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("modmed1.csv", header = TRUE)調節徑路分析模型：第二階段中介效果調節模型
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', m='M',w ='Z', model = 14,
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 : 14
## Y : Y
## X : X
## M : M
## W : Z
##
## Sample size: 1000
##
## Random seed: 393923
##
##
## ***********************************************************************
## Outcome Variable: M
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.4336 0.1880 1.0128 231.0512 1.0000 998.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 0.0410 0.0318 1.2895 0.1975 -0.0214 0.1035
## X 0.4880 0.0321 15.2004 0.0000 0.4250 0.5511
##
## ***********************************************************************
## Outcome Variable: Y
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.5706 0.3256 0.9795 120.1094 4.0000 995.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant -0.0063 0.0313 -0.1998 0.8417 -0.0678 0.0552
## X 0.0054 0.0351 0.1549 0.8770 -0.0634 0.0743
## M 0.4933 0.0311 15.8381 0.0000 0.4322 0.5544
## Z 0.3625 0.0323 11.2217 0.0000 0.2991 0.4259
## Int_1 0.1208 0.0305 3.9585 0.0001 0.0609 0.1806
##
## Product terms key:
## Int_1 : M x Z
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## M*W 0.0106 15.6698 1.0000 995.0000 0.0001
## ----------
## Focal predictor: M (M)
## Moderator: Z (W)
##
## Conditional effects of the focal predictor at values of the moderator(s):
## Z effect se t p LLCI ULCI
## -0.9985 0.3727 0.0439 8.4811 0.0000 0.2865 0.4590
## -0.0201 0.4909 0.0312 15.7517 0.0000 0.4297 0.5520
## 0.9582 0.6090 0.0423 14.3835 0.0000 0.5259 0.6921
##
## Moderator value(s) defining Johnson-Neyman significance region(s):
## Value % below % above
## -2.6627 0.1000 99.9000
##
## Conditional effect of focal predictor at values of the moderator:
## Z effect se t p LLCI ULCI
## -2.8485 0.1493 0.0928 1.6081 0.1081 -0.0329 0.3314
## -2.6627 0.1717 0.0875 1.9624 0.0500 -0.0000 0.3435
## -2.5351 0.1871 0.0839 2.2310 0.0259 0.0225 0.3517
## -2.2216 0.2250 0.0751 2.9965 0.0028 0.0777 0.3723
## -1.9081 0.2629 0.0665 3.9526 0.0001 0.1324 0.3934
## -1.5946 0.3007 0.0582 5.1648 0.0000 0.1865 0.4150
## -1.2811 0.3386 0.0504 6.7173 0.0000 0.2397 0.4375
## -0.9677 0.3764 0.0433 8.6960 0.0000 0.2915 0.4614
## -0.6542 0.4143 0.0373 11.1114 0.0000 0.3411 0.4875
## -0.3407 0.4522 0.0330 13.6986 0.0000 0.3874 0.5169
## -0.0272 0.4900 0.0312 15.7197 0.0000 0.4288 0.5512
## 0.2863 0.5279 0.0322 16.3936 0.0000 0.4647 0.5911
## 0.5998 0.5657 0.0358 15.7824 0.0000 0.4954 0.6361
## 0.9132 0.6036 0.0414 14.5710 0.0000 0.5223 0.6849
## 1.2267 0.6414 0.0483 13.2890 0.0000 0.5467 0.7362
## 1.5402 0.6793 0.0559 12.1483 0.0000 0.5696 0.7890
## 1.8537 0.7172 0.0641 11.1912 0.0000 0.5914 0.8429
## 2.1672 0.7550 0.0726 10.4011 0.0000 0.6126 0.8975
## 2.4806 0.7929 0.0813 9.7486 0.0000 0.6333 0.9525
## 2.7941 0.8307 0.0902 9.2056 0.0000 0.6537 1.0078
## 3.1076 0.8686 0.0993 8.7495 0.0000 0.6738 1.0634
## 3.4211 0.9065 0.1084 8.3624 0.0000 0.6937 1.1192
##
## Data for visualizing the conditional effect of the focal predictor:
## M Z Y
## -1.0674 -0.9985 -0.7659
## 0.0489 -0.9985 -0.3499
## 1.1652 -0.9985 0.0662
## -1.0674 -0.0201 -0.5374
## 0.0489 -0.0201 0.0105
## 1.1652 -0.0201 0.5585
## -1.0674 0.9582 -0.3089
## 0.0489 0.9582 0.3710
## 1.1652 0.9582 1.0508
##
## ***********************************************************************
## Bootstrapping progress:
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##
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
##
## Direct effect of X on Y:
## effect se t p LLCI ULCI
## 0.0054 0.0351 0.1549 0.8770 -0.0634 0.0743
##
## Conditional indirect effects of X on Y:
##
## INDIRECT EFFECT:
##
## X -> M -> Y
##
## Z Effect BootSE BootLLCI BootULCI
## -0.9985 0.1819 0.0251 0.1347 0.2339
## -0.0201 0.2396 0.0223 0.1977 0.2839
## 0.9582 0.2972 0.0282 0.2442 0.3544
##
## Index of moderated mediation:
## Index BootSE BootLLCI BootULCI
## Z 0.0589 0.0150 0.0302 0.0887
##
## ********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********
##
## Outcome variable: M
##
## Coeff BootMean BootSE BootLLCI BootULCI
## constant 0.0410 0.0406 0.0318 -0.0220 0.1023
## X 0.4880 0.4886 0.0308 0.4282 0.5485
## ----------
## Outcome variable: Y
##
## Coeff BootMean BootSE BootLLCI BootULCI
## constant -0.0063 -0.0070 0.0312 -0.0676 0.0534
## X 0.0054 0.0053 0.0360 -0.0655 0.0756
## M 0.4933 0.4933 0.0327 0.4288 0.5582
## Z 0.3625 0.3626 0.0315 0.2996 0.4243
## Int_1 0.1208 0.1208 0.0302 0.0627 0.1801
##
## ******************** 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.#畫圖，這圖意義不大
m3 <- lm(Y ~ X+M+M:Z, data = dta)
interactions::interact_plot(m3, pred = M, modx = Z, interval = TRUE,
int.type = "confidence", int.width = .8)
調節徑路分析模型：第二階段中介效果調節模型
方法二：調節效果，用 lavaan 功能分析
dta$int1 <- dta$M*dta$Z
k1 <- mean(dta$Z)-sd(dta$Z)
k2 <- mean(dta$Z)
k3 <- mean(dta$Z)+sd(dta$Z)
round(c(k1,k2,k3),3)## [1] -0.998 -0.020 0.958model1 <-'
M ~ a*X
Y ~ c*X + b1*M + b2*int1
sslope1med1 := (b1+b2*(-.998))*a
sslope1med2 := (b1+b2*(-0.020))*a
sslope1med3 := (b1+b2*(0.958))*a
'#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 6
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 0.445
## Degrees of freedom 1
## P-value (Chi-square) 0.505
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## M ~
## X (a) 0.488 0.032 15.216 0.000
## Y ~
## X (c) -0.008 0.037 -0.219 0.827
## M (b1) 0.502 0.033 15.241 0.000
## int1 (b2) 0.166 0.032 5.178 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .M 1.011 0.045 22.361 0.000
## .Y 1.098 0.049 22.361 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## sslope1med1 0.164 0.025 6.610 0.000
## sslope1med2 0.244 0.023 10.731 0.000
## sslope1med3 0.323 0.031 10.566 0.000#以拔靴法看徑路係數與簡單中介效果信賴區間
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 |
|---|---|---|---|---|---|---|---|---|---|
| M | ~ | X | a | 0.488 | 0.0321 | 15.2 | 0 | 0.425 | 0.551 |
| Y | ~ | X | c | -0.00812 | 0.0371 | -0.219 | 0.827 | -0.0809 | 0.0647 |
| Y | ~ | M | b1 | 0.502 | 0.033 | 15.2 | 0 | 0.438 | 0.567 |
| Y | ~ | int1 | b2 | 0.166 | 0.032 | 5.18 | 2.25e-07 | 0.103 | 0.228 |
| M | ~~ | M | 1.01 | 0.0452 | 22.4 | 0 | 0.922 | 1.1 | |
| Y | ~~ | Y | 1.1 | 0.0491 | 22.4 | 0 | 1 | 1.19 | |
| X | ~~ | X | 0.982 | 0 | 0.982 | 0.982 | |||
| X | ~~ | int1 | 0.0492 | 0 | 0.0492 | 0.0492 | |||
| int1 | ~~ | int1 | 1.07 | 0 | 1.07 | 1.07 | |||
| sslope1med1 | := | (b1+b2*(-.998))*a | sslope1med1 | 0.164 | 0.0249 | 6.61 | 3.84e-11 | 0.116 | 0.213 |
| sslope1med2 | := | (b1+b2*(-0.020))*a | sslope1med2 | 0.244 | 0.0227 | 10.7 | 0 | 0.199 | 0.288 |
| sslope1med3 | := | (b1+b2*(0.958))*a | sslope1med3 | 0.323 | 0.0305 | 10.6 | 0 | 0.263 | 0.382 |
#畫圖看模型與估計值
lavaanPlot::lavaanPlot(model = fit,
edge_options = list(color = "grey"),
coefs = TRUE,
stand = TRUE)#畫圖，這圖意義不大
m3 <- lm(Y ~ X+M+M:Z, data = dta)
interactions::interact_plot(m3, pred = M, modx = Z, interval = TRUE,
int.type = "confidence", int.width = .8)