User Tools

Site Tools


sequential_regression

데이터

DATA for regression analysis
bankaccount income famnum
6 220 5
5 190 6
7 260 3
7 200 4
8 330 2
10 490 4
8 210 3
11 380 2
9 320 1
9 270 3
datavar <- read.csv("http://commres.net/wiki/_media/regression01-bankaccount.csv")

Enter

Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics
R Square Change F Change df1 df2 Sig. F Change
1 .893a .798 .740 .930 .798 13.838 2 7 .004
a. Predictors: (Constant), 가족숫자, 수입
b. Dependent Variable: 통장갯수
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 23.944 2 11.972 13.838 .004b
Residual 6.056 7 .865
Total 30.000 9
a. Dependent Variable: 통장갯수
b. Predictors: (Constant), 가족숫자, 수입
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations
B Std. Error Beta Zero-order Partial Part
1 (Constant) 6.399 1.517 4.220 .004
수입 .012 .004 .616 3.325 .013 .794 .783 .565
가족숫자 -.545 .226 -.446 -2.406 .047 -.692 -.673 -.409
a. Dependent Variable: 통장갯수

$\hat{Y} = 6.399 + .012 X_{1} + -.545 X_{2} $

The below is just an exercise for figuring out the unique part of r2 value for x1 and x2 (수입, 가족수). For more information see part and zero-order relationship: see determining_ivs_role in multiple regression

zero-order part
x1 x2 x1p x2p
.794 -.692 .565 -.409
zero-order square part (in spss) = semipartial (in general)
x1 sq (x1sq) x2 sq (x1sq) x1 part sq (x1psq) x2 part sq (x1psq)
.630436 .478864 .319225 .167281
a+b / a+b+c+d b+c / a+b+c+d a / a+b+c+d c / a+b+c+d

x1sq - x1psq ~= x2sq - x2psq
0.311211 ~= 0.311583

R에서 보는 예는 아래를 참조

Seq.

Model Summaryc
Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics
R Square Change F Change df1 df2 Sig. F Change
1 .794a .631 .585 1.176 .631 13.687 1 8 .006
2 .893b .798 .740 .930 .167 5.791 1 7 .047
a. Predictors: (Constant), 수입
b. Predictors: (Constant), 수입, 가족숫자
c. Dependent Variable: 통장갯수

증가한 r2값에 대한 F-test 결과는 Fdiff=5.791, p = .047 (less than .05)

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 18.934 1 18.934 13.687 .006b
Residual 11.066 8 1.383
Total 30.000 9
2 Regression 23.944 2 11.972 13.838 .004c
Residual 6.056 7 .865
Total 30.000 9
a. Dependent Variable: 통장갯수
b. Predictors: (Constant), 수입
c. Predictors: (Constant), 수입, 가족숫자
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations
B Std. Error Beta Zero-order Partial Part
1 (Constant) 3.618 1.242 2.914 .019
수입 .015 .004 .794 3.700 .006 .794 .794 .794
2 (Constant) 6.399 1.517 4.220 .004
수입 .012 .004 .616 3.325 .013 .794 .783 .565
가족숫자 -.545 .226 -.446 -2.406 .047 -.692 -.673 -.409
a. Dependent Variable: 통장갯수

http://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ/hier
https://ww2.coastal.edu/kingw/statistics/R-tutorials/multregr.html

r

datavar <- read.csv("http://commres.net/wiki/_media/regression01-bankaccount.csv")
datavar 
m1 <- lm(bankaccount~income+famnum, data=datavar)
summary(m1)
library(ppcor)
spcor(datavar)
pcor(datavar)
> datavar <- read.csv("http://commres.net/wiki/_media/regression01-bankaccount.csv")
> datavar 
   bankaccount income famnum
1            6    220      5
2            5    190      6
3            7    260      3
4            7    200      4
5            8    330      2
6           10    490      4
7            8    210      3
8           11    380      2
9            9    320      1
10           9    270      3
> m1 <- lm(bankaccount~income+famnum, data=datavar)
> summary(m1)

Call:
lm(formula = bankaccount ~ income + famnum, data = datavar)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2173 -0.5779 -0.1515  0.6642  1.1906 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)  6.399103   1.516539   4.220  0.00394 **
income       0.011841   0.003561   3.325  0.01268 * 
famnum      -0.544727   0.226364  -2.406  0.04702 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9301 on 7 degrees of freedom
Multiple R-squared:  0.7981,	Adjusted R-squared:  0.7404 
F-statistic: 13.84 on 2 and 7 DF,  p-value: 0.003696

> library(ppcor)
> spcor(datavar)
$estimate
            bankaccount    income     famnum
bankaccount   1.0000000 0.5646726 -0.4086619
income        0.7171965 1.0000000  0.2078919
famnum       -0.6166940 0.2470028  1.0000000

$p.value
            bankaccount   income    famnum
bankaccount  0.00000000 0.113182 0.2748117
income       0.02964029 0.000000 0.5914441
famnum       0.07691195 0.521696 0.0000000

$statistic
            bankaccount    income     famnum
bankaccount    0.000000 1.8101977 -1.1846548
income         2.722920 0.0000000  0.5623159
famnum        -2.072679 0.6744045  0.0000000

$n
[1] 10

$gp
[1] 1

$method
[1] "pearson"

> pcor(datavar)
$estimate
            bankaccount    income     famnum
bankaccount   1.0000000 0.7825112 -0.6728560
income        0.7825112 1.0000000  0.3422911
famnum       -0.6728560 0.3422911  1.0000000

$p.value
            bankaccount     income     famnum
bankaccount  0.00000000 0.01267595 0.04702022
income       0.01267595 0.00000000 0.36723388
famnum       0.04702022 0.36723388 0.00000000

$statistic
            bankaccount    income     famnum
bankaccount    0.000000 3.3251023 -2.4064253
income         3.325102 0.0000000  0.9638389
famnum        -2.406425 0.9638389  0.0000000

$n
[1] 10

$gp
[1] 1

$method
[1] "pearson"

## zero-order correlation
> cor(datavar)
            bankaccount     income     famnum
bankaccount   1.0000000  0.7944312 -0.6922935
income        0.7944312  1.0000000 -0.3999614
famnum       -0.6922935 -0.3999614  1.0000000
> 

semipartial (part): spcor()

bankaccount income famnum
bankaccount 1.0000000 0.5646726$(1)$ -0.4086619$(2)$
income 0.7171965 1.0000000 0.2078919
famnum -0.6166940 0.2470028 1.0000000
bankaccount income famnum
bankaccount 1.0000000 0.7825112 -0.6728560
income 0.7825112 1.0000000 0.3422911
famnum -0.6728560 0.3422911 1.0000000
bankaccount income famnum
bankaccount 1.0000000 0.7944312$(3)$ -0.6922935$(4)$
income 0.7944312 1.0000000 -0.3999614
famnum -0.6922935 -0.3999614 1.0000000
sp.b.i <- 0.5646726 ## (1)
c.b.i <- 0.7944312 ## (3)

sp.b.f <- -0.4086619 ## (2)
c.b.f <- -0.6922935 ## (4)

c.b.i.sq <- c.b.i^2 ## (3)^2
sp.b.i.sq <- sp.b.i^2 ## (1)^2
c.b.i.sq - sp.b.i.sq 

c.b.f.sq <- c.b.f^2 ## (4)^2
sp.b.f.sq <- sp.b.f^2 ## (1)^2
c.b.f.sq - sp.b.f.sq
> sp.b.i <- 0.5646726
> c.b.i <- 0.7944312
> 
> sp.b.f <- -0.4086619
> c.b.f <- -0.6922935
> 
> c.b.i.sq <- c.b.i^2 ## (3)^2
> sp.b.i.sq <- sp.b.i^2
> 
> c.b.i.sq - sp.b.i.sq 
[1] 0.3123
> 
> c.b.f.sq <- c.b.f^2 ## (4)^2
> sp.b.f.sq <- sp.b.f^2
> 
> c.b.f.sq - sp.b.f.sq
[1] 0.3123

0.3123 가 두 독립변인이 DV에 같이 (공히) 미치는 영향력 분량이다.

pcor.test(datavar$bankaccount, datavar$income, datavar$famnum)
pcor.test(datavar$bankaccount, datavar$famnum, datavar$income)

spcor.test(datavar$bankaccount, datavar$income, datavar$famnum)
spcor.test(datavar$bankaccount, datavar$famnum, datavar$income)

. . .

> pcor.test(datavar$bankaccount, datavar$income, datavar$famnum)
   estimate    p.value statistic  n gp  Method
1 0.7825112 0.01267595  3.325102 10  1 pearson
> pcor.test(datavar$bankaccount, datavar$famnum, datavar$income)
   estimate    p.value statistic  n gp  Method
1 -0.672856 0.04702022 -2.406425 10  1 pearson
>
> spcor.test(datavar$bankaccount, datavar$income, datavar$famnum)
   estimate  p.value statistic  n gp  Method
1 0.5646726 0.113182  1.810198 10  1 pearson
> spcor.test(datavar$bankaccount, datavar$famnum, datavar$income)
    estimate   p.value statistic  n gp  Method
1 -0.4086619 0.2748117 -1.184655 10  1 pearson
> 
> 
 

e.g. 3. College enrollment in New Mexico University

> datavar <- read.csv("http://commres.net/wiki/_media/r/dataset_hlr.csv")
> str(datavar)
'data.frame':	29 obs. of  5 variables:
 $ YEAR : int  1 2 3 4 5 6 7 8 9 10 ...
 $ ROLL : int  5501 5945 6629 7556 8716 9369 9920 10167 11084 12504 ...
 $ UNEM : num  8.1 7 7.3 7.5 7 6.4 6.5 6.4 6.3 7.7 ...
 $ HGRAD: int  9552 9680 9731 11666 14675 15265 15484 15723 16501 16890 ...
 $ INC  : int  1923 1961 1979 2030 2112 2192 2235 2351 2411 2475 ...
> 
onePredictorModel <- lm(ROLL ~ UNEM, data = datavar)
twoPredictorModel <- lm(ROLL ~ UNEM + HGRAD, data = datavar)
threePredictorModel <- lm(ROLL ~ UNEM + HGRAD + INC, data = datavar)
summary(onePredictorModel)
summary(twoPredictorModel)
summary(threePredictorModel)
> summary(onePredictorModel)

Call:
lm(formula = ROLL ~ UNEM, data = datavar)

Residuals:
    Min      1Q  Median      3Q     Max 
-7640.0 -1046.5   602.8  1934.3  4187.2 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   3957.0     4000.1   0.989   0.3313  
UNEM          1133.8      513.1   2.210   0.0358 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3049 on 27 degrees of freedom
Multiple R-squared:  0.1531,	Adjusted R-squared:  0.1218 
F-statistic: 4.883 on 1 and 27 DF,  p-value: 0.03579
> summary(twoPredictorModel)

Call:
lm(formula = ROLL ~ UNEM + HGRAD, data = datavar)

Residuals:
    Min      1Q  Median      3Q     Max 
-2102.2  -861.6  -349.4   374.5  3603.5 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -8.256e+03  2.052e+03  -4.023  0.00044 ***
UNEM         6.983e+02  2.244e+02   3.111  0.00449 ** 
HGRAD        9.423e-01  8.613e-02  10.941 3.16e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1313 on 26 degrees of freedom
Multiple R-squared:  0.8489,	Adjusted R-squared:  0.8373 
F-statistic: 73.03 on 2 and 26 DF,  p-value: 2.144e-11

> 
> summary(threePredictorModel)

Call:
lm(formula = ROLL ~ UNEM + HGRAD + INC, data = datavar)

Residuals:
     Min       1Q   Median       3Q      Max 
-1148.84  -489.71    -1.88   387.40  1425.75 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -9.153e+03  1.053e+03  -8.691 5.02e-09 ***
UNEM         4.501e+02  1.182e+02   3.809 0.000807 ***
HGRAD        4.065e-01  7.602e-02   5.347 1.52e-05 ***
INC          4.275e+00  4.947e-01   8.642 5.59e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 670.4 on 25 degrees of freedom
Multiple R-squared:  0.9621,	Adjusted R-squared:  0.9576 
F-statistic: 211.5 on 3 and 25 DF,  p-value: < 2.2e-16
anova(onePredictorModel, twoPredictorModel, threePredictorModel)
Analysis of Variance Table

Model 1: ROLL ~ UNEM
Model 2: ROLL ~ UNEM + HGRAD
Model 3: ROLL ~ UNEM + HGRAD + INC
  Res.Df       RSS Df Sum of Sq      F    Pr(>F)    
1     27 251084710                                  
2     26  44805568  1 206279143 458.92 < 2.2e-16 ***
3     25  11237313  1  33568255  74.68 5.594e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 

e.g. 4. Happiness

hierarchical.regression.data.csv

# Import data (simulated data for this example)
# myData <- read.csv('http://static.lib.virginia.edu/statlab/materials/data/hierarchicalRegressionData.csv')  
myData <- read.csv("http://commres.net/wiki/_media/hierarchical.regression.data.csv")
> str(myData)
'data.frame':	100 obs. of  5 variables:
 $ happiness: int  5 5 6 4 3 5 5 5 4 4 ...
 $ age      : int  24 28 25 26 20 25 24 24 26 26 ...
 $ gender   : chr  "Male" "Male" "Female" "Male" ...
 $ friends  : int  12 8 6 4 8 9 5 6 8 6 ...
 $ pets     : int  3 1 0 2 0 0 5 2 1 4 ...
> myData$gender <- factor(myData$gender)
> str(myData)
'data.frame':	100 obs. of  5 variables:
 $ happiness: int  5 5 6 4 3 5 5 5 4 4 ...
 $ age      : int  24 28 25 26 20 25 24 24 26 26 ...
 $ gender   : Factor w/ 2 levels "Female","Male": 2 2 1 2 1 2 2 2 2 2 ...
 $ friends  : int  12 8 6 4 8 9 5 6 8 6 ...
 $ pets     : int  3 1 0 2 0 0 5 2 1 4 ...
> 
> m0 <- lm(happiness ~ 1, data = myData)
> anova(m0)
Analysis of Variance Table

Response: happiness
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals 99 240.84  2.4327               
> 
# 불필요하지만 위의 분석이 variance와 
# 같은 것이라는 것을 아래처럼 확인한다.
> attach(myData)
The following objects are masked from myData (pos = 3):

    age, friends, gender, happiness, pets

> var(happiness)
[1] 2.432727
> length(happiness)
[1] 100
> df.happiness <- length(happiness) - 1
> df.happiness # degrees of freedom 
[1] 99
> ss.happiness <- var(happiness)* df.happiness # sum of square (ss) value for happiness variable
> ss.happiness 
[1] 240.84
> 
> m1 <- lm(happiness ~ age + gender, data=myData)  # Model 1
> summary(m1)

Call:
lm(formula = happiness ~ age + gender, data = myData)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6688 -1.0094 -0.1472  0.8273  4.2973 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  7.66778    2.01364   3.808 0.000246 ***
age         -0.13039    0.07936  -1.643 0.103611    
genderMale   0.16430    0.31938   0.514 0.608106    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.553 on 97 degrees of freedom
Multiple R-squared:  0.02855,	Adjusted R-squared:  0.008515 
F-statistic: 1.425 on 2 and 97 DF,  p-value: 0.2455

> 
# m1은 이미 위에서 실행
> m2 <- lm(happiness ~ age + gender + friends, data=myData)  # Model 2: Adding friends variable 
> m3 <- lm(happiness ~ age + gender + friends + pets, data = myData) # Model 3: Adding pets variable
> anova(m1, m2, m3)
Analysis of Variance Table

Model 1: happiness ~ age + gender
Model 2: happiness ~ age + gender + friends
Model 3: happiness ~ age + gender + friends + pets
  Res.Df    RSS Df Sum of Sq       F    Pr(>F)    
1     97 233.97                                   
2     96 209.27  1    24.696 12.1293 0.0007521 ***
3     95 193.42  1    15.846  7.7828 0.0063739 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
> summary(m1)

Call:
lm(formula = happiness ~ age + gender, data = myData)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.6688 -1.0094 -0.1472  0.8273  4.2973 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  7.66778    2.01364   3.808 0.000246 ***
age         -0.13039    0.07936  -1.643 0.103611    
genderMale   0.16430    0.31938   0.514 0.608106    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.553 on 97 degrees of freedom
Multiple R-squared:  0.02855,	Adjusted R-squared:  0.008515 
F-statistic: 1.425 on 2 and 97 DF,  p-value: 0.2455

> summary(m2)

Call:
lm(formula = happiness ~ age + gender + friends, data = myData)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5758 -1.0204  0.0156  0.8087  3.7299 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  6.21730    1.96220   3.169  0.00206 **
age         -0.12479    0.07546  -1.654  0.10146   
genderMale   0.14931    0.30365   0.492  0.62405   
friends      0.18985    0.05640   3.366  0.00110 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.476 on 96 degrees of freedom
Multiple R-squared:  0.1311,	Adjusted R-squared:  0.1039 
F-statistic: 4.828 on 3 and 96 DF,  p-value: 0.003573

> summary(m3)

Call:
lm(formula = happiness ~ age + gender + friends + pets, data = myData)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0556 -1.0183 -0.1109  0.8832  3.5911 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)  5.78540    1.90266   3.041  0.00305 **
age         -0.11146    0.07309  -1.525  0.13057   
genderMale  -0.14267    0.31157  -0.458  0.64806   
friends      0.17134    0.05491   3.120  0.00239 **
pets         0.36391    0.13044   2.790  0.00637 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.427 on 95 degrees of freedom
Multiple R-squared:  0.1969,	Adjusted R-squared:  0.1631 
F-statistic: 5.822 on 4 and 95 DF,  p-value: 0.0003105

> 

Report in research paper

e.g. 5: Stock Market

e.g. 6: SWISS

sequential_regression.txt · Last modified: 2020/12/01 18:31 by hkimscil