FAQ
A couple weeks ago I posted a message on this topic to r-help, the response
was that this seemed like odd behavior, and that I ought to post it to one
of the developer lists. I posted to r-sig-mixed-models, but didn't get any
response. So, with good intentions, I decided to try posting once more, but
to this more general list.

The goal is (1) FYI, to make you aware of this issue, in case it is an
error in gls (2) For my information, in case I have made an error, in the
hope that one of you folks might be able to correct me. Thanks in advance
for your time.

The issue is in 2 parts.

(A) I've used gls to fit a model with two fixed effects and a corExp
object. By my count, this fitting process estimates 5 parameters:
(Intercept), l10area, newx, range, and nugget. With 118 total df, there
should be 118 - 5 = 113 residual df. However, the output from summary.gls
reports 115 residual degrees of freedom. Is this an error in summary or
gls, or is there an error in my count?

(B) Summary.gls reports logLik=-273.6. Using my count of 5 estimated
parameters, the AIC should be -2*(-273.6) + 2*5 = 557.2. However,
summary.gls reports an AIC of 559.2. If one works backwards from the
reported AIC of 559.2, it seems that gls believes it has estimated 6
parameters in the fitting process. Again, is this an error in gls, or an
error on my part?

Copied from R terminal:
summary(sppl.i.ex)
Generalized least squares fit by maximum likelihood
Model: all.all.rch ~ l10area + newx
Data: gtemp
AIC BIC logLik
559.167 575.7911 -273.5835

Correlation Structure: Exponential spatial correlation
Formula: ~x + y | area
Parameter estimate(s):
range nugget
15.4448835 0.3741476

Coefficients:
Value Std.Error t-value p-value
(Intercept) 7.621306 0.7648135 9.964921 0.0000
l10area 6.332931 0.5589199 11.330659 0.0000
newx 0.066535 0.0204417 3.254857 0.0015

Correlation:
(Intr) l10are
l10area -0.605
newx 0.358 -0.024

Standardized residuals:
Min Q1 Med Q3 Max
-3.0035983 -0.5990432 -0.2226852 0.5113270 2.4444263

Residual standard error: 2.820337
Degrees of freedom: 118 total; 115 residual

Tim Handley
Fire Effects Monitor
Santa Monica Mountains National Recreation Area
401 W. Hillcrest Dr.
Thousand Oaks, CA 91360
805-370-2347

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  • Uwe Ligges at Sep 22, 2009 at 6:07 pm

    Timothy_Handley at nps.gov wrote:
    A couple weeks ago I posted a message on this topic to r-help, the response
    was that this seemed like odd behavior, and that I ought to post it to one
    of the developer lists. I posted to r-sig-mixed-models, but didn't get any
    response. So, with good intentions, I decided to try posting once more, but
    to this more general list.

    The goal is (1) FYI, to make you aware of this issue, in case it is an
    error in gls (2) For my information, in case I have made an error, in the
    hope that one of you folks might be able to correct me. Thanks in advance
    for your time.

    The issue is in 2 parts.

    (A) I've used gls to fit a model with two fixed effects and a corExp
    object. By my count, this fitting process estimates 5 parameters:
    (Intercept), l10area, newx, range, and nugget. With 118 total df, there
    should be 118 - 5 = 113 residual df. However, the output from summary.gls
    reports 115 residual degrees of freedom. Is this an error in summary or
    gls, or is there an error in my count?

    Those for the correlation structure are not counted for these residuals
    as you can find in

    nlme:::print.summary.gls

    that contains the line

    cat("Degrees of freedom:", dd[["N"]], "total;", dd[["N"]] -
    dd[["p"]], "residual\n")

    (B) Summary.gls reports logLik=-273.6. Using my count of 5 estimated
    parameters, the AIC should be -2*(-273.6) + 2*5 = 557.2. However,
    summary.gls reports an AIC of 559.2. If one works backwards from the
    reported AIC of 559.2, it seems that gls believes it has estimated 6
    parameters in the fitting process. Again, is this an error in gls, or an
    error on my part?

    1 additional was used for the estimation of the variance. Accordingly

    nlme:::logLik.gls

    contains the line

    attr(val, "df") <- p + length(coef(object[["modelStruct"]])) + 1

    The AICs should be comparable at least within R and if others think 5
    rather than 6 should be used its still fine since the difference will be
    there in all models to be compared.


    Best wishes,
    Uwe Ligges


    Copied from R terminal:
    summary(sppl.i.ex)
    Generalized least squares fit by maximum likelihood
    Model: all.all.rch ~ l10area + newx
    Data: gtemp
    AIC BIC logLik
    559.167 575.7911 -273.5835

    Correlation Structure: Exponential spatial correlation
    Formula: ~x + y | area
    Parameter estimate(s):
    range nugget
    15.4448835 0.3741476

    Coefficients:
    Value Std.Error t-value p-value
    (Intercept) 7.621306 0.7648135 9.964921 0.0000
    l10area 6.332931 0.5589199 11.330659 0.0000
    newx 0.066535 0.0204417 3.254857 0.0015

    Correlation:
    (Intr) l10are
    l10area -0.605
    newx 0.358 -0.024

    Standardized residuals:
    Min Q1 Med Q3 Max
    -3.0035983 -0.5990432 -0.2226852 0.5113270 2.4444263

    Residual standard error: 2.820337
    Degrees of freedom: 118 total; 115 residual

    Tim Handley
    Fire Effects Monitor
    Santa Monica Mountains National Recreation Area
    401 W. Hillcrest Dr.
    Thousand Oaks, CA 91360
    805-370-2347

    ______________________________________________
    R-devel at r-project.org mailing list
    https://stat.ethz.ch/mailman/listinfo/r-devel

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postedSep 22, '09 at 4:15p
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