summary.betareg: Methods for betareg Objects
In betareg: Beta Regression
Description
Usage
Arguments
Details
References
See Also
Examples
Description
Methods for extracting information from fitted beta
regression model objects of class "betareg".
Usage
1
2
3
4
5
6
7
8
9
10
11
## S3 method for class 'betareg'
summary(object, phi = NULL, type = "sweighted2", ...)
## S3 method for class 'betareg'
coef(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
vcov(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
bread(x, phi = NULL, ...)
## S3 method for class 'betareg'
estfun(x, phi = NULL, ...)
Arguments
object, x
fitted model object of class "betareg".
phi
logical indicating whether the parameters in the precision model
(for phi) should be reported as full model parameters (TRUE) or
nuisance parameters (FALSE). The default is taken from
object$phi.
type
character specifying type of residuals to be included in the
summary output, see residuals.betareg.
model
character specifying for which component of the model coefficients/covariance
should be extracted. (Only used if phi is NULL.)
...
currently not used.
Details
A set of standard extractor functions for fitted model objects is available for
objects of class "betareg", including methods to the generic functions
print and summary which print the estimated
coefficients along with some further information. The summary in particular
supplies partial Wald tests based on the coefficients and the covariance matrix.
As usual, the summary method returns an object of class "summary.betareg"
containing the relevant summary statistics which can subsequently be printed
using the associated print method. Note that the default residuals
"sweighted2" might be burdensome to compute in large samples and hence might
need modification in such applications.
A logLik method is provided, hence AIC
can be called to compute information criteria.
References
Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R.
Journal of Statistical Software, 34(2), 1–24.
http://www.jstatsoft.org/v34/i02/.
Ferrari, S.L.P., and Cribari-Neto, F. (2004).
Beta Regression for Modeling Rates and Proportions.
Journal of Applied Statistics, 31(7), 799–815.
Simas, A.B., and Barreto-Souza, W., and Rocha, A.V. (2010).
Improved Estimators for a General Class of Beta Regression Models.
Computational Statistics & Data Analysis, 54(2), 348–366.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Example output
Call:
betareg(formula = yield ~ batch + temp | temp, data = GasolineYield)
Standardized weighted residuals 2:
Min 1Q Median 3Q Max
-2.540 -0.779 -0.117 0.862 2.942
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.923236 0.183526 -32.27 < 2e-16 ***
batch1 1.601988 0.063856 25.09 < 2e-16 ***
batch2 1.297266 0.099100 13.09 < 2e-16 ***
batch3 1.565338 0.099739 15.69 < 2e-16 ***
batch4 1.030072 0.063288 16.28 < 2e-16 ***
batch5 1.154163 0.065643 17.58 < 2e-16 ***
batch6 1.019445 0.066351 15.36 < 2e-16 ***
batch7 0.622259 0.065632 9.48 < 2e-16 ***
batch8 0.564583 0.060185 9.38 < 2e-16 ***
batch9 0.359439 0.067141 5.35 8.6e-08 ***
temp 0.010359 0.000436 23.75 < 2e-16 ***
Phi coefficients (precision model with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.36409 1.22578 1.11 0.27
temp 0.01457 0.00362 4.03 5.7e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Type of estimator: ML (maximum likelihood)
Log-likelihood: 87 on 13 Df
Pseudo R-squared: 0.952
Number of iterations: 33 (BFGS) + 28 (Fisher scoring)
(Intercept) batch1 batch2 batch3
-5.92324 1.60199 1.29727 1.56534
batch4 batch5 batch6 batch7
1.03007 1.15416 1.01944 0.62226
batch8 batch9 temp (phi)_(Intercept)
0.56458 0.35944 0.01036 1.36409
(phi)_temp
0.01457
(Intercept) batch1 batch2 batch3 batch4
(Intercept) 3.368e-02 -4.124e-03 -8.216e-03 -8.839e-03 -3.672e-03
batch1 -4.124e-03 4.078e-03 2.483e-03 2.524e-03 2.189e-03
batch2 -8.216e-03 2.483e-03 9.821e-03 3.401e-03 2.395e-03
batch3 -8.839e-03 2.524e-03 3.401e-03 9.948e-03 2.427e-03
batch4 -3.672e-03 2.189e-03 2.395e-03 2.427e-03 4.005e-03
batch5 -4.461e-03 2.240e-03 2.548e-03 2.594e-03 2.206e-03
batch6 -3.902e-03 2.204e-03 2.439e-03 2.475e-03 2.178e-03
batch7 -3.007e-03 2.146e-03 2.267e-03 2.285e-03 2.133e-03
batch8 -6.259e-04 1.993e-03 1.804e-03 1.775e-03 2.013e-03
batch9 -1.801e-03 2.068e-03 2.031e-03 2.026e-03 2.072e-03
temp -7.753e-05 4.999e-06 1.504e-05 1.657e-05 3.891e-06
(phi)_(Intercept) -1.860e-02 1.682e-04 9.769e-04 1.420e-03 1.409e-04
(phi)_temp 4.618e-05 2.069e-07 -1.937e-06 -2.948e-06 6.530e-08
batch5 batch6 batch7 batch8 batch9
(Intercept) -4.461e-03 -3.902e-03 -3.007e-03 -6.259e-04 -1.801e-03
batch1 2.240e-03 2.204e-03 2.146e-03 1.993e-03 2.068e-03
batch2 2.548e-03 2.439e-03 2.267e-03 1.804e-03 2.031e-03
batch3 2.594e-03 2.475e-03 2.285e-03 1.775e-03 2.026e-03
batch4 2.206e-03 2.178e-03 2.133e-03 2.013e-03 2.072e-03
batch5 4.309e-03 2.223e-03 2.156e-03 1.977e-03 2.065e-03
batch6 2.223e-03 4.402e-03 2.140e-03 2.003e-03 2.070e-03
batch7 2.156e-03 2.140e-03 4.308e-03 2.044e-03 2.078e-03
batch8 1.977e-03 2.003e-03 2.044e-03 3.622e-03 2.100e-03
batch9 2.065e-03 2.070e-03 2.078e-03 2.100e-03 4.508e-03
temp 5.827e-06 4.454e-06 2.259e-06 -3.585e-06 -7.000e-07
(phi)_(Intercept) 1.011e-03 5.045e-04 -4.523e-04 -1.307e-03 -3.533e-04
(phi)_temp -2.185e-06 -8.969e-07 1.470e-06 3.675e-06 1.119e-06
temp (phi)_(Intercept) (phi)_temp
(Intercept) -7.753e-05 -1.860e-02 4.618e-05
batch1 4.999e-06 1.682e-04 2.069e-07
batch2 1.504e-05 9.769e-04 -1.937e-06
batch3 1.657e-05 1.420e-03 -2.948e-06
batch4 3.891e-06 1.409e-04 6.530e-08
batch5 5.827e-06 1.011e-03 -2.185e-06
batch6 4.454e-06 5.045e-04 -8.969e-07
batch7 2.259e-06 -4.523e-04 1.470e-06
batch8 -3.585e-06 -1.307e-03 3.675e-06
batch9 -7.000e-07 -3.533e-04 1.119e-06
temp 1.902e-07 4.666e-05 -1.175e-07
(phi)_(Intercept) 4.666e-05 1.503e+00 -4.342e-03
(phi)_temp -1.175e-07 -4.342e-03 1.309e-05
'log Lik.' 86.98 (df=13)
[1] -148
(Intercept) batch1 batch2 batch3 batch4 batch5
-5.92324 1.60199 1.29727 1.56534 1.03007 1.15416
batch6 batch7 batch8 batch9 temp
1.01944 0.62226 0.56458 0.35944 0.01036
(Intercept) temp
1.36409 0.01457
Call:
betareg(formula = yield ~ batch + temp | temp, data = GasolineYield)
Standardized weighted residuals 2:
Min 1Q Median 3Q Max
-2.540 -0.779 -0.117 0.862 2.942
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.923236 0.183526 -32.27 < 2e-16 ***
batch1 1.601988 0.063856 25.09 < 2e-16 ***
batch2 1.297266 0.099100 13.09 < 2e-16 ***
batch3 1.565338 0.099739 15.69 < 2e-16 ***
batch4 1.030072 0.063288 16.28 < 2e-16 ***
batch5 1.154163 0.065643 17.58 < 2e-16 ***
batch6 1.019445 0.066351 15.36 < 2e-16 ***
batch7 0.622259 0.065632 9.48 < 2e-16 ***
batch8 0.564583 0.060185 9.38 < 2e-16 ***
batch9 0.359439 0.067141 5.35 8.6e-08 ***
temp 0.010359 0.000436 23.75 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Type of estimator: ML (maximum likelihood)
Log-likelihood: 87 on 13 Df
Pseudo R-squared: 0.952
Number of iterations: 33 (BFGS) + 28 (Fisher scoring)
betareg documentation built on May 2, 2019, 5:20 p.m.
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Description Usage Arguments Details References See Also Examples
Description
Methods for extracting information from fitted beta
regression model objects of class "betareg".
Usage
1 2 3 4 5 6 7 8 9 10 11 | ## S3 method for class 'betareg'
summary(object, phi = NULL, type = "sweighted2", ...)
## S3 method for class 'betareg'
coef(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
vcov(object, model = c("full", "mean", "precision"), phi = NULL, ...)
## S3 method for class 'betareg'
bread(x, phi = NULL, ...)
## S3 method for class 'betareg'
estfun(x, phi = NULL, ...)
|
Arguments
object, x |
fitted model object of class |
phi |
logical indicating whether the parameters in the precision model
(for phi) should be reported as full model parameters ( |
type |
character specifying type of residuals to be included in the
summary output, see |
model |
character specifying for which component of the model coefficients/covariance
should be extracted. (Only used if |
... |
currently not used. |
Details
A set of standard extractor functions for fitted model objects is available for
objects of class "betareg", including methods to the generic functions
print and summary which print the estimated
coefficients along with some further information. The summary in particular
supplies partial Wald tests based on the coefficients and the covariance matrix.
As usual, the summary method returns an object of class "summary.betareg"
containing the relevant summary statistics which can subsequently be printed
using the associated print method. Note that the default residuals
"sweighted2" might be burdensome to compute in large samples and hence might
need modification in such applications.
A logLik method is provided, hence AIC
can be called to compute information criteria.
References
Cribari-Neto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. http://www.jstatsoft.org/v34/i02/.
Ferrari, S.L.P., and Cribari-Neto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
Simas, A.B., and Barreto-Souza, W., and Rocha, A.V. (2010). Improved Estimators for a General Class of Beta Regression Models. Computational Statistics & Data Analysis, 54(2), 348–366.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
Example output
Call:
betareg(formula = yield ~ batch + temp | temp, data = GasolineYield)
Standardized weighted residuals 2:
Min 1Q Median 3Q Max
-2.540 -0.779 -0.117 0.862 2.942
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.923236 0.183526 -32.27 < 2e-16 ***
batch1 1.601988 0.063856 25.09 < 2e-16 ***
batch2 1.297266 0.099100 13.09 < 2e-16 ***
batch3 1.565338 0.099739 15.69 < 2e-16 ***
batch4 1.030072 0.063288 16.28 < 2e-16 ***
batch5 1.154163 0.065643 17.58 < 2e-16 ***
batch6 1.019445 0.066351 15.36 < 2e-16 ***
batch7 0.622259 0.065632 9.48 < 2e-16 ***
batch8 0.564583 0.060185 9.38 < 2e-16 ***
batch9 0.359439 0.067141 5.35 8.6e-08 ***
temp 0.010359 0.000436 23.75 < 2e-16 ***
Phi coefficients (precision model with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.36409 1.22578 1.11 0.27
temp 0.01457 0.00362 4.03 5.7e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Type of estimator: ML (maximum likelihood)
Log-likelihood: 87 on 13 Df
Pseudo R-squared: 0.952
Number of iterations: 33 (BFGS) + 28 (Fisher scoring)
(Intercept) batch1 batch2 batch3
-5.92324 1.60199 1.29727 1.56534
batch4 batch5 batch6 batch7
1.03007 1.15416 1.01944 0.62226
batch8 batch9 temp (phi)_(Intercept)
0.56458 0.35944 0.01036 1.36409
(phi)_temp
0.01457
(Intercept) batch1 batch2 batch3 batch4
(Intercept) 3.368e-02 -4.124e-03 -8.216e-03 -8.839e-03 -3.672e-03
batch1 -4.124e-03 4.078e-03 2.483e-03 2.524e-03 2.189e-03
batch2 -8.216e-03 2.483e-03 9.821e-03 3.401e-03 2.395e-03
batch3 -8.839e-03 2.524e-03 3.401e-03 9.948e-03 2.427e-03
batch4 -3.672e-03 2.189e-03 2.395e-03 2.427e-03 4.005e-03
batch5 -4.461e-03 2.240e-03 2.548e-03 2.594e-03 2.206e-03
batch6 -3.902e-03 2.204e-03 2.439e-03 2.475e-03 2.178e-03
batch7 -3.007e-03 2.146e-03 2.267e-03 2.285e-03 2.133e-03
batch8 -6.259e-04 1.993e-03 1.804e-03 1.775e-03 2.013e-03
batch9 -1.801e-03 2.068e-03 2.031e-03 2.026e-03 2.072e-03
temp -7.753e-05 4.999e-06 1.504e-05 1.657e-05 3.891e-06
(phi)_(Intercept) -1.860e-02 1.682e-04 9.769e-04 1.420e-03 1.409e-04
(phi)_temp 4.618e-05 2.069e-07 -1.937e-06 -2.948e-06 6.530e-08
batch5 batch6 batch7 batch8 batch9
(Intercept) -4.461e-03 -3.902e-03 -3.007e-03 -6.259e-04 -1.801e-03
batch1 2.240e-03 2.204e-03 2.146e-03 1.993e-03 2.068e-03
batch2 2.548e-03 2.439e-03 2.267e-03 1.804e-03 2.031e-03
batch3 2.594e-03 2.475e-03 2.285e-03 1.775e-03 2.026e-03
batch4 2.206e-03 2.178e-03 2.133e-03 2.013e-03 2.072e-03
batch5 4.309e-03 2.223e-03 2.156e-03 1.977e-03 2.065e-03
batch6 2.223e-03 4.402e-03 2.140e-03 2.003e-03 2.070e-03
batch7 2.156e-03 2.140e-03 4.308e-03 2.044e-03 2.078e-03
batch8 1.977e-03 2.003e-03 2.044e-03 3.622e-03 2.100e-03
batch9 2.065e-03 2.070e-03 2.078e-03 2.100e-03 4.508e-03
temp 5.827e-06 4.454e-06 2.259e-06 -3.585e-06 -7.000e-07
(phi)_(Intercept) 1.011e-03 5.045e-04 -4.523e-04 -1.307e-03 -3.533e-04
(phi)_temp -2.185e-06 -8.969e-07 1.470e-06 3.675e-06 1.119e-06
temp (phi)_(Intercept) (phi)_temp
(Intercept) -7.753e-05 -1.860e-02 4.618e-05
batch1 4.999e-06 1.682e-04 2.069e-07
batch2 1.504e-05 9.769e-04 -1.937e-06
batch3 1.657e-05 1.420e-03 -2.948e-06
batch4 3.891e-06 1.409e-04 6.530e-08
batch5 5.827e-06 1.011e-03 -2.185e-06
batch6 4.454e-06 5.045e-04 -8.969e-07
batch7 2.259e-06 -4.523e-04 1.470e-06
batch8 -3.585e-06 -1.307e-03 3.675e-06
batch9 -7.000e-07 -3.533e-04 1.119e-06
temp 1.902e-07 4.666e-05 -1.175e-07
(phi)_(Intercept) 4.666e-05 1.503e+00 -4.342e-03
(phi)_temp -1.175e-07 -4.342e-03 1.309e-05
'log Lik.' 86.98 (df=13)
[1] -148
(Intercept) batch1 batch2 batch3 batch4 batch5
-5.92324 1.60199 1.29727 1.56534 1.03007 1.15416
batch6 batch7 batch8 batch9 temp
1.01944 0.62226 0.56458 0.35944 0.01036
(Intercept) temp
1.36409 0.01457
Call:
betareg(formula = yield ~ batch + temp | temp, data = GasolineYield)
Standardized weighted residuals 2:
Min 1Q Median 3Q Max
-2.540 -0.779 -0.117 0.862 2.942
Coefficients (mean model with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.923236 0.183526 -32.27 < 2e-16 ***
batch1 1.601988 0.063856 25.09 < 2e-16 ***
batch2 1.297266 0.099100 13.09 < 2e-16 ***
batch3 1.565338 0.099739 15.69 < 2e-16 ***
batch4 1.030072 0.063288 16.28 < 2e-16 ***
batch5 1.154163 0.065643 17.58 < 2e-16 ***
batch6 1.019445 0.066351 15.36 < 2e-16 ***
batch7 0.622259 0.065632 9.48 < 2e-16 ***
batch8 0.564583 0.060185 9.38 < 2e-16 ***
batch9 0.359439 0.067141 5.35 8.6e-08 ***
temp 0.010359 0.000436 23.75 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Type of estimator: ML (maximum likelihood)
Log-likelihood: 87 on 13 Df
Pseudo R-squared: 0.952
Number of iterations: 33 (BFGS) + 28 (Fisher scoring)
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