HSI
From: Bayesian Models for Astrophysical Data, Cambridge Univ. Press
(c) 2017, Joseph M. Hilbe, Rafael S. de Souza and Emille E. O. Ishida
you are kindly asked to include the complete citation if you used this material in a publication
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Code 5.3 Synthetic lognormal data generated in R
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require(gamlss)
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# Data
set.seed(1056) # set seed to replicate example
nobs = 5000 # number of observations in model
x1 <- runif(nobs) # random uniform variable
xb <- 2 + 3*x1 # linear predictor, xb
y <- rlnorm(nobs, xb, sdlog=1) # create y as random lognormal variate
summary(mylnm <- gamlss(y ~ x1, family=LOGNO))
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Output on screen:
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GAMLSS-RS iteration 1: Global Deviance = 49159.59
GAMLSS-RS iteration 2: Global Deviance = 49159.59
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Family: c("LOGNO", "Log Normal")
Call: gamlss(formula = y ~ x1, family = LOGNO)
Fitting method: RS()
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Mu link function: identity
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.99350 0.02816 70.78 <2e-16 ***
x1 3.00663 0.04868 61.76 <2e-16 ***
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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.001669 0.010000 -0.167 0.867
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No. of observations in the fit: 5000
Degrees of Freedom for the fit: 3
Residual Deg. of Freedom: 4997
at cycle: 2
Global Deviance: 49159.59
AIC: 49165.59
SBC: 49185.14
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