# VarReg

The goal of VarReg is to provide methods for fitting semi-parametric mean and variance models, with normal or censored data. This has also been extended to allow a regression in the location, scale and shape parameters. This algorithm is based upon an EM (Expectation Maximisation) algorithm, so is more stable than other similar methods like GAMLSS.

## :raising_hand: Author

Kristy Robledo https://github.com/kristyrobledo

NHMRC Clinical Trials Centre, University of Sydney

## :arrow_double_down: Installation

You can install the released version of VarReg from CRAN with:

``install.packages("VarReg")``

And the development version from GitHub with:

``````# install.packages("devtools")
devtools::install_github("kristyrobledo/VarReg")``````

## :book: Examples

This is a basic example to read in the mcycle dataset and perform a linear model in the mean and the variance:

``````library(VarReg)
#> Welcome to the 'VarReg' package to perform semi-parametric regression
data(mcycle)
## run a model with linear mean and linear variance:
linmodel<-semiVarReg(mcycle\$accel, mcycle\$times, meanmodel="linear", varmodel="linear",
maxit=10000)``````

Now we can plot the model:

``plotVarReg(linmodel)`` ``````

plotVarReg(linmodel, ci=TRUE, ci.type = "im")
#>  "CI=true, type=information matrix"`````` Or we can look at the results:

``````linmodel\$loglik
#>  -683.5092

linmodel\$mean
#>    Intercept mcycle\$times
#>    -53.69517      1.11797

linmodel\$variance
#>    Intercept mcycle\$times
#>   3824.07225    -66.39011``````

We can also run a model with semi-parametric mean (4 internal knots) and semi-parametric variance (2 knots):

``````semimodel<-semiVarReg(mcycle\$accel, mcycle\$times, meanmodel="semi", varmodel="semi",
knots.m=4, knots.v=2, maxit=10000)
plotVarReg(semimodel)`````` ``````
## run a model with semi-parametric mean (4 internal knots) and semi-parametric monotonic
## variance (2 knots):
## not run
##semimodel_inc<-semiVarReg(mcycle\$accel, mcycle\$times, meanmodel="semi", varmodel="semi",
##knots.m=4, knots.v=2, mono.var="inc")``````

Lastly, we can fit a model with a model in the location, scale and shape. Im not going to run this, just show the code, as it takes a while to run on my laptop!

`````` ##  LSS model followed by the basic plot command
#lssmodel<-lssVarReg(mcycle\$accel, mcycle\$times,  locationmodel="linear", scale2model="linear", shapemodel="constant", maxit=10000)
#plotlssVarReg(lssmodel, xlab="Time in seconds", ylab="Acceleration")``````

Enjoy!