I have a major problem: I am an absolute beginner in R programming :) using libaray PMCMRplus I want to perform a dunnetTest. However, I cant change the methods to adjust the p value. Whatever command I've used I will allways get a single-stept adjustment. Can please anyone help me?? The commant I am using is:
dunnettTest(v~Gruppe, alternative="greater", p.adjust.methods ="BH")
Maybe a little bit late but three inputs to your question that may help:
1.) For v~Gruppe have you specified what content should be used? You can define this by using $ such as v~Gruppe$columnofyourdataframe
2.) Also for v~Gruppe: according to the PMCMRplus documentation you have to use dunnettTest(x, g). You use v instead of x and did not define g.
3.) For alternative="greater" have you tried to further specify by using alternative = c("two.sided", "greater", "less")? This is also explained in the PMCMRplus documentation.
Related
I am trying to integrate a Heaviside theta function with two signs inside and Mathematica won't give me a solution. Is there any way of improving the approach before just acknowledging that Mathematica cannot integrate it?
Some things you do really worry me.
You use X as a variable and as the name of a function. I've changed those to X and Xf.
You use ω as a variable and as the name of a function. I've changed those to ω and ωf.
You use = and not := in your function definitions. I've changed that.
With those changes I have
Xf[s1_,s2_,α_]:=(1+s2(-2+α)-α+s1(-1+2s2+α))/((-1+2 s1)(-1+2s2));
ωf[s1_,s2_,α_]:=((1-s2+s1(-3+4s2))(1+s2(-2+α)-α+s1(-1+2s2+α)))/((-1+2s1)(-1+s1+s2)(-1+2s2));
λ1[s1_,s2_,α_,X_,ω_]:=1/2(s1-2s2-3s1 X+3s2 X-α+s1 α+s2 α+ω-s1 ω-s2 ω-
1/2Sqrt[(4(α-ω+s2(2-3X-α-ω)+s1(-1+3X-α+ω))^2+8(2-5X+4X^2-2α+2X α+
s1(-2+4s2(1-2X)^2+7X-8X^2+2α-2X α-ω)+ω-s2(4-11X+8X^2-2α+2X α+ω)))]);
λ2[s1_,s2_,α_,X_,ω_]:=1/2(s1-2s2-3s1 X+3s2 X-α+s1 α+s2 α+ω-s1 ω-s2 ω+
1/2Sqrt[(4(α-ω+s2(2-3X-α-ω)+s1(-1+3X-α+ω))^2+8(2-5X+4X^2-2α+2X α+
s1(-2+4s2(1-2X)^2+7X-8X^2+2α-2X α-ω)+ω-s2(4-11X+8X^2-2α+2X α+ω)))]);
λ1simp[s1_,s2_,α_]:=λ1[s1,s2,α,Xf[s1,s2,α],ωf[s1,s2,α]];
λ2simp[s1_,s2_,α_]:=λ2[s1,s2,α,Xf[s1,s2,α],ωf[s1,s2,α]];
fint[s1_,s2_]:=HeavisideTheta[Sign[-λ1simp[s1,s2,α]]*Sign[-λ2simp[s1,s2,α]]];
Please check that very carefully to see if I've made any mistakes.
Now I want to look at your integrand and see what Mathematica sees.
Simplify[fint[s1,s2],1/2<=s1<=1&&1/2<=s2<=s1]
And it responds with
HeavisideTheta[CompexInfinity] 2s1==1||2s2==1||s1+s2==1
HeavisideTheta[True Sign[...]*Sign[...]]
so it looks like your integrand is blowing up at the boundary.
I check that with
Simplify[fint[1/2,s2]]
or
Simplify[fint[s1,1/2]]
and it responds with 1/0 and Indeterminate and HeavisideTheta[Indeterminate]
When it isn't at the boundary, for example
Simplify[fint[3/4,3/4]]
it returns
HeavisideTheta[Sign[5-4*Sqrt[7-28*α+25*α^2]]*Sign[5+4*Sqrt[7-28*α+25*α^2]]]
and that probably says that α is a free variable and we aren't able to determine the value of the Sign without more information.
So I think this is a strong hint where I would begin looking for why your integration is not simply completing.
If you are curious what that integrand looks like then try
α=1/4;
Plot3D[fint[s1,s2],{s1,1/2,1},{s2,1/2,s1}]
I am trying to learn Julia and I read this article about the quick success of Julia. In the last page of the article the author works a small example showing the benefits of multiple dispatch. They define a custom class Spect and define a plot() function for it. Then for an object sqw of type Spect they can call plot(sqw) without having to edit the original plot function. Moreover, this definition also affects similar plotting functions so that you can also call scatter(sqw) without problems. My issue is that author does not show the code, so I do not understand how can you achieve this. I am specially interested in the fact that just defining plot() for this new class is enough to also call other functions like scatter() without defining them for the new class.
Can someone write a small example of this like that of the article so that I can understand how all of this is achieved? Thank you in advance.
Cross posting my answer from Discourse:
It’s a shame the article doesn’t link to the code. Here’s my rough reproduction attempt. My version uses the dct and idct so I’m not getting the nice harmonics, but I think it shows the ideas pretty well.
using RecipesBase, FFTW
struct Spect
points :: AbstractRange
weights :: Vector{Float64}
end
function Spect(f::Function, min, max, n)
points = range(min, max, n)
Spect(points, dct(f.(points)))
end
#recipe function f(S::Spect)
S.points, idct(S.weights)
end
These definitions are enough for
using Plots
squarewave(x) = iseven(floor(x)) ? 1.0 : 0.0
sqw = Spect(squarewave, 0, 5, 20);
plot(sqw)
scatter(sqw)
and
Sage seems to want to evaluate derivatives as far as possible using the chain rule. A simple example is:
var('theta')
f = function('f')(theta)
g = function('g')(theta)
h = f*g
diff(h,theta)
which would display
g(theta)*diff(f(theta), theta) + f(theta)*diff(g(theta), theta)
My question is, is there a way to control just how far Sage will take derivatives? In the example above for instance, how would I get Sage to display instead:
diff(f(theta)*g(theta))
I'm working through some pretty intensive derivations in fluid mechanics, and being able to not evaluate derivatives all the way like discussed above would really help with this. Thanks in advance. Would appreciate any help on this.
This would be called "holding" the derivative.
Adding this possibility to Sage has already been considered.
Progress on this is tracked at:
Sage Trac ticket 24861
and the ticket even links to a branch with code implementing this.
Although progress on this is stalled, and the branch has not been merged,
you could use the code from the branch.
I want to use Simulated Annealing. My objective function exist of multiple variables, for some of them there are only a few options possible. I saw the same question on Stack here:
How to use simulated annealing for a function with discrete paremeters?, but there was no answer but a reference to: How to put mathematical constraints with GenSA function in R.
I don't understand how to apply the advice from the second link to my situation (but I think the answer can be found there).
For example:
v <- c(50, 50, 25, 25)
lower <- c(0,0,0,20)
upper <- c(100,100,50,40)
out <- GenSA(v, lower = lower, upper = upper, fn = efficientFunction)
Assume that the fourth parameter, v[4], only can be in {20,25,30,35,40}. They suggested the use of Lagrange multipliers, hence, I was thinking of something like: lambda * ceil(v[4] / 5). Is this a good idea ?
But what can I do it the sample space of a variable does not have a nice pattern, for example third parameter, v[3], only can be in {0,21,33,89,100}. I don't understand why a Lagrange multiplier can help in this situation. Do I need to make the form of my parameters different that they follow a pattern or is there another option?
In case Lagrange multipliers are the only option, I'll end up with with 8 of these formulations in my objective. It seems to me that there is another option, but I don't know how!
With kind regards and thanks in advance,
Roos
With SA, you could start with a very simple neighbourhood sheme,
pick 1 of the parameters, and change it by selecting a new valid setting, 1 above, or 1 below the current one (we assume that they have a order, like I feel is your case).
There are no Lagrange multipliers involved in SA as I know. But there are many variations and maybe some with Constrainsts or other make use of them.
How should I plot this function:
z^(1/n) [complex roots of z]
with ezsurf(), ezmesh(), ...? In the official documentation is clearly stated that ezsurf() and ezsurfc() for example, do not accept complex inputs.
I understand the trick is probably in using both real() and imag() functions, but even so, I can't get rid of the problem.
The basic idea seems to work for me alright. Of course you can go on to tweak the axis limits, grid spacing, color look-up table, etc.. The online documentation at http://www.mathworks.com/help/techdoc/ref/ezsurf.html has some nice examples that aren't found in the built-in help system. Good luck!
syms z n
subplot(2,1,1)
ezsurf(real(z^(1/n)))
subplot(2,1,2)
ezsurf(imag(z^(1/n)))