NBF: Negative Binomial Family distribution for fitting a GAMLSS
In gamlss.dist: Distributions for Generalized Additive Models for Location Scale and Shape
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
The NBF() function defines the Negative Binomial family distribution, a three parameter distribution, for a gamlss.family object to be used in GAMLSS fitting using the function gamlss().
The functions dNBF, pNBF, qNBF and rNBF define the density, distribution function, quantile function and random generation for the negative binomial family, NBF(), distribution.
The functions dZINBF, pZINBF, qZINBF and rZINBF define the density, distribution function, quantile function and random generation for the zero inflated negative binomial family, ZINBF(), distribution a four parameter distribution.
Usage
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NBF(mu.link = "log", sigma.link = "log", nu.link = "log")
dNBF(x, mu = 1, sigma = 1, nu = 2, log = FALSE)
pNBF(q, mu = 1, sigma = 1, nu = 2, lower.tail = TRUE, log.p = FALSE)
qNBF(p, mu = 1, sigma = 1, nu = 2, lower.tail = TRUE, log.p = FALSE)
rNBF(n, mu = 1, sigma = 1, nu = 2)
ZINBF(mu.link = "log", sigma.link = "log", nu.link = "log",
tau.link = "logit")
dZINBF(x, mu = 1, sigma = 1, nu = 2, tau = 0.1, log = FALSE)
pZINBF(q, mu = 1, sigma = 1, nu = 2, tau = 0.1, lower.tail = TRUE,
log.p = FALSE)
qZINBF(p, mu = 1, sigma = 1, nu = 2, tau = 0.1, lower.tail = TRUE,
log.p = FALSE)
rZINBF(n, mu = 1, sigma = 1, nu = 2, tau = 0.1)
Arguments
mu.link
The link function for mu
sigma.link
The link function for sigma
nu.link
The link function for nu
tau.link
The link function for tau
x
vector of (non-negative integer)
mu
vector of positive means
sigma
vector of positive dispersion parameter
nu
vector of power parameter
tau
vector of inflation parameter
log, log.p
logical; if TRUE, probabilities p are given as log(p)
lower.tail
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x]
p
vector of probabilities
q
vector of quantiles
n
number of random values to return
Details
The definition for Negative Binomial Family distribution , NBF, is similar to the Negative Binomial type I. The probability function of the NBF can be obtained by replacing σ with σ μ^{ν-2} where ν is a power parameter.
The distribution has mean μ and variance μ+σ μ^{ν}.
Value
returns a gamlss.family object which can be used to fit a Negative Binomial Family distribution in the gamlss() function.
Author(s)
Bob Rigby and Mikis Stasinopoulos
References
Anscombe, F. J. (1950) Sampling theory of the negative binomial and logarithmic distributions, Biometrika, 37, 358-382.
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion),
Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in http://www.gamlss.com/.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.com/).
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R.
Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017)
Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
See Also
Examples
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NBF() # default link functions for the Negative Binomial Family
# plotting the distribution
plot(function(y) dNBF(y, mu = 10, sigma = 0.5, nu=2 ), from=0,
to=40, n=40+1, type="h")
# creating random variables and plot them
tN <- table(Ni <- rNBF(1000, mu=5, sigma=0.5, nu=2))
r <- barplot(tN, col='lightblue')
# zero inflated NBF
ZINBF() # default link functions for the zero inflated NBF
# plotting the distribution
plot(function(y) dZINBF(y, mu = 10, sigma = 0.5, nu=2, tau=.1 ),
from=0, to=40, n=40+1, type="h")
# creating random variables and plot them
tN <- table(Ni <- rZINBF(1000, mu=5, sigma=0.5, nu=2, tau=0.1))
r <- barplot(tN, col='lightblue')
## Not run:
library(gamlss)
data(species)
species <- transform(species, x=log(lake))
m6 <- gamlss(fish~poly(x,2), sigma.fo=~1, data=species, family=NBF,
n.cyc=200)
fitted(m6, "nu")[1]
## End(Not run)
Example output
Loading required package: MASS
GAMLSS Family: NBF NB Family
Link function for mu : log
Link function for sigma: log
Link function for nu : log
GAMLSS Family: ZINBF
Link function for mu : log
Link function for sigma: log
Link function for nu : log
Link function for tau : logit
Loading required package: splines
Loading required package: gamlss.data
Attaching package: 'gamlss.data'
The following object is masked from 'package:datasets':
sleep
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.1-3 **********
For more on GAMLSS look at http://www.gamlss.org/
Type gamlssNews() to see new features/changes/bug fixes.
GAMLSS-RS iteration 1: Global Deviance = 614.1349
GAMLSS-RS iteration 2: Global Deviance = 613.7821
GAMLSS-RS iteration 3: Global Deviance = 613.4428
GAMLSS-RS iteration 4: Global Deviance = 613.1159
GAMLSS-RS iteration 5: Global Deviance = 612.8035
GAMLSS-RS iteration 6: Global Deviance = 612.5025
GAMLSS-RS iteration 7: Global Deviance = 612.2054
GAMLSS-RS iteration 8: Global Deviance = 611.9217
GAMLSS-RS iteration 9: Global Deviance = 611.6472
GAMLSS-RS iteration 10: Global Deviance = 611.3866
GAMLSS-RS iteration 11: Global Deviance = 611.1413
GAMLSS-RS iteration 12: Global Deviance = 610.9056
GAMLSS-RS iteration 13: Global Deviance = 610.6826
GAMLSS-RS iteration 14: Global Deviance = 610.4682
GAMLSS-RS iteration 15: Global Deviance = 610.2649
GAMLSS-RS iteration 16: Global Deviance = 610.0689
GAMLSS-RS iteration 17: Global Deviance = 609.8839
GAMLSS-RS iteration 18: Global Deviance = 609.705
GAMLSS-RS iteration 19: Global Deviance = 609.5371
GAMLSS-RS iteration 20: Global Deviance = 609.3739
GAMLSS-RS iteration 21: Global Deviance = 609.2215
GAMLSS-RS iteration 22: Global Deviance = 609.0729
GAMLSS-RS iteration 23: Global Deviance = 608.9346
GAMLSS-RS iteration 24: Global Deviance = 608.7995
GAMLSS-RS iteration 25: Global Deviance = 608.6739
GAMLSS-RS iteration 26: Global Deviance = 608.5513
GAMLSS-RS iteration 27: Global Deviance = 608.4374
GAMLSS-RS iteration 28: Global Deviance = 608.3263
GAMLSS-RS iteration 29: Global Deviance = 608.2228
GAMLSS-RS iteration 30: Global Deviance = 608.1224
GAMLSS-RS iteration 31: Global Deviance = 608.0284
GAMLSS-RS iteration 32: Global Deviance = 607.9376
GAMLSS-RS iteration 33: Global Deviance = 607.8522
GAMLSS-RS iteration 34: Global Deviance = 607.7701
GAMLSS-RS iteration 35: Global Deviance = 607.6927
GAMLSS-RS iteration 36: Global Deviance = 607.6184
GAMLSS-RS iteration 37: Global Deviance = 607.5481
GAMLSS-RS iteration 38: Global Deviance = 607.4808
GAMLSS-RS iteration 39: Global Deviance = 607.417
GAMLSS-RS iteration 40: Global Deviance = 607.356
GAMLSS-RS iteration 41: Global Deviance = 607.2981
GAMLSS-RS iteration 42: Global Deviance = 607.2428
GAMLSS-RS iteration 43: Global Deviance = 607.1903
GAMLSS-RS iteration 44: Global Deviance = 607.1401
GAMLSS-RS iteration 45: Global Deviance = 607.0924
GAMLSS-RS iteration 46: Global Deviance = 607.0468
GAMLSS-RS iteration 47: Global Deviance = 607.0034
GAMLSS-RS iteration 48: Global Deviance = 606.9621
GAMLSS-RS iteration 49: Global Deviance = 606.9226
GAMLSS-RS iteration 50: Global Deviance = 606.885
GAMLSS-RS iteration 51: Global Deviance = 606.8491
GAMLSS-RS iteration 52: Global Deviance = 606.8149
GAMLSS-RS iteration 53: Global Deviance = 606.7823
GAMLSS-RS iteration 54: Global Deviance = 606.7511
GAMLSS-RS iteration 55: Global Deviance = 606.7214
GAMLSS-RS iteration 56: Global Deviance = 606.693
GAMLSS-RS iteration 57: Global Deviance = 606.6659
GAMLSS-RS iteration 58: Global Deviance = 606.64
GAMLSS-RS iteration 59: Global Deviance = 606.6153
GAMLSS-RS iteration 60: Global Deviance = 606.5917
GAMLSS-RS iteration 61: Global Deviance = 606.5622
GAMLSS-RS iteration 62: Global Deviance = 606.5334
GAMLSS-RS iteration 63: Global Deviance = 606.506
GAMLSS-RS iteration 64: Global Deviance = 606.4802
GAMLSS-RS iteration 65: Global Deviance = 606.4559
GAMLSS-RS iteration 66: Global Deviance = 606.4329
GAMLSS-RS iteration 67: Global Deviance = 606.4112
GAMLSS-RS iteration 68: Global Deviance = 606.3907
GAMLSS-RS iteration 69: Global Deviance = 606.3714
GAMLSS-RS iteration 70: Global Deviance = 606.3532
GAMLSS-RS iteration 71: Global Deviance = 606.336
GAMLSS-RS iteration 72: Global Deviance = 606.3198
GAMLSS-RS iteration 73: Global Deviance = 606.3045
GAMLSS-RS iteration 74: Global Deviance = 606.29
GAMLSS-RS iteration 75: Global Deviance = 606.2764
GAMLSS-RS iteration 76: Global Deviance = 606.2636
GAMLSS-RS iteration 77: Global Deviance = 606.2515
GAMLSS-RS iteration 78: Global Deviance = 606.2405
GAMLSS-RS iteration 79: Global Deviance = 606.2369
GAMLSS-RS iteration 80: Global Deviance = 606.2332
GAMLSS-RS iteration 81: Global Deviance = 606.2296
GAMLSS-RS iteration 82: Global Deviance = 606.226
GAMLSS-RS iteration 83: Global Deviance = 606.2225
GAMLSS-RS iteration 84: Global Deviance = 606.2191
GAMLSS-RS iteration 85: Global Deviance = 606.2158
GAMLSS-RS iteration 86: Global Deviance = 606.2125
GAMLSS-RS iteration 87: Global Deviance = 606.2093
GAMLSS-RS iteration 88: Global Deviance = 606.2062
GAMLSS-RS iteration 89: Global Deviance = 606.2031
GAMLSS-RS iteration 90: Global Deviance = 606.2001
GAMLSS-RS iteration 91: Global Deviance = 606.1972
GAMLSS-RS iteration 92: Global Deviance = 606.1943
GAMLSS-RS iteration 93: Global Deviance = 606.1915
GAMLSS-RS iteration 94: Global Deviance = 606.1888
GAMLSS-RS iteration 95: Global Deviance = 606.1861
GAMLSS-RS iteration 96: Global Deviance = 606.1799
GAMLSS-RS iteration 97: Global Deviance = 606.1753
GAMLSS-RS iteration 98: Global Deviance = 606.1707
GAMLSS-RS iteration 99: Global Deviance = 606.1664
GAMLSS-RS iteration 100: Global Deviance = 606.1623
GAMLSS-RS iteration 101: Global Deviance = 606.1582
GAMLSS-RS iteration 102: Global Deviance = 606.1544
GAMLSS-RS iteration 103: Global Deviance = 606.1507
GAMLSS-RS iteration 104: Global Deviance = 606.1471
GAMLSS-RS iteration 105: Global Deviance = 606.1437
GAMLSS-RS iteration 106: Global Deviance = 606.1404
GAMLSS-RS iteration 107: Global Deviance = 606.1372
GAMLSS-RS iteration 108: Global Deviance = 606.1342
GAMLSS-RS iteration 109: Global Deviance = 606.1312
GAMLSS-RS iteration 110: Global Deviance = 606.1284
GAMLSS-RS iteration 111: Global Deviance = 606.1257
GAMLSS-RS iteration 112: Global Deviance = 606.1231
GAMLSS-RS iteration 113: Global Deviance = 606.1205
GAMLSS-RS iteration 114: Global Deviance = 606.1181
GAMLSS-RS iteration 115: Global Deviance = 606.1157
GAMLSS-RS iteration 116: Global Deviance = 606.1135
GAMLSS-RS iteration 117: Global Deviance = 606.1113
GAMLSS-RS iteration 118: Global Deviance = 606.1092
GAMLSS-RS iteration 119: Global Deviance = 606.1072
GAMLSS-RS iteration 120: Global Deviance = 606.1052
GAMLSS-RS iteration 121: Global Deviance = 606.1033
GAMLSS-RS iteration 122: Global Deviance = 606.1015
GAMLSS-RS iteration 123: Global Deviance = 606.0998
GAMLSS-RS iteration 124: Global Deviance = 606.0981
GAMLSS-RS iteration 125: Global Deviance = 606.0964
GAMLSS-RS iteration 126: Global Deviance = 606.0949
GAMLSS-RS iteration 127: Global Deviance = 606.0933
GAMLSS-RS iteration 128: Global Deviance = 606.0919
GAMLSS-RS iteration 129: Global Deviance = 606.0905
GAMLSS-RS iteration 130: Global Deviance = 606.0891
GAMLSS-RS iteration 131: Global Deviance = 606.088
GAMLSS-RS iteration 132: Global Deviance = 606.0862
GAMLSS-RS iteration 133: Global Deviance = 606.0837
GAMLSS-RS iteration 134: Global Deviance = 606.0827
[1] 2.84052
gamlss.dist documentation built on July 13, 2020, 5:08 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
The NBF() function defines the Negative Binomial family distribution, a three parameter distribution, for a gamlss.family object to be used in GAMLSS fitting using the function gamlss().
The functions dNBF, pNBF, qNBF and rNBF define the density, distribution function, quantile function and random generation for the negative binomial family, NBF(), distribution.
The functions dZINBF, pZINBF, qZINBF and rZINBF define the density, distribution function, quantile function and random generation for the zero inflated negative binomial family, ZINBF(), distribution a four parameter distribution.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | NBF(mu.link = "log", sigma.link = "log", nu.link = "log")
dNBF(x, mu = 1, sigma = 1, nu = 2, log = FALSE)
pNBF(q, mu = 1, sigma = 1, nu = 2, lower.tail = TRUE, log.p = FALSE)
qNBF(p, mu = 1, sigma = 1, nu = 2, lower.tail = TRUE, log.p = FALSE)
rNBF(n, mu = 1, sigma = 1, nu = 2)
ZINBF(mu.link = "log", sigma.link = "log", nu.link = "log",
tau.link = "logit")
dZINBF(x, mu = 1, sigma = 1, nu = 2, tau = 0.1, log = FALSE)
pZINBF(q, mu = 1, sigma = 1, nu = 2, tau = 0.1, lower.tail = TRUE,
log.p = FALSE)
qZINBF(p, mu = 1, sigma = 1, nu = 2, tau = 0.1, lower.tail = TRUE,
log.p = FALSE)
rZINBF(n, mu = 1, sigma = 1, nu = 2, tau = 0.1)
|
Arguments
mu.link |
The link function for |
sigma.link |
The link function for |
nu.link |
The link function for |
tau.link |
The link function for |
x |
vector of (non-negative integer) |
mu |
vector of positive means |
sigma |
vector of positive dispersion parameter |
nu |
vector of power parameter |
tau |
vector of inflation parameter |
log, log.p |
logical; if TRUE, probabilities p are given as log(p) |
lower.tail |
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x] |
p |
vector of probabilities |
q |
vector of quantiles |
n |
number of random values to return |
Details
The definition for Negative Binomial Family distribution , NBF, is similar to the Negative Binomial type I. The probability function of the NBF can be obtained by replacing σ with σ μ^{ν-2} where ν is a power parameter.
The distribution has mean μ and variance μ+σ μ^{ν}.
Value
returns a gamlss.family object which can be used to fit a Negative Binomial Family distribution in the gamlss() function.
Author(s)
Bob Rigby and Mikis Stasinopoulos
References
Anscombe, F. J. (1950) Sampling theory of the negative binomial and logarithmic distributions, Biometrika, 37, 358-382.
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in http://www.gamlss.com/.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.com/).
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
See Also
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 | NBF() # default link functions for the Negative Binomial Family
# plotting the distribution
plot(function(y) dNBF(y, mu = 10, sigma = 0.5, nu=2 ), from=0,
to=40, n=40+1, type="h")
# creating random variables and plot them
tN <- table(Ni <- rNBF(1000, mu=5, sigma=0.5, nu=2))
r <- barplot(tN, col='lightblue')
# zero inflated NBF
ZINBF() # default link functions for the zero inflated NBF
# plotting the distribution
plot(function(y) dZINBF(y, mu = 10, sigma = 0.5, nu=2, tau=.1 ),
from=0, to=40, n=40+1, type="h")
# creating random variables and plot them
tN <- table(Ni <- rZINBF(1000, mu=5, sigma=0.5, nu=2, tau=0.1))
r <- barplot(tN, col='lightblue')
## Not run:
library(gamlss)
data(species)
species <- transform(species, x=log(lake))
m6 <- gamlss(fish~poly(x,2), sigma.fo=~1, data=species, family=NBF,
n.cyc=200)
fitted(m6, "nu")[1]
## End(Not run)
|
Example output
Loading required package: MASS
GAMLSS Family: NBF NB Family
Link function for mu : log
Link function for sigma: log
Link function for nu : log
GAMLSS Family: ZINBF
Link function for mu : log
Link function for sigma: log
Link function for nu : log
Link function for tau : logit
Loading required package: splines
Loading required package: gamlss.data
Attaching package: 'gamlss.data'
The following object is masked from 'package:datasets':
sleep
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.1-3 **********
For more on GAMLSS look at http://www.gamlss.org/
Type gamlssNews() to see new features/changes/bug fixes.
GAMLSS-RS iteration 1: Global Deviance = 614.1349
GAMLSS-RS iteration 2: Global Deviance = 613.7821
GAMLSS-RS iteration 3: Global Deviance = 613.4428
GAMLSS-RS iteration 4: Global Deviance = 613.1159
GAMLSS-RS iteration 5: Global Deviance = 612.8035
GAMLSS-RS iteration 6: Global Deviance = 612.5025
GAMLSS-RS iteration 7: Global Deviance = 612.2054
GAMLSS-RS iteration 8: Global Deviance = 611.9217
GAMLSS-RS iteration 9: Global Deviance = 611.6472
GAMLSS-RS iteration 10: Global Deviance = 611.3866
GAMLSS-RS iteration 11: Global Deviance = 611.1413
GAMLSS-RS iteration 12: Global Deviance = 610.9056
GAMLSS-RS iteration 13: Global Deviance = 610.6826
GAMLSS-RS iteration 14: Global Deviance = 610.4682
GAMLSS-RS iteration 15: Global Deviance = 610.2649
GAMLSS-RS iteration 16: Global Deviance = 610.0689
GAMLSS-RS iteration 17: Global Deviance = 609.8839
GAMLSS-RS iteration 18: Global Deviance = 609.705
GAMLSS-RS iteration 19: Global Deviance = 609.5371
GAMLSS-RS iteration 20: Global Deviance = 609.3739
GAMLSS-RS iteration 21: Global Deviance = 609.2215
GAMLSS-RS iteration 22: Global Deviance = 609.0729
GAMLSS-RS iteration 23: Global Deviance = 608.9346
GAMLSS-RS iteration 24: Global Deviance = 608.7995
GAMLSS-RS iteration 25: Global Deviance = 608.6739
GAMLSS-RS iteration 26: Global Deviance = 608.5513
GAMLSS-RS iteration 27: Global Deviance = 608.4374
GAMLSS-RS iteration 28: Global Deviance = 608.3263
GAMLSS-RS iteration 29: Global Deviance = 608.2228
GAMLSS-RS iteration 30: Global Deviance = 608.1224
GAMLSS-RS iteration 31: Global Deviance = 608.0284
GAMLSS-RS iteration 32: Global Deviance = 607.9376
GAMLSS-RS iteration 33: Global Deviance = 607.8522
GAMLSS-RS iteration 34: Global Deviance = 607.7701
GAMLSS-RS iteration 35: Global Deviance = 607.6927
GAMLSS-RS iteration 36: Global Deviance = 607.6184
GAMLSS-RS iteration 37: Global Deviance = 607.5481
GAMLSS-RS iteration 38: Global Deviance = 607.4808
GAMLSS-RS iteration 39: Global Deviance = 607.417
GAMLSS-RS iteration 40: Global Deviance = 607.356
GAMLSS-RS iteration 41: Global Deviance = 607.2981
GAMLSS-RS iteration 42: Global Deviance = 607.2428
GAMLSS-RS iteration 43: Global Deviance = 607.1903
GAMLSS-RS iteration 44: Global Deviance = 607.1401
GAMLSS-RS iteration 45: Global Deviance = 607.0924
GAMLSS-RS iteration 46: Global Deviance = 607.0468
GAMLSS-RS iteration 47: Global Deviance = 607.0034
GAMLSS-RS iteration 48: Global Deviance = 606.9621
GAMLSS-RS iteration 49: Global Deviance = 606.9226
GAMLSS-RS iteration 50: Global Deviance = 606.885
GAMLSS-RS iteration 51: Global Deviance = 606.8491
GAMLSS-RS iteration 52: Global Deviance = 606.8149
GAMLSS-RS iteration 53: Global Deviance = 606.7823
GAMLSS-RS iteration 54: Global Deviance = 606.7511
GAMLSS-RS iteration 55: Global Deviance = 606.7214
GAMLSS-RS iteration 56: Global Deviance = 606.693
GAMLSS-RS iteration 57: Global Deviance = 606.6659
GAMLSS-RS iteration 58: Global Deviance = 606.64
GAMLSS-RS iteration 59: Global Deviance = 606.6153
GAMLSS-RS iteration 60: Global Deviance = 606.5917
GAMLSS-RS iteration 61: Global Deviance = 606.5622
GAMLSS-RS iteration 62: Global Deviance = 606.5334
GAMLSS-RS iteration 63: Global Deviance = 606.506
GAMLSS-RS iteration 64: Global Deviance = 606.4802
GAMLSS-RS iteration 65: Global Deviance = 606.4559
GAMLSS-RS iteration 66: Global Deviance = 606.4329
GAMLSS-RS iteration 67: Global Deviance = 606.4112
GAMLSS-RS iteration 68: Global Deviance = 606.3907
GAMLSS-RS iteration 69: Global Deviance = 606.3714
GAMLSS-RS iteration 70: Global Deviance = 606.3532
GAMLSS-RS iteration 71: Global Deviance = 606.336
GAMLSS-RS iteration 72: Global Deviance = 606.3198
GAMLSS-RS iteration 73: Global Deviance = 606.3045
GAMLSS-RS iteration 74: Global Deviance = 606.29
GAMLSS-RS iteration 75: Global Deviance = 606.2764
GAMLSS-RS iteration 76: Global Deviance = 606.2636
GAMLSS-RS iteration 77: Global Deviance = 606.2515
GAMLSS-RS iteration 78: Global Deviance = 606.2405
GAMLSS-RS iteration 79: Global Deviance = 606.2369
GAMLSS-RS iteration 80: Global Deviance = 606.2332
GAMLSS-RS iteration 81: Global Deviance = 606.2296
GAMLSS-RS iteration 82: Global Deviance = 606.226
GAMLSS-RS iteration 83: Global Deviance = 606.2225
GAMLSS-RS iteration 84: Global Deviance = 606.2191
GAMLSS-RS iteration 85: Global Deviance = 606.2158
GAMLSS-RS iteration 86: Global Deviance = 606.2125
GAMLSS-RS iteration 87: Global Deviance = 606.2093
GAMLSS-RS iteration 88: Global Deviance = 606.2062
GAMLSS-RS iteration 89: Global Deviance = 606.2031
GAMLSS-RS iteration 90: Global Deviance = 606.2001
GAMLSS-RS iteration 91: Global Deviance = 606.1972
GAMLSS-RS iteration 92: Global Deviance = 606.1943
GAMLSS-RS iteration 93: Global Deviance = 606.1915
GAMLSS-RS iteration 94: Global Deviance = 606.1888
GAMLSS-RS iteration 95: Global Deviance = 606.1861
GAMLSS-RS iteration 96: Global Deviance = 606.1799
GAMLSS-RS iteration 97: Global Deviance = 606.1753
GAMLSS-RS iteration 98: Global Deviance = 606.1707
GAMLSS-RS iteration 99: Global Deviance = 606.1664
GAMLSS-RS iteration 100: Global Deviance = 606.1623
GAMLSS-RS iteration 101: Global Deviance = 606.1582
GAMLSS-RS iteration 102: Global Deviance = 606.1544
GAMLSS-RS iteration 103: Global Deviance = 606.1507
GAMLSS-RS iteration 104: Global Deviance = 606.1471
GAMLSS-RS iteration 105: Global Deviance = 606.1437
GAMLSS-RS iteration 106: Global Deviance = 606.1404
GAMLSS-RS iteration 107: Global Deviance = 606.1372
GAMLSS-RS iteration 108: Global Deviance = 606.1342
GAMLSS-RS iteration 109: Global Deviance = 606.1312
GAMLSS-RS iteration 110: Global Deviance = 606.1284
GAMLSS-RS iteration 111: Global Deviance = 606.1257
GAMLSS-RS iteration 112: Global Deviance = 606.1231
GAMLSS-RS iteration 113: Global Deviance = 606.1205
GAMLSS-RS iteration 114: Global Deviance = 606.1181
GAMLSS-RS iteration 115: Global Deviance = 606.1157
GAMLSS-RS iteration 116: Global Deviance = 606.1135
GAMLSS-RS iteration 117: Global Deviance = 606.1113
GAMLSS-RS iteration 118: Global Deviance = 606.1092
GAMLSS-RS iteration 119: Global Deviance = 606.1072
GAMLSS-RS iteration 120: Global Deviance = 606.1052
GAMLSS-RS iteration 121: Global Deviance = 606.1033
GAMLSS-RS iteration 122: Global Deviance = 606.1015
GAMLSS-RS iteration 123: Global Deviance = 606.0998
GAMLSS-RS iteration 124: Global Deviance = 606.0981
GAMLSS-RS iteration 125: Global Deviance = 606.0964
GAMLSS-RS iteration 126: Global Deviance = 606.0949
GAMLSS-RS iteration 127: Global Deviance = 606.0933
GAMLSS-RS iteration 128: Global Deviance = 606.0919
GAMLSS-RS iteration 129: Global Deviance = 606.0905
GAMLSS-RS iteration 130: Global Deviance = 606.0891
GAMLSS-RS iteration 131: Global Deviance = 606.088
GAMLSS-RS iteration 132: Global Deviance = 606.0862
GAMLSS-RS iteration 133: Global Deviance = 606.0837
GAMLSS-RS iteration 134: Global Deviance = 606.0827
[1] 2.84052
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