Many thanks,
Stéphane
Le 30 mars 2015 à 10:42, peter dalgaard a écrit :
>
>> On 30 Mar 2015, at 09:59 , Stéphane Adamowicz
>> wrote:
>>
>>
>> However, in order to help me understand, would you be so kind as to give me
>> a matrix or data.frame example where « complete.cases(X)== T » or «
> On 30 Mar 2015, at 09:59 , Stéphane Adamowicz
> wrote:
>
>
> However, in order to help me understand, would you be so kind as to give me a
> matrix or data.frame example where « complete.cases(X)== T » or «
> complete.cases(X)== TRUE » would give some unwanted result ?
The standard proble
> On 30-03-2015, at 09:59, Stéphane Adamowicz
> wrote:
>
>
> Le 27 mars 2015 à 18:01, David Winsemius a écrit :
>
>>
>> On Mar 27, 2015, at 3:41 AM, Stéphane Adamowicz wrote:
>>
>>> Well, it seems to work with me.
>>>
>>
>> No one is doubting that it worked for you in this instance. What
Le 27 mars 2015 � 18:01, David Winsemius a �crit :
>
> On Mar 27, 2015, at 3:41 AM, St�phane Adamowicz wrote:
>
>> Well, it seems to work with me.
>>
>
> No one is doubting that it worked for you in this instance. What Peter D. was
> criticizing was the construction :
>
> complete.cases(t(
On 2015-03-27 11:41, Stéphane Adamowicz wrote:
Well, it seems to work with me.
Y <- as.matrix(airquality)
head(Y, n=8)
Ozone Solar.R Wind Temp Month Day
[1,]41 190 7.4 67 5 1
[2,]36 118 8.0 72 5 2
[3,]12 149 12.6 74 5 3
[4,]18 313
Thanks Richard,
This works, rather obvious now that i think of it!
=)
On 27/03/2015 4:30 pm, Richard M. Heiberger wrote:
just reverse what you did before.
newdata <- data
newdata[] <- NA
newdata[,!apply(is.na(data), 2, any)] <- myfunction(data_no_NA)
On Fri, Mar 27, 2015 at 1:13 AM, Jatin Kala
On Mar 27, 2015, at 3:41 AM, Stéphane Adamowicz wrote:
> Well, it seems to work with me.
>
No one is doubting that it worked for you in this instance. What Peter D. was
criticizing was the construction :
complete.cases(t(Y))==T
... and it was on two bases that it is "wrong". The first is tha
>
>> example. Furthermore in my example no unwanted format occurred. You can
>
> Yes because data.frame was (luckily) numeric.
>
Luck has nothing to do with this. I Chose this example on purpose …
Stéphane
__
R-help@r-project.org mailing list -- To
Hi
> -Original Message-
> From: Stéphane Adamowicz [mailto:stephane.adamow...@avignon.inra.fr]
> Sent: Friday, March 27, 2015 1:26 PM
> To: PIKAL Petr
> Cc: peter dalgaard; r-help@r-project.org
> Subject: Re: [R] matrix manipulation question
>
>
> Le 27 mars
Le 27 mars 2015 à 12:34, PIKAL Petr a écrit :
> Very, very, very bad solution.
>
> as.matrix can change silently your data to unwanted format,
> complete.cases()==T is silly as Peter already pointed out.
>
>
Perhaps, but it happens that in the original message, the question dealt with a
: [R] matrix manipulation question
Well, it seems to work with me.
Y <- as.matrix(airquality)
head(Y, n=8)
Ozone Solar.R Wind Temp Month Day
[1,]41 190 7.4 67 5 1
[2,]36 118 8.0 72 5 2
[3,]12 149 12.6 74 5 3
[4,]18 313 11.5 62
Well, it seems to work with me.
Y <- as.matrix(airquality)
head(Y, n=8)
Ozone Solar.R Wind Temp Month Day
[1,]41 190 7.4 67 5 1
[2,]36 118 8.0 72 5 2
[3,]12 149 12.6 74 5 3
[4,]18 313 11.5 62 5 4
[5,]NA NA 14.3 56
On 27 Mar 2015, at 09:58 , Stéphane Adamowicz
wrote:
> data_no_NA <- data[, complete.cases(t(data))==T]
Ouch! logical == TRUE is bad, logical == T is worse:
data[, complete.cases(t(data))]
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 200
Why not use complete.cases() ?
data_no_NA <- data[, complete.cases(t(data))==T]
Le 27 mars 2015 à 06:13, Jatin Kala a écrit :
> Hi,
> I've got a rather large matrix of about 800 rows and 60 columns.
> Each column is a time-series 800 long.
>
> Out of these 60 time series, some have mi
just reverse what you did before.
newdata <- data
newdata[] <- NA
newdata[,!apply(is.na(data), 2, any)] <- myfunction(data_no_NA)
On Fri, Mar 27, 2015 at 1:13 AM, Jatin Kala wrote:
> Hi,
> I've got a rather large matrix of about 800 rows and 60 columns.
> Each column is a time-series 800 lon
Hi,
I've got a rather large matrix of about 800 rows and 60 columns.
Each column is a time-series 800 long.
Out of these 60 time series, some have missing values (NA).
I want to strip out all columns that have one or more NA values, i.e.,
only want full time series.
This should do the
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