調節分析
資料與管理
#讀檔案，這是 CSV 檔（用逗號分隔的檔），可以用 notepad 或 EXCEL 開啟
dta <- read.csv("TwoModerators2.csv", header = TRUE)一個獨變項，兩個調節變項
方法一：調節效果，用 PROCESS 功能分析
#載入 PROCESS，特別記得要讓 process.r 可讀取（在同目錄，或特定目錄）
source('process.r')##
## ********************* PROCESS for R Version 4.3.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## PROCESS is now ready for use.
## Copyright 2020-2023 by Andrew F. Hayes ALL RIGHTS RESERVED
## Workshop schedule at http://haskayne.ucalgary.ca/CCRAM
## #用 PROCESS 處理
#套件的變項要用字串符號括入（統計能力好，程式能力待加強）
process (data = dta, y = 'Y', x = 'X', w ='Z1',z='Z2', model = 3,
moments = 1,jn = 1,plot=1, modelbt= 1, boot = 999)##
## ********************* PROCESS for R Version 4.3.1 *********************
##
## Written by Andrew F. Hayes, Ph.D. www.afhayes.com
## Documentation available in Hayes (2022). www.guilford.com/p/hayes3
##
## ***********************************************************************
##
## Model : 3
## Y : Y
## X : X
## W : Z1
## Z : Z2
##
## Sample size: 500
##
## Random seed: 936195
##
##
## ***********************************************************************
## Outcome Variable: Y
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6104 0.3725 1.0190 41.7300 7.0000 492.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 0.0608 0.0454 1.3382 0.1814 -0.0284 0.1499
## X 0.4460 0.0467 9.5559 0.0000 0.3543 0.5378
## Z1 0.4296 0.0455 9.4334 0.0000 0.3401 0.5191
## Int_1 0.0248 0.0472 0.5254 0.5995 -0.0680 0.1177
## Z2 0.0305 0.0458 0.6657 0.5059 -0.0595 0.1206
## Int_2 0.1014 0.0502 2.0202 0.0439 0.0028 0.2000
## Int_3 0.0096 0.0435 0.2208 0.8254 -0.0758 0.0950
## Int_4 0.4911 0.0487 10.0826 0.0000 0.3954 0.5869
##
## Product terms key:
## Int_1 : X x Z1
## Int_2 : X x Z2
## Int_3 : Z1 x Z2
## Int_4 : X x Z1 x Z2
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## X*W*Z 0.1296 101.6592 1.0000 492.0000 0.0000
## ----------
## Focal predictor: X (X)
## Moderator: Z1 (W)
## Moderator: Z2 (Z)
##
## Test of conditional X*W interaction at value(s) of Z:
## Z2 effect F df1 df2 p
## -0.9666 -0.4499 38.0268 1.0000 492.0000 0.0000
## 0.0262 0.0377 0.6428 1.0000 492.0000 0.4231
## 1.0190 0.5253 73.1136 1.0000 492.0000 0.0000
##
## Conditional effects of the focal predictor at values of the moderator(s):
## Z1 Z2 effect se t p LLCI
## -1.0132 -0.9666 0.8039 0.1021 7.8724 0.0000 0.6033
## -1.0132 0.0262 0.4105 0.0655 6.2720 0.0000 0.2819
## -1.0132 1.0190 0.0171 0.0913 0.1874 0.8515 -0.1622
## -0.0023 -0.9666 0.3491 0.0674 5.1780 0.0000 0.2166
## -0.0023 0.0262 0.4486 0.0467 9.6085 0.0000 0.3569
## -0.0023 1.0190 0.5482 0.0692 7.9263 0.0000 0.4123
## 1.0086 -0.9666 -0.1058 0.0977 -1.0827 0.2795 -0.2977
## 1.0086 0.0262 0.4867 0.0678 7.1818 0.0000 0.3536
## 1.0086 1.0190 1.0792 0.0946 11.4093 0.0000 0.8934
## ULCI
## 1.0046
## 0.5391
## 0.1965
## 0.4815
## 0.5404
## 0.6840
## 0.0862
## 0.6199
## 1.2651
##
## Moderator value(s) defining Johnson-Neyman significance region(s):
## Value % below % above
## -0.2552 38.0000 62.0000
## 0.1351 52.6000 47.4000
##
## Conditional X*W interaction at values of the moderator Z:
## Z2 effect se t p LLCI ULCI
## -2.6953 -1.2990 0.1480 -8.7759 0.0000 -1.5898 -1.0082
## -2.3750 -1.1417 0.1333 -8.5655 0.0000 -1.4035 -0.8798
## -2.0547 -0.9843 0.1188 -8.2871 0.0000 -1.2177 -0.7510
## -1.7344 -0.8270 0.1046 -7.9073 0.0000 -1.0325 -0.6215
## -1.4141 -0.6697 0.0909 -7.3705 0.0000 -0.8483 -0.4912
## -1.0938 -0.5124 0.0779 -6.5819 0.0000 -0.6654 -0.3594
## -0.7735 -0.3551 0.0660 -5.3829 0.0000 -0.4847 -0.2255
## -0.4532 -0.1978 0.0559 -3.5354 0.0004 -0.3077 -0.0879
## -0.2552 -0.1005 0.0512 -1.9648 0.0500 -0.2010 -0.0000
## -0.1329 -0.0405 0.0489 -0.8269 0.4087 -0.1366 0.0557
## 0.1351 0.0912 0.0464 1.9648 0.0500 -0.0000 0.1823
## 0.1874 0.1168 0.0463 2.5224 0.0120 0.0258 0.2079
## 0.5077 0.2742 0.0488 5.6145 0.0000 0.1782 0.3701
## 0.8280 0.4315 0.0558 7.7372 0.0000 0.3219 0.5410
## 1.1483 0.5888 0.0657 8.9560 0.0000 0.4596 0.7180
## 1.4686 0.7461 0.0776 9.6151 0.0000 0.5936 0.8986
## 1.7889 0.9034 0.0906 9.9722 0.0000 0.7254 1.0814
## 2.1092 1.0607 0.1043 10.1692 0.0000 0.8558 1.2657
## 2.4295 1.2180 0.1185 10.2797 0.0000 0.9852 1.4509
## 2.7498 1.3754 0.1330 10.3418 0.0000 1.1141 1.6367
## 3.0701 1.5327 0.1477 10.3758 0.0000 1.2424 1.8229
## 3.3904 1.6900 0.1626 10.3932 0.0000 1.3705 2.0095
##
## Data for visualizing the conditional effect of the focal predictor:
## X Z1 Z2 Y
## -0.9382 -1.0132 -0.9666 -1.1489
## 0.0346 -1.0132 -0.9666 -0.3668
## 1.0074 -1.0132 -0.9666 0.4152
## -0.9382 -1.0132 0.0262 -0.7591
## 0.0346 -1.0132 0.0262 -0.3598
## 1.0074 -1.0132 0.0262 0.0395
## -0.9382 -1.0132 1.0190 -0.3694
## 0.0346 -1.0132 1.0190 -0.3528
## 1.0074 -1.0132 1.0190 -0.3361
## -0.9382 -0.0023 -0.9666 -0.2972
## 0.0346 -0.0023 -0.9666 0.0424
## 1.0074 -0.0023 -0.9666 0.3819
## -0.9382 -0.0023 0.0262 -0.3603
## 0.0346 -0.0023 0.0262 0.0761
## 1.0074 -0.0023 0.0262 0.5125
## -0.9382 -0.0023 1.0190 -0.4234
## 0.0346 -0.0023 1.0190 0.1098
## 1.0074 -0.0023 1.0190 0.6430
## -0.9382 1.0086 -0.9666 0.5544
## 0.0346 1.0086 -0.9666 0.4515
## 1.0074 1.0086 -0.9666 0.3486
## -0.9382 1.0086 0.0262 0.0385
## 0.0346 1.0086 0.0262 0.5119
## 1.0074 1.0086 0.0262 0.9854
## -0.9382 1.0086 1.0190 -0.4775
## 0.0346 1.0086 1.0190 0.5723
## 1.0074 1.0086 1.0190 1.6221
##
## ***********************************************************************
## Bootstrapping progress:
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##
## ********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********
##
## Outcome variable: Y
##
## Coeff BootMean BootSE BootLLCI BootULCI
## constant 0.0608 0.0613 0.0455 -0.0268 0.1484
## X 0.4460 0.4469 0.0488 0.3543 0.5422
## Z1 0.4296 0.4287 0.0469 0.3352 0.5218
## Int_1 0.0248 0.0264 0.0466 -0.0662 0.1176
## Z2 0.0305 0.0304 0.0435 -0.0551 0.1156
## Int_2 0.1014 0.1048 0.0442 0.0178 0.1907
## Int_3 0.0096 0.0094 0.0412 -0.0733 0.0893
## Int_4 0.4911 0.4911 0.0450 0.4011 0.5762
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 5000
##
## W values in conditional tables are the mean and +/- SD from the mean.
##
## Z values in conditional tables are the mean and +/- SD from the mean.#畫圖
m3 <- lm(Y ~ X+Z1+Z2+X:Z1+X:Z2+Z1:Z2+X:Z1:Z2, data = dta)
interactions::interact_plot(m3, pred = X, modx = Z1,mod2=Z2, interval = TRUE,
int.type = "confidence", int.width = .8)
一個獨變項，兩個調節變項
方法二：調節效果，用 lavaan 功能分析
dta$int1 <- dta$X*dta$Z1
dta$int2 <- dta$X*dta$Z2
dta$int3 <- dta$Z1*dta$Z2
dta$int4 <- dta$X*dta$Z1*dta$Z2
k11 <- mean(dta$Z1)-sd(dta$Z1)
k12 <- mean(dta$Z1)
k13 <- mean(dta$Z1)+sd(dta$Z1)
round(c(k11,k12,k13),3)## [1] -1.013 -0.002 1.009k21 <- mean(dta$Z2)-sd(dta$Z2)
k22 <- mean(dta$Z2)
k23 <- mean(dta$Z2)+sd(dta$Z2)
round(c(k21,k22,k23),3)## [1] -0.967 0.026 1.019model1 <-'
Y ~ b1*X + b2*Z1 + b3*Z2 + b4*int1 + b5*int2 + b6*int3 + b7*int4
sslope11 := b1+b4*(-1.013)+b5*(-0.967)+b7*(-1.013*-0.967)
sslope12 := b1+b4*(-1.013)+b5*(0.026)+b7*(-1.013*0.026)
sslope13 := b1+b4*(-1.013)+b5*(1.019)+b7*(-1.013*1.019)
sslope21 := b1+b4*(-0.002)+b5*(-0.967)+b7*(-0.002*-0.967)
sslope22 := b1+b4*(-0.002)+b5*(0.026)+b7*(-0.002*0.026)
sslope23 := b1+b4*(-0.002)+b5*(1.019)+b7*(-0.002*1.019)
sslope31 := b1+b4*(1.009)+b5*(-0.967)+b7*(1.009*-0.967)
sslope32 := b1+b4*(1.009)+b5*(0.026)+b7*(1.009*0.026)
sslope33 := b1+b4*(1.009)+b5*(1.019)+b7*(1.009*1.019)
'#徑路分析報表
fit <- lavaan::sem(model1, data=dta)
summary(fit)## lavaan 0.6.15 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 8
##
## Number of observations 500
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## Y ~
## X (b1) 0.446 0.046 9.633 0.000
## Z1 (b2) 0.430 0.045 9.510 0.000
## Z2 (b3) 0.031 0.045 0.671 0.502
## int1 (b4) 0.025 0.047 0.530 0.596
## int2 (b5) 0.101 0.050 2.037 0.042
## int3 (b6) 0.010 0.043 0.223 0.824
## int4 (b7) 0.491 0.048 10.164 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Y 1.003 0.063 15.811 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## sslope11 0.804 0.101 7.936 0.000
## sslope12 0.411 0.065 6.325 0.000
## sslope13 0.017 0.091 0.190 0.849
## sslope21 0.349 0.067 5.216 0.000
## sslope22 0.449 0.046 9.686 0.000
## sslope23 0.548 0.069 7.993 0.000
## sslope31 -0.106 0.097 -1.095 0.273
## sslope32 0.487 0.067 7.237 0.000
## sslope33 1.079 0.094 11.502 0.000#以拔靴法看徑路係數與簡單效果信賴區間
set.seed(1234)
fit <- lavaan::sem(model1, data=dta, test="bootstrap", bootstrap=501)
parameterEstimates(fit,ci=TRUE,boot.ci.type="bca.simple")| lhs | op | rhs | label | est | se | z | pvalue | ci.lower | ci.upper |
|---|---|---|---|---|---|---|---|---|---|
| Y | ~ | X | b1 | 0.446 | 0.0463 | 9.63 | 0 | 0.355 | 0.537 |
| Y | ~ | Z1 | b2 | 0.43 | 0.0452 | 9.51 | 0 | 0.341 | 0.518 |
| Y | ~ | Z2 | b3 | 0.0305 | 0.0455 | 0.671 | 0.502 | -0.0586 | 0.12 |
| Y | ~ | int1 | b4 | 0.0248 | 0.0469 | 0.53 | 0.596 | -0.067 | 0.117 |
| Y | ~ | int2 | b5 | 0.101 | 0.0498 | 2.04 | 0.0417 | 0.00382 | 0.199 |
| Y | ~ | int3 | b6 | 0.0096 | 0.0431 | 0.223 | 0.824 | -0.0749 | 0.0941 |
| Y | ~ | int4 | b7 | 0.491 | 0.0483 | 10.2 | 0 | 0.396 | 0.586 |
| Y | ~~ | Y | 1 | 0.0634 | 15.8 | 0 | 0.878 | 1.13 | |
| X | ~~ | X | 0.944 | 0 | 0.944 | 0.944 | |||
| X | ~~ | Z1 | -0.051 | 0 | -0.051 | -0.051 | |||
| X | ~~ | Z2 | 0.0565 | 0 | 0.0565 | 0.0565 | |||
| X | ~~ | int1 | -0.0485 | 0 | -0.0485 | -0.0485 | |||
| X | ~~ | int2 | 0.00633 | 0 | 0.00633 | 0.00633 | |||
| X | ~~ | int3 | -0.0223 | 0 | -0.0223 | -0.0223 | |||
| X | ~~ | int4 | -0.0442 | 0 | -0.0442 | -0.0442 | |||
| Z1 | ~~ | Z1 | 1.02 | 0 | 1.02 | 1.02 | |||
| Z1 | ~~ | Z2 | 0.051 | 0 | 0.051 | 0.051 | |||
| Z1 | ~~ | int1 | 0.134 | 0 | 0.134 | 0.134 | |||
| Z1 | ~~ | int2 | -0.0204 | 0 | -0.0204 | -0.0204 | |||
| Z1 | ~~ | int3 | 0.131 | 0 | 0.131 | 0.131 | |||
| Z1 | ~~ | int4 | 0.0454 | 0 | 0.0454 | 0.0454 | |||
| Z2 | ~~ | Z2 | 0.984 | 0 | 0.984 | 0.984 | |||
| Z2 | ~~ | int1 | -0.0192 | 0 | -0.0192 | -0.0192 | |||
| Z2 | ~~ | int2 | 0.0288 | 0 | 0.0288 | 0.0288 | |||
| Z2 | ~~ | int3 | 0.0401 | 0 | 0.0401 | 0.0401 | |||
| Z2 | ~~ | int4 | -0.0644 | 0 | -0.0644 | -0.0644 | |||
| int1 | ~~ | int1 | 0.972 | 0 | 0.972 | 0.972 | |||
| int1 | ~~ | int2 | -0.042 | 0 | -0.042 | -0.042 | |||
| int1 | ~~ | int3 | 0.0481 | 0 | 0.0481 | 0.0481 | |||
| int1 | ~~ | int4 | 0.191 | 0 | 0.191 | 0.191 | |||
| int2 | ~~ | int2 | 0.817 | 0 | 0.817 | 0.817 | |||
| int2 | ~~ | int3 | -0.0679 | 0 | -0.0679 | -0.0679 | |||
| int2 | ~~ | int4 | 0.0214 | 0 | 0.0214 | 0.0214 | |||
| int3 | ~~ | int3 | 1.11 | 0 | 1.11 | 1.11 | |||
| int3 | ~~ | int4 | 0.0832 | 0 | 0.0832 | 0.0832 | |||
| int4 | ~~ | int4 | 0.908 | 0 | 0.908 | 0.908 | |||
| sslope11 | := | b1+b4*(-1.013)+b5*(-0.967)+b7*(-1.013*-0.967) | sslope11 | 0.804 | 0.101 | 7.94 | 2e-15 | 0.605 | 1 |
| sslope12 | := | b1+b4*(-1.013)+b5*(0.026)+b7*(-1.013*0.026) | sslope12 | 0.411 | 0.0649 | 6.32 | 2.54e-10 | 0.283 | 0.538 |
| sslope13 | := | b1+b4*(-1.013)+b5*(1.019)+b7*(-1.013*1.019) | sslope13 | 0.0172 | 0.0905 | 0.19 | 0.849 | -0.16 | 0.195 |
| sslope21 | := | b1+b4*(-0.002)+b5*(-0.967)+b7*(-0.002*-0.967) | sslope21 | 0.349 | 0.0669 | 5.22 | 1.83e-07 | 0.218 | 0.48 |
| sslope22 | := | b1+b4*(-0.002)+b5*(0.026)+b7*(-0.002*0.026) | sslope22 | 0.449 | 0.0463 | 9.69 | 0 | 0.358 | 0.539 |
| sslope23 | := | b1+b4*(-0.002)+b5*(1.019)+b7*(-0.002*1.019) | sslope23 | 0.548 | 0.0686 | 7.99 | 1.33e-15 | 0.414 | 0.683 |
| sslope31 | := | b1+b4*(1.009)+b5*(-0.967)+b7*(1.009*-0.967) | sslope31 | -0.106 | 0.0969 | -1.1 | 0.273 | -0.296 | 0.0838 |
| sslope32 | := | b1+b4*(1.009)+b5*(0.026)+b7*(1.009*0.026) | sslope32 | 0.487 | 0.0672 | 7.24 | 4.59e-13 | 0.355 | 0.618 |
| sslope33 | := | b1+b4*(1.009)+b5*(1.019)+b7*(1.009*1.019) | sslope33 | 1.08 | 0.0938 | 11.5 | 0 | 0.895 | 1.26 |
#畫圖看模型與估計值
lavaanPlot::lavaanPlot(model = fit,
edge_options = list(color = "grey"),
coefs = TRUE,
stand = TRUE)#畫圖
m3 <- lm(Y ~ X+Z1+Z2+X:Z1+X:Z2+Z1:Z2+X:Z1:Z2, data = dta)
interactions::interact_plot(m3, pred = X, modx = Z1,mod2=Z2, interval = TRUE,
int.type = "confidence", int.width = .8)