis using a mixed model with array + > dye > + treatment + time + treatment*time, and then cluster genes that showed a > significant time effect, using the lsmeans for each mutant*timepoint > group. > The lack of replication of the controls may cause this not to work. > > Cheers, > Jenny I agree with your statement about clustering, and prehaps I didn't word my question very clearly. The timeseires clustering will indeed be performed on genes selected as differential with respect to time. In the past I have used the MAANOVA package to select these differential genes, however in r error in inheritsx data.frame particular case, the samples were all compared back to a single reference sample rather than multiple references that are then compared to eachother in an incomplete loop. The issue I am concerned with is how to both, select genes that have a time effect, and how/what to use as a standardised expression level for these genes so that it can then be used in the clustering. Cheers Pete > > > >>I am unsure quite how to achieve this second point and welcome any >>suggestions or references that may help. Is this something I could do in >>Limma or MAanova? >> >> >>The data are from spotted, two-colour, oligo arrays. There are 6 >>timepoints. >>At each timepoint, tissue samples from 3 individual mutant animals are >>compared to a control pool of WT animals at the same timepoint, with dye >>swaps. In addition each control pool has then been compared in a dye swap >>to >>the next timepoint control pool. See diagram below (if it comes out >>correctly!) or the table further below where a1 a2 a3 represent any 3 >>individual animals. >> >> >> >>a1t1 a2t1 r error in inheritsx data.frame a3t1 a1t2 a2t2 a3t2 etc. >> \\

Thanks for your answer. I have the following questions: 1. How can I produce data.frame correct without Preprocessing (MAS5, RMA, liwong.)? I know that the instruction is not correct: data.raw <- ReadAffy(filenames="./R/ME_cel/Expt1_R1.CEL", . "./R/ME_cel/Expt7_R2.CEL") x<- exprs(data.raw) but I have unfortunately no new Idea! 2. (in affycomp) Can I use directly *.CEL files instead of *.csv? If yes, how? Thanks, Mohmmad Esad-Djou "Rafael A. Irizarry" <r[email protected]> schrieb am 18.05.05 20:35:36: > > the instructions say you should do this: > > x <- exprs(eset) > write.table(data.frame(x,check.names=FALSE),file="filename.csv",sep= ",",col.names=NA,quote=FALSE) > > > -r > > On Wed, 18 > May 2005, Mohammad Esad-Djou wrote: > > > Hello, > > > > I try through affycomp different methods with one another compare. > > > > I have step by step instructions of http://affycomp.biostat.jhsph.edu/#whatisthis used: > > > > >>Data and instructions > r error in inheritsx data.frame >>Download the spike-in and dilution data sets. > > > > >>Spike-in hgu133a Data > > >>Affymetrix's Spike-in hgu133a Experiment CEL files [gzip- compressed tar-archive] > > > > >>Description file for this data [text] > > > > ::: I downloaded. > > > > >>2. Obtain expression measures (in original scale, NOT log scale) for any or all of the datasets, and write r error in inheritsx data.frame as a comma-delimited text file as follows: > > >>For R users, if x is matrix with probe set IDs as rownames and filenames as colnames, the command write.table(data.frame(x,check.name s=FALSE),file="filename.csv",sep=",",col.names=NA,quote=FALSE) should do the trick. > > >>For convenience, we offer two example files [compressed archive] in the correct format, one for dilution and one for spike-in. > > > > ::: Iwrote: > > > > library(affy) > > library(affycomp) > > r error in inheritsx data.frame > data.raw <- ReadAffy(filenames="./R/ME_cel/Expt1_R1.CEL", > > . > > date error bios "./R/ME_cel/Expt7_R2.CEL") > > > > eset <- mas5(data.raw) > > > > :::For data.frame I receive error message: > > #write.table(eset(x,check.names=FALSE),file="filename.csv",sep="," ,col.names=NA,quote=FALSE) > > #Error in inherits(x, "data.frame") : couldn't find function "eset" > > > > ::: I can the following commands use, but csv file is not stored like given examples: > > write.table(eset,file="filename.csv",sep=",",col.names=NA,quote=FALSE) > > > > How can I produce correct data.frame? > > > > Thanks, > > Mohammad Esad-Djou > > > > _______________________________________________ > > Bioconductor mailing list > > [email protected] > > https://stat.ethz.ch/mailman/listinfo/bioconductor > >

youtube video

Error in data frame undefined columns selected - R

R error in inheritsx data.frame - useful

knit_print.data.frame shoud not pass ... to print() #2047

For more context, the packages that could be impacted by this issue and have CRAN check failure based on the emails received are

It seems there are 2 false positives but the others have indeed the same errors related to :

  • Vignette failed on Solaris which as no pandoc ()
  • The error is with being passed and unused ()
pkgs<- c("DriveML","SmartEDA","anomalize","bayestestR", "broom", "effectsize", "heemod", "isoreader", "mlrCPO", "panelr", "parameters", "tibbletime") checks<-purrr::map(pkgs, ~ { rcmdcheck::cran_check_results(.x, flavours="r-patched-solaris-x86") }) xfun::raw_string(checks[[1]][[1]]$warnings) #> checking re-building of vignette outputs ... [8s/13s] WARNING#> Error(s) in re-building vignettes:#> ...#> --- re-building ‘SmartML.Rmd’ using rmarkdown#> Warning in engine$weave(file, quiet = quiet, encoding = enc) :#> Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.#> Quitting from lines 169-170 (SmartML.Rmd) #> Error: processing vignette 'SmartML.Rmd' failed with diagnostics:#> unused argument (options = list(TRUE, FALSE, "markup", FALSE, NULL, FALSE, FALSE, "##", TRUE, TRUE, "normalsize", "#F7F7F7", 0, "cache/", NULL, TRUE, NULL, FALSE, FALSE, "high", "asis", "default", "figure/", "png", NULL, 72, "png", 7, 7, "figure", NULL, NULL, "fig:", NULL, "", NULL, NULL, NULL, 1, TRUE, FALSE, 1, "controls,loop", FALSE, FALSE, TRUE, list(c("age", "age", "age", "chol", "chol", "chol", "oldpeak", "oldpeak", "oldpeak", "thalach", "thalach", "thalach", "trestbps", "trestbps", "trestbps"), c("target_var:All", #> "target_var:1", "target_var:0", "target_var:All", "target_var:1", "target_var:0", "target_var:All", "target_var:1", "target_var:0", "target_var:All", "target_var:1", "target_var:0", "target_var:All", "target_var:1", "target_var:0"), c(303, 165, 138, 303, 165, 138, 303, 165, 138, 303, 165, 138, 303, 165, 138), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 99, 74, 25, 0, 0, 0, 0, 0, 0), c(303, 165, 138, 303, 165, 138, 204, 91, 113, 303, 165, 138, 303, 165, 138), c(0, 0, 0, 0, #> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c(16473, 8662, 7811, 74618, 39968, 34650, 315, 96.2, 218.8, 45343, 26147, 19196, 39882, 21335, 18547), c(29, 29, 35, 126, 126, 131, 0, 0, 0, 71, 96, 71, 94, 94, 100), c(77, 76, 77, 564, 564, 409, 6.2, 4.2, 6.2, 202, 202, 195, 200, 180, 200), c(54.37, 52.5, 56.6, 246.26, 242.23, 251.09, 1.04, 0.58, 1.59, 149.65, 158.47, 139.1, #> 131.62, 129.3, 134.4), c(55, 52, 58, 240, 234, 249, 0.8, 0.2, 1.4, 153, 161, 142, 130, 130, 130), c(9.08, 9.55, 7.96, 51.83, 53.55, 49.45, 1.16, 0.78, 1.3, 22.91, 19.17, 22.6, 17.54, 16.17, 18.73), c(0.17, 0.18, 0.14, 0.21, 0.22, 0.2, 1.12, 1.34, 0.82, 0.15, 0.12, 0.16, 0.13, 0.13, 0.14), c(13.5, 15, 10, 63.5, 59, 65.75, 1.6, 1, 1.9, 32.5, 23, 31, 20, 20, 24.75), c(-0.2, 0.12, -0.54, 1.14, 1.73, 0.31, 1.26, 1.63, 0.73, -0.53, -0.7, -0.29, 0.71, 0.42, 0.85), c(-0.55, -0.63, 0.08, 4.41, 7.37, 0.32, #> 1.53, 3.06, 0.35, -0.08, 0.41, -0.23, 0.89, 0.35, 0.84), c(29, 29, 35, 126, 126, 131, 0, 0, 0, 71, 96, 71, 94, 94, 100), c(42, 41, 44.7, 188, 192, 187.7, 0, 0, 0, 116, 131, 108.7, 110, 110, 110), c(45, 43, 50, 204, 202.6, 206.4, 0, 0, 0.14, 130, 144, 120, 120, 118, 120), c(50, 46, 54, 217.6, 212.2, 225.3, 0, 0, 0.8, 140.6, 152, 127.1, 120, 120, 124), c(53, 50.6, 56.8, 230, 223, 235.6, 0.38, 0, 1, 146, 157, 132.8, 126, 125, 128), c(55, 52, 58, 240, 234, 249, 0.8, 0.2, 1.4, 153, 161, 142, 130, 130, #> 130), c(58, 54, 59, 254, 245, 261.4, 1.12, 0.5, 1.8, 159, 164.4, 145.2, 134, 130, 138), c(59, 57.8, 61, 268, 260.8, 275.9, 1.4, 0.8, 2.19, 163, 170, 152.9, 140, 138, 140), c(62, 62, 63, 285.2, 273.4, 289, 1.9, 1.2, 2.8, 170, 173.2, 160, 144, 140, 150), c(66, 65.6, 66, 308.8, 305.2, 312.2, 2.8, 1.6, 3.4, 176.6, 179.6, 166.6, 152, 150, 160), c(77, 76, 77, 564, 564, 409, 6.2, 4.2, 6.2, 202, 202, 195, 200, 180, 200), c(27.25, 21.5, 37, 115.75, 119.5, 118.62, -2.4, -1.5, -2.25, 84.75, 114.5, 78.5, 90, #> 90, 82.88), c(81.25, 81.5, 77, 369.75, 355.5, 381.62, 4, 2.5, 5.35, 214.75, 206.5, 202.5, 170, 170, 181.88), c(0, 0, 2, 5, 4, 2, 5, 4, 2, 1, 4, 1, 9, 3, 2)), NULL, NULL, "R", FALSE, TRUE, TRUE, "snc3", "paged_table(snc)", 504, 504, "snc3,warning=FALSE,eval=T,render=snc,echo=F"))#> --- failed re-building ‘SmartML.Rmd’#> #> SUMMARY: processing the following file failed:#> ‘SmartML.Rmd’#> #> Error: Vignette re-building failed.#> Execution halted# the error is the same purrr::map_dfr(checks, ~{ res<-.x[[1]] data.frame( pkg=res$package, unused_argument= grepl("unused argument \\(options", res$warnings) && grepl("Error: processing vignette", res$warnings) ) }) #> pkg unused_argument#> 1 DriveML TRUE#> 2 SmartEDA TRUE#> 3 anomalize TRUE#> 4 bayestestR TRUE#> 5 broom NA#> 6 effectsize TRUE#> 7 heemod TRUE#> 8 isoreader NA#> 9 mlrCPO TRUE#> 10 panelr TRUE#> 11 parameters TRUE#> 12 tibbletime TRUE

Created on 2021-02-18 by the reprex package (v1.0.0)

[R] Error in inherits(x, "data.frame") : object "Dataset" not found

Dimitris Rizopoulosdimitris.rizopoulos at med.kuleuven.be
Tue Jun 6 14:11:24 CEST 2006
you probably want to use: model <- glm(cbind(successes, failures) ~ medyear + age + sex + where + who + dxbroad + firstep + standard, family = binomial, data = logreg) since you store the data you imported in the data.frame 'logreg' not 'Dataset'. I hope it helps. Best, Dimitris ---- Dimitris Rizopoulos Ph.D. Student Biostatistical Centre School of Public Health Catholic University of Leuven Address: Kapucijnenvoer 35, Leuven, Belgium Tel: +32/(0)16/336899 Fax: +32/(0)16/337015 Web: http://med.kuleuven.be/biostat/http://www.student.kuleuven.be/~m0390867/dimitris.htm ----- Original Message ----- From: "Bob Green" <bgreen at dyson.brisnet.org.au> To: <r-help at stat.math.ethz.ch> Sent: Tuesday, June 06, 2006 1:52 PM Subject: [R] Error in inherits(x, "data.frame") : object "Dataset" not found >I have been trying to run a logistic regression using a number of >studies. > Below is the syntax, error message & data. >> Any advice regarding what I am doing wrong or solutions are > appreciated, >> regards >> Bob Green >>> > logreg <- read.csv("c:\\logregtest.csv",header=T) > > attach(logreg) > > names(logreg) > [1] "medyear" "where" "who" "dxbroad" "firstep" > "standard" > [7] "age" "sex" "successes" "failures" > > model <- glm(cbind(successes, failures) ~medyear + age + sex + > > where + > who + dxbroad + firstep + standard, family=binomial, data=Dataset) > Error in inherits(x, "data.frame") : object "Dataset" not found >>> medyear where who dxbroad firstep standard age sex successes > failures > 89 3 2 1 0 0 31.5 71 28 117 > 98 2 2 1 0 1 48 62 15 72 > 98 4 1 1 0 0 45.2 61 42 57 > 89 3 0 1 0 1 28.7 63 19 48 > 99 2 2 1 0 1 34.7 73 27 73 > 88 3 0 1 0 1 30.6 58 26 57 > 94 1 1 1 0 1 36.3 81 70 124 > 96 3 1 1 0 1 40 57 27 40 > 96 2 2 1 0 1 33.1 64 9 41 > 88 2 0 1 1 0 29.5 47 30 202 > 98 1 2 0 0 1 39.3 60 246 734 > 97 4 0 0 0 1 38.4 67 17 85 > 92 3 0 1 0 1 34.3 67 15 127 > 88 2 0 1 0 1 NA 46 9 90 > 85 3 0 1 0 1 30.3 64 58 87 > 94 3 0 1 0 1 38.8 47 47 126 > 88 3 0 1 0 1 33.8 54 25 134 > 92 3 0 1 1 1 NA NA 67 157 > 90 3 0 1 1 1 26 52 17 101 > 90 3 0 1 0 0 NA NA 39 32 > 90 2 0 1 0 1 36.1 38 10 173 > 90 2 0 1 0 1 38.9 53 64 383 > 97 2 0 1 0 1 31.5 61 12 52 > 99 1 1 1 0 1 NA NA 25 56 > 100 4 1 1 0 1 45 62 46 270 > 101 2 0 1 0 1 32.4 100 33 92 >> ______________________________________________ >R-help at stat.math.ethz.ch mailing list >https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide! >http://www.R-project.org/posting-guide.html> Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

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// >> Control t1 ========= Control t2 ==== etc............... math.ethz.ch >>https://stat.ethz.ch/mailman/listinfo/bioconductor > > Jenny Drnevich, Ph.D. > > Functional Genomics Bioinformatics Specialist > W.M. Keck Center for Comparative and Functional Genomics > Roy J. Carver Biotechnology Center > University of Illinois, Urbana-Champaign > > 330 ERML > 1201 W. Gregory Dr. > Urbana, IL 61801 > USA > > ph: 217-244-7355 > fax: 217-265-5066 > e-mail: drnevich at uiuc.edu > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > ------------------------------ Message: 6 Date: Wed, 15 Feb 2006 11:01:13 -0500 From: "James W. MacDonald" <[email protected]> Subject: Re: [BioC] Timeseries loop design analysis using Limma or Maanova? To: Pete <p.underhill at="" har.mrc.ac.uk=""> Cc: bioconductor at stat.math.ethz.ch Message-ID: <43F35049.6000701 at med.umich.edu> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Hi Pete, Pete wrote: > I'm not quite sure I understand your point here? I was going to treat this > as a simple dye swap experiment, ignoring time and comparing mutant to WT. > Is this not a statistically valid approach? There are 3 independ mutant > samples compared in dyeswaps to the WT pool. I understand that there is no > biological replicate for the WT pool, however it is technically replicated > at the dyeswap level and cDNA synthesis level. The biological variation of > the WT population is not of immediate interest in this case, hence a pool > was used. Individual mutant samples were used instead of a pool, because > only a limited number of mutants were available. You can certainly do something like this, but there are some caveats. First, by comparing WT to mutant and ignoring time you are essentially looking at a main effect that might not be of much interest (hence why would you make the effort to do a time series?). Usually a more interesting question is to look for genes that are differentially expressed between mutant and WT at particular times, which I assume is why Jenny said you have no replication. Second, when you compare biological replicates to technical replicates you are underestimating the true variability of the WT samples, which may result in apparent significance where there may have been none had biological replicates been used for WT samples as well. This is usually only a problem when you try to validate the results (using new biologically replicated samples), if there are many genes that fail to validate. Since the validation step is usually much slower and laborious, decreasing the number of false positives in the microarray step is often worth the time and effort. Best, Jim -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 ------------------------------ Message: 7 Date: Wed, 15 Feb 2006 09:49:12 -0800 From: Maurice Melancon <[email protected]> Subject: [BioC] interpretation of vsn normalized data To: bioconductor at stat.math.ethz.ch Message-ID: <87ba8bf70602150949s6e9e2af4t5ca024b1b77d394b at mail.gmail.com> Content-Type: text/plain Hello All, I used vsn to normalze my one-channel cDNA microarray experiment. I'm sorry if this is an elementary question (I'm not a math person) but can vsn data be interpreted in similar fashion to log2 data, e.g. 1 log vale = 2-fold induction? What would be the appropriate transformation to get to either log2 or raw data from vsn data? Briefly, what I did was to normalize using vsn, then I used SAS to run anovas with pairwise comparisons and anova slicing. Using the estimate function returns estimated differences between the reported means. I am seeking then to bridge the gap between these estimates and actual fold changes. I think this can be done, but I am unsure about how to either reverse-transform the vsn data or how to interpret it biologically (e.g. 1 log = 2x fold change) WIth thanks Maurice [[alternative HTML version deleted]] ------------------------------ Message: 8 Date: Tue, 14 Feb 2006 16:16:49 -0800 From: Maurice Melancon <[email protected]> Subject: [BioC] interpretation of vsn normalized data To: bioconductor at stat.math.ethz.ch Message-ID: <87ba8bf70602141616t5fb5275o29c41c9a950f4825 at mail.gmail.com> Content-Type: text/plain Hello All, I used vsn to normalze my one-channel cDNA microarray experiment. I'm sorry if this is an elementary question (I'm not a math person) but can vsn data be interpreted in similar fashion to log2 data, e.g. 1 log vale = 2-fold induction? What would be the appropriate transformation to get to either log2 or raw data from vsn data? WIth thanks Maurice [[alternative HTML version deleted]] ------------------------------ Message: 9 Date: Tue, 14 Feb 2006 10:30:23 +0100 (CET) From: [email protected] Subject: [BioC] Fold Change values after RMA To: bioconductor at stat.math.ethz.ch Message-ID: <1759269561kfbargad at ehu.es> Content-Type: text/plain; charset="ISO-8859-1" Dear List, I have come across an article (Choudary et al. 2005, PNAS 102,15653- 15658) where they state that the FC values after RMA preprocessing "always" remain below a maximum of 2.0 Is this right? I have performed some analyses using RMA, quantile normalisation and limma and am getting M values higher than 2, and if M = log2(FC), then FC values are higher than 4. What am I missing? Any comments on this? Thanks in advance, David ------------------------------ Message: 10 Date: Thu, 16 Feb 2006 18:16:24 +0000 From: Wolfgang Huber <[email protected]> Subject: Re: [BioC] interpretation of vsn normalized data To: Maurice Melancon <dmso12 at="" gmail.com=""> Cc: bioconductor at stat.math.ethz.ch Message-ID: <43F4C178.6020305 at ebi.ac.uk> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Hi Maurice, in statistics it is sometimes useful to differentiate between (a) the estimator and (b) the true underlying quantity that you want to estimate. For example, if you want to estimate the expectation value of a symmetric distribution, you can use the mean, or the median as estimators. They are both correct, but depending on the data they can provide different, and more or less appropriate answers. With microarrays, (b) is the fold-change, that is the change in mRNA abundance. The log-ratio of fluorescence intensities is a simple and intuitive estimator for this, but if the fluorescence intensities become small, this estimator can have unpleasant properties, like large variance. The glog-ratio (what vsn provides) is an alternative estimator, which avoids the variance explosion, for the price of being biased towards 0 when the fluorescence intensities are small. Note that the vsn function returns glog to base e (so a glog-ratio of 1 corresponds to an estimated fold change of exp(1) = 2.718..) while many other packages use log2. Hope this helps Wolfgang Maurice Melancon wrote: > Hello All, > > I used vsn to normalze my one-channel cDNA microarray experiment. I'm sorry > if this is an elementary question (I'm not a math person) but can vsn data > be interpreted in similar fashion to log2 data, e.g. 1 log vale = 2-fold > induction? What would be the appropriate transformation to get to either > log2 or raw data from vsn data? > > Briefly, what I did was to normalize using vsn, then I used SAS to run > anovas with pairwise comparisons and anova slicing. Using the estimate > function returns estimated differences between the reported means. I am > seeking then to bridge the gap between these estimates and actual fold > changes. I think this can be done, but I am unsure about how to either > reverse-transform the vsn data or how to interpret it biologically (e.g. 1 > log = 2x fold change) > > WIth thanks > > Maurice > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- Best regards Wolfgang ------------------------------------- Wolfgang Huber European Bioinformatics Institute European Molecular Biology Laboratory Cambridge CB10 1SD England Phone: +44 1223 494642 Fax: +44 1223 494486 Http: www.ebi.ac.uk/huber ------------------------------ Message: 11 Date: Wed, 15 Feb 2006 13:37:00 -0500 From: "James W. MacDonald" <[email protected]> Subject: Re: [BioC] Fold Change values after RMA To: kfbargad at ehu.es Cc: bioconductor at stat.math.ethz.ch Message-ID: <43F374CC.4060407 at med.umich.edu> Content-Type: text/plain; charset=ISO-8859-1; format=flowed Hi David, kfbargad at ehu.es wrote: > Dear List, > > I have come across an article (Choudary et al. 2005, PNAS 102,15653- > 15658) where they state that the FC values after RMA > preprocessing "always" remain below a maximum of 2.0 Is this right? No, this is not correct. There are other factual errors in that paper as well, which makes me wonder if there was a breakdown in communication between the statisticians and those who wrote the paper. That said, it is my understanding that fold change values in brain are often very small, so they may simply be trying to indicate that using a fold change of two is not reasonable in that context. Best, Jim > > I have performed some analyses using RMA, quantile normalisation and > limma and am getting M values higher than 2, and if M = log2(FC), then > FC values are higher than 4. What am I missing? Any comments on this? > > Thanks in advance, > > David > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 ------------------------------ Message: 12 Date: Wed, 15 Feb 2006 11:28:39 -0800 From: Nianhua Li <[email protected]> Subject: [BioC] ANN: BioC2006 Conference Scheduled for August in Seattle To: bioconductor at stat.math.ethz.ch, r-help at stat.math.ethz.ch Message-ID: <43F380E7.9010603 at fhcrc.org> Content-Type: text/plain; charset=ISO-8859-1; format=flowed ================================== BioC2006 Where Software and Biology Connect ================================== This conference will highlight current developments within and beyond Bioconductor, a world-wide open source and open development software project for the analysis and comprehension of genomic data. Our goal is to provide a forum in which to discuss the use and design of software for analyzing data arising in biology with a focus on Bioconductor and genomic data. Where: Fred Hutchinson Cancer Research Center Seattle WA. When: August 3 and 4, 2006 What: Morning Talks: 8:30-12:00 Afternoon Practicals: 2:00-5:00 Thursday Evening 5:00-7:30 Posters and Wine & Cheese Fees: 300 USD for attendees registered before July 1 250 USD for Bioconductor package maintainers or FHCRC employees 125 USD for enrolled full-time students The online registration form and conference details are now available at http://www.bioconductor.org/BioC2006 (You will be redirected to our secure server: https://cobra.fhcrc.org/BioC2006 ------------------------------ Message: 13 Date: Wed, 15 Feb 2006 20:41:53 -0500 From: [email protected] Subject: [BioC] Run GOHyperG without specifying a chip To: bioconductor at stat.math.ethz.ch Message-ID: <20060215204153.9bs7tl84f4c4s0sw at wwwmail-new.urz.uni- heidelberg.de> Content-Type: text/plain; charset=ISO-8859-1 Hello everyone, here's a really trivial question, but I can't find the answer anywhere: can I use GOHyperG() in GOstats without specifying a particular microarray chip? I'd simply like to pass a list of EntrezGene IDs as my "total population". Thanks!!!! Kamila ------------------------------ Message: 14 Date: Thu, 16 Feb 2006 09:07:58 +0100 From: [email protected] Subject: [BioC] Data Frame Error in Affycomp To: "bioconductor at stat.math.ethz.ch" <bioconductor at="" stat.math.ethz.ch=""> Message-ID: <ofbf9d5073.df6d05ad-onc1257117.002c3b0e-c1257117.002cad01 at="" genfit.com=""> Content-Type: text/plain Dear all, I encountered the same problem described by monnie McGee using the Affycomp library. As adviced by the vignette I've used the " read.newspikein" function with HG-U133A spikein data. >library(affy) >library(affycomp) >spike133 <- ReadAffy(...) >eset <- expresso(spike133, bgcorrect.method="rma", normalize.method="quantiles", pmcorrect.method="pmonly", summary.method="medianpolish") >new.eset <- exprs(eset) >write.table(data.frame(new.eset,check.names=FALSE),"rma-133.csv",sep= ",",col.names=NA,quote=FALSE) >read.newspikein("rma-133.csv") Error in "[.data.frame"(s, , rownames(pData(pd))) : undefined columns selected Any suggestions ? Thanks John Brozek [[alternative HTML version deleted]] ------------------------------ Message: 15 Date: Wed, 15 Feb 2006 10:15:34 -0800 From: Ben Bolstad <[email protected]> Subject: Re: [BioC] Fold Change values after RMA To: kfbargad at ehu.es Cc: bioconductor at stat.math.ethz.ch Message-ID: <1140027334.3781.19.camel at localhost.localdomain> Content-Type: text/plain Two points: 1. In general estimates of FC off microarrays tend to be smaller than the truth, irrespective of processing algorithm. 2. There is no specific reason why RMA should limit to FC values of 2.0 (and it does not do this in general, as you have observed with your own dataset). In the case of Choudary et al they are studying gene expression changes in the brain and my understanding is that these fold changes are typically small, perhaps explaining the comment. Ben On Tue, 2006-02-14 at 10:30 +0100, kfbargad at ehu.es wrote: > Dear List, > > I have come across an article (Choudary et al. 2005, PNAS 102,15653- > 15658) where they state that the FC values after RMA > preprocessing "always" remain below a maximum of 2.0 Is this right? > > I have performed some analyses using RMA, quantile normalisation and > limma and am getting M values higher than 2, and if M = log2(FC), then > FC values are higher than 4. What am I missing? Any comments on this? > > Thanks in advance, > > David > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor ------------------------------ Message: 16 Date: Thu, 16 Feb 2006 10:24:34 +0100 From: "Benjamin Otto" <[email protected]> Subject: [BioC] Differences: mas5/mas5calls vs. call.expr/pairwise.comparison To: "BioClist" <bioconductor at="" stat.math.ethz.ch=""> Message-ID: <noeokkcpbgiaippdonmgceljcbaa.b.otto at="" uke.uni-="" hamburg.de=""> Content-Type: text/plain; charset="iso-8859-1" Dear BioC members, in my last calculations I noticed that the affy-package combination of mas5() and mas5calls() results in different Present/Absent calls than the simpleaffy-package version with call.exprs() and pairwise.comparison(). That was the more surprising for me as I thought simpleaffy was just some wrapper around the affy-package automating some high-level steps. A comparison of the results with the ones returned by the affymetrix software revealed that the simpleaffy version is nearly identical while the affy version is different one. Is there some error in my code? The exact commands I used were: x <- read.affy() #version 1: mas <- mas5(x,sc=sometgt) mas.call <- mas5calls(x) #version 2: simplemas <- call.exprs(x,"mas5",sc=sometgt) simplemas.cmp <- pairwise.comparison(simplemas,"treatment",spots=x) regards Benjamin ------------------------------ Message: 17 Date: Thu, 16 Feb 2006 10:03:50 +0000 (GMT) From: Ilhem Diboun <[email protected]> Subject: [BioC] comparing correlation coefficients (fwd) To: bioconductor at stat.math.ethz.ch Message-ID: <pine.lnx.4.44.0602161001490.19097-100000 at="" w3pain=""> Content-Type: TEXT/PLAIN; charset=US-ASCII Dear all I would greaty appreaciate any help with the following. Can Pearson correlation coefficients from data on different range or scale be compared??.For example, if I compute the (r) value from a pair of intensity ratio datasets spanning the range -10 to +10, can I compare it with an the (r) value from another pair of intensity ratio datasets spanning a different range say -5 to +5. Similarily, can I compare the (r) value from correlating a pair of intensity ratio datasets with that from correlating a pair of absolute intensity datasets (where the scale of the data is different). The question that I would want to address from such comparison is whether the ratios covary better than the raw intensities. Please let me know if this is not clear enough... Many thanks. ------------------------------ Message: 18 Date: Thu, 16 Feb 2006 11:21:44 +0100 From: "Benjamin Otto" <[email protected]> Subject: Re: [BioC] Differences: mas5/mas5calls vs. call.expr/pairwise.comparison To: "Benjamin Otto" <b.otto at="" uke.uni-hamburg.de="">, "BioClist" <bioconductor at="" stat.math.ethz.ch=""> Message-ID: <noeokkcpbgiaippdonmggelkcbaa.b.otto at="" uke.uni-="" hamburg.de=""> Content-Type: text/plain; charset="us-ascii" I just checked the expresson values returned by the two methods. The interesting thing is, mas5(x,sc=sometgt) yields results nearly identical to the affymetrix ones. The simplaffy call.exprs(x,"mas5",sc=sometgt) return values a little bit different different. Looking the resulting values it makes no big difference if I call call.exprs() with "mas5" or "mas5-R". So why do I get such different results? Here are my first five rows: > smp2 <- call.exprs(x,"mas5-R",sc=sometgt) > exprs(smp2)[1:5,] 1026_HG-U133.CEL 62_HG-U133.CEL 1007_s_at 4.850861 4.657905 1053_at 2.438049 3.517322 117_at 4.199809 4.803548 121_at 6.758776 6.241071 1255_g_at 4.450771 1.855238 > exprs(simplemas)[1:5,] 1026_HG-U133.CEL 62_HG-U133.CEL 1007_s_at 4.851125 4.658149 1053_at 2.438313 3.517566 117_at 4.200072 4.803792 121_at 6.759039 6.241314 1255_g_at 4.451034 1.855482 > exprs(mas)[1:5,] 1026_HG-U133.CEL 62_HG-U133.CEL 1007_s_at 28.857236 25.244638 1053_at 5.419085 11.450371 117_at 18.376736 27.926217 121_at 108.291468 75.639653 1255_g_at 21.868322 3.618115 > log(exprs(mas))[1:5,] 1026_HG-U133.CEL 62_HG-U133.CEL 1007_s_at 3.362361 3.228614 1053_at 1.689927 2.438022 117_at 2.911086 3.329566 121_at 4.684826 4.325981 1255_g_at 3.085039 1.285953 and here is a copy of the corresponding function calls of mas5() and call.exprs(): > mas5 function (object, normalize = TRUE, sc = 500, analysis = "absolute", ...) { res <- expresso(object, bgcorrect.method = "mas", pmcorrect.method = "mas", normalize = FALSE, summary.method = "mas", ...) if (normalize) res <- affy.scalevalue.exprSet(res, sc = sc, analysis = analysis) return(res) } #calls.exprs ... else if (algorithm == "mas5-R") { if is.na(method)) { tmp1 <- expresso(x, normalize = FALSE, bgcorrect.method = "mas", pmcorrect.method = "mas", summary.method = "mas") tmp <- affy.scalevalue.exprSet(tmp1, sc = sc) } else { tmp1 <- expresso(x, normalize.method = method, bgcorrect.method = "mas", pmcorrect.method = "mas", summary.method = "mas") tmp <- affy.scalevalue.exprSet(tmp1, sc = sc) } ... else if (algorithm == "mas5") { tmp <- justMAS(x, tgt = sc) if (!do.log) { exprs(tmp) <- 2^exprs(tmp) } ... I don't really see the difference. regards, benjamin > -----Original Message----- > From: bioconductor-bounces at stat.math.ethz.ch > [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of Benjamin > Otto > Sent: 16 February 2006 10:25 > To: BioClist > Subject: [BioC] Differences: mas5/mas5calls vs. > call.expr/pairwise.comparison > > > Dear BioC members, > > in my last calculations I noticed that the affy-package combination of > mas5() and mas5calls() results in different Present/Absent calls than the > simpleaffy-package version with call.exprs() and > pairwise.comparison(). That > was the more surprising for me as I thought simpleaffy was just > some wrapper > around the affy-package automating some high-level steps. A comparison of > the results with the ones returned by the affymetrix software > revealed that > the simpleaffy version is nearly identical while the affy version is > different one. Is there some error in my code? > The exact commands I used were: > > x <- read.affy() > > #version 1: > mas <- mas5(x,sc=sometgt) > mas.call <- mas5calls(x) > > #version 2: > simplemas <- call.exprs(x,"mas5",sc=sometgt) > simplemas.cmp <- pairwise.comparison(simplemas,"treatment",spots=x) > > > regards > Benjamin > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > ------------------------------ Message: 19 Date: Thu, 16 Feb 2006 11:55:11 +0100 From: Antoine Lucas <[email protected]> Subject: [BioC] GeneR To: bioconductor at stat.math.ethz.ch Message-ID: <20060216115511.1817fcb4.antoinelucas at libertysurf.fr> Content-Type: text/plain; charset=ISO-8859-15 Dear R users, There is a new release of package GeneR. Briefly, GeneR package provides tools to manipulate large DNA/protein sequences (like a whole chromosome). By "manipulate" I mean of course extract, append, concatenate; but also look for "word", orfs or masked positions, or to returns compositions of nono-di-tri nucleotides. It has been designed to work with large vectors so that it can concatenate all exons of a chromosome at once, or the composition of same exons. There are I/O functions to work with standard bank files like Fasta, Genebank and Embl. And last, but not least we provide a complete arithmetic on "segments" i.e. functions like "union", "intersection", "not" between two sets of segments. One application could be to "substract" all CDS from genes and deduce UTR regions. In new release we add some functions working on Embl files (to read headers, or features), and correct some bugs. I hope you will enjoy it ! Regards, Antoine. -- Antoine Lucas Centre de g?n?tique Mol?culaire, CNRS 91198 Gif sur Yvette Cedex Tel: (33)1 69 82 38 89 Fax: (33)1 69 82 38 77 ------------------------------ _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor End of Bioconductor Digest, Vol 36, Issue 14 ********************************************