title: “EFT-training_2” output: html_document date: “2025-09-23”
fixed_d_from_fit <- function(fit){
sig <- sigma(fit)
co <- as.data.frame(coef(summary(fit)))
tibble::rownames_to_column(co, "term") %>%
transmute(
term,
estimate = Estimate,
std.error = `Std. Error`,
df,
t = `t value`,
p = `Pr(>|t|)`,
d = estimate / sig,
SE_d = std.error / sig,
d_low = d + qt(0.025, df) * SE_d,
d_high = d + qt(0.975, df) * SE_d
)
}
# 组内简单效应 d（post−pre / fu−pre）
within_d_from_fit <- function(fit, window){
emm <- emmeans(fit, ~ time | group)
sig <- sigma(fit)
if (identical(window, c("pre","post"))) {
w <- summary(contrast(emm, list(post_minus_pre = c(-1, 1)),
by = "group", adjust = "none"))
} else if (identical(window, c("pre","fu"))) {
w <- summary(contrast(emm, list(fu_minus_pre = c(-1, 1)),
by = "group", adjust = "none"))
} else {
return(NULL) # post–fu 不做组内
}
as.data.frame(w) %>%
mutate(
d = estimate / sig,
SE_d = SE / sig,
d_low = d + qt(0.025, df) * SE_d,
d_high= d + qt(0.975, df) * SE_d
)
}
# 窗口
win_list <- list(
pre_post = c("pre","post"),
pre_fu = c("pre","fu"),
post_fu = c("post","fu")
)
# 只取“积极条件”的 7 个指标
metrics_posi <- dat_long %>%
filter(cond == "posi") %>%
distinct(metric) %>%
pull() %>% as.character()
# 主循环：对每个指标 × 窗口建立模型并计算指标
res_fixed <- list()
res_within <- list()
for (m in metrics_posi) {
d_metric <- dat_long %>% filter(cond=="posi", metric==m)
for (wname in names(win_list)) {
win <- win_list[[wname]]
dsub <- d_metric %>%
filter(time %in% win) %>%
droplevels() %>%
mutate(
time = fct_relevel(time, win),
group = fct_relevel(group, "control","training")
)
# 1) 建模
fit <- lmer(score ~ group * time + (1 | ID), data = dsub, REML = TRUE)
# 2) 固定效应 d
fx <- fixed_d_from_fit(fit) %>%
mutate(cond="posi", metric=m, window=wname, .before=1)
res_fixed[[length(res_fixed)+1]] <- fx
# 3) 组内简单效应 d（仅 pre_post / pre_fu）
wx <- within_d_from_fit(fit, win)
if (!is.null(wx)) {
wx <- wx %>% mutate(cond="posi", metric=m, window=wname, .before=1)
res_within[[length(res_within)+1]] <- wx
}
}
}
# 汇总结果表
fixed_posi_tbl <- bind_rows(res_fixed) %>% select(cond, metric, window, everything())
within_posi_tbl <- bind_rows(res_within) %>%
select(cond, metric, window, group, contrast, estimate, SE, df, t.ratio, p.value,
d, SE_d, d_low, d_high)
# 查看前几行
print(head(fixed_posi_tbl, 84), row.names = FALSE)## cond metric window term estimate std.error
## posi 表象清晰度 pre_post (Intercept) 6.276206897 0.1821231
## posi 表象清晰度 pre_post grouptraining 0.285126437 0.2554056
## posi 表象清晰度 pre_post timepost 0.172068966 0.1716713
## posi 表象清晰度 pre_post grouptraining:timepost 0.135597701 0.2407482
## posi 表象清晰度 pre_fu (Intercept) 6.276206897 0.1933178
## posi 表象清晰度 pre_fu grouptraining 0.285126437 0.2711049
## posi 表象清晰度 pre_fu timefu 0.023103448 0.1882192
## posi 表象清晰度 pre_fu grouptraining:timefu 0.165712837 0.2656560
## posi 表象清晰度 post_fu (Intercept) 6.448275862 0.1657034
## posi 表象清晰度 post_fu grouptraining 0.420724138 0.2323789
## posi 表象清晰度 post_fu timefu -0.148965517 0.1498033
## posi 表象清晰度 post_fu grouptraining:timefu 0.025572861 0.2114928
## posi 发生时快感 pre_post (Intercept) 7.149655172 0.1489717
## posi 发生时快感 pre_post grouptraining 0.126344828 0.2089147
## posi 发生时快感 pre_post timepost -0.046206897 0.1662067
## posi 发生时快感 pre_post grouptraining:timepost 0.402540230 0.2330848
## posi 发生时快感 pre_fu (Intercept) 7.149655172 0.1735111
## posi 发生时快感 pre_fu grouptraining 0.126344828 0.2433283
## posi 发生时快感 pre_fu timefu 0.038965517 0.1770071
## posi 发生时快感 pre_fu grouptraining:timefu 0.305974677 0.2497825
## posi 发生时快感 post_fu (Intercept) 7.103448276 0.1588790
## posi 发生时快感 post_fu grouptraining 0.528885057 0.2228085
## posi 发生时快感 post_fu timefu 0.085172414 0.1847312
## posi 发生时快感 post_fu grouptraining:timefu -0.106331505 0.2605125
## posi 具体性 pre_post (Intercept) 2.931034483 0.2759811
## posi 具体性 pre_post grouptraining 0.168965517 0.3870301
## posi 具体性 pre_post timepost 0.586206897 0.2969236
## posi 具体性 pre_post grouptraining:timepost 1.313793103 0.4163994
## posi 具体性 pre_fu (Intercept) 2.931034483 0.2761323
## posi 具体性 pre_fu grouptraining 0.168965517 0.3872422
## posi 具体性 pre_fu timefu 0.965517241 0.3021992
## posi 具体性 pre_fu grouptraining:timefu 0.555482759 0.4237978
## posi 具体性 post_fu (Intercept) 3.517241379 0.2476500
## posi 具体性 post_fu grouptraining 1.482758621 0.3472991
## posi 具体性 post_fu timefu 0.379310345 0.2598981
## posi 具体性 post_fu grouptraining:timefu -0.758310345 0.3644757
## posi 可能性 pre_post (Intercept) 6.970344828 0.1562494
## posi 可能性 pre_post grouptraining -0.339344828 0.2191209
## posi 可能性 pre_post timepost -0.107931034 0.1912152
## posi 可能性 pre_post grouptraining:timepost 0.748597701 0.2681562
## posi 可能性 pre_fu (Intercept) 6.970344828 0.1569712
## posi 可能性 pre_fu grouptraining -0.339344828 0.2201331
## posi 可能性 pre_fu timefu -0.160000000 0.1851685
## posi 可能性 pre_fu grouptraining:timefu 0.778057181 0.2611075
## posi 可能性 post_fu (Intercept) 6.862413793 0.1490842
## posi 可能性 post_fu grouptraining 0.409252874 0.2090725
## posi 可能性 post_fu timefu -0.052068966 0.1768461
## posi 可能性 post_fu grouptraining:timefu 0.022722736 0.2493639
## posi 详细度 pre_post (Intercept) 5.729655172 0.2132435
## posi 详细度 pre_post grouptraining 0.119011494 0.2990482
## posi 详细度 pre_post timepost 0.166551724 0.2107245
## posi 详细度 pre_post grouptraining:timepost 0.322448276 0.2955155
## posi 详细度 pre_fu (Intercept) 5.729655172 0.2247681
## posi 详细度 pre_fu grouptraining 0.119011494 0.3152100
## posi 详细度 pre_fu timefu 0.245172414 0.1716116
## posi 详细度 pre_fu grouptraining:timefu 0.190842096 0.2424005
## posi 详细度 post_fu (Intercept) 5.896206897 0.2026103
## posi 详细度 post_fu grouptraining 0.441459770 0.2841364
## posi 详细度 post_fu timefu 0.078620690 0.1700191
## posi 详细度 post_fu grouptraining:timefu -0.148735903 0.2400904
## posi 想象时快感 pre_post (Intercept) 6.729655172 0.1519363
## posi 想象时快感 pre_post grouptraining 0.117011494 0.2130723
## posi 想象时快感 pre_post timepost 0.080689655 0.1876753
## posi 想象时快感 pre_post grouptraining:timepost 0.361977011 0.2631918
## posi 想象时快感 pre_fu (Intercept) 6.729655172 0.1835295
## posi 想象时快感 pre_fu grouptraining 0.117011494 0.2573778
## posi 想象时快感 pre_fu timefu 0.092758621 0.1887559
## posi 想象时快感 pre_fu grouptraining:timefu 0.380843553 0.2663523
## posi 想象时快感 post_fu (Intercept) 6.810344828 0.1668115
## posi 想象时快感 post_fu grouptraining 0.478988506 0.2339330
## posi 想象时快感 post_fu timefu 0.012068966 0.1948217
## posi 想象时快感 post_fu grouptraining:timefu 0.005080311 0.2747354
## posi 知觉控制 pre_post (Intercept) 6.660000000 0.1759823
## posi 知觉控制 pre_post grouptraining -0.499333333 0.2467939
## posi 知觉控制 pre_post timepost 0.092758621 0.1827225
## posi 知觉控制 pre_post grouptraining:timepost 0.782241379 0.2562461
## posi 知觉控制 pre_fu (Intercept) 6.660000000 0.1793498
## posi 知觉控制 pre_fu grouptraining -0.499333333 0.2515164
## posi 知觉控制 pre_fu timefu 0.046206897 0.1997563
## posi 知觉控制 pre_fu grouptraining:timefu 0.982737958 0.2817671
## posi 知觉控制 post_fu (Intercept) 6.752758621 0.1531167
## posi 知觉控制 post_fu grouptraining 0.282908046 0.2147276
## posi 知觉控制 post_fu timefu -0.046551724 0.1868175
## posi 知觉控制 post_fu grouptraining:timefu 0.190690100 0.2633792
## df t p d SE_d d_low
## 87.09942 34.46134714 1.561070e-52 9.600980702 0.2786014 9.047239098
## 87.09942 1.11636730 2.673353e-01 0.436170041 0.3907048 -0.340385518
## 57.00000 1.00231653 3.204269e-01 0.263221217 0.2626129 -0.262651976
## 57.00000 0.56323456 5.754842e-01 0.207429572 0.3682827 -0.530043914
## 88.83252 32.46574166 4.648583e-51 8.756877002 0.2697267 8.220921893
## 88.83252 1.05172013 2.957805e-01 0.397822630 0.3782590 -0.353789523
## 56.21384 0.12274754 9.027448e-01 0.032235084 0.2626129 -0.493797543
## 56.55519 0.62378731 5.352753e-01 0.231210819 0.3706565 -0.511142547
## 84.16124 38.91457187 1.397010e-55 11.304158350 0.2904865 10.726510228
## 84.16124 1.81050905 7.378559e-02 0.737550995 0.4073722 -0.072530562
## 56.27703 -0.99440746 3.242811e-01 -0.261144193 0.2626129 -0.787163837
## 56.58349 0.12091598 9.041860e-01 0.044830538 0.3707578 -0.697717546
## 99.77310 47.99338475 9.524995e-71 11.296724743 0.2353809 10.829722793
## 99.77310 0.60476740 5.467065e-01 0.199629591 0.3300932 -0.455284105
## 57.00000 -0.27800861 7.820117e-01 -0.073008639 0.2626129 -0.598881831
## 57.00000 1.72701203 8.958153e-02 0.636028741 0.3682827 -0.101444745
## 92.14060 41.20574595 3.733262e-61 10.607436689 0.2574262 10.096176522
## 92.14060 0.51923597 6.048412e-01 0.187448867 0.3610090 -0.529531619
## 56.21353 0.22013536 8.265632e-01 0.057810376 0.2626129 -0.468222316
## 56.57724 1.22496448 2.256642e-01 0.453952944 0.3705846 -0.288250065
## 102.50042 44.70980254 4.745642e-69 10.098226012 0.2258616 9.650256918
## 102.50042 2.37371978 1.947272e-02 0.751860313 0.3167435 0.123637894
## 56.53632 0.46106146 6.465234e-01 0.121080671 0.2626129 -0.404886009
## 56.96047 -0.40816284 6.846844e-01 -0.151160327 0.3703432 -0.892770921
## 96.82016 10.62041569 6.300729e-18 2.592341381 0.2440904 2.107878165
## 96.82016 0.43656940 6.633957e-01 0.149440856 0.3423072 -0.529960151
## 57.00000 1.97426838 5.320382e-02 0.518468276 0.2626129 -0.007404917
## 57.00000 3.15512714 2.561600e-03 1.161978901 0.3682827 0.424505415
## 98.19826 10.61460113 5.506083e-18 2.547085829 0.2399606 2.070903886
## 98.19826 0.43633038 6.635549e-01 0.146832007 0.3365157 -0.520955518
## 57.00000 3.19496928 2.280038e-03 0.839040038 0.2626129 0.313166845
## 57.00000 1.31072583 1.952070e-01 0.482717713 0.3682827 -0.254755773
## 94.85076 14.20246882 3.272323e-25 3.553980280 0.2502368 3.057187349
## 94.85076 4.26939906 4.648586e-05 1.498246588 0.3509268 0.801554650
## 57.00000 1.45945775 1.499286e-01 0.383272383 0.2626129 -0.142600810
## 57.00000 -2.08055137 4.198049e-02 -0.766231180 0.3682827 -1.503704666
## 107.23447 44.61036618 4.564472e-71 9.572994938 0.2145913 9.147603418
## 107.23447 -1.54866463 1.244087e-01 -0.466052455 0.3009383 -1.062612559
## 57.00000 -0.56444796 5.746639e-01 -0.148231296 0.2626129 -0.674104489
## 57.00000 2.79164813 7.123765e-03 1.028115851 0.3682827 0.290642365
## 103.55280 44.40525167 3.123705e-69 9.885602609 0.2226224 9.444111645
## 103.55280 -1.54154401 1.262349e-01 -0.481271472 0.3122009 -1.100409098
## 56.25364 -0.86407784 3.912147e-01 -0.226917958 0.2626129 -0.752942404
## 56.68387 2.97983421 4.241489e-03 1.103469669 0.3703124 0.361842164
## 104.08527 46.03045714 5.131623e-71 10.190546259 0.2213870 9.751531701
## 104.08527 1.95746833 5.297012e-02 0.607732274 0.3104685 -0.007932493
## 56.57642 -0.29443099 7.695062e-01 -0.077321365 0.2626129 -0.603279898
## 57.00892 0.09112280 9.277144e-01 0.033742805 0.3703004 -0.707768348
## 90.34131 26.86907133 7.293989e-45 7.140515322 0.2657522 6.612579328
## 90.34131 0.39796759 6.915930e-01 0.148316674 0.3726853 -0.592049641
## 57.00000 0.79037684 4.325838e-01 0.207563126 0.2626129 -0.318310067
## 57.00000 1.09113818 2.798030e-01 0.401847369 0.3682827 -0.335626117
## 75.58070 25.49140760 7.383951e-39 8.767945161 0.3439569 8.082834188
## 75.58070 0.37756251 7.068138e-01 0.182120254 0.4823579 -0.778664940
## 56.03698 1.42864703 1.586563e-01 0.375181091 0.2626129 -0.150888034
## 56.27103 0.78730062 4.344096e-01 0.292040791 0.3709394 -0.450961075
## 80.07406 29.10122040 2.431646e-44 9.107327451 0.3129535 8.484539072
## 80.07406 1.55368954 1.242022e-01 0.681882226 0.4388793 -0.191503076
## 56.25986 0.46242264 6.455618e-01 0.121438134 0.2626129 -0.404585034
## 56.53294 -0.61949970 5.380761e-01 -0.229738643 0.3708454 -0.972476824
## 107.93185 44.29260087 4.675322e-71 9.416765987 0.2126036 8.995345773
## 107.93185 0.54916332 5.840278e-01 0.163733480 0.2981508 -0.427257350
## 57.00000 0.42994295 6.688582e-01 0.112908549 0.2626129 -0.412964644
## 57.00000 1.37533527 1.744090e-01 0.506512254 0.3682827 -0.230961232
## 92.78265 36.66798385 5.265744e-57 9.362852585 0.2553414 8.855779610
## 92.78265 0.45462924 6.504379e-01 0.162796064 0.3580853 -0.548312393
## 56.28309 0.49142094 6.250397e-01 0.129053461 0.2626129 -0.396964940
## 56.65071 1.42984890 1.582525e-01 0.529861034 0.3705713 -0.212294459
## 102.84712 40.82658651 2.309915e-65 9.180108987 0.2248561 8.734151972
## 102.84712 2.04754607 4.315331e-02 0.645659919 0.3153335 0.020259196
## 56.51695 0.06194878 9.508222e-01 0.016268548 0.2626129 -0.509702073
## 56.94299 0.01849165 9.853113e-01 0.006848083 0.3703338 -0.734748620
## 94.02138 37.84470702 1.061889e-58 9.571903484 0.2529258 9.069714831
## 94.02138 -2.02328051 4.588329e-02 -0.717653224 0.3546978 -1.421912010
## 57.00000 0.50764763 6.136592e-01 0.133314799 0.2626129 -0.392558393
## 57.00000 3.05269552 3.442398e-03 1.124255103 0.3682827 0.386781617
## 98.92545 37.13412670 6.799362e-60 8.755676358 0.2357852 8.287823066
## 98.92545 -1.98529096 4.988060e-02 -0.656456616 0.3306602 -1.312564215
## 56.18952 0.23131632 8.179093e-01 0.060746641 0.2626129 -0.465290990
## 56.59464 3.48776730 9.495733e-04 1.291972298 0.3704296 0.550084628
## 106.23075 44.10205081 4.030629e-70 9.492480091 0.2152390 9.065758594
## 106.23075 1.31752080 1.904986e-01 0.397689173 0.3018466 -0.200736062
## 56.59213 -0.24918290 8.041215e-01 -0.065438636 0.2626129 -0.591393982
## 57.03565 0.72401357 4.720168e-01 0.268056668 0.3702371 -0.473320248
## d_high
## 10.1547223057
## 1.2127255990
## 0.7890944096
## 0.9449030581
## 9.2928321109
## 1.1494347834
## 0.5582677109
## 0.9735641849
## 11.8818064722
## 1.5476325512
## 0.2648754507
## 0.7873786214
## 11.7637266939
## 0.8545432877
## 0.4528645540
## 1.3735022266
## 11.1186968553
## 0.9044293526
## 0.5838430689
## 1.1961559541
## 10.5461951066
## 1.3800827326
## 0.6470473514
## 0.5904502662
## 3.0768045967
## 0.8288418632
## 1.0443414689
## 1.8994523872
## 3.0232677723
## 0.8146195309
## 1.3649132306
## 1.2201911992
## 4.0507732097
## 2.1949385269
## 0.9091455758
## -0.0287576937
## 9.9983864570
## 0.1305076497
## 0.3776418969
## 1.7655893369
## 10.3270935736
## 0.1378661550
## 0.2991064876
## 1.8450971736
## 10.6295608173
## 1.2233970407
## 0.4486371682
## 0.7752539568
## 7.6684513159
## 0.8886829890
## 0.7334363186
## 1.1393208545
## 9.4530561340
## 1.1429054483
## 0.9012502166
## 1.0350426570
## 9.7301158308
## 1.5552675280
## 0.6474613029
## 0.5129995382
## 9.8381862020
## 0.7547243100
## 0.6387817417
## 1.2439857401
## 9.8699255606
## 0.8739045205
## 0.6550718618
## 1.2720165259
## 9.6260660026
## 1.2710606411
## 0.5422391681
## 0.7484447859
## 10.0740921361
## -0.0133944386
## 0.6591879922
## 1.8617285886
## 9.2235296495
## -0.0003490168
## 0.5867842733
## 2.0338599693
## 9.9192015865
## 0.9961144089
## 0.4605167093
## 1.0094335843print(head(within_posi_tbl, 72), row.names = FALSE)## cond metric window group contrast estimate SE
## posi 表象清晰度 pre_post control post_minus_pre 0.17206897 0.1716713
## posi 表象清晰度 pre_post training post_minus_pre 0.30766667 0.1687858
## posi 表象清晰度 pre_fu control fu_minus_pre 0.02310345 0.1882192
## posi 表象清晰度 pre_fu training fu_minus_pre 0.18881629 0.1875362
## posi 发生时快感 pre_post control post_minus_pre -0.04620690 0.1662067
## posi 发生时快感 pre_post training post_minus_pre 0.35633333 0.1634131
## posi 发生时快感 pre_fu control fu_minus_pre 0.03896552 0.1770071
## posi 发生时快感 pre_fu training fu_minus_pre 0.34494019 0.1762980
## posi 具体性 pre_post control post_minus_pre 0.58620690 0.2969236
## posi 具体性 pre_post training post_minus_pre 1.90000000 0.2919329
## posi 具体性 pre_fu control fu_minus_pre 0.96551724 0.3021992
## posi 具体性 pre_fu training fu_minus_pre 1.52100000 0.2971199
## posi 可能性 pre_post control post_minus_pre -0.10793103 0.1912152
## posi 可能性 pre_post training post_minus_pre 0.64066667 0.1880013
## posi 可能性 pre_fu control fu_minus_pre -0.16000000 0.1851685
## posi 可能性 pre_fu training fu_minus_pre 0.61805718 0.1841573
## posi 详细度 pre_post control post_minus_pre 0.16655172 0.2107245
## posi 详细度 pre_post training post_minus_pre 0.48900000 0.2071826
## posi 详细度 pre_fu control fu_minus_pre 0.24517241 0.1716116
## posi 详细度 pre_fu training fu_minus_pre 0.43601451 0.1712376
## posi 想象时快感 pre_post control post_minus_pre 0.08068966 0.1876753
## posi 想象时快感 pre_post training post_minus_pre 0.44266667 0.1845208
## posi 想象时快感 pre_fu control fu_minus_pre 0.09275862 0.1887559
## posi 想象时快感 pre_fu training fu_minus_pre 0.47360217 0.1879866
## posi 知觉控制 pre_post control post_minus_pre 0.09275862 0.1827225
## posi 知觉控制 pre_post training post_minus_pre 0.87500000 0.1796513
## posi 知觉控制 pre_fu control fu_minus_pre 0.04620690 0.1997563
## posi 知觉控制 pre_fu training fu_minus_pre 1.02894485 0.1987916
## df t.ratio p.value d SE_d d_low d_high
## 57.00000 1.0023165 3.204269e-01 0.26322122 0.2626129 -0.262651976 0.7890944
## 57.00000 1.8228227 7.357637e-02 0.47065079 0.2581989 -0.046383569 0.9876851
## 56.05558 0.1227475 9.027460e-01 0.03223508 0.2626129 -0.493830191 0.5583004
## 56.74309 1.0068255 3.182929e-01 0.26344590 0.2616599 -0.260570493 0.7874623
## 57.00000 -0.2780086 7.820117e-01 -0.07300864 0.2626129 -0.598881832 0.4528646
## 57.00000 2.1805675 3.335787e-02 0.56302010 0.2581989 0.045985744 1.0800545
## 56.06704 0.2201354 8.265653e-01 0.05781038 0.2626129 -0.468252529 0.5838733
## 56.80003 1.9565745 5.532023e-02 0.51176332 0.2615609 -0.012043234 1.0355699
## 57.00000 1.9742684 5.320382e-02 0.51846828 0.2626129 -0.007404917 1.0443415
## 57.00000 6.5083439 2.088828e-08 1.68044718 0.2581989 1.163412819 2.1974815
## 57.00000 3.1949693 2.280038e-03 0.83904004 0.2626129 0.313166845 1.3649132
## 57.00000 5.1191458 3.769345e-06 1.32175775 0.2581989 0.804723393 1.8387921
## 57.00000 -0.5644480 5.746639e-01 -0.14823130 0.2626129 -0.674104489 0.3776419
## 57.00000 3.4077782 1.207626e-03 0.87988455 0.2581989 0.362850197 1.3969189
## 56.12011 -0.8640778 3.912234e-01 -0.22691796 0.2626129 -0.752969899 0.2991340
## 56.98895 3.3561374 1.412041e-03 0.87655171 0.2611787 0.353548129 1.3995553
## 57.00000 0.7903768 4.325838e-01 0.20756313 0.2626129 -0.318310067 0.7334363
## 57.00000 2.3602367 2.170880e-02 0.60941049 0.2581989 0.092376136 1.1264449
## 56.02097 1.4286470 1.586579e-01 0.37518109 0.2626129 -0.150891350 0.9012535
## 56.49119 2.5462540 1.364119e-02 0.66722188 0.2620406 0.142392222 1.1920515
## 57.00000 0.4299429 6.688582e-01 0.11290855 0.2626129 -0.412964644 0.6387817
## 57.00000 2.3990065 1.973289e-02 0.61942080 0.2581989 0.102386445 1.1364552
## 56.06926 0.4914209 6.250470e-01 0.12905346 0.2626129 -0.397008985 0.6551159
## 56.81020 2.5193397 1.459562e-02 0.65891449 0.2615425 0.135146666 1.1826823
## 57.00000 0.5076476 6.136592e-01 0.13331480 0.2626129 -0.392558393 0.6591880
## 57.00000 4.8705473 9.224219e-06 1.25756990 0.2581989 0.740535544 1.7746043
## 56.09537 0.2313163 8.179108e-01 0.06074664 0.2626129 -0.465310408 0.5868037
## 56.91280 5.1759976 3.075220e-06 1.35271894 0.2613446 0.829368055 1.8760698# neg条件 同上
metrics_neg <- dat_long %>%
filter(cond == "neg") %>%
distinct(metric) %>% pull() %>% as.character()
neg_fixed <- list()
neg_within <- list()
for (m in metrics_neg) {
d_m <- dat_long %>% filter(cond == "neg", metric == m)
for (wname in names(win_list)) {
win <- win_list[[wname]]
dsub <- d_m %>%
filter(time %in% win) %>%
droplevels() %>%
mutate(
time = fct_relevel(time, win),
group = fct_relevel(group, "control","training")
)
if (nrow(dsub) == 0) next
# 1) LMM
fit <- lmer(score ~ group * time + (1 | ID), data = dsub, REML = TRUE)
# 2) 固定效应 d
sig <- sigma(fit)
co <- as.data.frame(coef(summary(fit)))
fx <- tibble(
cond = "neg", metric = m, window = wname,
term = rownames(co),
estimate = co[, "Estimate"],
std.error = co[, "Std. Error"],
df = co[, "df"],
t = co[, "t value"],
p = co[, "Pr(>|t|)"],
d = estimate / sig,
SE_d = std.error / sig
) %>%
mutate(d_low = d + qt(0.025, df) * SE_d,
d_high= d + qt(0.975, df) * SE_d)
neg_fixed[[length(neg_fixed)+1]] <- fx
# 3) 组内简单效应 d（pre_post / pre_fu）
if (wname != "post_fu") {
emm <- emmeans(fit, ~ time | group)
con <- if (wname == "pre_post") list(post_minus_pre = c(-1, 1))
else list(fu_minus_pre = c(-1, 1))
w <- summary(contrast(emm, con, by = "group", adjust = "none"))
wx <- as.data.frame(w) %>%
mutate(
cond = "neg", metric = m, window = wname,
d = estimate / sig,
SE_d = SE / sig,
d_low = d + qt(0.025, df) * SE_d,
d_high = d + qt(0.975, df) * SE_d
) %>%
select(cond, metric, window, group, contrast, estimate, SE, df, t.ratio, p.value,
d, SE_d, d_low, d_high)
neg_within[[length(neg_within)+1]] <- wx
}
}
}
fixed_neg_tbl <- bind_rows(neg_fixed)
within_neg_tbl <- bind_rows(neg_within)## # A tibble: 84 × 13
## cond metric window term estimate std.error df t p d
## <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 neg 表象… pre_p… (Int… 6.34 0.199 89.9 31.8 1.11e-50 8.49
## 2 neg 表象… pre_p… grou… 0.245 0.280 89.9 0.877 3.83e- 1 0.329
## 3 neg 表象… pre_p… time… 0.0407 0.196 57.0 0.208 8.36e- 1 0.0545
## 4 neg 表象… pre_p… grou… -0.232 0.275 57.0 -0.843 4.03e- 1 -0.310
## 5 neg 表象… pre_fu (Int… 6.34 0.202 94.7 31.3 8.53e-52 7.79
## 6 neg 表象… pre_fu grou… 0.245 0.284 94.7 0.865 3.89e- 1 0.302
## 7 neg 表象… pre_fu time… -0.0734 0.214 56.2 -0.344 7.32e- 1 -0.0903
## 8 neg 表象… pre_fu grou… -0.0300 0.301 56.6 -0.0997 9.21e- 1 -0.0369
## 9 neg 表象… post_… (Int… 6.38 0.188 70.0 34.0 3.20e-45 13.8
## 10 neg 表象… post_… grou… 0.0137 0.263 70.0 0.0521 9.59e- 1 0.0297
## 11 neg 表象… post_… time… -0.114 0.121 56.1 -0.940 3.51e- 1 -0.247
## 12 neg 表象… post_… grou… 0.204 0.171 56.3 1.19 2.39e- 1 0.442
## 13 neg 发生… pre_p… (Int… 2.52 0.205 112. 12.3 1.98e-22 2.45
## 14 neg 发生… pre_p… grou… -0.401 0.288 112. -1.39 1.67e- 1 -0.390
## 15 neg 发生… pre_p… time… 0.490 0.269 57.0 1.82 7.45e- 2 0.477
## 16 neg 发生… pre_p… grou… 0.988 0.378 57.0 2.61 1.14e- 2 0.962
## 17 neg 发生… pre_fu (Int… 2.52 0.207 107. 12.2 6.68e-22 2.58
## 18 neg 发生… pre_fu grou… -0.401 0.290 107. -1.38 1.71e- 1 -0.410
## 19 neg 发生… pre_fu time… 0.810 0.256 56.6 3.16 2.52e- 3 0.830
## 20 neg 发生… pre_fu grou… 0.704 0.361 57.1 1.95 5.62e- 2 0.722
## 21 neg 发生… post_… (Int… 3.01 0.242 83.3 12.4 1.30e-20 3.65
## 22 neg 发生… post_… grou… 0.587 0.340 83.3 1.73 8.80e- 2 0.714
## 23 neg 发生… post_… time… 0.321 0.216 56.3 1.48 1.43e- 1 0.390
## 24 neg 发生… post_… grou… -0.270 0.305 56.6 -0.886 3.79e- 1 -0.329
## 25 neg 具体性 pre_p… (Int… 1.93 0.262 105. 7.37 4.07e-11 1.63
## 26 neg 具体性 pre_p… grou… 0.269 0.367 105. 0.732 4.66e- 1 0.228
## 27 neg 具体性 pre_p… time… 0.276 0.310 57.0 0.889 3.78e- 1 0.233
## 28 neg 具体性 pre_p… grou… 1.96 0.435 57.0 4.50 3.42e- 5 1.66
## 29 neg 具体性 pre_fu (Int… 1.93 0.301 109. 6.41 3.80e- 9 1.34
## 30 neg 具体性 pre_fu grou… 0.269 0.422 109. 0.637 5.26e- 1 0.186
## 31 neg 具体性 pre_fu time… 0.552 0.379 57.0 1.46 1.51e- 1 0.382
## 32 neg 具体性 pre_fu grou… 1.42 0.532 57.0 2.67 9.78e- 3 0.985
## 33 neg 具体性 post_… (Int… 2.21 0.279 101. 7.91 3.48e-12 1.84
## 34 neg 具体性 post_… grou… 2.23 0.391 101. 5.69 1.26e- 7 1.86
## 35 neg 具体性 post_… time… 0.276 0.314 57.0 0.877 3.84e- 1 0.230
## 36 neg 具体性 post_… grou… -0.537 0.441 57.0 -1.22 2.29e- 1 -0.448
## 37 neg 可能性 pre_p… (Int… 6.83 0.193 92.9 35.4 1.10e-55 9.08
## 38 neg 可能性 pre_p… grou… -0.328 0.271 92.9 -1.21 2.29e- 1 -0.436
## 39 neg 可能性 pre_p… time… -0.156 0.198 57.0 -0.787 4.34e- 1 -0.207
## 40 neg 可能性 pre_p… grou… 0.200 0.277 57.0 0.722 4.74e- 1 0.266
## 41 neg 可能性 pre_fu (Int… 6.83 0.179 85.0 38.1 3.22e-55 10.9
## 42 neg 可能性 pre_fu grou… -0.328 0.251 85.0 -1.30 1.96e- 1 -0.521
## 43 neg 可能性 pre_fu time… -0.333 0.165 55.7 -2.02 4.83e- 2 -0.530
## 44 neg 可能性 pre_fu grou… 0.520 0.233 56.0 2.23 2.97e- 2 0.827
## 45 neg 可能性 post_… (Int… 6.67 0.201 78.6 33.2 4.81e-48 10.7
## 46 neg 可能性 post_… grou… -0.128 0.282 78.6 -0.454 6.51e- 1 -0.205
## 47 neg 可能性 post_… time… -0.178 0.163 56.3 -1.09 2.81e- 1 -0.286
## 48 neg 可能性 post_… grou… 0.289 0.231 56.6 1.25 2.16e- 1 0.465
## 49 neg 详细度 pre_p… (Int… 5.83 0.222 82.5 26.2 8.21e-42 7.88
## 50 neg 详细度 pre_p… grou… 0.161 0.312 82.5 0.517 6.06e- 1 0.218
## 51 neg 详细度 pre_p… time… 0.143 0.194 57.0 0.734 4.66e- 1 0.193
## 52 neg 详细度 pre_p… grou… -0.235 0.273 57.0 -0.864 3.91e- 1 -0.318
## 53 neg 详细度 pre_fu (Int… 5.83 0.223 83.8 26.2 4.31e-42 7.63
## 54 neg 详细度 pre_fu grou… 0.161 0.313 83.8 0.516 6.07e- 1 0.211
## 55 neg 详细度 pre_fu time… 0.0686 0.201 56.0 0.342 7.34e- 1 0.0898
## 56 neg 详细度 pre_fu grou… 0.0229 0.283 56.3 0.0807 9.36e- 1 0.0299
## 57 neg 详细度 post_… (Int… 5.98 0.225 67.7 26.5 1.68e-37 11.8
## 58 neg 详细度 post_… grou… -0.0742 0.316 67.7 -0.235 8.15e- 1 -0.146
## 59 neg 详细度 post_… time… -0.0741 0.133 56.1 -0.557 5.80e- 1 -0.146
## 60 neg 详细度 post_… grou… 0.251 0.188 56.3 1.34 1.87e- 1 0.495
## 61 neg 想象… pre_p… (Int… 2.91 0.198 107. 14.7 2.04e-27 3.15
## 62 neg 想象… pre_p… grou… -0.335 0.278 107. -1.20 2.32e- 1 -0.362
## 63 neg 想象… pre_p… time… 0.548 0.243 57.0 2.25 2.81e- 2 0.592
## 64 neg 想象… pre_p… grou… 0.968 0.341 57.0 2.84 6.24e- 3 1.05
## 65 neg 想象… pre_fu (Int… 2.91 0.212 106. 13.7 2.73e-25 2.97
## 66 neg 想象… pre_fu grou… -0.335 0.297 106. -1.13 2.62e- 1 -0.342
## 67 neg 想象… pre_fu time… 0.811 0.257 56.5 3.15 2.59e- 3 0.828
## 68 neg 想象… pre_fu grou… 0.842 0.363 57.0 2.32 2.38e- 2 0.860
## 69 neg 想象… post_… (Int… 3.46 0.230 76.7 15.0 1.45e-24 5.06
## 70 neg 想象… post_… grou… 0.634 0.323 76.7 1.96 5.33e- 2 0.927
## 71 neg 想象… post_… time… 0.263 0.180 56.3 1.47 1.48e- 1 0.385
## 72 neg 想象… post_… grou… -0.129 0.254 56.5 -0.507 6.14e- 1 -0.188
## 73 neg 知觉… pre_p… (Int… 6.25 0.207 87.6 30.2 4.18e-48 8.36
## 74 neg 知觉… pre_p… grou… -0.297 0.290 87.6 -1.02 3.09e- 1 -0.397
## 75 neg 知觉… pre_p… time… -0.0797 0.196 57.0 -0.406 6.86e- 1 -0.107
## 76 neg 知觉… pre_p… grou… 0.300 0.275 57.0 1.09 2.80e- 1 0.401
## 77 neg 知觉… pre_fu (Int… 6.25 0.226 94.3 27.7 4.48e-47 6.90
## 78 neg 知觉… pre_fu grou… -0.297 0.317 94.3 -0.938 3.51e- 1 -0.328
## 79 neg 知觉… pre_fu time… -0.0579 0.238 55.6 -0.243 8.09e- 1 -0.0639
## 80 neg 知觉… pre_fu grou… 0.369 0.336 56.0 1.10 2.76e- 1 0.407
## 81 neg 知觉… post_… (Int… 6.17 0.220 75.2 28.0 1.57e-41 9.75
## 82 neg 知觉… post_… grou… 0.00289 0.309 75.2 0.00933 9.93e- 1 0.00456
## 83 neg 知觉… post_… time… 0.0217 0.166 56.2 0.131 8.97e- 1 0.0343
## 84 neg 知觉… post_… grou… 0.0591 0.235 56.4 0.252 8.02e- 1 0.0934
## # ℹ 3 more variables: SE_d <dbl>, d_low <dbl>, d_high <dbl>## # A tibble: 28 × 14
## cond metric window group contrast estimate SE df t.ratio p.value
## <chr> <chr> <chr> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 neg 表象清晰度 pre_po… cont… post_mi… 0.0407 0.196 57.0 0.208 8.36e- 1
## 2 neg 表象清晰度 pre_po… trai… post_mi… -0.191 0.193 57 -0.991 3.26e- 1
## 3 neg 表象清晰度 pre_fu cont… fu_minu… -0.0734 0.214 56.1 -0.344 7.32e- 1
## 4 neg 表象清晰度 pre_fu trai… fu_minu… -0.103 0.213 56.8 -0.487 6.28e- 1
## 5 neg 发生时快感 pre_po… cont… post_mi… 0.490 0.269 57 1.82 7.45e- 2
## 6 neg 发生时快感 pre_po… trai… post_mi… 1.48 0.265 57 5.58 7.05e- 7
## 7 neg 发生时快感 pre_fu cont… fu_minu… 0.810 0.256 56.1 3.16 2.53e- 3
## 8 neg 发生时快感 pre_fu trai… fu_minu… 1.51 0.255 57.1 5.95 1.76e- 7
## 9 neg 具体性 pre_po… cont… post_mi… 0.276 0.310 57 0.889 3.78e- 1
## 10 neg 具体性 pre_po… trai… post_mi… 2.23 0.305 57 7.32 9.30e-10
## 11 neg 具体性 pre_fu cont… fu_minu… 0.552 0.379 57.0 1.46 1.51e- 1
## 12 neg 具体性 pre_fu trai… fu_minu… 1.97 0.373 57 5.29 1.99e- 6
## 13 neg 可能性 pre_po… cont… post_mi… -0.156 0.198 57.0 -0.787 4.34e- 1
## 14 neg 可能性 pre_po… trai… post_mi… 0.0443 0.194 57 0.228 8.20e- 1
## 15 neg 可能性 pre_fu cont… fu_minu… -0.333 0.165 56.0 -2.02 4.83e- 2
## 16 neg 可能性 pre_fu trai… fu_minu… 0.187 0.165 56.7 1.13 2.62e- 1
## 17 neg 详细度 pre_po… cont… post_mi… 0.143 0.194 57.0 0.734 4.66e- 1
## 18 neg 详细度 pre_po… trai… post_mi… -0.0927 0.191 57 -0.485 6.30e- 1
## 19 neg 详细度 pre_fu cont… fu_minu… 0.0686 0.201 56.0 0.342 7.34e- 1
## 20 neg 详细度 pre_fu trai… fu_minu… 0.0915 0.200 56.7 0.457 6.49e- 1
## 21 neg 想象时快感 pre_po… cont… post_mi… 0.548 0.243 57 2.25 2.81e- 2
## 22 neg 想象时快感 pre_po… trai… post_mi… 1.52 0.239 57 6.34 3.91e- 8
## 23 neg 想象时快感 pre_fu cont… fu_minu… 0.811 0.257 56.1 3.15 2.60e- 3
## 24 neg 想象时快感 pre_fu trai… fu_minu… 1.65 0.256 57.0 6.46 2.47e- 8
## 25 neg 知觉控制 pre_po… cont… post_mi… -0.0797 0.196 57.0 -0.406 6.86e- 1
## 26 neg 知觉控制 pre_po… trai… post_mi… 0.220 0.193 57.0 1.14 2.58e- 1
## 27 neg 知觉控制 pre_fu cont… fu_minu… -0.0579 0.238 56.1 -0.243 8.09e- 1
## 28 neg 知觉控制 pre_fu trai… fu_minu… 0.311 0.237 56.8 1.31 1.94e- 1
## # ℹ 4 more variables: d <dbl>, SE_d <dbl>, d_low <dbl>, d_high <dbl>