Displaying 5 results from an estimated 5 matches for "gamma_".
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2011 Jan 03
1
Greetings. I have a question with mixed beta regression model in nlme.
...n model. The details of the model are:
Suppose that:*
*j in {1, ..., J}* *(level 1)*
*i in {1, ..., n_j}* *(level 2)*
*y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij})
y_{ij} = mu_{ij} + w_{ij}
*
*with*
*logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}
log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c2 * z2_{ij}
*
*Beta_{0i} = b_0 + u_{0i}
Beta_{1i} = b_1 + u_{1i}
Gamma_{0i} = c_0 + v_{0i}
Gamma_{1i} = c_1 + v_{1i}
*
*The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean*
*(0, 0)'* *and covariance matrix*
*sigma_{00} sigma_{01}
sigma_{10} sigm...
2011 Jan 03
0
Greetings. I have a question with mixed beta regression model in nlme (corrected version).
...send you the correct formulation.
**
Suppose that:*
*j in {1, ..., J}* *(level 1)*
*i in {1, ..., n_j}* *(level 2)*
*y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij})
y_{ij} = mu_{ij} + w_{ij}
*
*with*
*logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b_2 * x2_{ij}
log(phi_{ij}) = Gamma_{0i} + Gamma_{1i} * z1_{ij} + c_2 * z2_{ij}
*
*Beta_{0i} = b_0 + u_{0i}
Beta_{1i} = b_1 + u_{1i}
Gamma_{0i} = c_0 + v_{0i}
Gamma_{1i} = c_1 + v_{1i}
*
*The vector* *(u_{0i}, u_{1i})'* *has normal distribution with mean*
*(0, 0)'* *and covariance matrix*
*sigma_{00} sigma_{01}
sigma_{10} sig...
2010 Sep 29
1
generalized additive mixed models for ordinal data
? stato filtrato un testo allegato il cui set di caratteri non era
indicato...
Nome: non disponibile
URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20100929/bedab79b/attachment.pl>
2010 Oct 04
0
spatial interaction (gravity) model as Poisson regression
...e attractiveness of the jth location, and D_{ij} is the
distance between i and j. Note that \varepsilon_{ij} is just the
multiplicative error term of the ?ow from i to j, and \beta is the
distance decay parameter.
Similarly, the doubly constrained model follows the form:
T_{ij} = exp(\delta_{i} + \gamma_{j} - \beta D_{ij}) ~ \varepsilon_{ij}
where everything is defined as above, except exp(\gamma_{j}) is an
estimate of the attractiveness of location A_{j}.
Hopefully the above description makes things a bit clearer,
essentially my question is this:
What factors or in what form do I have to have my...
2010 Aug 23
2
Quantile Regression and Goodness of Fit
All -
Does anyone know if there is a method to calculate a goodness-of-fit
statistic for quantile regressions with package quantreg?
Specifically, I'm wondering if anyone has implemented the
goodness-of-fit process developed by Koenker and Machado (1999) for R?
Though I have used package quantreg in the past, I may have overlooked
this function, if it is included.
Citation:
Koenker, R. and