I would like to know if there is a way to get p-values when using the GLS function (part of the nlme package) for the ML estimate of the error-autoregressive parameters. I looked through the package information on nlme and have not found a clear example of how to do this.
Here is an example of some code that illustrates my question:
x <- rnorm(100) y <- rnorm(100) z <- rnorm(100) df <- data.frame(x,y,z) test.reg <- gls(x ~ y+z,data=df,correlation=corARMA(p=1),method="ML") summary(test.reg) Generalized least squares fit by maximum likelihood Model: x ~ y + z Data: df AIC BIC logLik 289.3276 302.3534 -139.6638 Correlation Structure: AR(1) Formula: ~1 Parameter estimate(s): Phi 0.01887445 Coefficients: Value Std.Error t-value p-value (Intercept) 0.08418063 0.10214246 0.8241492 0.4119 y 0.00236772 0.09410912 0.0251593 0.9800 z 0.07689263 0.09480738 0.8110405 0.4193 Correlation: (Intr) y y 0.135 z -0.013 0.074 Standardized residuals: Min Q1 Med Q3 Max -1.94366502 -0.66310726 0.09546043 0.64881478 2.78384648 Residual standard error: 0.9781187 Degrees of freedom: 100 total; 97 residual
What I would like to know more about is the Parameter estimate Phi. I have searched through the nlme package to get more information about p-values for Phi but have come up short. I am unsure if the gls function computes the p-values for Phi.
I do know this gives the coefficients of phi:
coeff <- test.reg$modelStruct$corStruct Correlation structure of class corAR1 representing Phi 0.01887445
But that is all I have really found. Any help would be great!
Thanks so much, John