binomial_sigma_G: Function for deriving the estimated standard deviation of the...
In igbucur/MASSIVE: MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Description
Usage
Arguments
Value
Examples
Description
Function for deriving the estimated standard deviation of the genetic variants
from the first-order and second-order statistics, assuming these are
binomially distributed.
Usage
1
binomial_sigma_G(SS, n = 2)
Arguments
SS
Numeric matrix of first-order and second-order statistics.
n
Integer number of alleles (trials) for the binomial genetic variables.
Value
Numeric vector containing the standard deviations of the genetic variants.
Examples
1
2
3
4
J <- 5 # number of instruments
par <- random_Gaussian_parameters(J)
dat <- generate_data_MASSIVE_model(N = 1000, n = 2, p = rep(0.3, J), par)
binomial_sigma_G(dat$SS)
igbucur/MASSIVE documentation built on Oct. 26, 2020, 1:26 a.m.
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Description Usage Arguments Value Examples
Description
Function for deriving the estimated standard deviation of the genetic variants from the first-order and second-order statistics, assuming these are binomially distributed.
Usage
1 | binomial_sigma_G(SS, n = 2)
|
Arguments
SS |
Numeric matrix of first-order and second-order statistics. |
n |
Integer number of alleles (trials) for the binomial genetic variables. |
Value
Numeric vector containing the standard deviations of the genetic variants.
Examples
1 2 3 4 | J <- 5 # number of instruments
par <- random_Gaussian_parameters(J)
dat <- generate_data_MASSIVE_model(N = 1000, n = 2, p = rep(0.3, J), par)
binomial_sigma_G(dat$SS)
|
R Package Documentation
Browse R Packages
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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