[R] rlnorm behaviour

[Thierry Onkelinx]

You need to study my examples and the helpfile of ifelse more carefully.
Then you'll understand why your code is wrong.

​
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
Op 14 jun. 2016 17:47 schreef "Ayyappa Chaturvedula" <ayyappach op gmail.com>:

> I am sorry, I missed that.  I think I made it more appropriate and not
> using unnecessary simulated values.  Thank you for your help.
>
> fulldata$Wt<-ifelse(fulldata$Sex==1,rlnorm(length(fulldata$Sex[fulldata$Sex==1]),
> meanlog = log(85.1), sdlog = sqrt(0.0329)),
>                     rlnorm(length(fulldata$Sex[fulldata$Sex==0]), meanlog
> = log(73), sdlog = sqrt(0.0442)))
>
> On Tue, Jun 14, 2016 at 10:42 AM, Thierry Onkelinx <
> thierry.onkelinx op inbo.be> wrote:
>
>> Please keep r-help in cc.
>>
>> Yes. Have a look at this example
>>
>> ifelse(
>>   sample(c(TRUE, FALSE), size = 0.5 * length(letters), replace = TRUE),
>>   letters,
>>   LETTERS
>> )
>>
>>
>> ir. Thierry Onkelinx
>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>> and Forest
>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>> Kliniekstraat 25
>> 1070 Anderlecht
>> Belgium
>>
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> 2016-06-14 17:31 GMT+02:00 Ayyappa Chaturvedula <ayyappach op gmail.com>:
>>
>>> Thank you very much for your kind support.  The length of my condition
>>> vector is ~80 because I want only Sex==1 and else will be the other.  I
>>> understand now how ifelse works.  If the vector of the simulated vector is
>>> longer than the condition vector, then it takes the first few elements to
>>> match the length of condition vector and discards the rest?
>>>
>>> Regards,
>>> Ayyappa
>>>
>>> On Tue, Jun 14, 2016 at 10:15 AM, Thierry Onkelinx <
>>> thierry.onkelinx op inbo.be> wrote:
>>>
>>>> Dear Ayyappa,
>>>>
>>>> ifelse works on a vector. See the example below.
>>>>
>>>> ifelse(
>>>>   sample(c(TRUE, FALSE), size = length(letters), replace = TRUE),
>>>>   letters,
>>>>   LETTERS
>>>> )
>>>>
>>>> However, note that it will recycle short vectors when they are not of
>>>> equal length.
>>>>
>>>> ifelse(
>>>>   sample(c(TRUE, FALSE), size = 2 * length(letters), replace = TRUE),
>>>>   letters,
>>>>   LETTERS
>>>> )
>>>>
>>>> In your code the length of the condition vector is 200, the length of
>>>> the two other vectors is 100.
>>>>
>>>> Best regards,
>>>>
>>>> ir. Thierry Onkelinx
>>>> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>>>> and Forest
>>>> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
>>>> Kliniekstraat 25
>>>> 1070 Anderlecht
>>>> Belgium
>>>>
>>>> To call in the statistician after the experiment is done may be no more
>>>> than asking him to perform a post-mortem examination: he may be able to say
>>>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>>>> The plural of anecdote is not data. ~ Roger Brinner
>>>> The combination of some data and an aching desire for an answer does
>>>> not ensure that a reasonable answer can be extracted from a given body of
>>>> data. ~ John Tukey
>>>>
>>>> 2016-06-14 17:02 GMT+02:00 Ayyappa Chaturvedula <ayyappach op gmail.com>:
>>>>
>>>>> Dear Group,
>>>>>
>>>>> I am trying to simulate a dataset with 200 individuals with random
>>>>> assignment of Sex (1,0) and Weight from lognormal distribution
>>>>> specific to
>>>>> Sex.  I am intrigued by the behavior of rlnorm function to impute a
>>>>> value
>>>>> of Weight from the specified distribution.  Here is the code:
>>>>> ID<-1:200
>>>>> Sex<-sample(c(0,1),200,replace=T,prob=c(0.4,0.6))
>>>>> fulldata<-data.frame(ID,Sex)
>>>>> fulldata$Wt<-ifelse(fulldata$Sex==1,rlnorm(100, meanlog = log(85.1),
>>>>> sdlog
>>>>> = sqrt(0.0329)),
>>>>>                     rlnorm(100, meanlog = log(73), sdlog =
>>>>> sqrt(0.0442)))
>>>>>
>>>>> mean(fulldata$Wt[fulldata$Sex==0]);to check the mean is close to 73
>>>>> mean(fulldata$Wt[fulldata$Sex==1]);to check the mean is close to 85
>>>>>
>>>>> I see that the number of simulated values has an effect on the mean
>>>>> calculated after imputation. That is, the code rlnorm(100, meanlog =
>>>>> log(73), sdlog = sqrt(0.0442)) gives much better match compared to
>>>>> rlnorm(1, meanlog = log(73), sdlog = sqrt(0.0442)) in ifelse statement
>>>>> in
>>>>> the code above.
>>>>>
>>>>> My understanding is that ifelse will be imputing only one value where
>>>>> the
>>>>> condition is met as specified.  I appreciate your insights on the
>>>>> behavior
>>>>> for better performance of increasing sample number.  I appreciate your
>>>>> comments.
>>>>>
>>>>> Regards,
>>>>> Ayyappa
>>>>>
>>>>>         [[alternative HTML version deleted]]
>>>>>
>>>>> ______________________________________________
>>>>> R-help op r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>>> 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.
>>>>>
>>>>
>>>>
>>>
>>
>

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