R2MLwiN

Examples:Draws a sixway plot of MCMC diagnostics.
## Not run:
library(R2MLwiN)
## Modify the following paths as appropriate.
## MLwiN folder
mlwin = "C:/Program Files (x86)/MLwiN v2.27/"
## MLwiN sample worksheet folder
wspath=paste(mlwin,"/samples/",sep="")
## Example: Normal
formula="normexam~(0|cons+standlrt)+(2|cons+standlrt)+(1|cons)"
levID=c('school','student')
estoptions= list(EstM=1,resi.store.levs=2)
wsfile=paste(wspath,"tutorial.ws",sep="")
inputfile=paste(tempdir(),"/tutorial.dta",sep="")
ws2foreign(wsfile, foreignfile=inputfile, MLwiNPath=mlwin)
library(foreign);indata =read.dta(inputfile)
mymodel=runMLwiN(formula, levID, D="Normal", indata, estoptions,MLwiNPath=mlwin)
chain=mymodel["chains"][,"FP_standlrt"]
sixway(chain,"beta_1")
## End(Not run)

Example: Plots MCMC chain trajectories

## Not run:
library(R2MLwiN)
## Modify the following paths as appropriate.
## MLwiN folder
mlwin = "C:/Program Files (x86)/MLwiN v2.27/"
## MLwiN sample worksheet folder
wspath=paste(mlwin,"/samples/",sep="")
## Example: Normal
formula="normexam~(0|cons+standlrt)+(2|cons+standlrt)+(1|cons)"
levID=c('school','student')
estoptions= list(EstM=1)
wsfile=paste(wspath,"tutorial.ws",sep="")
inputfile=paste(tempdir(),"/tutorial.dta",sep="")
ws2foreign(wsfile, foreignfile=inputfile, MLwiNPath=mlwin)
library(foreign);indata =read.dta(inputfile)
mymodel=runMLwiN(formula, levID, D="Normal", indata, estoptions, MLwiNPath=mlwin)
trajectories(mymodel["chains"],Range=c(4501,5000))
## End(Not run)

Example: Converts a categorical variable into several separate binary variables

## Not run:
library(R2MLwiN)
## Modify the following paths as appropriate.
## MLwiN folder
mlwin = "C:/Program Files (x86)/MLwiN v2.27/"
## MLwiN sample worksheet folder
wspath=paste(mlwin,"/samples/",sep="")
wsfile=paste(wspath,"tutorial.ws",sep="")
inputfile=paste(tempdir(),"/tutorial.dta",sep="")
ws2foreign(wsfile, foreignfile=inputfile, MLwiNPath=mlwin)
library(foreign);indata =read.dta(inputfile)
names(indata)
indata=cbind(indata,Untoggle(indata[["school"]],"school"))
names(indata)

## End(Not run)

Multilevel Analysis using MLwiN - Example

• 01 Introduction to MCMC Estimation and Bayesian Modelling (R)
• 02 Single Level Normal Response Modelling (R)
• 03 Variance Components Models (R)
• 04 Other Features of Variance Components Models (R)
• 05 Prior Distributions, Starting Values and Random Number Seeds (R)
• 06 Random Slopes Regression Models (R)
• 07 Using the WinBUGS Interface in MLwiN (R)
• 08 Running a Simulation Study in MLwiN (R)
• 09 Modelling Complex Variance at Level 1 / Heteroscedasticity (R)
• 10 Modelling Binary Responses (R)
• 11 Poisson Response Modelling (R)
• 12 Unordered Categorical Responses (R)
• 13 Ordered Categorical Responses (R)
• 14 Adjusting for Measurement Errors in Predictor Variables (R)
• 15 Cross Classified Models (R)
• 16 Multiple Membership Models (R)
• 17 Modelling Spatial Data (R)
• 18 Multivariate Normal Response Models and Missing Data (R)
• 19 Mixed Response Models and Correlated Residuals (R)
• 20 Multilevel Factor Analysis Modelling (R)
• 21 Using Structured MCMC (R)
• 22 Using the Structured MVN framework for models (R)
• 23 Using Orthogonal fixed effect vectors (R)
• 24 Parameter expansion (R)
• 25 Hierarchical Centring (R)
  

多层次 回归模型 全部变量都放进去 不区分 是不是就是最小二成回归