search for: corar

Hello, I have been fitting nonlinear models with random effects using nlme with the corCAR1 correlation structure, as I have unequally spaced observations per subject, and serially correlated errors. My question is regarding the Phi parameter. Example of output: Correlation Structure: Continuous AR(1) Formula: ~day | subject Parameter estimate(s): Phi 0.8475842 After reading

.... Is anyone familiar with the inner workings of this function and can either explain the calculation of the standard errors or provide code that explains the inner workings of this function. Thanks! Example of the model I am running: model1<- gls(Y~ X1I + X2 + X3 + X4, data=Dat1, correlation = corAR1(), method = "ML") My understanding of model errors: Y = b_0 + X1 b_1+ ...Xk b_k + Z Z_t =phi Z_{t-1) + e_t The residuals reported by GLS are the Z's, while the white noise terms are the e's. I cannot replicate the reported residuals using this approach. I also do not know how Z...

Dears, I am specifying a panel model with GLS function and corCAR1 and cor AR1 parameters to correct for serial autocorrelation. But I have one seemingly trivial question: those models fitted that way are random effects models or pooling models? Thanks again, Tomas Notes [[alternative HTML version deleted]]

...rStruct constructor already written to calculate the shape of the spectral density for linear models using Fourier estimates (e.g. terms for a linear model derived from the frequency domain)? I have data with a long memory process but do not want to destroy it by taking n-differences for a suitable corAR, or corARIMA object. Thank you, KeithC.