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("depress.csv", header = TRUE)一個獨變項，一個調節變項
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
## #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 = 'depress', x = 'stress', w ='support', model = 1,
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 : 1
## Y : depress
## X : stress
## W : support
##
## Sample size: 100
##
## Random seed: 495750
##
##
## ***********************************************************************
## Outcome Variable: depress
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.9818 0.9638 1.9311 852.9588 3.0000 96.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 29.2583 0.6909 42.3506 0.0000 27.8870 30.6297
## stress 1.9956 0.1161 17.1855 0.0000 1.7651 2.2261
## support -0.2356 0.1109 -2.1246 0.0362 -0.4558 -0.0155
## Int_1 -0.3902 0.0188 -20.7538 0.0000 -0.4276 -0.3529
##
## Product terms key:
## Int_1 : stress x support
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## X*W 0.1622 430.7185 1.0000 96.0000 0.0000
## ----------
## Focal predictor: stress (X)
## Moderator: support (W)
##
## Conditional effects of the focal predictor at values of the moderator(s):
## support effect se t p LLCI ULCI
## 2.5607 0.9963 0.0765 13.0227 0.0000 0.8445 1.1482
## 5.3700 -0.1000 0.0531 -1.8841 0.0626 -0.2053 0.0054
## 8.1793 -1.1963 0.0732 -16.3429 0.0000 -1.3416 -1.0510
##
## Moderator value(s) defining Johnson-Neyman significance region(s):
## Value % below % above
## 4.8370 41.0000 59.0000
## 5.3837 51.0000 49.0000
##
## Conditional effect of focal predictor at values of the moderator:
## support effect se t p LLCI ULCI
## 1.0000 1.6054 0.0998 16.0921 0.0000 1.4073 1.8034
## 1.4737 1.4205 0.0923 15.3840 0.0000 1.2372 1.6038
## 1.9474 1.2357 0.0852 14.5035 0.0000 1.0666 1.4048
## 2.4211 1.0508 0.0784 13.3998 0.0000 0.8952 1.2065
## 2.8947 0.8660 0.0721 12.0093 0.0000 0.7228 1.0091
## 3.3684 0.6811 0.0664 10.2588 0.0000 0.5493 0.8129
## 3.8421 0.4963 0.0614 8.0769 0.0000 0.3743 0.6182
## 4.3158 0.3114 0.0575 5.4203 0.0000 0.1974 0.4255
## 4.7895 0.1266 0.0546 2.3165 0.0227 0.0181 0.2350
## 4.8370 0.1080 0.0544 1.9850 0.0500 0.0000 0.2161
## 5.2632 -0.0583 0.0532 -1.0957 0.2759 -0.1638 0.0473
## 5.3837 -0.1053 0.0530 -1.9850 0.0500 -0.2106 -0.0000
## 5.7368 -0.2431 0.0532 -4.5699 0.0000 -0.3487 -0.1375
## 6.2105 -0.4280 0.0547 -7.8256 0.0000 -0.5365 -0.3194
## 6.6842 -0.6128 0.0575 -10.6518 0.0000 -0.7270 -0.4986
## 7.1579 -0.7977 0.0615 -12.9609 0.0000 -0.9198 -0.6755
## 7.6316 -0.9825 0.0665 -14.7716 0.0000 -1.1146 -0.8505
## 8.1053 -1.1674 0.0722 -16.1586 0.0000 -1.3108 -1.0240
## 8.5789 -1.3522 0.0786 -17.2107 0.0000 -1.5082 -1.1963
## 9.0526 -1.5371 0.0854 -18.0080 0.0000 -1.7065 -1.3676
## 9.5263 -1.7219 0.0925 -18.6151 0.0000 -1.9055 -1.5383
## 10.0000 -1.9068 0.0999 -19.0808 0.0000 -2.1051 -1.7084
##
## Data for visualizing the conditional effect of the focal predictor:
## stress support depress
## 2.7542 2.5607 31.3990
## 5.3900 2.5607 34.0251
## 8.0258 2.5607 36.6512
## 2.7542 5.3700 27.7177
## 5.3900 5.3700 27.4542
## 8.0258 5.3700 27.1907
## 2.7542 8.1793 24.0363
## 5.3900 8.1793 20.8832
## 8.0258 8.1793 17.7302
##
## ***********************************************************************
## Bootstrapping progress:
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##
## ********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********
##
## Outcome variable: depress
##
## Coeff BootMean BootSE BootLLCI BootULCI
## constant 29.2583 29.2559 0.6455 27.9890 30.5696
## stress 1.9956 1.9963 0.1190 1.7608 2.2359
## support -0.2356 -0.2351 0.1010 -0.4367 -0.0348
## Int_1 -0.3902 -0.3905 0.0178 -0.4274 -0.3559
##
## ******************** 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，還是得要迴歸
m2 <- lm(depress ~ stress+support+ stress*support, data = dta)
interactions::interact_plot(m2, pred = stress, modx = support, interval = TRUE,
int.type = "confidence", int.width = .8)
一個獨變項，一個調節變項
方法二：調節效果，用 lavaan 功能分析
dta$int <- dta$stress*dta$support
k1 <- mean(dta$support)-sd(dta$support)
k2 <- mean(dta$support)
k3 <- mean(dta$support)+sd(dta$support)
round(c(k1,k2,k3),3)## [1] 2.561 5.370 8.179model1 <-'
depress ~ b1*stress + b2*support + b3*int
sslope1 := b1+b3*(2.561)
sslope2 := b1+b3*(5.370)
sslope3 := b1+b3*(8.179)
'#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 4
##
## Number of observations 100
##
## 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|)
## depress ~
## stress (b1) 1.996 0.114 17.540 0.000
## support (b2) -0.236 0.109 -2.168 0.030
## int (b3) -0.390 0.018 -21.182 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .depress 1.854 0.262 7.071 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## sslope1 0.996 0.075 13.290 0.000
## sslope2 -0.100 0.052 -1.923 0.054
## sslope3 -1.196 0.072 -16.679 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 |
|---|---|---|---|---|---|---|---|---|---|
| depress | ~ | stress | b1 | 2 | 0.114 | 17.5 | 0 | 1.77 | 2.22 |
| depress | ~ | support | b2 | -0.236 | 0.109 | -2.17 | 0.0301 | -0.449 | -0.0226 |
| depress | ~ | int | b3 | -0.39 | 0.0184 | -21.2 | 0 | -0.426 | -0.354 |
| depress | ~~ | depress | 1.85 | 0.262 | 7.07 | 1.54e-12 | 1.34 | 2.37 | |
| stress | ~~ | stress | 6.88 | 0 | 6.88 | 6.88 | |||
| stress | ~~ | support | -0.194 | 0 | -0.194 | -0.194 | |||
| stress | ~~ | int | 36.8 | 0 | 36.8 | 36.8 | |||
| support | ~~ | support | 7.81 | 0 | 7.81 | 7.81 | |||
| support | ~~ | int | 40.1 | 0 | 40.1 | 40.1 | |||
| int | ~~ | int | 468 | 0 | 468 | 468 | |||
| sslope1 | := | b1+b3*(2.561) | sslope1 | 0.996 | 0.075 | 13.3 | 0 | 0.849 | 1.14 |
| sslope2 | := | b1+b3*(5.370) | sslope2 | -0.1 | 0.052 | -1.92 | 0.0545 | -0.202 | 0.00192 |
| sslope3 | := | b1+b3*(8.179) | sslope3 | -1.2 | 0.0717 | -16.7 | 0 | -1.34 | -1.06 |
#畫圖看模型與估計值
lavaanPlot::lavaanPlot(model = fit,
edge_options = list(color = "grey"),
coefs = TRUE,
stand = TRUE)#畫圖，還是得要迴歸
m2 <- lm(depress ~ stress+support+ stress*support, data = dta)
interactions::interact_plot(m2, pred = stress, modx = support, interval = TRUE,
int.type = "confidence", int.width = .8)
一個獨變項，一個調節變項
方法三：調節效果，取用 lm 功能分析
#用迴歸分析並製表
m1 <- lm(depress ~ stress+support , data = dta)
m2 <- lm(depress ~ stress+support+ stress*support, data = dta)
#如果要中心化
#dta$int <- (dta$stress-mean(dta$stress))*(dta$support-(dta$support))
#m1 <- lm(depress ~ stress+support , data = dta)
#m2 <- lm(depress ~ stress+support+ int, data = dta)
options(huxtable.knitr_output_format="md")
jtools::export_summs(m1,m2,
model.names = c("depress", "depress"),
error_format = "[{conf.low},{conf.high}]")## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools## Warning in to_md.huxtable(structure(list(names = c("", "(Intercept)", "", :
## Markdown cannot handle cells with colspan/rowspan > 1## Warning in to_md.huxtable(structure(list(names = c("", "(Intercept)", "", :
## Can't vary column alignment in markdown; using first row| depress | depress | |
|---|---|---|
| (Intercept) | 40.64 *** | 29.26 *** |
| [38.70,42.59] | [27.89,30.63] | |
| stress | -0.15 | 2.00 *** |
| [-0.39,0.10] | [1.77,2.23] | |
| support | -2.29 *** | -0.24 * |
| [-2.52,-2.06] | [-0.46,-0.02] | |
| stress:support | -0.39 *** | |
| [-0.43,-0.35] | ||
| N | 100 | 100 |
| R2 | 0.80 | 0.96 |
| *** p < 0.001; | ** p < 0.01; * | p < 0.05. |
#兩模型的解釋量差異檢定
Rsquared_m1 <- broom::glance(m1)$r.squared
Rsquared_m2 <- broom::glance(m2)$r.squared
mrst <- c(m1_Rsquared=Rsquared_m1,m2_Rsquared=Rsquared_m2,deltaRsquared=Rsquared_m2-Rsquared_m1)
round(mrst,3)## m1_Rsquared m2_Rsquared deltaRsquared
## 0.802 0.964 0.162anova(m1,m2)| Res.Df | RSS | Df | Sum of Sq | F | Pr(>F) |
|---|---|---|---|---|---|
| 97 | 1.02e+03 | ||||
| 96 | 185 | 1 | 832 | 431 | 2.92e-37 |
#畫圖，還是得要迴歸
m2 <- lm(depress ~ stress+support+ stress*support, data = dta)
interactions::interact_plot(m2, pred = stress, modx = support, interval = TRUE,
int.type = "confidence", int.width = .8)
simple_slopes(m2)| stress | suppor t | Test Estima te | Std. Error | t value | df | Pr(>|t |) |
|---|---|---|---|---|---|---|
| 2.754213 | sstest | -1.31 | 0.0687 | -19.1 | 96 | 1.93e-34 |
| 5.39 | sstest | -2.34 | 0.0498 | -47 | 96 | 4.71e-68 |
| 8.025787 | sstest | -3.37 | 0.0718 | -46.9 | 96 | 5.5e-68 |
| sstest | 2.560726 | 0.996 | 0.0765 | 13 | 96 | 6.19e-23 |
| sstest | 5.37 | -0.1 | 0.0531 | -1.88 | 96 | 0.0626 |
| sstest | 8.179274 | -1.2 | 0.0732 | -16.3 | 96 | 1.75e-29 |