View source: R/gen.trun-21-06-13.R
| gen.trun | R Documentation | 
The gen.trun() function allows the user to generate d, p,  q, and r distribution functions plus  an extra
gamlss.family function for fitting a truncated  distribution with gamlss. 
For continuous distributions left truncation at 3 means that the random variable can take the value 3. For discrete distributions left truncation at 3 means that the random variable can take values from 4 onwards. This is the same for right truncation. Truncation at 15 for a discrete variable means that 15 and greater values are not allowed but for continuous variable it mean values greater that 15 are not allowed (so 15 is a possible value).
If the user want a different link (rather the default) for any of the parameters she/he has to declare at the generation of the functions, see example.
gen.trun(par = c(0), family = "NO", name = "tr", 
         type = c("left", "right", "both"), 
         varying = FALSE,  print=TRUE, ...)
| par |  a vector with one (for  | 
| family |  a  | 
| name | the extra characters to be added to the name of new truncated distribution, by default it adds  | 
| type | whether  | 
| varying | whether the truncation  varies for different observations. This can be useful in regression analysis. If  | 
| print | whether to print the names of the created distribution | 
| ... | for extra arguments | 
Returns  the d, the p, the q, the r and the fitting functions of a truncated gamlss.family distribution. 
Mikis Stasinopoulos d.stasinopoulos@gre.ac.uk and Bob Rigby
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 https://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, https://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.
(see also https://www.gamlss.com/).
trun.d, trun.p, trun.q, trun.r 
# generating a t-distribution from 0 to 100  	
gen.trun(par=c(0,100),family="TF", name="0to100", type="both")
op<-par(mfrow=c(2,2))
plot(function(x) dTF0to100(x, mu=80 ,sigma=20, nu=5), 0, 100, ylab="pdf")
plot(function(x) pTF0to100(x, mu=80 ,sigma=20, nu=5), 0, 100, ylab="cdf")
plot(function(x) qTF0to100(x, mu=80 ,sigma=20, nu=5), 0.01, .999, ylab="invcdf")
hist(s1<-rTF0to100(1000, mu=80 ,sigma=20, nu=5), ylab="hist", xlab="x", 
            main="generated data")
par(op)
m1<-histDist(s1, family=TF0to100, xlim=c(0,100))# fitting the data
# using the argumnt varying 
# left part varies right part equal 100
leftPAR <- rPO(100)
gen.trun(par=cbind(leftPAR,rep(100, 100)),family="TF", name="0to100Varying", 
            type="both", varying=TRUE)
YY<- rTF0to100Varying(100, mu=80, sigma=20, nu=5)
m1<-gamlss(YY~1, family=TF0to100Varying)
m1
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