# Min.Max.Multivar.PCA: Minimum and maximum values for the multivariate predictive... In EffectTreat: Prediction of Therapeutic Success

## Description

The function Min.Max.Multivar.PCA computes the minimum and maximum values for the multivariate predictive causal association (PCA) in the continuous-continuous case.

## Usage

 1 Min.Max.Multivar.PCA(gamma, Sigma_SS, Sigma_T0T0, Sigma_T1T1) 

## Arguments

 gamma The vector of regression coefficients for the S by treatment interactions. Sigma_SS The variance-covariance matrix of the pretreatment predictors. For example, when there are 2 pretreatment predictors \bold{Σ}_{SS}=≤ft(\begin{array}{cc}σ_{S1S1} & σ_{S1S2} \\ σ_{S1S2} & σ_{S2S2}\end{array}\right). Sigma_T0T0 The variance of T in the control treatment group. Sigma_T1T1 The variance of T in the experimental treatment group.

## Author(s)

Wim Van der Elst & Ariel Alonso

## References

Alonso, A., & Van der Elst, W. (submitted). Evaluating multivariate predictors of therapeutic success: a causal inference approach.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 # Specify vector of S by treatment interaction coefficients gamma <- matrix(data = c(-0.006, -0.002, 0.045), ncol=1) # Specify variances Sigma_SS = matrix(data=c(882.352, 49.234, 6.420, 49.234, 411.964, -26.205, 6.420, -26.205, 95.400), byrow = TRUE, nrow = 3) Sigma_T0T0 <- 82.274 Sigma_T1T1 <- 96.386 # Compute min and max PCA Min.Max.Multivar.PCA(gamma=gamma, Sigma_SS=Sigma_SS, Sigma_T0T0=Sigma_T0T0, Sigma_T1T1=Sigma_T1T1) 

### Example output


Min PCA:  0.0006419669

Max PCA:  0.4102911

\$Call
Min.Max.Multivar.PCA(gamma = gamma, Sigma_SS = Sigma_SS, Sigma_T0T0 = Sigma_T0T0,
Sigma_T1T1 = Sigma_T1T1)

attr(,"class")
[1] "Min.Max.Multivar.PCA"


EffectTreat documentation built on July 8, 2020, 7:17 p.m.