This is my first mathmatica code,
I defined the functions:
\[Beta] := v/c
\[Gamma] := 1/Sqrt[1 - \[Beta]^2]
TotalE[\[Gamma][\[Beta]]] := \[Gamma]mc^2
KE := TotalE[\[Gamma][\[Beta]]] - mc^2
No i want to make a series expansion of KE at β → 0 up to order 2,
I tried:
Series[KE, {\[Beta], 1, 2}]
But i got the error massage:
General::ivar: v/c is not a valid variable.
I also wanted to define Ekin as function of β,
so i used Solve function to get the inverse function, β[Ekin]:
Solve[KE, \[Beta]]
The same errors arises again:
Solve::ivar: v/c is not a valid variable.
Try this
Clear[\[Gamma],\[Beta],mc,KE,s,v,c]
\[Gamma] = 1/Sqrt[1 - \[Beta]^2];
TotalE[\[Gamma]*\[Beta]] = \[Gamma]*mc^2;
KE = TotalE[\[Gamma]*\[Beta]] - mc^2;
s=Normal[Series[KE, {\[Beta], 1, 2}]]/.\[Beta]->v/c
Reduce[KE==0, \[Beta]]/.\[Beta]->v/c
which returns
O-mc^2 + mc^2/(Sqrt[2]*Sqrt[1 - v/c]) -
(mc^2*(-1 + v/c))/(4*Sqrt[2]*Sqrt[1 - v/c]) +
(3*mc^2*(-1 + v/c)^2)/(32*Sqrt[2]*Sqrt[1 - v/c])
and
(mc != 0 && v/c == 0)||(-1+v^2/c^2 !=0 && mc == 0)
What that is trying to do is do your calculations with the simple variable beta, before you turn that into v/c and after the calculations replace beta with v/c.
But there are still things about the way you have written that which worry me. You are kind of writing TotalE like it is a function, but that is not the way to define a Mathematica function and I am concerned this may be going to get you into trouble.
Please let me know if I have misunderstood some of what you are trying to do and explain what I've done wrong and I will try to find a way to fix that.
Related
I ran this code to find the norm of some fundamnetal units of a biqaudratic number field, but I faced following problem
for (q=5, 200, for(p=q+1, 200, if (isprime(p)==1 && isprime(q)==1 ,k1=bnfinit(y^2-2*p,1); k2=bnfinit(y^2-q,1); k3=bnfinit(y^2-2*p*q,1); ep1=k1[8][5][1]; ep2=k2[8][5][1]; ep3=k3[8][5][1]; normep1=nfeltnorm(k1,ep1); normep2=nfeltnorm(k2,ep2); normep3=nfeltnorm(k3,ep3); li=[[q,p], [normep1, normep2, normep3]]; lis4=concat(lis4,[li]))))
and it works for small p and q. However, when I ran that for p and q greater than 150, it gives the following error:
First, I didn't use the flag=1 for bnf, but after adding that, still I get the same error.
Please, do not use indexing like ...[8][5][1] to get the fundamental units (FU). It seems that bnfinit omits FU matrix for some p and q. Instead, use the member function fu to receive FU. Please, find the example below:
> [q, p] = [23, 109];
> k = bnfinit(y^2 - 2*p*q, 1);
> k[8][5]
[;]
> k[8][5][1] \\ you will get the error here trying to index the empty matrix.
...
incorrect type in _[_] OCcompo1 [not a vector] (t_MAT).
> k.fu
[Mod(-355285121749346859670064114879166870*y - 25157598731408198132266996072608016699, y^2 - 5014)]
> norm(k.fu[1])
1
I'm trying to replicate values from pine script cci() function in golang. I've found this lib https://github.com/markcheno/go-talib/blob/master/talib.go#L1821
but it gives totally different values than cci function does
pseudo code how do I use the lib
cci := talib.Cci(latest14CandlesHighArray, latest14CandlesLowArray, latest14CandlesCloseArray, 14)
The lib gives me the following data
Timestamp: 2021-05-22 18:59:27.675, Symbol: BTCUSDT, Interval: 5m, Open: 38193.78000000, Close: 38122.16000000, High: 38283.55000000, Low: 38067.92000000, StartTime: 2021-05-22 18:55:00.000, EndTime: 2021-05-22 18:59:59.999, Sma: 38091.41020000, Cci0: -16.63898084, Cci1: -53.92565811,
While current cci values on TradingView are: cci0 - -136, cci1 - -49
could anyone guide what do I miss?
Thank you
P.S. cci0 - current candle cci, cci1 - previous candle cci
PineScript has really great reference when looking for functions, usually even supplying the pine code to recreate it.
https://www.tradingview.com/pine-script-reference/v4/#fun_cci
The code wasn't provided for cci, but a step-by-step explanation was.
Here is how I managed to recreate the cci function using Pine, following the steps in the reference:
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © bajaco
//#version=4
study("CCI Breakdown", overlay=false, precision=16)
cci_breakdown(src, p) =>
// The CCI (commodity channel index) is calculated as the
// 1. difference between the typical price of a commodity and its simple moving average,
// divided by the
// 2. mean absolute deviation of the typical price.
// 3. The index is scaled by an inverse factor of 0.015
// to provide more readable numbers
// 1. diff
ma = sma(src,p)
diff = src - ma
// 2. mad
s = 0.0
for i = 0 to p - 1
s := s + abs(src[i] - ma)
mad = s / p
// 3. Scaling
mcci = diff/mad / 0.015
mcci
plot(cci(close, 100))
plot(cci_breakdown(close,100))
I didn't know what mean absolute deviation meant, but at least in their implementation it appears to be taking the difference from the mean for each value in the range, but NOT changing the mean value as you go back.
I don't know Go but that's the logic.
I've been working on my own Julia Set Plot Implementation. I don't want to use JuliaSetPlot, (however I'm eager to use JuliaSetIterationPoints and JuliaSetCount, I just don't really know how).
I've come up with something like this, but I have a problem, I have no idea what is wrong and why it won't work.
Can anyone help?
'''mathematica
firstFun= Function[ {Typed[pixel0, "ComplexReal64"]},
Module[{i = 1, maksi=100, pixel = pixel0},
While[i < maksi && (Abs[pixel])^2 < 2,
temp = (Re[pixel])^2 - (Im[pixel])^2
Re[pixel] = 2 * Re[pixel] * Im[pixel] - 0.8\[Iota] * Im[pixel0]
Im[pixel] = temp - 0.8\[Iota]* Re[pixel0];
i++ ];
i]];
'''
my code
This
firstFun=Function[{Typed[pixel0,"ComplexReal64"]},
Module[{i=1,maksi=100,pixel=pixel0},
While[i<maksi&&Abs[pixel]^2<2,
pixel=2*Re[pixel]*Im[pixel]-0.8*I*Im[pixel0]+
I*(Re[pixel]^2-Im[pixel]^2-0.8*I*Re[pixel0]);
i++];
i]];
compFun[c_]=FunctionCompile[firstFun]
compiles without any compile-time error messages.
If I haven't made a mistake then I think your pixel calculation can be simplified to
pixel=I*Conjugate[pixel]^2+0.8*Conjugate[pixel0]
Please test all this very carefully to make certain that it is correct.
I have read a lot about the pain of replicate the easy robust option from STATA to R to use robust standard errors. I replicated following approaches: StackExchange and Economic Theory Blog. They work but the problem I face is, if I want to print my results using the stargazer function (this prints the .tex code for Latex files).
Here is the illustration to my problem:
reg1 <-lm(rev~id + source + listed + country , data=data2_rev)
stargazer(reg1)
This prints the R output as .tex code (non-robust SE) If i want to use robust SE, i can do it with the sandwich package as follow:
vcov <- vcovHC(reg1, "HC1")
if I now use stargazer(vcov) only the output of the vcovHC function is printed and not the regression output itself.
With the package lmtest() it is possible to print at least the estimator, but not the observations, R2, adj. R2, Residual, Residual St.Error and the F-Statistics.
lmtest::coeftest(reg1, vcov. = sandwich::vcovHC(reg1, type = 'HC1'))
This gives the following output:
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.54923 6.85521 -0.3719 0.710611
id 0.39634 0.12376 3.2026 0.001722 **
source 1.48164 4.20183 0.3526 0.724960
country -4.00398 4.00256 -1.0004 0.319041
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
How can I add or get an output with the following parameters as well?
Residual standard error: 17.43 on 127 degrees of freedom
Multiple R-squared: 0.09676, Adjusted R-squared: 0.07543
F-statistic: 4.535 on 3 and 127 DF, p-value: 0.00469
Did anybody face the same problem and can help me out?
How can I use robust standard errors in the lm function and apply the stargazer function?
You already calculated robust standard errors, and there's an easy way to include it in the stargazeroutput:
library("sandwich")
library("plm")
library("stargazer")
data("Produc", package = "plm")
# Regression
model <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc,
index = c("state","year"),
method="pooling")
# Adjust standard errors
cov1 <- vcovHC(model, type = "HC1")
robust_se <- sqrt(diag(cov1))
# Stargazer output (with and without RSE)
stargazer(model, model, type = "text",
se = list(NULL, robust_se))
Solution found here: https://www.jakeruss.com/cheatsheets/stargazer/#robust-standard-errors-replicating-statas-robust-option
Update I'm not so much into F-Tests. People are discussing those issues, e.g. https://stats.stackexchange.com/questions/93787/f-test-formula-under-robust-standard-error
When you follow http://www3.grips.ac.jp/~yamanota/Lecture_Note_9_Heteroskedasticity
"A heteroskedasticity-robust t statistic can be obtained by dividing an OSL estimator by its robust standard error (for zero null hypotheses). The usual F-statistic, however, is invalid. Instead, we need to use the heteroskedasticity-robust Wald statistic."
and use a Wald statistic here?
This is a fairly simple solution using coeftest:
reg1 <-lm(rev~id + source + listed + country , data=data2_rev)
cl_robust <- coeftest(reg1, vcov = vcovCL, type = "HC1", cluster = ~
country)
se_robust <- cl_robust[, 2]
stargazer(reg1, reg1, cl_robust, se = list(NULL, se_robust, NULL))
Note that I only included cl_robust in the output as a verification that the results are identical.
I am currently runnuing training in matlab on a matrix of logspecrum samples I am constantly dealing with underflow problems.I understood that I need to work with log's in order to deal with underflowing.
I am still strugling with uderflow though , when i calculate the mean (mue) bucause it is negetive i cant work with logs so i need the real values that underflow.
These are equasions i am working with:
In MATLAB code i calulate log_tau in oreder avoid underflow but when calulating mue i need exp(log(tau)) which goes to zero.
I am attaching relevent MATLAB code
**in the code i called the variable alpha is tau ...
for i = 1 : 50
log_c = Logsum(log_alpha,1) - log(N);
c = exp(log_c);
mue = DataMat*alpha./(repmat(exp(Logsum(log_alpha,1)),FrameSize,1));
log_abs_mue = log(abs(mue));
log_SigmaSqr = log((DataMat.^2)*alpha) - repmat(Logsum(log_alpha,1),FrameSize,1) - 2*log_abs_mue;
SigmaSqr = exp(log_SigmaSqr);
for j=1:N
rep_DataMat(:,:,j) = repmat(DataMat(:,j),1,M);
log_gamma(j,:) = log_c - 0.5*(FrameSize*log(2*pi)+sum(log_SigmaSqr)) + sum((rep_DataMat(:,:,j) - mue).^2./(2*SigmaSqr));
end
log_alpha = log_gamma - repmat(Logsum(log_gamma,2),1,M);
alpha = exp(log_alpha);
end
c = exp(log_c);
SigmaSqr = exp(log_SigmaSqr);
does any one see how i can avoid this? or what needs to be fixed in code?
What i did was add this line to the MATLAB code:
mue(isnan(mue))=0; %fix 0/0 problem
and this one:
SigmaSqr(SigmaSqr==0)=1;%fix if mue_k = x_k
not sure if this is the best solution but is seems to work...
any have a better idea?