similar to: Standardize GLS coefficients in R

When using the PLM package (version 1.2-8), I encounter the probem that calling the FGLS estimator evokes strange behavior, when choosing the "random" effects model. After calling the PGGLS function to estimate FGLS, PLM gives me a warning, stating that the "random" model has been replaced with the "pooling" model. I would, however, really like to estimate the random

I am working with panel data. I am using the plm package to do this. I would like to do be able to adjust for autocorrelation, as one does with glm models and correlation structures (eg corr=corARMA(q=4)) . In particular, I want to employ MA(4) error structure. Is there a way of doing this with the plm package? (Note: I do not really want to use the pggls function for various

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

Hi, my data set is data.frame(id, yr, y, l, e, k). I would like to estimate Lee and Schmidts (1993, OUP) model in R. My colleague wrote SAS code as follows: ** procedures for creating dummy variables are omitted ** ** di# and dt# are dummy variables for industry and time ** data a2; merge a1 a2 a; by id yr; proc sysnlin maxit=100 outest=beta2; endogenous y; exogenous l e k

I need some real help on this, really stuck how are the coefficients for ur.ers(y, type = c("DF-GLS", "P-test"), model = c("constant", "trend"), lag.max = 0) The max lag is set at zero, so the regression should simply be Diff(zt) = a*z(t-1) where a is the value i'm trying to find and z(t)'s are the detrended values. but through performing

Dears, Unfortunatelly, the packages relaimpo and relimp do not seem to work with plm function (plm package) or gls function (in nlm package). I've been studying on how to adapt one of them for this pourpose. In that sense, I have two questions regarding to this work: 1) have anyone hard of any workaround for those incompatibilities, or at least of any ideas on that - especially for plm? 2)

Dear Rers, I have threes series, x, y, z and I want to fit a model z ~ x + y. First of all, I fit a lm. I found the residuals are correlated, by looking at the acf() and pacf(). Then I tried to fit a gls model allowing residuals to be correlated (correlation = corARMA(p=5, q=1)): y.na <- as.data.frame(y[complete.cases(y),]) y.gls <- gls(z ~ x + y, data = y.na, correlation=corARMA(p=5,

Hi, I am trying to extract the coefficients matrix from a gls summary. Contrary to the lm function, the command fit$coefficients returns only the estimates of the model, not the whole matrix including the std errors, the t and the p values. example: ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <-

I am trying to analyse a data with gls/lm using the following set of models prcn.0.lm <- lm( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1()) prcn.0.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn) prcn.1m.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1(),method="ML") I get the following AICs for these models:

Hello listmembers, I've been thinking of using gls in the nlme package to test for serial correlation in my data set. I've simulated a sample data set and have found a large discrepancy in the results I get when using the default method REML vs. ML. The data set involves a response that is measured twice a day (once for each level of a treatment factor). In my simulated data set, I

Dear all, I have been using the function gls in the package nlme in R to fit some spatial regressions (as described in Dormann et al.). However, I have been struggling trying to find a way to calculate a measure of effect size from these models, so I wanted to know if any of you had an idea on how to do this. More precisely, I am producing a multiple model with an exponential correlation

Hi, I have fit a model using the rms package with the Gls() function. Is there a way to get the model estimates, std errors, and p-values (i.e. what you get with print(fit)) into latex format? I have tried: f <- Gls(...) latex(f, file='') ... but I get the following error Error in replace.substring.wild(s, old, new, test = test, front = front, : does not handle > 1 * in

Hi I have a huge array with series of data. For each cell in the array I fit a linear model, either using lm() or gls() with lm() there is no problem, but with gls() I get an error: Error in glsEstimate(glsSt, control = glsEstControl) : computed gls fit is singular, rank 2 as soon as there are data like this: > y1 <- c(0,0,0,0) > x1 <- c(0,1,1.3,0) > gls(y1~x1)

Dear R-help, I don't understand why Gls gives me an error when trying to fit a model with AR(2) errors, while gls (from nlme) does not. For example: library(nlme) library(rms) set.seed(1) d <- data.frame(x = rnorm(50), y = rnorm(50)) gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error # Error in

Hello everybody, Please help with connecting the AIC and BIC numbers printed by summary.gls to the logLik number. 1. is the logLik number the true ML or density scaling constants have been omitted? 2. what is the formula for calculating the AIC and BIC from logLik (and how can I see it)? I tried printing summary.gls but it says object not found. Thank you very much. Stephen [[alternative

Dear all, I am confused. I have encountered some strange behaviour of gls > data(co2) > co2.y <- aggregate(co2,1,mean) > co2.y.data <- data.frame(co2=as.numeric(co2.y),year=seq(1959-1980,along=co2.y)) > co2.1.gls <- gls(co2~year+I(year^2), co2.y.data) > co2.2.gls <- update(CO2.1.gls, corr=corAR1()) > summary(CO2.2.gls) > plot(CO2.2.gls) plot shows standardized

Hi, r-users I got a problem when I try to call a *gls* function in loop structure. The gls function seems not able to recognize the parameters that I pass into the loop function! (But, if I use lm function, it works.) The code looks like this: ================================================= gls.lm <- function(Data, iv1, dv1) { gls.model <- gls(Data[ , dv1] ~ Data[ , iv1], correlation =

There is a bug in 'anova.gls' in the 'nlme' package (3.1-90). The=20 bug is triggered by calling the function with a single 'gls' object=20 and specifying the 'Terms' argument but not the 'L' argument: > library(nlme) > fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont, + correlation =3D corSymm(form =3D ~ 1 |

Dear all I use the gls function but in contrast to the lm function in which when I type summary(lm(...))$coef I receive all the coefficients (estimate, Std. Error, t-value and pvalue), with gls when I type summary(gls(...))$coef I only receive the estimate of the reg. coefficient without std. error and t- and p-values. Dou you have any suggestion how to solve my problem? With kind regards

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure