Prints association study results

Description

Prints formatted results from the association study returned by AssoTestProc.

Usage

  ## S3 method for class 'asso'
 print(x, ...)

Arguments

Author(s)

Meiling Liu

Examples

# Fit the data under the assumption that there are 3 clusters
asso.fit <- AssoTestProc(signal=signal,fam=fam,envirX=envirX,phi=phi,N=3,varSelection='PC.9')
print(asso.fit)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(PedCNV)
Loading required package: Rcpp
Loading required package: RcppArmadillo
Loading required package: ggplot2
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/PedCNV/print.asso.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print.asso
> ### Title: Prints association study results
> ### Aliases: print.asso
> 
> ### ** Examples
> 
> # Fit the data under the assumption that there are 3 clusters
> asso.fit <- AssoTestProc(signal=signal,fam=fam,envirX=envirX,phi=phi,N=3,varSelection='PC.9')
The first 5 principal components are used.
The logliklihood for signal model is -1477.954 when clustering number is 3.
Iteration 1:
AI algorithm for REML.
The individuals are correlated, LMM is used.
Iteration 2:
AI algorithm for REML.
The individuals are correlated, LMM is used.
Iteration 3:
AI algorithm for REML.
The individuals are correlated, LMM is used.
> print(asso.fit)


 Under H0:
The coefficent:
   bet alpha1 alpha2 
0.0000 0.4853 0.1096 
The estimated variance:
      sig2     sig2g
 0.8615555 0.2473661
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>