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("mathmod.csv", header = TRUE)一個獨變項，一個調節變項（類別）
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
process (data = dta, y = 'math', x = 'training', w ='gender', model = 1,wcatcode=1,
center = 2,jn = 1,modelbt= 1, seed = 20231029)##
## ********************* 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 : math
## X : training
## W : gender
##
## Sample size: 101
##
## Custom seed: 20231029
##
##
## ***********************************************************************
## Outcome Variable: math
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.6164 0.3799 0.9086 19.8111 3.0000 97.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.2928 0.1579 20.8486 0.0000 2.9794 3.6063
## training -0.3394 0.0539 -6.3009 0.0000 -0.4463 -0.2325
## gender -0.2355 0.2000 -1.1776 0.2418 -0.6325 0.1614
## Int_1 0.5043 0.0685 7.3666 0.0000 0.3684 0.6401
##
## Product terms key:
## Int_1 : training x gender
##
## Test(s) of highest order unconditional interaction(s):
## R2-chng F df1 df2 p
## X*W 0.3469 54.2674 1.0000 97.0000 0.0000
## ----------
## Focal predictor: training (X)
## Moderator: gender (W)
##
## Conditional effects of the focal predictor at values of the moderator(s):
## gender effect se t p LLCI ULCI
## 0.0000 -0.3394 0.0539 -6.3009 0.0000 -0.4463 -0.2325
## 1.0000 0.1648 0.0422 3.9029 0.0002 0.0810 0.2487
##
## ***********************************************************************
## Bootstrapping progress:
##
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##
## ********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS **********
##
## Outcome variable: math
##
## Coeff BootMean BootSE BootLLCI BootULCI
## constant 3.2928 3.2897 0.1582 2.9773 3.5933
## training -0.3394 -0.3396 0.0546 -0.4502 -0.2368
## gender -0.2355 -0.2292 0.1993 -0.6050 0.1804
## Int_1 0.5043 0.5045 0.0707 0.3672 0.6470
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 5000
##
## NOTE: The following variables were mean centered prior to analysis:
## training#畫圖
dta$gender <- as.factor(dta$gender)
m2 <- lm(math ~ training+gender+training:gender, data = dta)
interactions::interact_plot(m2, pred = training, modx = gender, 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 <- dummy_cols(dta,select_columns=c("gender"), remove_first_dummy=TRUE)
dta$int <- dta$train*dta$gender_1model1 <-'
math ~ b1*training + b2*gender_1 + b3*int
sslope1 := b1+b3*0
sslope2 := b1+b3*1
'#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 101
##
## 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|)
## math ~
## training (b1) -0.339 0.053 -6.429 0.000
## gender_1 (b2) -2.757 0.372 -7.420 0.000
## int (b3) 0.504 0.067 7.517 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .math 0.873 0.123 7.106 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## sslope1 -0.339 0.053 -6.429 0.000
## sslope2 0.165 0.041 3.983 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 |
|---|---|---|---|---|---|---|---|---|---|
| math | ~ | training | b1 | -0.339 | 0.0528 | -6.43 | 1.28e-10 | -0.443 | -0.236 |
| math | ~ | gender_1 | b2 | -2.76 | 0.372 | -7.42 | 1.17e-13 | -3.49 | -2.03 |
| math | ~ | int | b3 | 0.504 | 0.0671 | 7.52 | 5.6e-14 | 0.373 | 0.636 |
| math | ~~ | math | 0.873 | 0.123 | 7.11 | 1.19e-12 | 0.632 | 1.11 | |
| training | ~~ | training | 8.5 | 0 | 8.5 | 8.5 | |||
| training | ~~ | gender_1 | 0.291 | 0 | 0.291 | 0.291 | |||
| training | ~~ | int | 6.64 | 0 | 6.64 | 6.64 | |||
| gender_1 | ~~ | gender_1 | 0.237 | 0 | 0.237 | 0.237 | |||
| gender_1 | ~~ | int | 1.3 | 0 | 1.3 | 1.3 | |||
| int | ~~ | int | 12.1 | 0 | 12.1 | 12.1 | |||
| sslope1 | := | b1+b3*0 | sslope1 | -0.339 | 0.0528 | -6.43 | 1.28e-10 | -0.443 | -0.236 |
| sslope2 | := | b1+b3*1 | sslope2 | 0.165 | 0.0414 | 3.98 | 6.82e-05 | 0.0837 | 0.246 |
#畫圖看模型與估計值
lavaanPlot::lavaanPlot(model = fit,
edge_options = list(color = "grey"),
coefs = TRUE,
stand = TRUE)#畫圖
dta$gender <- as.factor(dta$gender)
m2 <- lm(math ~ training+gender+training:gender, data = dta)
interactions::interact_plot(m2, pred = training, modx = gender, interval = TRUE,
int.type = "confidence", int.width = .8)
一個獨變項，一個調節變項
方法三：調節效果，取用 lm 功能分析
#用迴歸分析並製表
dta$gender <- as.factor(dta$gender)
m1 <- lm(math ~ training+gender, data = dta)
m2 <- lm(math ~ training+gender+training:gender, data = dta)
options(huxtable.knitr_output_format="md")
jtools::export_summs(m1,m2,
model.names = c("math", "math"),
error_format = "[{conf.low},{conf.high}]")## Registered S3 methods overwritten by 'broom':
## method from
## tidy.glht jtools
## tidy.summary.glht jtools## Warning in to_md.huxtable(structure(list(names = c("", "(Intercept)", "", :
## Markdown cannot handle cells with colspan/rowspan > 1## Warning in to_md.huxtable(structure(list(names = c("", "(Intercept)", "", :
## Can't vary column alignment in markdown; using first row| math | math | |
|---|---|---|
| (Intercept) | 3.66 *** | 4.99 *** |
| [3.15,4.18] | [4.44,5.54] | |
| training | -0.03 | -0.34 *** |
| [-0.11,0.05] | [-0.45,-0.23] | |
| gender1 | -0.38 | -2.76 *** |
| [-0.87,0.11] | [-3.51,-2.00] | |
| training:gender1 | 0.50 *** | |
| [0.37,0.64] | ||
| N | 101 | 101 |
| R2 | 0.03 | 0.38 |
| *** p < 0.001; * | * p < 0.01; * | p < 0.05. |
#兩模型的解釋量差異檢定
Rsquared_m1 <- broom::glance(m1)$r.squared
Rsquared_m2 <- broom::glance(m2)$r.squared
mrst <- c(m1_Rsquared=Rsquared_m1,m2_Rsquared=Rsquared_m2,deltaRsquared=Rsquared_m2-Rsquared_m1)
round(mrst,3)## m1_Rsquared m2_Rsquared deltaRsquared
## 0.033 0.380 0.347anova(m1,m2)| Res.Df | RSS | Df | Sum of Sq | F | Pr(>F) |
|---|---|---|---|---|---|
| 98 | 137 | ||||
| 97 | 88.1 | 1 | 49.3 | 54.3 | 5.8e-11 |
#畫圖
dta$gender <- as.factor(dta$gender)
m2 <- lm(math ~ training+gender+training:gender, data = dta)
interactions::interact_plot(m2, pred = training, modx = gender, interval = TRUE,
int.type = "confidence", int.width = .8)
#檢驗簡單斜率
simple_slopes(m2,
levels=list(gender=c('0','1', 'sstest'))) | traini ng | gender | Test Estima te | Std. Error | t value | df | Pr(>|t |) |
|---|---|---|---|---|---|---|
| sstest | 0 | -0.339 | 0.0539 | -6.3 | 97 | 8.7e-09 |
| sstest | 1 | 0.165 | 0.0422 | 3.9 | 97 | 0.000175 |
| 2.069983 | sstest | -1.71 | 0.269 | -6.37 | 97 | 6.39e-09 |
| 5 | sstest | -0.236 | 0.2 | -1.18 | 97 | 0.242 |
| 7.930017 | sstest | 1.24 | 0.297 | 4.18 | 97 | 6.28e-05 |