View source: R/HelpFiles_Calc CovMx stand Phi.r
calc.CovMxStandPhi | R Documentation |
This function calculates the (vectorized) standardized lagged effects matrix, their covariance matrix, and corresponding elliptical 95% confidence interval (CI). There is also an interactive web application on my website: Standardizing and/or transforming lagged regression estimates (https://www.uu.nl/staff/RMKuiper/Websites%20%2F%20Shiny%20apps).
calc.CovMxStandPhi(N, Phi, Gamma = NULL, SigmaVAR = NULL, alpha = 0.05)
N |
Number of persons (panel data) or number of measurement occasions - 1 (time series data). This is used in determining the covariance matrix of the vectorized standardized lagged effects. |
Phi |
(Un)standardized lagged effects matrix. If necessary, it is standardized and for the standardized and vectorized Phi the covariance matrix is determined. It also takes a fitted object from the classes "varest" (from the VAR() function in vars package) and "ctsemFit" (from the ctFit() function in the ctsem package); see example below. From such an object, the Phi, SigmaVAR, and Gamma matrices are calculated/extracted. |
Gamma |
Stationary covariance matrix, that is, the contemporaneous covariance matrix of the data. Note that if Phi and SigmaVAR are known, Gamma can be calculated; hence, only SigmaVAR or Gamma is needed as input (if only Gamma, then use 'Gamma = Gamma' or set SigmaVAR to NULL, see examples below). |
SigmaVAR |
Residual covariance matrix of the first-order discrete-time vector autoregressive (DT-VAR(1)) model. Note that if Phi and SigmaVAR are known, Gamma can be calculated; hence, only SigmaVAR or Gamma is needed as input (if only Gamma, then use 'Gamma = Gamma' or set SigmaVAR to NULL, see examples below). |
alpha |
The alpha level in determining the (1-alpha)*100% CI. By default, alpha is set to 0.05, resulting in a 95% CI. |
This function returns the vectorized standardized lagged effects, their covariance matrix, and the corresponding elliptical/multivariate 95% CI.
## Example 1 ##
# Input for examples below
N <- 643
Phi <- myPhi[1:2,1:2]
#Phi <- matrix(c(0.25, 0.10,
# 0.20, 0.36), byrow=T, ncol = 2)
SigmaVAR <- diag(2) # for ease
# Calculate the Gamma corresponding to Phi and SigmaVAR - used in the second example
Gamma <- calc.Gamma.fromVAR(Phi, SigmaVAR) # ?calc.Gamma.fromVAR
#Example where only SigmaVAR is known and not Gamma
calc.CovMxStandPhi(N, Phi, NULL, SigmaVAR)
# or
calc.CovMxStandPhi(N, Phi, SigmaVAR = SigmaVAR)
#Example where only Gamma is known and not SigmaVAR
calc.CovMxStandPhi(N, Phi, Gamma)
# or
calc.CovMxStandPhi(N, Phi, Gamma, NULL)
## Example 2: input from fitted object of class "varest" ##
#
N <- 643
data <- myData
if (!require("vars")) install.packages("vars")
library(vars)
out_VAR <- VAR(data, p = 1)
calc.CovMxStandPhi(N, out_VAR)
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