Estimateurs LuiComment les estimations de Liu peuvent-elles être calculées à l'aide de JMP pro ? !
Attaché
Données réelles obtenues de l'hôpital central pour enfants de Bagdad - Irak, représentant le nombre de cas de malformations congénitales des enfants dans le cœur et le système circulatoire pour la période (2006-2011).
Cordialement
AnalytiqueTableau de donnéesScriptRe : estimateurs LuiJe ne trouve pas cette statistique dans l'index des statistiques de JMP. Je ne connais pas cet estimateur. Veuillez fournir une référence pour cela. Merci!
Re : estimateurs LuiLa méthode de Liu est une méthode similaire à la méthode de régression ridge, utilisée pour estimer les paramètres d'un modèle de régression de Poisson en cas de données souffrant d'un problème de multicolinéarité.
doi:10.1016/j.jspi.2010.05.030
https://doi.org/10.1080%2F03610929508831585
Le réseau complet d'archives R (r-project.org)
Re : estimateurs LuiMerci pour le fond !
En attendant, vous pouvez exécuter le code R à partir de JMP , si cela pouvait aider.
Re : estimateurs LuiCher M. markbailey
Je ne sais pas comment exécuter les codes de programme R via JMP pro.
(j'ai essayé une fois et je n'y arrive pas)
S'il vous plaît, si vous savez, aidez-moi
Salutations
Re : estimateurs LuiAvez-vous lu la documentation JMP que j'ai citée ci-dessus ? Vous pourriez trouver ce blanc papier utile aussi.
Voici un exemple d'utilisation de R avec JMP dans un de mes projets récents. Je montre deux versions. Le premier utilise des matrices pour transférer des données entre JMP et 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( "$SAMPLE_DATA/Big Class.jmp" );
x = :height << Get As Matrix;
y = :weight << Get As Matrix;
Close( dt, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library( mcr )
mc <- mcreg( x, y, alpha = 0.05, method.reg = \!"PaBa\!", method.ci = \!"analytical\!", slope.measure = \!"tangent\!" )
print( mc )
parms <- getCoefficients( mc )
pred <- parms[1] + parms[2]*x
print( pred )
resid <- y - pred
print( resid )
");
// close R connection
R Term();
// select View > Log (Windows) or Windows > Log (macOS) to see results
Les deuxièmes versions utilisent à la place la table de données JMP. Il devient une trame de données dans R.
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// use Big Class from JMP Sample Data Folder
dt = Open( "$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
Vous pouvez envoyer des commandes individuelles à R ou, comme je l'ai illustré, un script R entier sous forme de chaîne de caractères. Vous pouvez utiliser toutes les fonctionnalités JSL pour les chaînes et les expressions afin de créer des scripts R personnalisés "à la volée".
Re : estimateurs LuiCher Monsieur
JMP ne se connecte pas avec R, a lu et appliqué les commentaires que vous avez envoyés.
Mais je n'ai pas réussi !
Salutations
Re : estimateurs Luiquelle version de JMP Pro utilisez-vous et sur quel système d'exploitation ?
Avez-vous installé R sur votre ordinateur ou l'exécutez-vous sur un ordinateur séparé ?
S'il est installé sur le même ordinateur que JMP, quels messages sont imprimés lorsque vous exécutez le script suivant ?
Names Default To Here( 1 );
R Init();
R Term();
Re : estimateurs LuiCher Monsieur
J'utilise JMPPro version 16 ainsi que R version 4.1.3
Les deux programmes sont installés sur le même ordinateur qui exécute Windows Pro 11
et j'ai lu les notes dans ce lien:
wp-jmp-synergies-python-r.pdf
Mais je n'ai pas réussi !
Re : estimateurs 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)
");ceci est mon script à exécuter (régression liu) de jmp à R.
pas courir ?!
#note : les données jointes ci-dessous
Re : estimateurs 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)
");ceci est mon script à exécuter (régression liu) de jmp à R.
pas courir ?!
#note : les données jointes ci-dessous
Re : estimateurs LuiJ'ai modifié votre JSL pour qu'il corresponde aux noms de colonne de la table de données incluse.J'ai également ajouté un Send() pour déplacer les données dta vers 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();
Les résultats de r étaient
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'interface semble fonctionner, il semble y avoir un problème de données.
Re : estimateurs Luien utilisant le code 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)
# endsortie de 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
-------------------------------Cher Monsieur,
En fait, je veux appliquer la régression de Liu En utilisant Jmppro, j'adore la technique et le lissage de Jmppro.
Cordialement
