solve_pcc: Find a unique solution of CIV (internal use).
In LaiJiang/CIVMR: Constrained Instrumental Variables in Mendelian Randomization with Pleiotropy
Description
Usage
Arguments
Value
Examples
View source: R/CIVMR_function.R
Description
This function find a unique solutin to the constrained instrument problem given matrix A and B.
Usage
1
solve_pcc(A, B)
Arguments
A:
matrix given by LA_decomposition().
B:
matrix given by LA_decomposition().
Value
c: solution to the constrained maximization problem.
max_value: the maximized correlation value.
Examples
1
2
3
4
5
6
7
data(simulation)
#CIV linear algebra decomposition components
civ.deco <- LA_decomposition(simulation$G,simulation$X,simulation$Z)
#solve the CIV solution \eqn{u}
civ.c <- solve_pcc(civ.deco$A, civ.deco$B)
#plot the weight c
plot(civ.c$c)
LaiJiang/CIVMR documentation built on July 16, 2020, 12:45 a.m.
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Description Usage Arguments Value Examples
View source: R/CIVMR_function.R
Description
This function find a unique solutin to the constrained instrument problem given matrix A and B.
Usage
1 | solve_pcc(A, B)
|
Arguments
A: |
matrix given by LA_decomposition(). |
B: |
matrix given by LA_decomposition(). |
Value
c: solution to the constrained maximization problem.
max_value: the maximized correlation value.
Examples
1 2 3 4 5 6 7 | data(simulation)
#CIV linear algebra decomposition components
civ.deco <- LA_decomposition(simulation$G,simulation$X,simulation$Z)
#solve the CIV solution \eqn{u}
civ.c <- solve_pcc(civ.deco$A, civ.deco$B)
#plot the weight c
plot(civ.c$c)
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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