Re: [Rd] Question re: NA, NaNs in R
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+ does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b) \
if (ISNA (a) || ISNA (b)) y = NA_REAL; \
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
IIUC, there is only one bit pattern used to encode R NA values, so
this should be safe. But I would like to be sure:
Is there any guarantee that the different functions in R would return
NA as identical to the bit pattern defined for NA_REAL, for a given
architecture? Similarly for NaN value(s) and R_NaN?
My guess is that it is possible some functions used internally by R
might encode NaN values differently; ie, setting the lower word to a
value different than 1954 (hence being NaN, but potentially not
identical to R_NaN), or perhaps this is architecture-dependent.
However, NA should be one specific bit pattern (?). And, I wonder if
there is any guarantee that the different functions used in R would
return an NaN value as identical to R_NaN (which appears to be the
'IEEE NaN')?
(interested parties can see + run a simple benchmark from the gist at
https://gist.github.com/kevinushey/8911432)
Thanks,
Kevin
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor
Re: [Rd] Question re: NA, NaNs in R
Thanks Tim, this is exactly the explanation I was hoping to see. Much
appreciated!
On Mon, Feb 10, 2014 at 7:21 AM, Tim Hesterberg timhesterb...@gmail.com wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+ does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b) \
if (ISNA (a) || ISNA (b)) y = NA_REAL; \
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
IIUC, there is only one bit pattern used to encode R NA values, so
this should be safe. But I would like to be sure:
Is there any guarantee that the different functions in R would return
NA as identical to the bit pattern defined for NA_REAL, for a given
architecture? Similarly for NaN value(s) and R_NaN?
My guess is that it is possible some functions used internally by R
might encode NaN values differently; ie, setting the lower word to a
value different than 1954 (hence being NaN, but potentially not
identical to R_NaN), or perhaps this is architecture-dependent.
However, NA should be one specific bit pattern (?). And, I wonder if
there is any guarantee that the different functions used in R would
return an NaN value as identical to R_NaN (which appears to be the
'IEEE NaN')?
(interested parties can see + run a simple benchmark from the gist at
Re: [Rd] Question re: NA, NaNs in R
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but
the answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've
shown are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+ does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b) \
if (ISNA (a) || ISNA (b)) y = NA_REAL; \
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
IIUC, there is only one bit pattern used to encode R NA values, so
this should be safe. But I would like to be sure:
Is there any guarantee that the different functions in R would return
NA as identical to the bit pattern defined for NA_REAL, for a given
architecture? Similarly for NaN value(s) and R_NaN?
My guess is that it is possible some functions used internally by R
might encode NaN values differently; ie, setting the lower word to a
value different than 1954 (hence being NaN, but potentially not
identical to R_NaN), or perhaps this is architecture-dependent.
Re: [Rd] Question re: NA, NaNs in R
Also, similarly, to clarify, should there be _one_ unique bit pattern
for R's NA_REAL, or two? Because I see (for a function hex that
produces the hex representation of a number):
hex(NA_real_)
[1] 7FF007A2
hex(NA_real_+1)
[1] 7FF807A2
hex(NaN)
[1] 7FF8
This is with 64-bit R (on OS X Mavericks, R-devel r64910), as well. I
also noticed in a conversation of Arun (co-author of data.table) that:
On 32-bit R-2.15.3:
NA: 7ff807a2
NaN: 7ff8
On 64-bit version of R-2.15.3
NA: 7ff007a2
NaN: 7ff8
Notice that the initial bit pattern is 7ff0, rather than 7ff8, for
64-bit R. Is this intentional?
Thanks,
Kevin
(function follows:)
// assume size of double, unsigned long long is the same
SEXP hex(SEXP x) {
// double is 8 bytes, each byte can be represented by 2 hex chars,
// so need a str with 16+1 slots
int n = sizeof(unsigned long long) * 2 + 1;
unsigned long long *xx = (unsigned long long*) REAL(x);
char buf[n];
snprintf(buf, n, %016llX, *xx);
SEXP output = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(output, 0, mkChar(buf));
UNPROTECT(1);
return output;
}
On Mon, Feb 10, 2014 at 10:07 AM, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but the
answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've shown
are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+
does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any
kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b) \
if (ISNA (a) || ISNA (b)) y = NA_REAL; \
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52,
Re: [Rd] Question re: NA, NaNs in R
On 10/02/2014 1:43 PM, Kevin Ushey wrote:
Also, similarly, to clarify, should there be _one_ unique bit pattern
for R's NA_REAL, or two? Because I see (for a function hex that
produces the hex representation of a number):
I don't think the language definition defines bit patterns, it defines
behaviour. If both of those bit patterns behave correctly, then it's
fine. (I see the same patterns as you do in 64 bit R-patched, but only
the 2nd in 32 bit R).
Now, perhaps we should define the bit pattern for NA_real_ to make the
optimization you're looking into a bit easier; I don't know if that's
trivial or hard.
Duncan Murdoch
hex(NA_real_)
[1] 7FF007A2
hex(NA_real_+1)
[1] 7FF807A2
hex(NaN)
[1] 7FF8
This is with 64-bit R (on OS X Mavericks, R-devel r64910), as well. I
also noticed in a conversation of Arun (co-author of data.table) that:
On 32-bit R-2.15.3:
NA: 7ff807a2
NaN: 7ff8
On 64-bit version of R-2.15.3
NA: 7ff007a2
NaN: 7ff8
Notice that the initial bit pattern is 7ff0, rather than 7ff8, for
64-bit R. Is this intentional?
Thanks,
Kevin
(function follows:)
// assume size of double, unsigned long long is the same
SEXP hex(SEXP x) {
// double is 8 bytes, each byte can be represented by 2 hex chars,
// so need a str with 16+1 slots
int n = sizeof(unsigned long long) * 2 + 1;
unsigned long long *xx = (unsigned long long*) REAL(x);
char buf[n];
snprintf(buf, n, %016llX, *xx);
SEXP output = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(output, 0, mkChar(buf));
UNPROTECT(1);
return output;
}
On Mon, Feb 10, 2014 at 10:07 AM, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but the
answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've shown
are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+
does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any
kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b) \
if (ISNA (a) || ISNA (b)) y = NA_REAL; \
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
Re: [Rd] Question re: NA, NaNs in R
On 10/02/2014 1:43 PM, Kevin Ushey wrote:
Also, similarly, to clarify, should there be _one_ unique bit pattern
for R's NA_REAL, or two? Because I see (for a function hex that
produces the hex representation of a number):
hex(NA_real_)
[1] 7FF007A2
hex(NA_real_+1)
[1] 7FF807A2
hex(NaN)
[1] 7FF8
This is with 64-bit R (on OS X Mavericks, R-devel r64910), as well. I
also noticed in a conversation of Arun (co-author of data.table) that:
I've just taken a look at the IEEE Std 754-2008. The encoding we use
for NA is a signaling NaN. The standard says that operations on
signaling NaN values convert them to quiet NaN values, with the most
significant bit of the mantissa set. That's the difference between your
first and second rows.
R doesn't do all this bit twiddling, it's done in hardware. Perhaps we
should have chosen a quiet NaN for NA from the beginning; they stay
quiet when you do operations on them. However, I think the choice of
bit pattern was made before that behaviour was mandated, and a change
now would be quite disruptive.
Similarly, the standard doesn't say which bit pattern propagates when
you do binary operations on two NaNs. That's why sometimes NA + NaN is
NA, and sometimes NaN.
Duncan Murdoch
On 32-bit R-2.15.3:
NA: 7ff807a2
NaN: 7ff8
On 64-bit version of R-2.15.3
NA: 7ff007a2
NaN: 7ff8
Notice that the initial bit pattern is 7ff0, rather than 7ff8, for
64-bit R. Is this intentional?
Thanks,
Kevin
(function follows:)
// assume size of double, unsigned long long is the same
SEXP hex(SEXP x) {
// double is 8 bytes, each byte can be represented by 2 hex chars,
// so need a str with 16+1 slots
int n = sizeof(unsigned long long) * 2 + 1;
unsigned long long *xx = (unsigned long long*) REAL(x);
char buf[n];
snprintf(buf, n, %016llX, *xx);
SEXP output = PROTECT(allocVector(STRSXP, 1));
SET_STRING_ELT(output, 0, mkChar(buf));
UNPROTECT(1);
return output;
}
On Mon, Feb 10, 2014 at 10:07 AM, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but the
answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've shown
are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that S+
does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey non-response
xx = I'll uses this for the union of those, or missing value of any
kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define
Re: [Rd] Question re: NA, NaNs in R
On 02/10/14, 19:07 , Duncan Murdoch wrote:
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
But under 3.0.2 patched 64 bit on Maverick:
version
_
platform x86_64-apple-darwin10.8.0
arch x86_64
os darwin10.8.0
system x86_64, darwin10.8.0
status Patched
major 3
minor 0.2
year 2014
month 01
day07
svn rev64692
language R
version.string R version 3.0.2 Patched (2014-01-07 r64692)
nickname Frisbee Sailing
NA+NaN
[1] NA
NaN+NA
[1] NaN
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but
the answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've
shown are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that
S+ does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey
non-response
xx = I'll uses this for the union of those, or missing value of
any kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b)\
if (ISNA (a) || ISNA (b)) y = NA_REAL;\
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
Re: [Rd] Question re: NA, NaNs in R
Hi Duncan,
Thanks a ton -- I appreciate your taking the time to investigate this,
and especially even checking into the IEEE standard to clarify.
Cheers,
Kevin
On Mon, Feb 10, 2014 at 11:54 AM, Rainer M Krug rai...@krugs.de wrote:
On 02/10/14, 19:07 , Duncan Murdoch wrote:
On 10/02/2014 10:21 AM, Tim Hesterberg wrote:
This isn't quite what you were asking, but might inform your choice.
R doesn't try to maintain the distinction between NA and NaN when
doing calculations, e.g.:
NA + NaN
[1] NA
NaN + NA
[1] NaN
So for the aggregate package, I didn't attempt to treat them differently.
This looks like a bug to me. In 32 bit 3.0.2 and R-patched I see
NA + NaN
[1] NA
NaN + NA
[1] NA
But under 3.0.2 patched 64 bit on Maverick:
version
_
platform x86_64-apple-darwin10.8.0
arch x86_64
os darwin10.8.0
system x86_64, darwin10.8.0
status Patched
major 3
minor 0.2
year 2014
month 01
day07
svn rev64692
language R
version.string R version 3.0.2 Patched (2014-01-07 r64692)
nickname Frisbee Sailing
NA+NaN
[1] NA
NaN+NA
[1] NaN
This seems more reasonable to me. NA should propagate. (I can see an
argument for NaN for the answer here, as I can't think of any possible
non-missing value that would give anything else when added to NaN, but
the answer should not depend on the order of operands.)
However, I get the same as you in 64 bit 3.0.2. All calculations I've
shown are on 64 bit Windows 7.
Duncan Murdoch
The aggregate package is available at
http://www.timhesterberg.net/r-packages
Here is the inst/doc/missingValues.txt file from that package:
--
Copyright 2012 Google Inc. All Rights Reserved.
Author: Tim Hesterberg roc...@google.com
Distributed under GPL 2 or later.
Handling of missing values and not-a-numbers.
Here I'll note how this package handles missing values.
I do it the way R handles them, rather than the more strict way that
S+ does.
First, for terminology,
NaN = not-a-number, e.g. the result of 0/0
NA = missing value or true missing value, e.g. survey
non-response
xx = I'll uses this for the union of those, or missing value of
any kind.
For background, at the hardware level there is an IEEE standard that
specifies that certain bit patterns are NaN, and specifies that
operations involving an NaN result in another NaN.
That standard doesn't say anything about missing values, which are
important in statistics.
So what R and S+ do is to pick one of the bit patterns and declare
that to be a NA. In other words, the NA bit pattern is a subset of
the NaN bit patterns.
At the user level, the reverse seems to hold.
You can assign either NA or NaN to an object.
But:
is.na(x) returns TRUE for both
is.nan(x) returns TRUE for NaN and FALSE for NA
Based on that, you'd think that NaN is a subset of NA.
To tell whether something is a true missing value do:
(is.na(x) !is.nan(x))
The S+ convention is that any operation involving NA results in an NA;
otherwise any operation involving NaN results in NaN.
The R convention is that any operation involving xx results in an xx;
a missing value of any kind results in another missing value of any
kind. R considers NA and NaN equivalent for testing purposes:
all.equal(NA_real_, NaN)
gives TRUE.
Some R functions follow the S+ convention, e.g. the Math2 functions
in src/main/arithmetic.c use this macro:
#define if_NA_Math2_set(y,a,b)\
if (ISNA (a) || ISNA (b)) y = NA_REAL;\
else if (ISNAN(a) || ISNAN(b)) y = R_NaN;
Other R functions, like the basic arithmetic operations +-/*^,
do not (search for PLUSOP in src/main/arithmetic.c).
They just let the hardware do the calculations.
As a result, you can get odd results like
is.nan(NA_real_ + NaN)
[1] FALSE
is.nan(NaN + NA_real_)
[1] TRUE
The R help files help(is.na) and help(is.nan) suggest that
computations involving NA and NaN are indeterminate.
It is faster to use the R convention; most operations are just
handled by the hardware, without extra work.
In cases like sum(x, na.rm=TRUE), the help file specifies that both NA
and NaN are removed.
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see
[Rd] Question re: NA, NaNs in R
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
IIUC, there is only one bit pattern used to encode R NA values, so
this should be safe. But I would like to be sure:
Is there any guarantee that the different functions in R would return
NA as identical to the bit pattern defined for NA_REAL, for a given
architecture? Similarly for NaN value(s) and R_NaN?
My guess is that it is possible some functions used internally by R
might encode NaN values differently; ie, setting the lower word to a
value different than 1954 (hence being NaN, but potentially not
identical to R_NaN), or perhaps this is architecture-dependent.
However, NA should be one specific bit pattern (?). And, I wonder if
there is any guarantee that the different functions used in R would
return an NaN value as identical to R_NaN (which appears to be the
'IEEE NaN')?
(interested parties can see + run a simple benchmark from the gist at
https://gist.github.com/kevinushey/8911432)
Thanks,
Kevin
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Question re: NA, NaNs in R
There is one NA but mulitple NaNs.
And please re-read 'man memcmp': your cast is wrong.
On 10/02/2014 06:52, Kevin Ushey wrote:
Hi R-devel,
I have a question about the differentiation between NA and NaN values
as implemented in R. In arithmetic.c, we have
int R_IsNA(double x)
{
if (isnan(x)) {
ieee_double y;
y.value = x;
return (y.word[lw] == 1954);
}
return 0;
}
ieee_double is just used for type punning so we can check the final
bits and see if they're equal to 1954; if they are, x is NA, if
they're not, x is NaN (as defined for R_IsNaN).
My question is -- I can see a substantial increase in speed (on my
computer, in certain cases) if I replace this check with
int R_IsNA(double x)
{
return memcmp(
(char*)(x),
(char*)(NA_REAL),
sizeof(double)
) == 0;
}
IIUC, there is only one bit pattern used to encode R NA values, so
this should be safe. But I would like to be sure:
Is there any guarantee that the different functions in R would return
NA as identical to the bit pattern defined for NA_REAL, for a given
architecture? Similarly for NaN value(s) and R_NaN?
My guess is that it is possible some functions used internally by R
might encode NaN values differently; ie, setting the lower word to a
value different than 1954 (hence being NaN, but potentially not
identical to R_NaN), or perhaps this is architecture-dependent.
However, NA should be one specific bit pattern (?). And, I wonder if
there is any guarantee that the different functions used in R would
return an NaN value as identical to R_NaN (which appears to be the
'IEEE NaN')?
(interested parties can see + run a simple benchmark from the gist at
https://gist.github.com/kevinushey/8911432)
Thanks,
Kevin
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UKFax: +44 1865 272595
__
R-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
