Lui estimatoriCome possono essere calcolate le stime di Liu utilizzando JMP pro?!
Allegato
Dati reali ottenuti dal Central Child Hospital di Baghdad - Iraq, che rappresentano il numero di casi di difetti congeniti dei bambini al cuore e al sistema circolatorio per il periodo (2006-2011).
Saluti
AnaliticaTabella datiSceneggiaturaRe: Estimatori LuiNon riesco a trovare questa statistica nell'indice delle statistiche in JMP. Non conosco questo stimatore. Si prega di fornire un riferimento per esso. Grazie!
Re: Estimatori LuiIl metodo di Liu è un metodo simile al metodo di regressione di cresta, utilizzato per stimare i parametri di un modello di regressione di Poisson in caso di dati affetti da un problema di multicollinearità.
doi:10.1016/j.jspi.2010.05.030
https://doi.org/10.1080%2F03610929508831585
La rete completa di archivi R (r-project.org)
Re: Lui estimatoriGrazie per lo sfondo!
Nel frattempo, puoi eseguire il codice R da JMP , se questo può aiutare.
Re: Estimatori LuiCaro signor Markbailey
Non so come eseguire i codici di programma R tramite JMP pro.
(Ho provato una volta e non ci sono riuscito)
Per favore, se lo sai, aiutami
Saluti
Re: Estimatori LuiHai letto la documentazione JMP che ho citato sopra? Potresti trovare questo bianco utile anche la carta.
Ecco un esempio di utilizzo di R con JMP in un mio recente progetto. Mostro due versioni. Il primo utilizza le matrici per trasferire i dati tra JMP e 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( "$SAMPLE_DATA/Big Class.jmp" );
x = :height << Get As Matrix;
y = :weight << Get As Matrix;
Close( dt, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library( mcr )
mc <- mcreg( x, y, alpha = 0.05, method.reg = \!"PaBa\!", method.ci = \!"analytical\!", slope.measure = \!"tangent\!" )
print( mc )
parms <- getCoefficients( mc )
pred <- parms[1] + parms[2]*x
print( pred )
resid <- y - pred
print( resid )
");
// close R connection
R Term();
// select View > Log (Windows) or Windows > Log (macOS) to see results
La seconda versione utilizza invece la tabella di dati JMP. Diventa un data frame in R.
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// use Big Class from JMP Sample Data Folder
dt = Open( "$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 results
Puoi inviare singoli comandi a R o, come ho illustrato, un intero script R come stringa di caratteri. Puoi utilizzare tutte le funzionalità JSL per stringhe ed espressioni per creare script R personalizzati "al volo".
Re: Estimatori Luicaro Signore
JMP non si connette con R, ha letto e applicato il feedback inviato.
Ma non ci sono riuscito!
Saluti
Re: Estimatori Luiquale versione di JMP Pro stai utilizzando e su quale sistema operativo?
Hai R installato sul tuo computer o lo esegui su un computer separato?
Se è installato sullo stesso computer su cui è installato JMP, quali messaggi vengono stampati quando si esegue lo script seguente?
Names Default To Here( 1 );
R Init();
R Term();
Re: Estimatori Luicaro Signore
Uso JMPPro versione 16 e R versione 4.1.3
I due programmi sono installati sullo stesso computer che esegue Windows Pro 11
e ho letto le note a questo link:
wp-jmp-synergies-python-r.pdf
Ma non ci sono riuscito!
Re: Estimatori LuiNames 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)
");questo è il mio script da eseguire (liu regression) da jmp a R.
non correre?!
#nota: i dati allegati di seguito
Re: Estimatori LuiNames 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)
");questo è il mio script da eseguire (liu regression) da jmp a R.
non correre?!
#nota: i dati allegati di seguito
Re: Estimatori LuiHo modificato il tuo JSL in modo che corrisponda ai nomi delle colonne dalla tabella dati inclusa.Ho anche aggiunto un Send() per spostare i dati dta su 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" );
// Open Data Table: fatima data.jmp
//dta = Open( "/D:/Rdata/fatima data.jmp" );
dta=current data table();
x1 = :"the totals of the children's weights 109 75.4 69.5 36.2 6.9"n << Get As Matrix;
x2 = :"Age sums of children's parents 1260 811 735 201 82"n << Get As Matrix;
x3 = :"Age sums of the mothers of the children 915 619 503 168 61"n << Get As Matrix;
x4 = :"The number of infected male children 29 18 11 3 2"n << Get As Matrix;
x5 = :"Number of infected female children 10 3 7 2 0"n << Get As Matrix;
x6 = :"Number of infected children born from consanguineous marriages 9 5 3 1 0"n << Get As Matrix;
x7 = :"Number of infected children whose mothers were exposed to radiation during pregnancy 11 3 1 1 0"n << Get As Matrix;
y = :"Total children with congenital heart defects 39 21 18 5 2"n << 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 );
R Send(dta);
// 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)
");
r term();
I risultati di r erano
TKIntRJMP.R version 14.0
Call:
liu.default(formula = y ~ ., data = as.data.frame(dta), d = seq(0,
0.1, 0.01), scaling = "centered")
Intercept no.1.2.3.4.5
d=0 NA NA
d=0.01 NA NA
d=0.02 NA NA
d=0.03 NA NA
d=0.04 NA NA
d=0.05 NA NA
d=0.06 NA NA
d=0.07 NA NA
d=0.08 NA NA
d=0.09 NA NA
d=0.1 NA NA
Total.children.with.congenital.heart.defects.39.21.18.5.2
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
the.totals.of.the.children.s.weights.109.75.4.69.5.36.2.6.9
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Age.sums.of.children.s.parents.1260.811.735.201.82
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Age.sums.of.the.mothers.of.the.children.915.619.503.168.61
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
The.number.of.infected.male.children.29.18.11.3.2
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Number.of.infected.female.children.10.3.7.2.0
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Number.of.infected.children.born.from.consanguineous.marriages.9.5.3.1.0
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Number.of.infected.children.whose.mothers.were.exposed.to.radiation.during.pregnancy.11.3.1.1.0
d=0 NA
d=0.01 NA
d=0.02 NA
d=0.03 NA
d=0.04 NA
d=0.05 NA
d=0.06 NA
d=0.07 NA
d=0.08 NA
d=0.09 NA
d=0.1 NA
Error: Error in solve.default(t(x) %*% x) :
system is computationally singular: reciprocal condition number = 1.28204e-20
0
l'interfaccia sembra funzionare, sembra esserci un problema con i dati.
Re: Estimatori Luiutilizzando il codice R:
# R code
library ("Matrix")
library ("aml")
library ("glmnet")
library("liureg")
library (readxl)
#SET THE WORK FILE DIRECTORY & READ EXCEL FILES
setwd("D:/Rdata")
da109 <- read_excel("D:/Rdata/PoiReg.xlsx", sheet = 1)
DT <- data.frame (da109, row.names=TRUE, stringsAsFactors=FALSE)
attach (DT)
## fitted Liu estimators
md<-liu(y ~ ., data = as.data.frame(DT), d = seq(0, 1, 0.05), scaling = "sc")
summary(md)
## Liu trace
plot(md)
# enduscita da R
Call:
liu.default(formula = y ~ ., data = as.data.frame(DT), d = seq(0,
1, 0.05), scaling = "sc")
Coefficients for Liu parameter d= 0
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 8.970e-01 -2.457e+03 7.974e+01 -30.814 <2e-16 ***
x1 4.061e-02 5.102e+00 3.315e-01 15.392 <2e-16 ***
x2 3.820e-03 5.373e+00 2.601e-01 20.661 <2e-16 ***
x3 5.260e-03 5.335e+00 2.577e-01 20.704 <2e-16 ***
x4 1.785e-01 5.452e+00 2.850e-01 19.132 <2e-16 ***
x5 4.453e-01 4.945e+00 4.755e-01 10.399 <2e-16 ***
x6 5.537e-01 5.241e+00 3.030e-01 17.297 <2e-16 ***
x7 4.625e-01 4.903e+00 5.292e-01 9.266 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0 0.9805 0.9611 98.67 13.91 55.37 803.4
-------------------------------
Coefficients for Liu parameter d= 0.05
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 8.521e-01 -2.343e+03 3.033e+03 -0.772 0.439942
x1 3.858e-02 4.847e+00 1.202e+00 4.033 5.50e-05 ***
x2 3.629e-03 5.105e+00 1.082e+01 0.472 0.636995
x3 4.997e-03 5.069e+00 8.082e+00 0.627 0.530538
x4 2.196e-01 6.707e+00 3.956e+00 1.695 0.090007 .
x5 4.730e-01 5.253e+00 2.114e+00 2.485 0.012965 *
x6 5.260e-01 4.979e+00 1.086e+00 4.586 4.53e-06 ***
x7 4.394e-01 4.658e+00 1.415e+00 3.292 0.000994 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.05 0.9824 0.9649 105 12.95 54.61 931.3
-------------------------------
Coefficients for Liu parameter d= 0.1
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 8.073e-01 -2.228e+03 5.858e+03 -0.380 0.7037
x1 3.655e-02 4.592e+00 2.184e+00 2.103 0.0355 *
x2 3.438e-03 4.836e+00 2.089e+01 0.231 0.8170
x3 4.734e-03 4.802e+00 1.560e+01 0.308 0.7583
x4 2.607e-01 7.961e+00 7.609e+00 1.046 0.2954
x5 5.007e-01 5.561e+00 3.975e+00 1.399 0.1619
x6 4.983e-01 4.717e+00 1.954e+00 2.414 0.0158 *
x7 4.163e-01 4.413e+00 2.543e+00 1.735 0.0827 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.1 0.9842 0.9685 112.4 11.91 53.78 1419
-------------------------------
Coefficients for Liu parameter d= 0.15
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 7.624e-01 -2.114e+03 8.462e+03 -0.250 0.803
x1 3.452e-02 4.337e+00 3.099e+00 1.399 0.162
x2 3.247e-03 4.567e+00 3.018e+01 0.151 0.880
x3 4.471e-03 4.535e+00 2.253e+01 0.201 0.840
x4 3.017e-01 9.216e+00 1.098e+01 0.840 0.401
x5 5.285e-01 5.869e+00 5.705e+00 1.029 0.304
x6 4.706e-01 4.455e+00 2.764e+00 1.612 0.107
x7 3.931e-01 4.168e+00 3.608e+00 1.155 0.248
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.15 0.9859 0.9719 121.2 10.78 52.85 2182
-------------------------------
Coefficients for Liu parameter d= 0.2
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 7.176e-01 -1.999e+03 1.082e+04 -0.185 0.853
x1 3.249e-02 4.082e+00 3.932e+00 1.038 0.299
x2 3.056e-03 4.299e+00 3.860e+01 0.111 0.911
x3 4.208e-03 4.268e+00 2.882e+01 0.148 0.882
x4 3.428e-01 1.047e+01 1.403e+01 0.746 0.456
x5 5.562e-01 6.177e+00 7.277e+00 0.849 0.396
x6 4.429e-01 4.193e+00 3.500e+00 1.198 0.231
x7 3.700e-01 3.923e+00 4.581e+00 0.856 0.392
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.2 0.9875 0.9751 131.7 9.542 51.82 3133
-------------------------------
Coefficients for Liu parameter d= 0.25
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 6.727e-01 -1.884e+03 1.292e+04 -0.146 0.884
x1 3.046e-02 3.827e+00 4.673e+00 0.819 0.413
x2 2.865e-03 4.030e+00 4.609e+01 0.087 0.930
x3 3.945e-03 4.002e+00 3.441e+01 0.116 0.907
x4 3.839e-01 1.173e+01 1.675e+01 0.700 0.484
x5 5.839e-01 6.485e+00 8.675e+00 0.748 0.455
x6 4.152e-01 3.931e+00 4.154e+00 0.946 0.344
x7 3.469e-01 3.678e+00 5.447e+00 0.675 0.500
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.25 0.9891 0.9781 144.4 8.188 50.67 4184
-------------------------------
Coefficients for Liu parameter d= 0.3
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 6.279e-01 -1.770e+03 1.474e+04 -0.120 0.904
x1 2.843e-02 3.571e+00 5.314e+00 0.672 0.502
x2 2.674e-03 3.761e+00 5.256e+01 0.072 0.943
x3 3.682e-03 3.735e+00 3.924e+01 0.095 0.924
x4 4.250e-01 1.298e+01 1.910e+01 0.680 0.497
x5 6.117e-01 6.793e+00 9.885e+00 0.687 0.492
x6 3.876e-01 3.669e+00 4.720e+00 0.777 0.437
x7 3.238e-01 3.432e+00 6.197e+00 0.554 0.580
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.3 0.9905 0.9809 159.8 6.7 49.39 5247
-------------------------------
Coefficients for Liu parameter d= 0.35
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 5.830e-01 -1.655e+03 1.625e+04 -0.102 0.919
x1 2.639e-02 3.316e+00 5.846e+00 0.567 0.571
x2 2.483e-03 3.493e+00 5.795e+01 0.060 0.952
x3 3.419e-03 3.468e+00 4.326e+01 0.080 0.936
x4 4.660e-01 1.423e+01 2.105e+01 0.676 0.499
x5 6.394e-01 7.101e+00 1.089e+01 0.652 0.514
x6 3.599e-01 3.407e+00 5.190e+00 0.656 0.512
x7 3.006e-01 3.187e+00 6.821e+00 0.467 0.640
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.35 0.9918 0.9836 179 5.059 47.96 6238
-------------------------------
Coefficients for Liu parameter d= 0.4
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 5.382e-01 -1.541e+03 1.743e+04 -0.088 0.930
x1 2.437e-02 3.061e+00 6.262e+00 0.489 0.625
x2 2.292e-03 3.224e+00 6.217e+01 0.052 0.959
x3 3.156e-03 3.201e+00 4.641e+01 0.069 0.945
x4 5.071e-01 1.549e+01 2.258e+01 0.686 0.493
x5 6.672e-01 7.409e+00 1.168e+01 0.634 0.526
x6 3.322e-01 3.144e+00 5.557e+00 0.566 0.571
x7 2.775e-01 2.942e+00 7.309e+00 0.403 0.687
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.4 0.993 0.986 203.1 3.239 46.34 7077
-------------------------------
Coefficients for Liu parameter d= 0.45
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 4.933e-01 -1.426e+03 1.826e+04 -0.078 0.938
x1 2.234e-02 2.806e+00 6.553e+00 0.428 0.669
x2 2.101e-03 2.955e+00 6.514e+01 0.045 0.964
x3 2.893e-03 2.935e+00 4.863e+01 0.060 0.952
x4 5.482e-01 1.674e+01 2.366e+01 0.708 0.479
x5 6.949e-01 7.717e+00 1.223e+01 0.631 0.528
x6 3.045e-01 2.882e+00 5.814e+00 0.496 0.620
x7 2.544e-01 2.697e+00 7.651e+00 0.352 0.724
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.45 0.9941 0.9882 234.1 1.211 44.52 7695
-------------------------------
Coefficients for Liu parameter d= 0.5
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 4.485e-01 -1.312e+03 1.873e+04 -0.070 0.944
x1 2.030e-02 2.551e+00 6.713e+00 0.380 0.704
x2 1.910e-03 2.687e+00 6.679e+01 0.040 0.968
x3 2.630e-03 2.668e+00 4.986e+01 0.054 0.957
x4 5.893e-01 1.800e+01 2.426e+01 0.742 0.458
x5 7.226e-01 8.025e+00 1.254e+01 0.640 0.522
x6 2.768e-01 2.620e+00 5.954e+00 0.440 0.660
x7 2.313e-01 2.452e+00 7.840e+00 0.313 0.754
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.5 0.9951 0.9903 274.9 -1.067 42.45 8036
-------------------------------
Coefficients for Liu parameter d= 0.55
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 4.036e-01 -1.197e+03 1.880e+04 -0.064 0.949
x1 1.827e-02 2.296e+00 6.735e+00 0.341 0.733
x2 1.719e-03 2.418e+00 6.705e+01 0.036 0.971
x3 2.367e-03 2.401e+00 5.006e+01 0.048 0.962
x4 6.303e-01 1.925e+01 2.435e+01 0.791 0.429
x5 7.504e-01 8.333e+00 1.259e+01 0.662 0.508
x6 2.491e-01 2.358e+00 5.972e+00 0.395 0.693
x7 2.081e-01 2.207e+00 7.867e+00 0.280 0.779
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.55 0.9961 0.9921 330 -3.645 40.08 8059
-------------------------------
Coefficients for Liu parameter d= 0.6
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 3.588e-01 -1.083e+03 1.847e+04 -0.059 0.953
x1 1.624e-02 2.041e+00 6.611e+00 0.309 0.758
x2 1.528e-03 2.149e+00 6.586e+01 0.033 0.974
x3 2.104e-03 2.134e+00 4.917e+01 0.043 0.965
x4 6.714e-01 2.051e+01 2.392e+01 0.857 0.391
x5 7.781e-01 8.641e+00 1.236e+01 0.699 0.484
x6 2.215e-01 2.096e+00 5.861e+00 0.358 0.721
x7 1.850e-01 1.961e+00 7.723e+00 0.254 0.800
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.6 0.9969 0.9938 407.1 -6.597 37.33 7747
-------------------------------
Coefficients for Liu parameter d= 0.65
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 3.139e-01 -9.682e+02 1.771e+04 -0.055 0.956
x1 1.421e-02 1.786e+00 6.336e+00 0.282 0.778
x2 1.337e-03 1.881e+00 6.316e+01 0.030 0.976
x3 1.841e-03 1.867e+00 4.715e+01 0.040 0.968
x4 7.125e-01 2.176e+01 2.294e+01 0.949 0.343
x5 8.058e-01 8.949e+00 1.185e+01 0.755 0.450
x6 1.938e-01 1.834e+00 5.617e+00 0.327 0.744
x7 1.619e-01 1.716e+00 7.404e+00 0.232 0.817
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.65 0.9976 0.9952 519.5 -10.02 34.11 7104
-------------------------------
Coefficients for Liu parameter d= 0.7
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 2.691e-01 -8.537e+02 1.651e+04 -0.052 0.959
x1 1.218e-02 1.531e+00 5.907e+00 0.259 0.796
x2 1.146e-03 1.612e+00 5.891e+01 0.027 0.978
x3 1.578e-03 1.601e+00 4.397e+01 0.036 0.971
x4 7.536e-01 2.302e+01 2.139e+01 1.076 0.282
x5 8.336e-01 9.257e+00 1.105e+01 0.838 0.402
x6 1.661e-01 1.572e+00 5.235e+00 0.300 0.764
x7 1.388e-01 1.471e+00 6.903e+00 0.213 0.831
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.7 0.9982 0.9965 692.7 -14.06 30.28 6166
-------------------------------
Coefficients for Liu parameter d= 0.75
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 2.242e-01 -7.392e+02 1.488e+04 -0.050 0.960
x1 1.015e-02 1.276e+00 5.319e+00 0.240 0.810
x2 9.551e-04 1.343e+00 5.306e+01 0.025 0.980
x3 1.315e-03 1.334e+00 3.961e+01 0.034 0.973
x4 7.946e-01 2.427e+01 1.927e+01 1.259 0.208
x5 8.613e-01 9.565e+00 9.955e+00 0.961 0.337
x6 1.384e-01 1.310e+00 4.714e+00 0.278 0.781
x7 1.156e-01 1.226e+00 6.217e+00 0.197 0.844
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.75 0.9988 0.9976 979.9 -18.95 25.6 4995
-------------------------------
Coefficients for Liu parameter d= 0.8
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 1.794e-01 -6.247e+02 1.279e+04 -0.049 0.961
x1 8.122e-03 1.020e+00 4.571e+00 0.223 0.823
x2 7.641e-04 1.075e+00 4.562e+01 0.024 0.981
x3 1.052e-03 1.067e+00 3.405e+01 0.031 0.975
x4 8.357e-01 2.553e+01 1.657e+01 1.541 0.123
x5 8.891e-01 9.873e+00 8.557e+00 1.154 0.249
x6 1.107e-01 1.048e+00 4.050e+00 0.259 0.796
x7 9.250e-02 9.807e-01 5.343e+00 0.184 0.854
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.8 0.9992 0.9984 1509 -25.06 19.69 3686
-------------------------------
Coefficients for Liu parameter d= 0.85
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 1.345e-01 -5.101e+02 1.025e+04 -0.050 0.9603
x1 6.091e-03 7.653e-01 3.663e+00 0.209 0.8345
x2 5.730e-04 8.060e-01 3.656e+01 0.022 0.9824
x3 7.890e-04 8.003e-01 2.729e+01 0.029 0.9766
x4 8.768e-01 2.678e+01 1.328e+01 2.017 0.0437 *
x5 9.168e-01 1.018e+01 6.858e+00 1.485 0.1377
x6 8.305e-02 7.861e-01 3.245e+00 0.242 0.8086
x7 6.938e-02 7.355e-01 4.282e+00 0.172 0.8636
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.85 0.9996 0.9991 2651 -33.11 11.85 2366
-------------------------------
Coefficients for Liu parameter d= 0.9
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 8.970e-02 -3.956e+02 7.266e+03 -0.054 0.95658
x1 4.061e-03 5.102e-01 2.596e+00 0.197 0.84416
x2 3.820e-04 5.373e-01 2.592e+01 0.021 0.98346
x3 5.260e-04 5.335e-01 1.935e+01 0.028 0.97800
x4 9.179e-01 2.803e+01 9.412e+00 2.979 0.00289 **
x5 9.445e-01 1.049e+01 4.861e+00 2.158 0.03094 *
x6 5.537e-02 5.241e-01 2.300e+00 0.228 0.81973
x7 4.625e-02 4.903e-01 3.035e+00 0.162 0.87163
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.9 0.9998 0.9996 5915 -44.69 0.4706 1188
-------------------------------
Coefficients for Liu parameter d= 0.95
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept 4.485e-02 -2.811e+02 3.845e+03 -0.073 0.942
x1 2.030e-03 2.551e-01 1.373e+00 0.186 0.853
x2 1.910e-04 2.687e-01 1.371e+01 0.020 0.984
x3 2.630e-04 2.668e-01 1.024e+01 0.026 0.979
x4 9.589e-01 2.929e+01 4.980e+00 5.882 4.06e-09 ***
x5 9.723e-01 1.080e+01 2.572e+00 4.198 2.69e-05 ***
x6 2.768e-02 2.620e-01 1.216e+00 0.215 0.829
x7 2.313e-02 2.452e-01 1.605e+00 0.153 0.879
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=0.95 1 0.9999 23538 -64.91 -19.54 332.2
-------------------------------
Coefficients for Liu parameter d= 1
Estimate Estimate (Sc) StdErr (Sc) t-val (Sc) Pr(>|t|)
Intercept -3.553e-15 -1.665e+02 2.816e+00 -5.914e+01 <2e-16 ***
x1 -3.606e-17 -4.531e-15 1.589e-13 -2.900e-02 0.977
x2 -2.012e-17 -2.829e-14 1.587e-12 -1.800e-02 0.986
x3 -1.293e-17 -1.312e-14 1.185e-12 -1.100e-02 0.991
x4 1.000e+00 3.054e+01 5.763e-13 5.300e+13 <2e-16 ***
x5 1.000e+00 1.111e+01 2.977e-13 3.731e+13 <2e-16 ***
x6 1.187e-15 1.123e-14 1.407e-13 8.000e-02 0.936
x7 1.004e-15 1.065e-14 1.858e-13 5.700e-02 0.954
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Liu Summary
R2 adj-R2 F AIC BIC MSE
d=1 1 1 1.947e+30 -959.5 -913.9 4.423e-24
-------------------------------Caro Signore,
In effetti, voglio applicare la regressione Liu Usando Jmppro, adoro la tecnica e lo smoothing Jmppro.
Saluti
Riguardo ai suoi stimatoriHo due commenti che potrebbero essere utili.
Innanzitutto, ho mostrato come assicurarsi che JMP trovi R dopo l'installazione. Ecco il codice JSL a tale scopo. Il percorso che ho utilizzato corrisponde a quello che dovresti utilizzare? Assicurati che il percorso sia corretto.
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
Il tuo script R fa riferimento a un data frame dta, ma si tratta di una tabella di dati JMP che non hai inviato. Hai invece inviato le colonne di dati JMP come vettori individuali a R. Pertanto, devi creare il data frame in R a partire dai vettori che hai inviato, oppure inviare la tabella di dati anziché i vettori e utilizzarla in R. JMP crea automaticamente un data frame per R, quindi quest'ultimo approccio sembra più semplice ed efficiente. Vedi il mio secondo esempio qui sopra.
Assicurati inoltre di chiamare questa funzione alla fine del tuo script:
R Term();
