How can Liu's estimations be calculated using JMP pro?!
Attached
Real data obtained from the Central Child Hospital in Baghdad - Iraq, representing the number of cases of congenital defects of children in the heart and circulatory system for the period (2006-2011).
regards
You send the individual vectors but then you refer to the data table in the liureg function call. You didn't send the data table as a data frame. You didn't make a data frame in R from the vectors. There are several approaches but it seems to me that the easiest way is to send the JMP data table as a data frame instead of individual vectors. I should you how to do it both ways but you mixed them together and left out the important parts in the process.
Try this script:
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// Open Data Table: PoiReg.jmp
dta = Open( "/D:/Rdata/PoiReg.jmp" );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( dta );
// use centered function in liureg package with entire data set
R Submit("
library(Matrix)
library(glmnet)
library(liureg)
library (readxl)
## liu regression
lu.reg <- liu(y~., data=dta, d=seq(0, 0.1, 0.1), scaling= \!"centered\!")
print(lu.reg)
");
// close R connection
R Term();I cannot find this statistic in the Statistics Index in JMP. I am not familiar with this estimator. Please provide a reference for it. Thanks!
Liu's method is a method similar to ridge regression method, used to estimate the parameters of a Poisson regression model in case of data suffering from a multicollinearity problem.
doi:10.1016/j.jspi.2010.05.030
https://doi.org/10.1080%2F03610929508831585
The Comprehensive R Archive Network (r-project.org)
Dear Mr. markbailey
I don't know how to run R program codes through JMP pro.
(I tried once and couldn't do it)
Please, if you know, help me
Regards
Did you read the JMP documentation that I cited above? You might find this white paper useful, too.
Here is an example of using R with JMP in a recent project of mine. I show two versions. The first uses matrices to transfer data between JMP and R.
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// use Big Class from JMP Sample Data Folder
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
x = :height << Get As Matrix;
y = :weight << Get As Matrix;
Close( dt, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library( mcr )
mc <- mcreg( x, y, alpha = 0.05, method.reg = \!"PaBa\!", method.ci = \!"analytical\!", slope.measure = \!"tangent\!" )
print( mc )
parms <- getCoefficients( mc )
pred <- parms[1] + parms[2]*x
print( pred )
resid <- y - pred
print( resid )
");
// close R connection
R Term();
// select View > Log (Windows) or Windows > Log (macOS) to see resultsThe second versions uses the JMP data table instead. It becomes a data frame in R.
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// use Big Class from JMP Sample Data Folder
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
x = :height << Get As Matrix;
y = :weight << Get As Matrix;
Close( dt, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library( mcr )
mc <- mcreg( x, y, alpha = 0.05, method.reg = \!"PaBa\!", method.ci = \!"analytical\!", slope.measure = \!"tangent\!" )
print( mc )
parms <- getCoefficients( mc )
pred <- parms[1] + parms[2]*x
print( pred )
resid <- y - pred
print( resid )
");
// close R connection
R Term();
// select View > Log (Windows) or Windows > Log (macOS) to see resultsYou can send individual commands to R or, as I illustrated, an entire R script as a character string. You can use all the JSL features for strings and expressions to create custom R scripts 'on the fly.'
Dear Sir
JMP does not connect with R, has read and applied the feedback you sent.
But I did not succeed!
Regards
what version of JMP Pro are you running and on what Operating System?
Do you have R installed on your computer or do you run it on a separate computer?
If it is installed on the same computer as JMP is installed on, what messages are printed out when you run the following script?
Names Default To Here( 1 );
R Init();
R Term();
Dear Sir
I use JMPPro version 16 as well as R version 4.1.3
The two programs are installed on the same computer that is running Windows Pro 11
and i read the notes in this link:
But I did not succeed!
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// Open Data Table: fatima data.jmp
dta = Open( "/D:/Rdata/fatima data.jmp" );
x1 = :the totals of the children weights << Get As Matrix;
x2 = :age sums of children parents << Get As Matrix;
x3 = :age sums of the mothers of the children << Get As Matrix;
x4 = :number of infected male children << Get As Matrix;
x5 = :number of infected female children << Get As Matrix;
x6 = :number of infected children born from consanguineous marriages << Get As Matrix;
x7 = :children whose mothers were exposed to radiation during pregnancy << Get As Matrix;
y = :total children with congenital heart defects << Get As Matrix;
Close( dta, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x1 );
R Send( x2 );
R Send( x3 );
R Send( x4 );
R Send( x5 );
R Send( x6 );
R Send( x7 );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library(liureg)
mod <-liu(y~., data = as.data.frame(dta), d = seq(0, 0.1, 0.01), scaling = "centered")
print( mod )
## Liu Var-Cov matrix
vcov(mod)
## Liu biasing parameters by researchers
dest(mod)
## Liu related statistics
lstats(mod)
");this is my script to run (liu regression) from jmp to R.
not run?!
#note: the data attached in below