ndl.package: Naive Discriminative Learning
In ndl: Naive Discriminative Learning
Description
Details
Author(s)
References
Examples
Description
Naive discriminative learning implements learning and
classification models based on the Rescorla-Wagner equations and their
equilibrium equations.
Naive discriminative learning implements classification models based
on the Rescorla-Wagner equations and the equilibrium equations of
the Rescorla-Wagner equations. This package provides three kinds of
functionality: (1) discriminative learning based directly on the
Rescorla-Wagner equations, (2) a function implementing the naive
discriminative reader, and a model for silent (single-word) reading,
and (3) a classifier based on the equilibrium equations. The
functions and datasets for the naive discriminative reader model
make it possible to replicate the simulation results for Experiment
1 of Baayen et al. (2011). The classifier is provided to allow for
comparisons between machine learning (svm, TiMBL, glm, random
forests, etc.) and discrimination learning. Compared to standard
classification algorithms, naive discriminative learning may overfit
the data, albeit gracefully.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
For more detailed information on the core Rescorla-Wagner equations, see
the functions RescorlaWagner and
plot.RescorlaWagner, as well as the data sets
danks, numbers (data courtesy of Michael
Ramscar), and lexample (an example discussed in Baayen et
al. 2011).
The functions for the naive discriminative learning (at the user level)
are estimateWeights and
estimateActivations. The relevant data sets are
serbian, serbianUniCyr,serbianUniLat, and
serbianLex. The examples for serbianLex
present the full simulation for Experiment 1 of Baayen et al. (2011).
Key functionality for the user is provided by the functions
orthoCoding, estimateWeights, and
estimateActivations. orthoCoding calculates the letter
n-grams for character strings, to be used as cues. It is assumed that
meaning or meanings (separated by underscores if there are more then
one) are available as outcomes. The frequency with which each (unique)
combination of cues and outcomes occurs are required. For some example
input data sets, see: danks, plurals,
serbian, serbianUniCyr and
serbianUniLat.
The function estimateWeights estimates the association
strengths of cues to outcomes, using the equilibrium equations presented
in Danks (2003). The function estimateActivations estimates the
activations of outcomes (meanings) given cues (n-grams).
The Rcpp-based learn and learnLegacy
functions use a C++ function to compute the conditional co-occurrence
matrices required in the equilibrium equations. These are internally
used by estimateWeights and should not be used directly by users
of the package.
The key function for naive discriminative classification is
ndlClassify; see data sets think and
dative for examples.
Author(s)
Antti Arppe [aut],
Peter Hendrix [aut],
Petar Milin [aut],
R. Harald Baayen [aut],
Tino Sering [aut, cre],
Cyrus Shaoul [aut]
Maintainer: Tino Sering <konstantin.sering@uni-tuebingen.de>
Author Contributions:
Initial concept by R. Harald Baayen with contributions from Petar Milin
and Peter Hendrix. First R coding done by R. Harald Baayen.
Initial R package development until version 0.1.6 by Antti
Arppe. Initial documentation by Antti Arppe. Initial optimizations in
C by Petar Milin and Antti Arppe.
Classification functionality developed further by Antti Arppe.
In version 0.2.14 to version 0.2.16, improvements to the NDL algorithm by Petar
Milin and Cyrus Shaoul. In version 0.2.14 to version 0.2.16, improved
performance optimizations (C++ and Rcpp) by Cyrus Shaoul.
From version 0.2.17 onwards bug fixes and cran compliance by Tino Sering.
References
Baayen, R. H. and Milin, P. and Filipovic Durdevic, D. and
Hendrix, P. and Marelli, M., An amorphous model for morphological
processing in visual comprehension based on naive discriminative
learning. Psychological Review, 118, 438-482.
Baayen, R. H. (2011) Corpus linguistics and naive discriminative
learning. Brazilian Journal of Applied Linguistics, 11, 295-328.
Arppe, A. and Baayen, R. H. (in prep.) Statistical classification
and principles of human learning.
Examples
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## Not run:
# Rescorla-Wagner
data(lexample)
lexample$Cues <- orthoCoding(lexample$Word, grams=1)
lexample.rw <- RescorlaWagner(lexample, nruns=25, traceCue="h",
traceOutcome="hand")
plot(lexample.rw)
mtext("h - hand", 3, 1)
data(numbers)
traceCues <- c( "exactly1", "exactly2", "exactly3", "exactly4", "exactly5",
"exactly6", "exactly7", "exactly10", "exactly15")
traceOutcomes <- c("1", "2", "3", "4", "5", "6", "7", "10", "15")
ylimit <- c(0,1)
par(mfrow=c(3,3), mar=c(4,4,1,1))
for (i in 1:length(traceCues)) {
numbers.rw <- RescorlaWagner(numbers, nruns=1, traceCue=traceCues[i],
traceOutcome=traceOutcomes[i])
plot(numbers.rw, ylimit=ylimit)
mtext(paste(traceCues[i], " - ", traceOutcomes[i], sep=""), side=3, line=-1,
cex=0.7)
}
par(mfrow=c(1,1))
# naive discriminative learning (for complete example, see serbianLex)
# This function uses a Unicode dataset.
data(serbianUniCyr)
serbianUniCyr$Cues <- orthoCoding(serbianUniCyr$WordForm, grams=2)
serbianUniCyr$Outcomes <- serbianUniCyr$LemmaCase
sw <- estimateWeights(cuesOutcomes=serbianUniCyr,hasUnicode=T)
desiredItems <- unique(serbianUniCyr["Cues"])
desiredItems$Outcomes=""
activations <- estimateActivations(desiredItems, sw)$activationMatrix
rownames(activations) <- unique(serbianUniCyr[["WordForm"]])
syntax <- c("acc", "dat", "gen", "ins", "loc", "nom", "Pl", "Sg")
activations2 <- activations[,!is.element(colnames(activations), syntax)]
head(rownames(activations2),50)
head(colnames(activations2),8)
image(activations2, xlab="word forms", ylab="meanings", xaxt="n", yaxt="n")
mtext(c("yena", "...", "zvuke"), side=1, line=1, at=c(0, 0.5, 1), adj=c(0,0,1))
mtext(c("yena", "...", "zvuk"), side=2, line=1, at=c(0, 0.5, 1), adj=c(0,0,1))
# naive discriminative classification
data(think)
think.ndl <- ndlClassify(Lexeme ~ Person + Number + Agent + Patient + Register,
data=think)
summary(think.ndl)
plot(think.ndl, values="weights", type="hist", panes="multiple")
plot(think.ndl, values="probabilities", type="density")
## End(Not run)
Example output
This is ndl version 0.2.18.
For an overview of the package, type 'help("ndl.package")'.
[1] "<U+0436><U+0435><U+043D><U+0430>"
[2] "<U+0436><U+0435><U+043D><U+0435>"
[3] "<U+0436><U+0435><U+043D><U+0438>"
[4] "<U+0436><U+0435><U+043D><U+0443>"
[5] "<U+0436><U+0435><U+043D><U+043E><U+043C>"
[6] "<U+0436><U+0435><U+043D><U+0430><U+043C><U+0430>"
[7] "<U+0436><U+0435><U+0459><U+0430>"
[8] "<U+0436><U+0435><U+0459><U+0435>"
[9] "<U+0436><U+0435><U+0459><U+0438>"
[10] "<U+0436><U+0435><U+0459><U+0443>"
[11] "<U+0436><U+0435><U+0459><U+043E><U+043C>"
[12] "<U+0436><U+0435><U+0459><U+0430><U+043C><U+0430>"
[13] "<U+0436><U+0438><U+0432><U+043E><U+0442>"
[14] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0430>"
[15] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0443>"
[16] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+043E><U+043C>"
[17] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0438>"
[18] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0438><U+043C><U+0430>"
[19] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0435>"
[20] "<U+0448><U+0435><U+0442><U+045A><U+0430>"
[21] "<U+0448><U+0435><U+0442><U+045A><U+0435>"
[22] "<U+0448><U+0435><U+0442><U+045A><U+0438>"
[23] "<U+0448><U+0435><U+0442><U+045A><U+0443>"
[24] "<U+0448><U+0435><U+0442><U+045A><U+043E><U+043C>"
[25] "<U+0448><U+0435><U+0442><U+045A><U+0430><U+043C><U+0430>"
[26] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0430>"
[27] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0435>"
[28] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0438>"
[29] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0443>"
[30] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+043E><U+043C>"
[31] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0430><U+043C><U+0430>"
[32] "<U+0448><U+043A><U+043E><U+043B><U+0430>"
[33] "<U+0448><U+043A><U+043E><U+043B><U+0435>"
[34] "<U+0448><U+043A><U+043E><U+043B><U+0438>"
[35] "<U+0448><U+043A><U+043E><U+043B><U+0443>"
[36] "<U+0448><U+043A><U+043E><U+043B><U+043E><U+043C>"
[37] "<U+0448><U+043A><U+043E><U+043B><U+0430><U+043C><U+0430>"
[38] "<U+0448><U+0443><U+043C><U+0430>"
[39] "<U+0448><U+0443><U+043C><U+0435>"
[40] "<U+0448><U+0443><U+043C><U+0438>"
[41] "<U+0448><U+0443><U+043C><U+0443>"
[42] "<U+0448><U+0443><U+043C><U+043E><U+043C>"
[43] "<U+0448><U+0443><U+043C><U+0430><U+043C><U+0430>"
[44] "<U+0447><U+0430><U+043C><U+0430><U+0446>"
[45] "<U+0447><U+0430><U+043C><U+0446><U+0430>"
[46] "<U+0447><U+0430><U+043C><U+0446><U+0443>"
[47] "<U+0447><U+0430><U+043C><U+0446><U+0435><U+043C>"
[48] "<U+0447><U+0430><U+043C><U+0446><U+0438>"
[49] "<U+0447><U+0430><U+043C><U+0430><U+0446><U+0430>"
[50] "<U+0447><U+0430><U+043C><U+0446><U+0438><U+043C><U+0430>"
[1] "\320\260\320\272\320\260\320\264\320\265\320\274\320\270\321\230\320\260"
[2] "\320\260\320\277\320\260\321\200\320\260\321\202"
[3] "\320\261\320\270\321\202\320\272\320\260"
[4] "\320\261\320\276\320\263"
[5] "\320\261\320\276\320\273"
[6] "\320\261\320\276\321\200"
[7] "\320\261\320\276\321\200\320\260\321\206"
[8] "\320\261\320\276\321\230\320\260"
Call:
ndlClassify(formula = Lexeme ~ Person + Number + Agent + Patient +
Register, data = think)
Formula:
Lexeme ~ Person + Number + Agent + Patient + Register
Weights:
ajatella harkita miettia pohtia
AgentGroup -0.0024238 0.0484437 -0.0195531 0.1726282
AgentIndividual 0.1377378 0.0011873 0.0730958 -0.0129260
AgentNone 0.1481698 0.0115980 0.0540880 -0.0147608
NumberOther 0.1240703 0.0326252 0.0936144 0.0483326
NumberPlural 0.1594136 0.0286038 0.0140163 0.0966088
PatientAbstraction -0.1868402 -0.0124441 0.0874109 0.1661720
PatientActivity -0.2692613 0.3313997 -0.0140782 0.0062384
PatientCommunication -0.3390196 0.0820339 0.2322344 0.0790500
PatientDirectQuote -0.3940583 -0.1073144 0.1906831 0.3649883
PatientEvent 0.2320365 -0.0692502 0.0007568 -0.1092445
... [ omitted 12 rows ] ...
Null deviance: 8701 on 13616 degrees of freedom
Residual (model) deviance: 7335 on 13528 degrees of freedom
R2.likelihood: 0.157
AIC: 7511
BIC: 8051
ndl documentation built on May 2, 2019, 10:28 a.m.
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Description Details Author(s) References Examples
Description
Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations.
Naive discriminative learning implements classification models based on the Rescorla-Wagner equations and the equilibrium equations of the Rescorla-Wagner equations. This package provides three kinds of functionality: (1) discriminative learning based directly on the Rescorla-Wagner equations, (2) a function implementing the naive discriminative reader, and a model for silent (single-word) reading, and (3) a classifier based on the equilibrium equations. The functions and datasets for the naive discriminative reader model make it possible to replicate the simulation results for Experiment 1 of Baayen et al. (2011). The classifier is provided to allow for comparisons between machine learning (svm, TiMBL, glm, random forests, etc.) and discrimination learning. Compared to standard classification algorithms, naive discriminative learning may overfit the data, albeit gracefully.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
For more detailed information on the core Rescorla-Wagner equations, see
the functions RescorlaWagner and
plot.RescorlaWagner, as well as the data sets
danks, numbers (data courtesy of Michael
Ramscar), and lexample (an example discussed in Baayen et
al. 2011).
The functions for the naive discriminative learning (at the user level)
are estimateWeights and
estimateActivations. The relevant data sets are
serbian, serbianUniCyr,serbianUniLat, and
serbianLex. The examples for serbianLex
present the full simulation for Experiment 1 of Baayen et al. (2011).
Key functionality for the user is provided by the functions
orthoCoding, estimateWeights, and
estimateActivations. orthoCoding calculates the letter
n-grams for character strings, to be used as cues. It is assumed that
meaning or meanings (separated by underscores if there are more then
one) are available as outcomes. The frequency with which each (unique)
combination of cues and outcomes occurs are required. For some example
input data sets, see: danks, plurals,
serbian, serbianUniCyr and
serbianUniLat.
The function estimateWeights estimates the association
strengths of cues to outcomes, using the equilibrium equations presented
in Danks (2003). The function estimateActivations estimates the
activations of outcomes (meanings) given cues (n-grams).
The Rcpp-based learn and learnLegacy
functions use a C++ function to compute the conditional co-occurrence
matrices required in the equilibrium equations. These are internally
used by estimateWeights and should not be used directly by users
of the package.
The key function for naive discriminative classification is
ndlClassify; see data sets think and
dative for examples.
Author(s)
Antti Arppe [aut], Peter Hendrix [aut], Petar Milin [aut], R. Harald Baayen [aut], Tino Sering [aut, cre], Cyrus Shaoul [aut]
Maintainer: Tino Sering <konstantin.sering@uni-tuebingen.de>
Author Contributions: Initial concept by R. Harald Baayen with contributions from Petar Milin and Peter Hendrix. First R coding done by R. Harald Baayen.
Initial R package development until version 0.1.6 by Antti Arppe. Initial documentation by Antti Arppe. Initial optimizations in C by Petar Milin and Antti Arppe.
Classification functionality developed further by Antti Arppe.
In version 0.2.14 to version 0.2.16, improvements to the NDL algorithm by Petar Milin and Cyrus Shaoul. In version 0.2.14 to version 0.2.16, improved performance optimizations (C++ and Rcpp) by Cyrus Shaoul.
From version 0.2.17 onwards bug fixes and cran compliance by Tino Sering.
References
Baayen, R. H. and Milin, P. and Filipovic Durdevic, D. and Hendrix, P. and Marelli, M., An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118, 438-482.
Baayen, R. H. (2011) Corpus linguistics and naive discriminative learning. Brazilian Journal of Applied Linguistics, 11, 295-328.
Arppe, A. and Baayen, R. H. (in prep.) Statistical classification and principles of human learning.
Examples
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 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | ## Not run:
# Rescorla-Wagner
data(lexample)
lexample$Cues <- orthoCoding(lexample$Word, grams=1)
lexample.rw <- RescorlaWagner(lexample, nruns=25, traceCue="h",
traceOutcome="hand")
plot(lexample.rw)
mtext("h - hand", 3, 1)
data(numbers)
traceCues <- c( "exactly1", "exactly2", "exactly3", "exactly4", "exactly5",
"exactly6", "exactly7", "exactly10", "exactly15")
traceOutcomes <- c("1", "2", "3", "4", "5", "6", "7", "10", "15")
ylimit <- c(0,1)
par(mfrow=c(3,3), mar=c(4,4,1,1))
for (i in 1:length(traceCues)) {
numbers.rw <- RescorlaWagner(numbers, nruns=1, traceCue=traceCues[i],
traceOutcome=traceOutcomes[i])
plot(numbers.rw, ylimit=ylimit)
mtext(paste(traceCues[i], " - ", traceOutcomes[i], sep=""), side=3, line=-1,
cex=0.7)
}
par(mfrow=c(1,1))
# naive discriminative learning (for complete example, see serbianLex)
# This function uses a Unicode dataset.
data(serbianUniCyr)
serbianUniCyr$Cues <- orthoCoding(serbianUniCyr$WordForm, grams=2)
serbianUniCyr$Outcomes <- serbianUniCyr$LemmaCase
sw <- estimateWeights(cuesOutcomes=serbianUniCyr,hasUnicode=T)
desiredItems <- unique(serbianUniCyr["Cues"])
desiredItems$Outcomes=""
activations <- estimateActivations(desiredItems, sw)$activationMatrix
rownames(activations) <- unique(serbianUniCyr[["WordForm"]])
syntax <- c("acc", "dat", "gen", "ins", "loc", "nom", "Pl", "Sg")
activations2 <- activations[,!is.element(colnames(activations), syntax)]
head(rownames(activations2),50)
head(colnames(activations2),8)
image(activations2, xlab="word forms", ylab="meanings", xaxt="n", yaxt="n")
mtext(c("yena", "...", "zvuke"), side=1, line=1, at=c(0, 0.5, 1), adj=c(0,0,1))
mtext(c("yena", "...", "zvuk"), side=2, line=1, at=c(0, 0.5, 1), adj=c(0,0,1))
# naive discriminative classification
data(think)
think.ndl <- ndlClassify(Lexeme ~ Person + Number + Agent + Patient + Register,
data=think)
summary(think.ndl)
plot(think.ndl, values="weights", type="hist", panes="multiple")
plot(think.ndl, values="probabilities", type="density")
## End(Not run)
|
Example output
This is ndl version 0.2.18.
For an overview of the package, type 'help("ndl.package")'.
[1] "<U+0436><U+0435><U+043D><U+0430>"
[2] "<U+0436><U+0435><U+043D><U+0435>"
[3] "<U+0436><U+0435><U+043D><U+0438>"
[4] "<U+0436><U+0435><U+043D><U+0443>"
[5] "<U+0436><U+0435><U+043D><U+043E><U+043C>"
[6] "<U+0436><U+0435><U+043D><U+0430><U+043C><U+0430>"
[7] "<U+0436><U+0435><U+0459><U+0430>"
[8] "<U+0436><U+0435><U+0459><U+0435>"
[9] "<U+0436><U+0435><U+0459><U+0438>"
[10] "<U+0436><U+0435><U+0459><U+0443>"
[11] "<U+0436><U+0435><U+0459><U+043E><U+043C>"
[12] "<U+0436><U+0435><U+0459><U+0430><U+043C><U+0430>"
[13] "<U+0436><U+0438><U+0432><U+043E><U+0442>"
[14] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0430>"
[15] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0443>"
[16] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+043E><U+043C>"
[17] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0438>"
[18] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0438><U+043C><U+0430>"
[19] "<U+0436><U+0438><U+0432><U+043E><U+0442><U+0435>"
[20] "<U+0448><U+0435><U+0442><U+045A><U+0430>"
[21] "<U+0448><U+0435><U+0442><U+045A><U+0435>"
[22] "<U+0448><U+0435><U+0442><U+045A><U+0438>"
[23] "<U+0448><U+0435><U+0442><U+045A><U+0443>"
[24] "<U+0448><U+0435><U+0442><U+045A><U+043E><U+043C>"
[25] "<U+0448><U+0435><U+0442><U+045A><U+0430><U+043C><U+0430>"
[26] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0430>"
[27] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0435>"
[28] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0438>"
[29] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0443>"
[30] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+043E><U+043C>"
[31] "<U+0448><U+0438><U+0440><U+0438><U+043D><U+0430><U+043C><U+0430>"
[32] "<U+0448><U+043A><U+043E><U+043B><U+0430>"
[33] "<U+0448><U+043A><U+043E><U+043B><U+0435>"
[34] "<U+0448><U+043A><U+043E><U+043B><U+0438>"
[35] "<U+0448><U+043A><U+043E><U+043B><U+0443>"
[36] "<U+0448><U+043A><U+043E><U+043B><U+043E><U+043C>"
[37] "<U+0448><U+043A><U+043E><U+043B><U+0430><U+043C><U+0430>"
[38] "<U+0448><U+0443><U+043C><U+0430>"
[39] "<U+0448><U+0443><U+043C><U+0435>"
[40] "<U+0448><U+0443><U+043C><U+0438>"
[41] "<U+0448><U+0443><U+043C><U+0443>"
[42] "<U+0448><U+0443><U+043C><U+043E><U+043C>"
[43] "<U+0448><U+0443><U+043C><U+0430><U+043C><U+0430>"
[44] "<U+0447><U+0430><U+043C><U+0430><U+0446>"
[45] "<U+0447><U+0430><U+043C><U+0446><U+0430>"
[46] "<U+0447><U+0430><U+043C><U+0446><U+0443>"
[47] "<U+0447><U+0430><U+043C><U+0446><U+0435><U+043C>"
[48] "<U+0447><U+0430><U+043C><U+0446><U+0438>"
[49] "<U+0447><U+0430><U+043C><U+0430><U+0446><U+0430>"
[50] "<U+0447><U+0430><U+043C><U+0446><U+0438><U+043C><U+0430>"
[1] "\320\260\320\272\320\260\320\264\320\265\320\274\320\270\321\230\320\260"
[2] "\320\260\320\277\320\260\321\200\320\260\321\202"
[3] "\320\261\320\270\321\202\320\272\320\260"
[4] "\320\261\320\276\320\263"
[5] "\320\261\320\276\320\273"
[6] "\320\261\320\276\321\200"
[7] "\320\261\320\276\321\200\320\260\321\206"
[8] "\320\261\320\276\321\230\320\260"
Call:
ndlClassify(formula = Lexeme ~ Person + Number + Agent + Patient +
Register, data = think)
Formula:
Lexeme ~ Person + Number + Agent + Patient + Register
Weights:
ajatella harkita miettia pohtia
AgentGroup -0.0024238 0.0484437 -0.0195531 0.1726282
AgentIndividual 0.1377378 0.0011873 0.0730958 -0.0129260
AgentNone 0.1481698 0.0115980 0.0540880 -0.0147608
NumberOther 0.1240703 0.0326252 0.0936144 0.0483326
NumberPlural 0.1594136 0.0286038 0.0140163 0.0966088
PatientAbstraction -0.1868402 -0.0124441 0.0874109 0.1661720
PatientActivity -0.2692613 0.3313997 -0.0140782 0.0062384
PatientCommunication -0.3390196 0.0820339 0.2322344 0.0790500
PatientDirectQuote -0.3940583 -0.1073144 0.1906831 0.3649883
PatientEvent 0.2320365 -0.0692502 0.0007568 -0.1092445
... [ omitted 12 rows ] ...
Null deviance: 8701 on 13616 degrees of freedom
Residual (model) deviance: 7335 on 13528 degrees of freedom
R2.likelihood: 0.157
AIC: 7511
BIC: 8051
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