On Fri, 24 Jun 2011, oyvfos wrote:
Dear all,
I get an error using betrag on this data set
:http://dl.dropbox.com/u/1866110/dump.csv.
I run it like this
regression f2.1=betareg(Y~X1+X2,data=dump)
summary(f2.1)
I get :
Call:
betareg(formula = Y ~ X1 + X2, data = dump)
Standardized weighted residuals 2:
Error in quantile.default(x$residuals) :
missing values and NaN's not allowed if 'na.rm' is FALSE
In addition: Warning message:
In sqrt(v * (1 - hatvalues(object))) : NaNs produced
Does anybody know how to fix this?
I cannot replicate this. I get no error but the results below for R 2.13.0
and betareg 2.3.0.
R> d <- read.table("http://dl.dropbox.com/u/1866110/dump.csv";)
R> m <- betareg(Y ~ X1 + X2, data = d)
R> summary(m)
Call:
betareg(formula = Y ~ X1 + X2, data = d)
Standardized weighted residuals 2:
Min 1Q Median 3Q Max
-1.2621 -0.5828 -0.1272 0.3490 3.5940
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.6830481 0.2697329 -2.532 0.0113 *
X1Y -0.4199696 0.2657974 -1.580 0.1141
X2 -0.0003639 0.0001561 -2.331 0.0198 *
Phi coefficients (precision model with identity link):
Estimate Std. Error z value Pr(>|z|)
(phi) 0.97087 0.04256 22.81 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Log-likelihood: 1820 on 4 Df
Pseudo R-squared: 0.01671
Number of iterations in BFGS optimization: 17
Your results suggest that something goes wrong in the computation of
residuals. So you may try to use summary(m, type = "deviance") or
summary(m, type = "pearson"). If these work, then you should look at
residuals(m, type = "sweighted2") which is the default type of residuals.
As these involve computations of quadratic (n x n) complexity, they may be
numerically less stable compared to deviance or pearson residuals which
just require computations of linear complexity.
hth,
Z
Oyvind
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