PIVW-class | R Documentation |
An object containing the estimate produced using the penalized inverse-variance weighted (pIVW) method as well as various statistics.
Over.dispersion
Should the method consider overdispersion (balanced horizontal pleiotropy)? Default is TRUE.
Boot.Fieller
If Boot.Fieller=TRUE
, then the P-value and the confidence interval of the causal effect will be calculated based on the bootstrapping Fieller method. Otherwise, the P-value and the confidence interval of the causal effect will be calculated from the normal distribution. It is recommended to use the bootstrapping Fieller method when Condition
(the estimated effective sample size) is smaller than 10. By default, Boot.Fieller=TRUE
.
Lambda
The penalty parameter in the pIVW estimator. The penalty parameter plays a role in the bias-variance trade-off of the estimator. It is recommended to choose lambda=1
to achieve the smallest bias and valid inference. By default, lambda=1
.
Delta
The z-score threshold for IV selection. By default, delta=0
(i.e., no IV selection will be conducted).
Exposure
The name of the exposure variable.
Outcome
The name of the outcome variable.
Estimate
The causal point estimate from the pIVW estimator.
StdError
The standard error associated with Estimate
.
CILower
The lower bound of the confidence interval for Estimate
, which is derived from the bootstrapping Fieller method or normal distribution. For the bootstrapping Fieller's interval, if it contains multiple ranges, then lower limits of all ranges will be reported.
CIUpper
The upper bound of the confidence interval for Estimate
, which is derived from the bootstrapping Fieller method or normal distribution. For the bootstrapping Fieller's interval, if it contains multiple ranges, then upper limits of all ranges will be reported.
Alpha
The significance level used in constructing the confidence interval (default is 0.05).
Pvalue
P-value associated with the causal estimate from the pIVW estimator.
Tau2
The variance of the balanced horizontal pleiotropy. Tau2
is calculated by using all IVs in the data before conducting the IV selection.
SNPs
The number of SNPs after IV selection.
Condition
The estimated effective sample size. It is recommended to be greater than 5 for the pIVW estimator to achieve reliable asymptotic properties.
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