[R] rlnorm behaviour

[Ayyappa Chaturvedula]

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 at 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 at 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 at 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 at 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]]
>>>>
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>>>>
>>>
>>>
>>
>

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