루이 추정기JMP pro를 사용하여 Liu의 추정치를 어떻게 계산할 수 있습니까?!
첨부된
이라크 바그다드에 있는 중앙 아동 병원에서 얻은 실제 데이터로, 해당 기간(2006-2011년) 동안 심장 및 순환계에 선천적 아동의 선천적 결함 사례 수를 나타냅니다.
문안 인사
해석학데이터 테이블스크립팅Re: Lui 추정기JMP의 통계 색인에서 이 통계를 찾을 수 없습니다. 나는 이 추정기에 익숙하지 않습니다. 이에 대한 참조를 제공하십시오. 감사해요!
Re: Lui 추정기Liu의 방법은 다중공선성 문제가 있는 데이터의 경우 푸아송 회귀 모델의 매개변수를 추정하는 데 사용되는 능선 회귀 방법과 유사한 방법입니다.
doi:10.1016/j.jspi.2010.05.030
https://doi.org/10.1080%2F03610929508831585
포괄적인 R 아카이브 네트워크(r-project.org)
Re: Lui 추정기배경 감사합니다!
그동안 JMP에서 R 코드 실행 , 도움이 된다면.
Re: Lui 추정기친애하는 마크베일리 씨
JMP pro를 통해 R 프로그램 코드를 실행하는 방법을 모르겠습니다.
(한번 해봤는데 안되더라구요)
아시는 분 도와주세요.
문안 인사
Re: Lui 추정기위에서 인용한 JMP 문서를 읽었습니까? 당신은 찾을 수 있습니다 이 흰색 종이도 유용하다.
다음은 저의 최근 프로젝트에서 JMP와 함께 R을 사용하는 예입니다. 두 가지 버전을 보여줍니다. 첫 번째는 행렬을 사용하여 JMP와 R 간에 데이터를 전송합니다.
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( "$SAMPLE_DATA/Big Class.jmp" );
x = :height << Get As Matrix;
y = :weight << Get As Matrix;
Close( dt, No Save );
// connect to R session
R Init( );
// send JMP data as vectors
R Send( x );
R Send( y );
// use mcreg function in mcr package with entire data set
R Submit("
library( mcr )
mc <- mcreg( x, y, alpha = 0.05, method.reg = \!"PaBa\!", method.ci = \!"analytical\!", slope.measure = \!"tangent\!" )
print( mc )
parms <- getCoefficients( mc )
pred <- parms[1] + parms[2]*x
print( pred )
resid <- y - pred
print( resid )
");
// close R connection
R Term();
// select View > Log (Windows) or Windows > Log (macOS) to see results
두 번째 버전은 대신 JMP 데이터 테이블을 사용합니다. R에서는 데이터 프레임이 됩니다.
Names Default to Here( 1 );
// make sure JMP can find the desired installed version of R
Set Environment Variable( "R_HOME", "C:\Program Files\R\R-4.1.3" );
// use Big Class from JMP Sample Data Folder
dt = Open( "$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
개별 명령을 R에 보낼 수도 있고, 내가 설명한 대로 전체 R 스크립트를 문자열로 보낼 수도 있습니다. 문자열 및 표현식에 대한 모든 JSL 기능을 사용하여 '즉시' 사용자 정의 R 스크립트를 작성할 수 있습니다.
Re: Lui 추정기귀하에게
JMP는 R과 연결되지 않으며 귀하가 보낸 피드백을 읽고 적용했습니다.
하지만 나는 성공하지 못했다!
문안 인사
Re: Lui 추정기실행 중인 JMP Pro 버전과 운영 체제는 무엇입니까?
컴퓨터에 R이 설치되어 있습니까, 아니면 별도의 컴퓨터에서 실행합니까?
JMP가 설치된 컴퓨터와 동일한 컴퓨터에 설치된 경우 다음 스크립트를 실행하면 어떤 메시지가 출력됩니까?
Names Default To Here( 1 );
R Init();
R Term();
Re: Lui 추정기귀하에게
JMPPro 버전 16과 R 버전 4.1.3을 사용합니다.
두 프로그램은 Windows Pro 11을 실행하는 동일한 컴퓨터에 설치됩니다.
이 링크의 메모를 읽었습니다.
wp-jmp-synergies-python-r.pdf
하지만 나는 성공하지 못했다!
Re: Lui 추정기Names 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)
");이것은 jmp에서 R로 (liu 회귀)를 실행하는 스크립트입니다.
안 뛰다?!
#참고: 아래 첨부된 데이터
Re: Lui 추정기Names 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)
");이것은 jmp에서 R로 (liu 회귀)를 실행하는 스크립트입니다.
안 뛰다?!
#참고: 아래 첨부된 데이터
Re: Lui 추정기포함된 데이터 테이블의 열 이름과 일치하도록 JSL을 수정했습니다.또한 dta 데이터를 r로 이동하기 위해 Send()를 추가했습니다.
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();
r의 결과는 다음과 같습니다.
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
인터페이스가 작동하는 것으로 보이며 데이터 문제가 있는 것 같습니다.
Re: Lui 추정기R 코드를 사용하여:
# 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)
# endR에서 출력
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
-------------------------------귀하에게,
사실, 저는 Jmppro를 사용하여 Liu 회귀를 적용하고 싶습니다. 저는 Jmppro의 기술 및 스무딩을 좋아합니다.
문안 인사
