Hi folks,
I am trying to play around with RCall by calling some R packages from
Julia. Then I run into this issue (see below)
using RCall
@rimport highmean as highmean ## Import the package highmean from r
@rusing highmean
srand(1234)
Y1 = randn(5,10)
Y2 = rand(5,10)
julia> pval = rcopy(apval_B
Depending on what parts of their package you are interested in, to
implement a small subset of their functionality I recently wrote some code
in both Julia and R (to make sure I had an idea what they were doing to
replicate their results) based on the equations in page 258 of:
Hothorn T, Hornik
so do include("file.jl", [1:2;9:10]) to evaluate lines 1 through 2 and
> then 9 through 10.
>
> I’m going to put this is my .juliarc.jl :)
>
> On Tuesday, January 5, 2016 at 1:46:05 PM UTC-8, Taylor Maxwell wrote:
>
> Thanks, I will see if I can figure it out.
>
; On Tuesday, January 05, 2016 09:25:40 AM Taylor Maxwell wrote:
> > Ordinarily that would work but my problem is that I need to do it from
> the
> > command line on a remote workstation (the data files cannot be moved
> from
> > that computer) and I can only have on
ress ctrl+enter to execute
> them.
>
> -s
>
> [1]: https://github.com/JunoLab/atom-julia-client
>
>
> On Tue, Jan 5, 2016, at 11:54 AM, Taylor Maxwell wrote:
>
> I have looked around and haven't been able to find an answer (I am sure it
> is simple).
&g
I have looked around and haven't been able to find an answer (I am sure it
is simple).
Say I have a script.jl file with a bunch of commands that I want to work
with interactively in the REPL but only want to do a few lines at a time
and I am unable to copy/paste.
Is there a way to load/import
Thanks, I will try and figure out if I can work this out from your
suggestions.
On Tuesday, October 27, 2015 at 8:29:07 AM UTC-6, Yichao Yu wrote:
>
> On Tue, Oct 27, 2015 at 10:08 AM, Taylor Maxwell
> > wrote:
> > Thanks for your response. It looks like pointer(::Array) gi
Thanks for your response. It looks like pointer(::Array) gives me the same
pointer as if I defined the base.convert call from 0.3.
I am not passing it to a ccall. Below is an example of what i am doing.
The bedfreq function gets the genotype and missing counts (4 possibilities
encoded in eve
I am moving some of my code to 0.4 and I am having trouble figuring out how
to get a pointer for beginning of a UInt matrix. In the past I did:
*julia> **bpt = convert(Ptr{Uint8},b) #where b it my Uint8 matrix*
*Ptr{Uint8} @0x7fdd243fca70*
in 0.4 I get:
*julia> **bpt = convert(Ptr{UInt8}
I have just spent a few hours getting past this error and this thread is a
top google result, so I am adding my fix:
I had the same issue as Andrew. First, note that the tkf repo hasn't been
updated for years, and the current emacs-ipython-notebook repo is
millejoh's fork: https://github.com/mi
Suppose I have a heterogeneous-valued dictionary *foo_cache* where the keys
are a tuple of types (e.g. *(S,T,R)*) and the values are the results of a
call to a function *foo(x,S,T,R)* where the type of object *x* is known to
type inference. I would like to annotate the type of a call to
*foo_c
Hello folks,
This is a re-post from my previous post earlier in the week. My code
was referring t a non-existent file. I am attaching the code I am using
since I have no idea what part of the code is producing errors in Julia
0.3.6.
I am trying to run a little bootstrap in Julia using pmap
Hello folks,
I am trying to run a little bootstrap in Julia using pmap and I am getting
the following error from each process:
fatal error on fatal error on fatal error on fatal error on fatal error on
fatal error on fatal error on fatal error on fatal error on fatal error on
fatal error on fa
You do not need DataFrames to run a GLM. If you construct your now Float64
design matrix (X) and have you dependent variable y you can fit a GLM with
such a logistic regression with the following command:
fit(GeneralizedLinearModel,X,y,Binomial())
to fit an LM model you can:
fit(LinearModel,
Thanks everyone for the replies.
Jiahao: Thanks especially for the pointer to MAX_TYPE_DEPTH. This is
exactly what I hit, and now I'm trying to learn what its consequences are.
I appreciate the need for constraints to keep type inference efficient, but
I'm not sure why what I am doing is runni
I ran into the following when tracking down a type instability issue.
Consider the following type hierarchy:
```julia
type T1{I<:Number} end
type T2{T<:T1}
x::T
end
type T3{T<:T2}
x::T
end
type T4{T<:T3}
x::T
end
type T5{T<:T4}
x::T
end
```
Now, two functions, one which returns a `T4`
I agree Tim! I will work on that.
On Wednesday, December 17, 2014 5:13:50 AM UTC-6, Tim Holy wrote:
>
> Maxwell, it would be great if you could submit a pull request to add that
> documentation.
>
> Best,
> --Tim
>
> On Wednesday, December 17, 2014 07:23:50 AM Andre
gt; d1 = NoncentralF(1.2,2.3,3.4)
> NoncentralF(ndf=1.2, ddf=2.3, ncp=3.4)
>
> julia> rand(d)
> 33.94546618024409
>
> julia> d2 = NoncentralChisq(3, 1.1)
> NoncentralChisq(df=3.0, ncp=1.1)
>
> julia> rand(d2)
> 4.822266647364242
>
> 2014-12-17 2:31 GMT+01:00
Hi all,
I would like to simulate from a non-central F distribution. I don't think
the package Distributions supports non-central F or the non-central Chi
squared. Is there any other package(s) that can help me with that? or does
anybody have an idea how that can be done?
Thanks,
Sounds great, I remember the brief discussion we previously had about
pedigreemm. Thanks for working on this!
On Tuesday, December 16, 2014 12:57:14 PM UTC-7, Douglas Bates wrote:
>
> On Tuesday, December 16, 2014 1:32:19 PM UTC-6, Taylor Maxwell wrote:
>>
>> Pedigrees sound
Pedigrees sounds like a good name. Will this package be able to generate
and manipulate standard kinship matrices? This is not my specialty but
familial data is pretty common and many standard association methods for
"unrelated" individuals have analogous method that need to take into about
th
I just moved to Fort Collins in February (~hour north of Denver). I am not
a programming aficionado but I love Julia and it has opened my world of
possibilities. I primarily do human statistical genetics. My git name is
timema. Thanks for posting about the Spark/Julia night, I had not heard o
Are you looking for the fitted values? Is predict(OLS) what you are
looking for?
*julia> **X = [1;2;3.]*
*3-element Array{Float64,1}:*
* 1.0*
* 2.0*
* 3.0*
*julia> **Y = [1;0;1.]*
*3-element Array{Float64,1}:*
* 1.0*
* 0.0*
* 1.0*
*julia> **data = DataFrame(X=X,Y=Y)*
*3x2 DataFrame*
Sorry, I threw it up quickly, I should have spent more time making sure it
was correct.
On Thursday, August 7, 2014 8:17:22 AM UTC-6, Simon Kornblith wrote:
>
>
>
I haven't seen code to do it yet but it is very simple to calculate with a
LinearModel from GLM Below is some code to calculate r-squared or adjusted
r-squared from a linear model in GLM calculated from a dataframe or from a
model calculated without a data frame. At the bottom is some simple c
It looks like there is a group effort currently underway to make a nice
interface for much of the machine learning and statistics problems out
there. This is primarily being headed up by Dahua Lin. Your work
definitely fits within the scope they have talked about. Here are some
links:
ttp://
scratch what I just said
If your dataframe has 6 or more columns then it prints this summary. If
you index less than 6 columns it will print ok. You can force it to spit
it all out with showall(df) but that may not be entirely legible. There
may be other ways to display it.
I just added a pull request with code to the GLM package to add an
anovatable and an effects function. It gets the effects from a LmMod
(created from lm()) and makes a standard anova table with term, df, SS, MS,
F, and p-value.
On Wednesday, February 12, 2014 1:57:08 PM UTC-7, Edward Stembler w
You should be able to perform an anova or ancova with the GLM package.
https://github.com/JuliaStats/GLM.jl
You can use the lm() function and your variables with factors need to be
PooledDataArrays in the dataframe.
On Wednesday, February 12, 2014 2:57:08 PM UTC-6, Edward Stembler wrote:
>
>
>
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