I'm using the package rdrobust in R and Stata. I planned to fully implement the analysis in R, but encountered a problem with the function rdbwselect. This function computes different bandwidths depending on the selection procedure. By default, the procedure is Mean Square Error bwselect=mserd. However, I'm interested in exploring other procedures and comparing them. I then tried ALL=true; which is the option that according to the package "if specified, rdbwselect reports all available bandwidth selection procedures"
My issue is that, in R, rdbwselect is not showing me the bandwidths, not with the default not with the 'all' option or any other specification
x<-runif(1000,-1,1)
y<-5+3*x+2*(x>=0)+rnorm(1000)
## With default mserd
rdbwselect(y,x,)
## All selection procedures
rdbwselect(y,x,all= TRUE)
Output rdwselect
The output of both lines of rdbwselect code is exactly the same (see image), and it should not. I also try replicating the script from the rdrobust article in The R Journal (Page 49) and I don't get the same output as in the paper.
Nevertheless, the function is working in Stata 16
clear all
set obs 1000
set seed 1234
gen x = runiform(-1,1)
gen y = 5+3*x+2*(x>=0)+rnormal()
rdbwselect y x
rdbwselect y x, all
Could someone provide me with some guidance on why R is not showing me the complete expected output of the function rdbwselect? I'm wondering if this is an issue related to my version of R? Could this be a bug with the R package or the specific function rdbwselect? How can I verify the computation behind rdbwselect?
I appreciate any advice or follow-up questions.
Found the solution. All I needed to do was to embed the function within the summary() function
summary(rdbwselect(y,x,))
or add a pipe and the summary function
rdbwselect(y,x,all= TRUE) %>%
summary()
I want to post it as this is nowhere mentioned in the package documentation nor the article in The R Journal.
I have been trying to compute the wronskian using SymPy, and can not figure out how to use the function. I did look at the program itself but I am very new to python. For functions any sinusoidal is okay. I just want to observe how to use SymPy in this way for future reference. Any help would be great!
~I listed my imports below
import sympy as sp
from scipy import linalg
import numpy as np
sp.init_printing()
I don't this that 'var' is the only thing wrong with what I am inputting.
You have to define the var first. You have not defined it. Also the functions should go in a list.
x = sp.Symbol('x')
## Define your var here
Wronskian_Sol = sp.matrices.dense.wronskian([sp.sin(x), 1-sp.cos(x)**2], var, method="bareiss")
Here is an example in this book "Applied Differntial Equation with Boundary Value Problems" by Vladimir A. Dobrushkin at page 199.
I computed a Wronskian for these three functions using Sympy
x
x*sin(x)
x*cons(x)
import sympy as sp
x = sp.Symbol('x')
var = x
Wronskian_Sol = sp.matrices.dense.wronskian([x, x*sp.cos(x), x*sp.sin(x)], var, method="bareiss")
print(Wronskian_Sol)
print(Wronskian_Sol.simplify())
This gives the output. The first is not simplified, the last one is simplified. You can reduce the first one to simplified version easily by taking the common factor x**3 out which leaves (sin(x)**2 + cos(x)**2) ..and this is nothing but 1.
x**3*sin(x)**2 + x**3*cos(x)**2
x**3
You can confirm the solution by manually taking the determinant of the derivative matrix.
I have a function where I want to solve for many variables separately, do I have to write down the function every time in terms of the other variable?
x,xG,xR
y = e.^tan(x.^2)+cos.^2(x);
yG = e.^tan(xG.^2)+cos.^2(xG);
First you cannot write an expression like cos.^2(x). If x is a single variable (ie x=pi) you could write either cos(x)^2 or cos(x^2). If x is a vector (a column vector might be x=[3;4;pi] and a row vector might be x=[3,4,pi], then you might write cos(x).^2 or cos(x.^2). The role of the period (.) in octave is explained here: https://octave.org/doc/v4.0.3/Arithmetic-Ops.html
Another issue has to do with understanding the difference between an expression: x=e^tanh(y); and a function. The later is a separate chunk of code that can be invoked from anywhere in your program.
Consider this simple example
1;
function y=myfunc(x)
y=exp(tanh(x));
endfunction
## main program
xxx=pi/3;
yyy=myfunc(xxx);
printf('%7.3f %7.3f\n',xxx,yyy)
y=exp(tanh(pi/3))
comments: The '1' in the first line tells Octave that there is more to the script than just the following function: the main program has to be interpreted as well. The function line specifies that inside the function, the input will be called x and the output y, so when my function is called from main, the input is xxx(=pi/2) and the output is yyy. The last line in this tiny script is an expression that does the same thing as the function. Note that since I didn't include a semi-colon at the end of that line the result is printed out
I suggest you play with this for a while, then if you have more questions, ask them in a new question.
I have the following issue:
I want to use autowrap to generate a compiled version of a sympy matrix, with cells containing sympy expressions. Depending on the specification of my problem, the number of arguments can get very large.
I ran into the following 2 issues:
The number of arguments that autowrap accepts seems to be limited to 509.
i.e., this works:
import sympy
from sympy.utilities.autowrap import autowrap
x = sympy.symbols("x:509")
exp = sum(x)
cyt = autowrap(exp, backend="cython", args=x)
and this fails to compile:
x = sympy.symbols("x:510")
exp = sum(x)
cyt = autowrap(exp, backend="cython", args=x)
The message I get seems not very telling:
[...] (Full output upon request)
Generating code
c:\users\[classified]\appdata\local\temp\tmp2zer8vfe_sympy_compile\wrapper_module_17.c(6293) : fatal error C1001: An internal error has occurred in the compiler.
(compiler file 'f:\dd\vctools\compiler\utc\src\p2\hash.c', line 884)
To work around this problem, try simplifying or changing the program near the locations listed above.
Please choose the Technical Support command on the Visual C++
Help menu, or open the Technical Support help file for more information
LINK : fatal error LNK1257: code generation failed
error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\link.exe' failed with exit status 1257
Is there any way around this? I would like to use versions of my program that need ~1000 input variables.
(I have no understanding of C/cython. Is this an autowrap limitation, a C limitation ...?)
Partly connected to the above:
Can one compile functions that accept the arguments as array.
Is there any way to generate code that accepts a numpy array as input? I specifically mean one array for all the arguments, instead of providing the arguments as list. (Similar to lambdify using a DeferredVector). ufuncify supports array input, but as I understand only for broadcasting/vectorizing the function.
I would hope that an array as argument could circumvent the first problem above, which is most pressing for me. Apart from that, I would prefer array input anyways, both because it seems faster (no need to unpack the numpy array I have as input into a list), and also more straightforward and natural.
Does anyone have any suggestions what I can do?
Also, could anyone tell me whether f2py has similar limitations? This would also be an option for me if feasible, but I don't have it set up to work currently, and would prefer to know whether it helps at all before investing the time.
Thanks!
Edit:
I played around a bit with the different candidates for telling autowrap that the input argument will be something in array form, rather than a list of numbers. I'll document my steps here for posterity, and also to increase chances to get some input:
sympy.DeferredVector
Is what I use with lambdify for the same purpose, so I thought to give it a try. However, warning:
A = sympy.DeferredVector("A")
expression = A[0]+A[1]
cyt = autowrap(expression, backend="cython", args=A)
just completely crashed my OS - last statement started executing, (no feedback), everything got really slow, then no more reactions. (Can only speculate, perhaps it has to do with the fact that A has no shape information, which does not seem to bother lambdify, but might be a problem here. Anyways, seems not the right way to go.)
All sorts of array-type objects filled with the symbols in the expression to be wrapped.
e.g.
x0 ,x1 = sympy.symbols("x:2")
expression = x0 + x1
cyt = autowrap(expression, backend="cython", args=np.array([x0,x1]))
Still wants unpacked arguments. Replacing the last row by
cyt = autowrap(expression, backend="cython", args=[np.array([x0,x1])])
Gives the message
CodeGenArgumentListError: ("Argument list didn't specify: x0, x1 ", [InputArgument(x0), InputArgument(x1)])
Which is a recurrent theme to this approach: also happens when using a sympy matrix, a tuple, and so on inside the arguments list.
sympy.IndexedBase
This is actually used in the autowrap examples; however, in a (to me) inintuitive way, using an equation as the expression to be wrapped. Also, the way it is used there seems not really feasible to me: The expression I want to cythonize is a matrix, but its cells are themselves longish expressions, which I cannot obtain via index operations.
The upside is that I got a minimal example to work:
X = sympy.IndexedBase("X",shape=(1,1))
expression = 2*X[0,0]
cyt = autowrap(expression, backend="cython", args=[X])
actually compiles, and the resulting function correctly evaluates - when passed a 2d-np.array.
So this seems the most promising avenue, even though further extensions to this approach I keep trying fail.
For example this
X = sympy.IndexedBase("X",shape=(1,))
expression = 2*X[0]
cyt = autowrap(expression, backend="cython", args=[X])
gets me
[...]\site-packages\sympy\printing\codeprinter.py", line 258, in _get_expression_indices " rhs indices in %s" % expr)
ValueError: lhs indices must match non-dummy rhs indices in 2*X[0]
even though I don't see how it should be different from the working one above.
Same error message when sticking to two dimensions, but increasing the size of X:
X = sympy.IndexedBase("X",shape=(2,2))
expression = 2*X[0,0]+X[0,1]+X[1,0]+X[1,1]
cyt = autowrap(expression, backend="cython", args=[X])
ValueError: lhs indices must match non-dummy rhs indices in 2*X[0, 0] + X[0, 1] + X[1, 0] + X[1, 1]
I tried snooping around the code for autowrap, but I feel a bit lost there...
So I'm still searching for a solution and happy for any input.
Passing the argument as an array seems to work OK
x = sympy.MatrixSymbol('x', 520, 1)
exp = 0
for i in range(x.shape[0]):
exp += x[i]
cyt = autowrap(exp, backend='cython')
arr = np.random.randn(520, 1)
cyt(arr)
Out[48]: -42.59735861021934
arr.sum()
Out[49]: -42.597358610219345
I have a matrix that is outputted like this:
maximums =
[ -9.9043877608991468201413092380493, 426.34796945271797204125533010993]
[ 9.3758615553048990076305298649689, 441.87005169359418197397861057075]
But when I try and run any commands on it, I get an error saying that this matrix is still symbolic. I don't understand since it's just numeric values. Is there anyway of making this matrix outputted used by normal functions of Matlab?
To get this matrix, I did calculate derivatives of a symbolic equation and then evaluate. But I'd like to run functions on this output.
Thanks!
EDIT (Here's an example of the command/error):
[maxValue, rowIdx] = max(maximums(:,2),[],2)
Undefined function 'max' for input arguments of type 'sym'.
Since your matrix is symbolic, you have to convert it to numeric first:
maximums = double(maximums)
You have to convert it:
maximus=double(maximus)