DirichletRegModel: Methods for the Class 'DirichletRegModel'
In DirichletReg: Dirichlet Regression
Description
Usage
Arguments
Author(s)
Examples
Description
These are available methods for the results of Dirichlet regression models and objects of class DirichletRegModel.
These methods contain functions for print and summary of the data, generate fitted values and predicting new values using predict.
Various types of residuals are implemented and confint can be used to compute confidence intervals of the parameters.
Furthermore logLik extracts the log-likelihood of the model and vcov extracts the covariance matrix of the parameter estimates.
Usage
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## S3 method for class 'DirichletRegModel'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'DirichletRegModel'
summary(object, ...)
## S3 method for class 'DirichletRegModel'
fitted(object, mu = TRUE, alpha = FALSE, phi = FALSE, ...)
## S3 method for class 'DirichletRegModel'
predict(object, newdata, mu = TRUE, alpha = FALSE, phi = FALSE, ...)
## S3 method for class 'DirichletRegModel'
residuals(object, type = c("standardized", "composite", "raw"), ...)
## S3 method for class 'DirichletRegModel'
confint(object, parm, level, ..., type=c("all", "beta", "gamma"), exp = FALSE)
## S3 method for class 'DirichletRegConfint'
print(x, digits = 3, ...)
## S3 method for class 'DirichletRegModel'
logLik(object, ...)
## S3 method for class 'DirichletRegModel'
AIC(object, ..., k = 2)
## S3 method for class 'DirichletRegModel'
BIC(object, ...)
## S3 method for class 'DirichletRegModel'
nobs(object, ...)
## S3 method for class 'DirichletRegModel'
vcov(object, ...)
## S3 method for class 'DirichletRegModel'
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'DirichletRegModel'
drop1(object, scope, test = c("LRT", "none"), k = 2, sort = TRUE, ...)
Arguments
x
an object of class DirichletRegModel
object
an object of class DirichletRegModel or DirichletRegConfint for printing an object obtained by confint.DirichletRegModel
alpha
logical; returns alpha values
mu
logical; returns expected values
phi
logical; returns precision values
type
for residuals: defines the type of residuals to be computed "standardized" (i.e., Pearson), "composite", or "raw"
for confint: defines the type of parameter ("all", "beta", or "gamma") for which confidence values are returned
newdata
a data.frame containing new observations
k
number for the weighting of parameters
parm
a vector containing names of the parameters to print
level
(a vector of) confidence level(s), defaults to .95
exp
logical; returns parameters in exponentiated form
digits
the number of digits in the output
formula.
the new formula to be updated, see update.formula and update.Formula
evaluate
if FALSE the updated call will be returned, but not evaluated
scope
defines the scope of variables to be dropped, see drop1
test
defines the type of test for drop1
sort
if TRUE, p-values will be sorted in decreasing order.
...
further arguments
Author(s)
Marco J. Maier
Examples
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ALake <- ArcticLake
ALake$AL <- DR_data(ArcticLake[, 1:3])
mod1 <- DirichReg(AL ~ depth + I(depth^2) | depth, data = ALake, model="alternative")
update(mod1, . ~ . | . + I(depth^2), evaluate = FALSE)
mod1
drop1(mod1) ### issues a caveat when used for the first time in an R session
summary(mod1)
head(fitted(mod1))
predict(mod1, newdata = data.frame("depth" = seq(10, 100, 10)))
head(residuals(mod1))
confint(mod1)
confint(mod1, exp = TRUE)
logLik(mod1)
round(vcov(mod1), 5)
Example output
Loading required package: Formula
Warning in DR_data(ArcticLake[, 1:3]) :
not all rows sum up to 1 => normalization forced
DirichReg(formula = AL ~ depth + I(depth^2) | depth + I(depth^2),
data = ALake, model = "alternative")
Call:
DirichReg(formula = AL ~ depth + I(depth^2) | depth, data = ALake, model =
"alternative")
using the alternative parametrization
Log-likelihood: 107.8 on 8 df (95 BFGS + 2 NR Iterations)
MEAN MODELS:
-----------------------------------------
Coefficients for variable no. 1: sand
- variable omitted (reference category) -
-----------------------------------------
Coefficients for variable no. 2: silt
(Intercept) depth I(depth^2)
-1.2885187 0.0706992 -0.0003247
-----------------------------------------
Coefficients for variable no. 3: clay
(Intercept) depth I(depth^2)
-2.9754613 0.1064013 -0.0005161
-----------------------------------------
PRECISION MODEL:
-----------------------------------------
(Intercept) depth
1.32438 0.04177
-----------------------------------------
CAVEAT: drop1() is still an experimental feature. If you plan to use this
function, please double-check results, e.g., by comparing two models
using anova().
Single term deletions
Model:
AL ~ depth + I(depth^2) | depth
Df Deviance AIC LRT Pr(>Chi)
<none> -215.68 -199.68
Mean: I(depth^2) 2 -202.37 -190.37 13.308 0.001289 **
Mean: depth 2 -185.52 -173.52 30.157 2.829e-07 ***
Prec: depth 1 -182.59 -168.59 33.087 8.814e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
DirichReg(formula = AL ~ depth + I(depth^2) | depth, data = ALake, model =
"alternative")
Standardized Residuals:
Min 1Q Median 3Q Max
sand -1.7598 -0.7459 -0.1833 1.0148 2.7250
silt -1.1459 -0.5379 -0.1581 0.2467 1.5572
clay -1.9269 -0.6106 -0.0617 0.6294 1.9976
MEAN MODELS:
------------------------------------------------------------------
Coefficients for variable no. 1: sand
- variable omitted (reference category) -
------------------------------------------------------------------
Coefficients for variable no. 2: silt
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.2885187 0.3835030 -3.360 0.00078 ***
depth 0.0706992 0.0147385 4.797 1.61e-06 ***
I(depth^2) -0.0003247 0.0001210 -2.684 0.00727 **
------------------------------------------------------------------
Coefficients for variable no. 3: clay
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.9754613 0.4973405 -5.983 2.19e-09 ***
depth 0.1064013 0.0180002 5.911 3.40e-09 ***
I(depth^2) -0.0005161 0.0001429 -3.612 0.000303 ***
------------------------------------------------------------------
PRECISION MODEL:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.324382 0.357461 3.705 0.000211 ***
depth 0.041773 0.006604 6.325 2.53e-10 ***
------------------------------------------------------------------
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-likelihood: 107.8 on 8 df (95 BFGS + 2 NR Iterations)
AIC: -199.7, BIC: -186.4
Number of Observations: 39
Links: Logit (Means) and Log (Precision)
Parametrization: alternative
sand silt clay
[1,] 0.5878381 0.3263858 0.08577610
[2,] 0.5655708 0.3410547 0.09337446
[3,] 0.5467019 0.3532332 0.10006483
[4,] 0.5432735 0.3554206 0.10130588
[5,] 0.4972825 0.3839724 0.11874513
[6,] 0.4871840 0.3900359 0.12278004
[,1] [,2] [,3]
[1,] 0.59466851 0.3218234 0.08350812
[2,] 0.42659217 0.4247056 0.14870228
[3,] 0.28598015 0.4908362 0.22318362
[4,] 0.18696231 0.5184104 0.29462729
[5,] 0.12394802 0.5203210 0.35573099
[6,] 0.08568946 0.5103458 0.40396475
[7,] 0.06288266 0.4979281 0.43918924
[8,] 0.04950947 0.4884452 0.46204530
[9,] 0.04207675 0.4846797 0.47324354
[10,] 0.03871648 0.4879630 0.47332057
sand silt clay
1 0.9919461 -0.73098860 -0.5196036
2 0.8264901 -0.51849202 -0.5632384
3 -0.2172116 0.04425618 0.2898421
4 -0.1081184 0.31301128 -0.3180409
5 1.1641424 -0.70238386 -0.7433259
6 1.0327887 -0.40495044 -0.9711209
95% Confidence Intervals (original form)
- Beta-Parameters:
Variable: sand
variable omitted
Variable: silt
2.5% Est. 97.5%
(Intercept) -2.040 -1.289 -0.537
depth 0.042 0.071 0.100
I(depth^2) -0.001 0.000 0.000
Variable: clay
2.5% Est. 97.5%
(Intercept) -3.950 -2.975 -2.001
depth 0.071 0.106 0.142
I(depth^2) -0.001 -0.001 0.000
- Gamma-Parameters
2.5% Est. 97.5%
(Intercept) 0.624 1.324 2.025
depth 0.029 0.042 0.055
95% Confidence Intervals (exponentiated)
- Beta-Parameters:
Variable: sand
variable omitted
Variable: silt
2.5% exp(Est.) 97.5%
(Intercept) 0.130 0.276 0.585
depth 1.043 1.073 1.105
I(depth^2) 0.999 1.000 1.000
Variable: clay
2.5% exp(Est.) 97.5%
(Intercept) 0.019 0.051 0.135
depth 1.074 1.112 1.152
I(depth^2) 0.999 0.999 1.000
- Gamma-Parameters
2.5% exp(Est.) 97.5%
(Intercept) 1.87 3.76 7.58
depth 1.03 1.04 1.06
'log Lik.' 107.8401 (df=8)
silt:(Intercept) silt:depth silt:I(depth^2) clay:(Intercept)
silt:(Intercept) 0.14707 -0.00528 4e-05 0.11699
silt:depth -0.00528 0.00022 0e+00 -0.00471
silt:I(depth^2) 0.00004 0.00000 0e+00 0.00004
clay:(Intercept) 0.11699 -0.00471 4e-05 0.24735
clay:depth -0.00440 0.00020 0e+00 -0.00853
clay:I(depth^2) 0.00003 0.00000 0e+00 0.00006
(phi):(Intercept) -0.04142 0.00191 -2e-05 -0.09849
(phi):depth 0.00076 -0.00003 0e+00 0.00176
clay:depth clay:I(depth^2) (phi):(Intercept) (phi):depth
silt:(Intercept) -0.00440 3e-05 -0.04142 0.00076
silt:depth 0.00020 0e+00 0.00191 -0.00003
silt:I(depth^2) 0.00000 0e+00 -0.00002 0.00000
clay:(Intercept) -0.00853 6e-05 -0.09849 0.00176
clay:depth 0.00032 0e+00 0.00362 -0.00007
clay:I(depth^2) 0.00000 0e+00 -0.00003 0.00000
(phi):(Intercept) 0.00362 -3e-05 0.12778 -0.00212
(phi):depth -0.00007 0e+00 -0.00212 0.00004
DirichletReg documentation built on May 18, 2021, 5:06 p.m.
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Description Usage Arguments Author(s) Examples
Description
These are available methods for the results of Dirichlet regression models and objects of class DirichletRegModel.
These methods contain functions for print and summary of the data, generate fitted values and predicting new values using predict.
Various types of residuals are implemented and confint can be used to compute confidence intervals of the parameters.
Furthermore logLik extracts the log-likelihood of the model and vcov extracts the covariance matrix of the parameter estimates.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ## S3 method for class 'DirichletRegModel'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'DirichletRegModel'
summary(object, ...)
## S3 method for class 'DirichletRegModel'
fitted(object, mu = TRUE, alpha = FALSE, phi = FALSE, ...)
## S3 method for class 'DirichletRegModel'
predict(object, newdata, mu = TRUE, alpha = FALSE, phi = FALSE, ...)
## S3 method for class 'DirichletRegModel'
residuals(object, type = c("standardized", "composite", "raw"), ...)
## S3 method for class 'DirichletRegModel'
confint(object, parm, level, ..., type=c("all", "beta", "gamma"), exp = FALSE)
## S3 method for class 'DirichletRegConfint'
print(x, digits = 3, ...)
## S3 method for class 'DirichletRegModel'
logLik(object, ...)
## S3 method for class 'DirichletRegModel'
AIC(object, ..., k = 2)
## S3 method for class 'DirichletRegModel'
BIC(object, ...)
## S3 method for class 'DirichletRegModel'
nobs(object, ...)
## S3 method for class 'DirichletRegModel'
vcov(object, ...)
## S3 method for class 'DirichletRegModel'
update(object, formula., ..., evaluate = TRUE)
## S3 method for class 'DirichletRegModel'
drop1(object, scope, test = c("LRT", "none"), k = 2, sort = TRUE, ...)
|
Arguments
x |
an object of class |
object |
an object of class |
alpha |
logical; returns alpha values |
mu |
logical; returns expected values |
phi |
logical; returns precision values |
type |
for for |
newdata |
a |
k |
number for the weighting of parameters |
parm |
a vector containing names of the parameters to print |
level |
(a vector of) confidence level(s), defaults to |
exp |
logical; returns parameters in exponentiated form |
digits |
the number of digits in the output |
formula. |
the new formula to be updated, see |
evaluate |
if |
scope |
defines the scope of variables to be dropped, see |
test |
defines the type of test for |
sort |
if |
... |
further arguments |
Author(s)
Marco J. Maier
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ALake <- ArcticLake
ALake$AL <- DR_data(ArcticLake[, 1:3])
mod1 <- DirichReg(AL ~ depth + I(depth^2) | depth, data = ALake, model="alternative")
update(mod1, . ~ . | . + I(depth^2), evaluate = FALSE)
mod1
drop1(mod1) ### issues a caveat when used for the first time in an R session
summary(mod1)
head(fitted(mod1))
predict(mod1, newdata = data.frame("depth" = seq(10, 100, 10)))
head(residuals(mod1))
confint(mod1)
confint(mod1, exp = TRUE)
logLik(mod1)
round(vcov(mod1), 5)
|
Example output
Loading required package: Formula
Warning in DR_data(ArcticLake[, 1:3]) :
not all rows sum up to 1 => normalization forced
DirichReg(formula = AL ~ depth + I(depth^2) | depth + I(depth^2),
data = ALake, model = "alternative")
Call:
DirichReg(formula = AL ~ depth + I(depth^2) | depth, data = ALake, model =
"alternative")
using the alternative parametrization
Log-likelihood: 107.8 on 8 df (95 BFGS + 2 NR Iterations)
MEAN MODELS:
-----------------------------------------
Coefficients for variable no. 1: sand
- variable omitted (reference category) -
-----------------------------------------
Coefficients for variable no. 2: silt
(Intercept) depth I(depth^2)
-1.2885187 0.0706992 -0.0003247
-----------------------------------------
Coefficients for variable no. 3: clay
(Intercept) depth I(depth^2)
-2.9754613 0.1064013 -0.0005161
-----------------------------------------
PRECISION MODEL:
-----------------------------------------
(Intercept) depth
1.32438 0.04177
-----------------------------------------
CAVEAT: drop1() is still an experimental feature. If you plan to use this
function, please double-check results, e.g., by comparing two models
using anova().
Single term deletions
Model:
AL ~ depth + I(depth^2) | depth
Df Deviance AIC LRT Pr(>Chi)
<none> -215.68 -199.68
Mean: I(depth^2) 2 -202.37 -190.37 13.308 0.001289 **
Mean: depth 2 -185.52 -173.52 30.157 2.829e-07 ***
Prec: depth 1 -182.59 -168.59 33.087 8.814e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Call:
DirichReg(formula = AL ~ depth + I(depth^2) | depth, data = ALake, model =
"alternative")
Standardized Residuals:
Min 1Q Median 3Q Max
sand -1.7598 -0.7459 -0.1833 1.0148 2.7250
silt -1.1459 -0.5379 -0.1581 0.2467 1.5572
clay -1.9269 -0.6106 -0.0617 0.6294 1.9976
MEAN MODELS:
------------------------------------------------------------------
Coefficients for variable no. 1: sand
- variable omitted (reference category) -
------------------------------------------------------------------
Coefficients for variable no. 2: silt
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.2885187 0.3835030 -3.360 0.00078 ***
depth 0.0706992 0.0147385 4.797 1.61e-06 ***
I(depth^2) -0.0003247 0.0001210 -2.684 0.00727 **
------------------------------------------------------------------
Coefficients for variable no. 3: clay
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.9754613 0.4973405 -5.983 2.19e-09 ***
depth 0.1064013 0.0180002 5.911 3.40e-09 ***
I(depth^2) -0.0005161 0.0001429 -3.612 0.000303 ***
------------------------------------------------------------------
PRECISION MODEL:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.324382 0.357461 3.705 0.000211 ***
depth 0.041773 0.006604 6.325 2.53e-10 ***
------------------------------------------------------------------
Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Log-likelihood: 107.8 on 8 df (95 BFGS + 2 NR Iterations)
AIC: -199.7, BIC: -186.4
Number of Observations: 39
Links: Logit (Means) and Log (Precision)
Parametrization: alternative
sand silt clay
[1,] 0.5878381 0.3263858 0.08577610
[2,] 0.5655708 0.3410547 0.09337446
[3,] 0.5467019 0.3532332 0.10006483
[4,] 0.5432735 0.3554206 0.10130588
[5,] 0.4972825 0.3839724 0.11874513
[6,] 0.4871840 0.3900359 0.12278004
[,1] [,2] [,3]
[1,] 0.59466851 0.3218234 0.08350812
[2,] 0.42659217 0.4247056 0.14870228
[3,] 0.28598015 0.4908362 0.22318362
[4,] 0.18696231 0.5184104 0.29462729
[5,] 0.12394802 0.5203210 0.35573099
[6,] 0.08568946 0.5103458 0.40396475
[7,] 0.06288266 0.4979281 0.43918924
[8,] 0.04950947 0.4884452 0.46204530
[9,] 0.04207675 0.4846797 0.47324354
[10,] 0.03871648 0.4879630 0.47332057
sand silt clay
1 0.9919461 -0.73098860 -0.5196036
2 0.8264901 -0.51849202 -0.5632384
3 -0.2172116 0.04425618 0.2898421
4 -0.1081184 0.31301128 -0.3180409
5 1.1641424 -0.70238386 -0.7433259
6 1.0327887 -0.40495044 -0.9711209
95% Confidence Intervals (original form)
- Beta-Parameters:
Variable: sand
variable omitted
Variable: silt
2.5% Est. 97.5%
(Intercept) -2.040 -1.289 -0.537
depth 0.042 0.071 0.100
I(depth^2) -0.001 0.000 0.000
Variable: clay
2.5% Est. 97.5%
(Intercept) -3.950 -2.975 -2.001
depth 0.071 0.106 0.142
I(depth^2) -0.001 -0.001 0.000
- Gamma-Parameters
2.5% Est. 97.5%
(Intercept) 0.624 1.324 2.025
depth 0.029 0.042 0.055
95% Confidence Intervals (exponentiated)
- Beta-Parameters:
Variable: sand
variable omitted
Variable: silt
2.5% exp(Est.) 97.5%
(Intercept) 0.130 0.276 0.585
depth 1.043 1.073 1.105
I(depth^2) 0.999 1.000 1.000
Variable: clay
2.5% exp(Est.) 97.5%
(Intercept) 0.019 0.051 0.135
depth 1.074 1.112 1.152
I(depth^2) 0.999 0.999 1.000
- Gamma-Parameters
2.5% exp(Est.) 97.5%
(Intercept) 1.87 3.76 7.58
depth 1.03 1.04 1.06
'log Lik.' 107.8401 (df=8)
silt:(Intercept) silt:depth silt:I(depth^2) clay:(Intercept)
silt:(Intercept) 0.14707 -0.00528 4e-05 0.11699
silt:depth -0.00528 0.00022 0e+00 -0.00471
silt:I(depth^2) 0.00004 0.00000 0e+00 0.00004
clay:(Intercept) 0.11699 -0.00471 4e-05 0.24735
clay:depth -0.00440 0.00020 0e+00 -0.00853
clay:I(depth^2) 0.00003 0.00000 0e+00 0.00006
(phi):(Intercept) -0.04142 0.00191 -2e-05 -0.09849
(phi):depth 0.00076 -0.00003 0e+00 0.00176
clay:depth clay:I(depth^2) (phi):(Intercept) (phi):depth
silt:(Intercept) -0.00440 3e-05 -0.04142 0.00076
silt:depth 0.00020 0e+00 0.00191 -0.00003
silt:I(depth^2) 0.00000 0e+00 -0.00002 0.00000
clay:(Intercept) -0.00853 6e-05 -0.09849 0.00176
clay:depth 0.00032 0e+00 0.00362 -0.00007
clay:I(depth^2) 0.00000 0e+00 -0.00003 0.00000
(phi):(Intercept) 0.00362 -3e-05 0.12778 -0.00212
(phi):depth -0.00007 0e+00 -0.00212 0.00004
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