
R version 4.0.5 (2021-03-31) -- "Shake and Throw"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin17.0 (64-bit)

R ist freie Software und kommt OHNE JEGLICHE GARANTIE.
Sie sind eingeladen, es unter bestimmten Bedingungen weiter zu verbreiten.
Tippen Sie 'license()' or 'licence()' für Details dazu.

R ist ein Gemeinschaftsprojekt mit vielen Beitragenden.
Tippen Sie 'contributors()' für mehr Information und 'citation()',
um zu erfahren, wie R oder R packages in Publikationen zitiert werden können.

Tippen Sie 'demo()' für einige Demos, 'help()' für on-line Hilfe, oder
'help.start()' für eine HTML Browserschnittstelle zur Hilfe.
Tippen Sie 'q()', um R zu verlassen.

[R.app GUI 1.74 (7950) x86_64-apple-darwin17.0]

[Verlauf wiederhergestellt aus /Users/estherkaufmann/.Rapp.history]

> library(metafor)
Lade nötiges Paket: Matrix
Lade nötiges Paket: metadat
Lade nötiges Paket: numDeriv

Loading the 'metafor' package (version 4.0-0). For an
introduction to the package please type: help(metafor)

>  dat <- read.table("mode27.txt", header=T, na.strings = "-99")
> attach(dat)
> dat <- escalc(measure="PFT", xi=xi, ni=ni, data=dat, slab=paste(Authors, pubyear, sep=", "))
> res <- rma(yi, vi, method="HS", data=dat)
> res

Random-Effects Model (k = 64; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0304 (SE = 0.0126)
tau (square root of estimated tau^2 value):      0.1745
I^2 (total heterogeneity / total variability):   99.86%
H^2 (total variability / sampling variability):  703.23

Test for Heterogeneity:
Q(df = 63) = 60922.7440, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5496  0.0220  25.0318  <.0001  0.5065  0.5926  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=dat$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2724 0.2349 0.3116 0.0406 0.6082 

> data <- subset(dat, expert == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 16; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0182 (SE = 0.0095)
tau (square root of estimated tau^2 value):      0.1350
I^2 (total heterogeneity / total variability):   98.42%
H^2 (total variability / sampling variability):  63.44

Test for Heterogeneity:
Q(df = 15) = 1198.2306, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5975  0.0347  17.2305  <.0001  0.5295  0.6654  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3157 0.2541 0.3807 0.0998 0.5853 

> data <- subset(dat, samplegp == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 31; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0403 (SE = 0.0161)
tau (square root of estimated tau^2 value):      0.2006
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  950.63

Test for Heterogeneity:
Q(df = 30) = 31680.5157, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5110  0.0361  14.1488  <.0001  0.4403  0.5818  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2390 0.1814 0.3019 0.0120 0.6240 

> data <- subset(dat, countryAm == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 23; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0283 (SE = 0.0112)
tau (square root of estimated tau^2 value):      0.1683
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  890.62

Test for Heterogeneity:
Q(df = 22) = 40642.0549, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5203  0.0354  14.7045  <.0001  0.4509  0.5896  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2466 0.1893 0.3088 0.0323 0.5718 

> data <- subset(dat, health == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 41; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0374 (SE = 0.0137)
tau (square root of estimated tau^2 value):      0.1934
I^2 (total heterogeneity / total variability):   99.80%
H^2 (total variability / sampling variability):  490.42

Test for Heterogeneity:
Q(df = 40) = 21332.0847, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5349  0.0304  17.5919  <.0001  0.4753  0.5944  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2593 0.2088 0.3133 0.0217 0.6319 

> data <- subset(dat, method == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 13; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0231 (SE = 0.0083)
tau (square root of estimated tau^2 value):      0.1519
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  902.17

Test for Heterogeneity:
Q(df = 12) = 29740.5709, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5861  0.0423  13.8512  <.0001  0.5032  0.6691  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3058 0.2323 0.3846 0.0745 0.6090 

> data <- subset(dat, fieldp056 == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 34; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0294 (SE = 0.0121)
tau (square root of estimated tau^2 value):      0.1716
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  904.76

Test for Heterogeneity:
Q(df = 33) = 48901.8539, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5269  0.0296  17.7872  <.0001  0.4688  0.5849  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2524 0.2037 0.3045 0.0333 0.5824 

> data <- subset(dat, intRem014 == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 33; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0274 (SE = 0.0110)
tau (square root of estimated tau^2 value):      0.1657
I^2 (total heterogeneity / total variability):   99.86%
H^2 (total variability / sampling variability):  717.40

Test for Heterogeneity:
Q(df = 32) = 42079.3975, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4935  0.0290  16.9995  <.0001  0.4366  0.5504  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2240 0.1783 0.2732 0.0259 0.5378 

> data <- subset(dat, lenghtshort == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 18; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0173 (SE = 0.0094)
tau (square root of estimated tau^2 value):      0.1315
I^2 (total heterogeneity / total variability):   99.43%
H^2 (total variability / sampling variability):  176.79

Test for Heterogeneity:
Q(df = 17) = 4035.7797, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5464  0.0314  17.3980  <.0001  0.4848  0.6079  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2695 0.2167 0.3259 0.0763 0.5260 

> data <- subset(dat, lenghtmedmore == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 20; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0072 (SE = 0.0021)
tau (square root of estimated tau^2 value):      0.0849
I^2 (total heterogeneity / total variability):   99.45%
H^2 (total variability / sampling variability):  180.82

Test for Heterogeneity:
Q(df = 19) = 9259.6895, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5504  0.0192  28.6436  <.0001  0.5127  0.5881  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2734 0.2405 0.3076 0.1372 0.4357 

> data <- subset(dat, rr050 == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 29; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0277 (SE = 0.0115)
tau (square root of estimated tau^2 value):      0.1665
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  916.62

Test for Heterogeneity:
Q(df = 28) = 44318.7252, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5335  0.0312  17.1093  <.0001  0.4724  0.5946  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2581 0.2065 0.3135 0.0392 0.5799 

> data <- subset(dat, rr050 == 0)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 32; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0250 (SE = 0.0101)
tau (square root of estimated tau^2 value):      0.1581
I^2 (total heterogeneity / total variability):   99.63%
H^2 (total variability / sampling variability):  268.81

Test for Heterogeneity:
Q(df = 31) = 9389.0366, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5551  0.0281  19.7203  <.0001  0.4999  0.6102  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2774 0.2294 0.3281 0.0560 0.5841 

> data <- subset(dat, fieldp056 == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 34; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0294 (SE = 0.0121)
tau (square root of estimated tau^2 value):      0.1716
I^2 (total heterogeneity / total variability):   99.89%
H^2 (total variability / sampling variability):  904.76

Test for Heterogeneity:
Q(df = 33) = 48901.8539, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5269  0.0296  17.7872  <.0001  0.4688  0.5849  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2524 0.2037 0.3045 0.0333 0.5824 

> data <- subset(dat, fieldp57more == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 23; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0179 (SE = 0.0074)
tau (square root of estimated tau^2 value):      0.1338
I^2 (total heterogeneity / total variability):   99.29%
H^2 (total variability / sampling variability):  140.92

Test for Heterogeneity:
Q(df = 22) = 3786.8921, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5378  0.0282  19.0836  <.0001  0.4826  0.5930  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2620 0.2149 0.3120 0.0704 0.5204 

> data <- subset(dat, reminder12 == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 38; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0369 (SE = 0.0146)
tau (square root of estimated tau^2 value):      0.1921
I^2 (total heterogeneity / total variability):   99.86%
H^2 (total variability / sampling variability):  724.65

Test for Heterogeneity:
Q(df = 37) = 29705.7044, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5586  0.0314  17.8015  <.0001  0.4971  0.6201  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2804 0.2268 0.3373 0.0300 0.6525 

> data <- subset(dat, reminder3more == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 23; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0436 (SE = 0.0145)
tau (square root of estimated tau^2 value):      0.2088
I^2 (total heterogeneity / total variability):   99.65%
H^2 (total variability / sampling variability):  284.68

Test for Heterogeneity:
Q(df = 22) = 6664.8516, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5312  0.0437  12.1646  <.0001  0.4456  0.6168  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2563 0.1854 0.3344 0.0122 0.6610 

> data <- subset(dat, personalized == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 19; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0423 (SE = 0.0187)
tau (square root of estimated tau^2 value):      0.2057
I^2 (total heterogeneity / total variability):   99.79%
H^2 (total variability / sampling variability):  486.60

Test for Heterogeneity:
Q(df = 18) = 10589.9361, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5488  0.0473  11.5941  <.0001  0.4560  0.6415  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2719 0.1936 0.3580 0.0176 0.6736 

> data <- subset(dat, incentive == 1)
> res <- rma(yi, vi, method="HS", data=data)
> res

Random-Effects Model (k = 24; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0457 (SE = 0.0194)
tau (square root of estimated tau^2 value):      0.2137
I^2 (total heterogeneity / total variability):   99.79%
H^2 (total variability / sampling variability):  471.50

Test for Heterogeneity:
Q(df = 23) = 12722.9695, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5514  0.0438  12.5812  <.0001  0.4655  0.6373  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2741 0.2010 0.3538 0.0144 0.6891 

------------------------------------------------------------------------------
______________________________________________________________________________

> data <- subset(dat, mode == 1)
> data2 <- subset(data, expert == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 4; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0022 (SE = 0.0005)
tau (square root of estimated tau^2 value):      0.0469
I^2 (total heterogeneity / total variability):   74.32%
H^2 (total variability / sampling variability):  3.89

Test for Heterogeneity:
Q(df = 3) = 56.8066, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6375  0.0285  22.3701  <.0001  0.5816  0.6933  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3534 0.3007 0.4079 0.2541 0.4595 

> data2 <- subset(data, samplegp == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 4; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0066 (SE = 0.0038)
tau (square root of estimated tau^2 value):      0.0812
I^2 (total heterogeneity / total variability):   99.53%
H^2 (total variability / sampling variability):  211.76

Test for Heterogeneity:
Q(df = 3) = 1056.4602, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4665  0.0407  11.4531  <.0001  0.3866  0.5463  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2022 0.1421 0.2699 0.0808 0.3610 

> data2 <- subset(data, rr050 == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 9; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0038 (SE = 0.0007)
tau (square root of estimated tau^2 value):      0.0620
I^2 (total heterogeneity / total variability):   99.16%
H^2 (total variability / sampling variability):  118.68

Test for Heterogeneity:
Q(df = 8) = 4412.0117, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5322  0.0211  25.2049  <.0001  0.4908  0.5736  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2573 0.2220 0.2943 0.1541 0.3764 

> data2 <- subset(data, rr050 == 0)
> res <- rma(yi, vi, method="HS", data=data2)
> 
> res

Random-Effects Model (k = 8; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0173 (SE = 0.0077)
tau (square root of estimated tau^2 value):      0.1314
I^2 (total heterogeneity / total variability):   99.45%
H^2 (total variability / sampling variability):  181.84

Test for Heterogeneity:
Q(df = 7) = 2368.2595, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6050  0.0472  12.8076  <.0001  0.5124  0.6976  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3230 0.2397 0.4124 0.1048 0.5929 

> data2 <- subset(data, countryAm == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 11; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0098 (SE = 0.0025)
tau (square root of estimated tau^2 value):      0.0992
I^2 (total heterogeneity / total variability):   99.68%
H^2 (total variability / sampling variability):  309.54

Test for Heterogeneity:
Q(df = 10) = 11690.9943, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6244  0.0307  20.3669  <.0001  0.5643  0.6845  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3414 0.2856 0.3996 0.1662 0.5426 

> data2 <- subset(data, health == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 7; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0077 (SE = 0.0044)
tau (square root of estimated tau^2 value):      0.0876
I^2 (total heterogeneity / total variability):   98.75%
H^2 (total variability / sampling variability):  79.70

Test for Heterogeneity:
Q(df = 6) = 673.4053, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4958  0.0342  14.4773  <.0001  0.4287  0.5629  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2256 0.1719 0.2842 0.0928 0.3953 

> data2 <- subset(data, method == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 7; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0024 (SE = 0.0003)
tau (square root of estimated tau^2 value):      0.0490
I^2 (total heterogeneity / total variability):   98.48%
H^2 (total variability / sampling variability):  65.66

Test for Heterogeneity:
Q(df = 6) = 2639.9368, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6511  0.0189  34.4458  <.0001  0.6140  0.6881  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3673 0.3319 0.4033 0.2715 0.4686 

> data2 <- subset(data, fieldp056 == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 10; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0113 (SE = 0.0032)
tau (square root of estimated tau^2 value):      0.1065
I^2 (total heterogeneity / total variability):   99.80%
H^2 (total variability / sampling variability):  497.52

Test for Heterogeneity:
Q(df = 9) = 14018.8169, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5112  0.0340  15.0458  <.0001  0.4446  0.5777  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2391 0.1847 0.2981 0.0825 0.4449 

> data2 <- subset(data, fieldp57more == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 5; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0056 (SE = 0.0036)
tau (square root of estimated tau^2 value):      0.0750
I^2 (total heterogeneity / total variability):   94.47%
H^2 (total variability / sampling variability):  18.10

Test for Heterogeneity:
Q(df = 4) = 101.6398, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6996  0.0357  19.5908  <.0001  0.6296  0.7696  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.4143 0.3462 0.4841 0.2607 0.5769 

> data2 <- subset(data, intRem014 == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 12; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0114 (SE = 0.0032)
tau (square root of estimated tau^2 value):      0.1066
I^2 (total heterogeneity / total variability):   99.76%
H^2 (total variability / sampling variability):  410.03

Test for Heterogeneity:
Q(df = 11) = 14050.0746, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5152  0.0314  16.4251  <.0001  0.4537  0.5766  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2422 0.1915 0.2968 0.0851 0.4475 

> data2 <- subset(data, lenghtshort == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 8; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0220 (SE = 0.0098)
tau (square root of estimated tau^2 value):      0.1484
I^2 (total heterogeneity / total variability):   99.61%
H^2 (total variability / sampling variability):  257.21

Test for Heterogeneity:
Q(df = 7) = 3147.0595, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.6277  0.0528  11.8886  <.0001  0.5242  0.7312  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3448 0.2503 0.4459 0.0979 0.6489 

> data2 <- subset(data, lenghtmedmore == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 5; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0002)
tau (square root of estimated tau^2 value):      0.0363
I^2 (total heterogeneity / total variability):   97.90%
H^2 (total variability / sampling variability):  47.63

Test for Heterogeneity:
Q(df = 4) = 1435.9125, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5753  0.0172  33.5410  <.0001  0.5417  0.6090  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2959 0.2657 0.3271 0.2268 0.3700 

> data2 <- subset(data, reminder12 == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 8; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0124 (SE = 0.0071)
tau (square root of estimated tau^2 value):      0.1112
I^2 (total heterogeneity / total variability):   99.60%
H^2 (total variability / sampling variability):  247.65

Test for Heterogeneity:
Q(df = 7) = 2463.8729, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5442  0.0403  13.5050  <.0001  0.4652  0.6232  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2674 0.2004 0.3402 0.0934 0.4905 

> data2 <- subset(data, reminder3more == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 6; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0371 (SE = 0.0199)
tau (square root of estimated tau^2 value):      0.1925
I^2 (total heterogeneity / total variability):   99.58%
H^2 (total variability / sampling variability):  239.48

Test for Heterogeneity:
Q(df = 5) = 1494.3064, p-val < .0001

Model Results:

estimate      se    zval    pval   ci.lb   ci.ub      
  0.5834  0.0788  7.4054  <.0001  0.4290  0.7377  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3033 0.1728 0.4524 0.0302 0.7000 

> data2 <- subset(data, personalized == 1)
> 
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 8; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0200 (SE = 0.0099)
tau (square root of estimated tau^2 value):      0.1413
I^2 (total heterogeneity / total variability):   99.42%
H^2 (total variability / sampling variability):  171.01

Test for Heterogeneity:
Q(df = 7) = 1414.7738, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5777  0.0501  11.5250  <.0001  0.4795  0.6759  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2981 0.2126 0.3914 0.0782 0.5857 

> data2 <- subset(data, incentive == 1)
> res <- rma(yi, vi, method="HS", data=data2)
> res

Random-Effects Model (k = 10; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0109 (SE = 0.0054)
tau (square root of estimated tau^2 value):      0.1044
I^2 (total heterogeneity / total variability):   99.07%
H^2 (total variability / sampling variability):  107.93

Test for Heterogeneity:
Q(df = 9) = 1196.0582, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5861  0.0336  17.4605  <.0001  0.5203  0.6519  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data2$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3056 0.2467 0.3678 0.1309 0.5157 


------------------------------------------------------------------------------
______________________________________________________________________________


> data <- subset(dat, mode == 2)
> data3 <- subset(data, expert == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 12; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0247 (SE = 0.0126)
tau (square root of estimated tau^2 value):      0.1573
I^2 (total heterogeneity / total variability):   98.60%
H^2 (total variability / sampling variability):  71.25

Test for Heterogeneity:
Q(df = 11) = 1022.7555, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5844  0.0462  12.6390  <.0001  0.4937  0.6750  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.3036 0.2236 0.3901 0.0660 0.6195 

> data3 <- subset(data, samplegp == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> 
> res

Random-Effects Model (k = 27; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0483 (SE = 0.0211)
tau (square root of estimated tau^2 value):      0.2197
I^2 (total heterogeneity / total variability):   99.90%
H^2 (total variability / sampling variability):  1011.35

Test for Heterogeneity:
Q(df = 26) = 30267.1731, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5177  0.0424  12.2178  <.0001  0.4346  0.6007  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2447 0.1770 0.3193 0.0058 0.6676 

> data3 <- subset(data, rr050 == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 20; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0545 (SE = 0.0285)
tau (square root of estimated tau^2 value):      0.2334
I^2 (total heterogeneity / total variability):   99.90%
H^2 (total variability / sampling variability):  1015.70

Test for Heterogeneity:
Q(df = 19) = 24712.3982, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5363  0.0525  10.2150  <.0001  0.4334  0.6392  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2604 0.1755 0.3555 0.0033 0.7134 

> data3 <- subset(data, rr050 == 0)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 24; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0276 (SE = 0.0113)
tau (square root of estimated tau^2 value):      0.1662
I^2 (total heterogeneity / total variability):   99.60%
H^2 (total variability / sampling variability):  250.41

Test for Heterogeneity:
Q(df = 23) = 6589.7724, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5384  0.0341  15.7887  <.0001  0.4715  0.6052  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2626 0.2060 0.3235 0.0413 0.5851 

> data3 <- subset(data, countryAm == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 12; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0157 (SE = 0.0061)
tau (square root of estimated tau^2 value):      0.1251
I^2 (total heterogeneity / total variability):   99.46%
H^2 (total variability / sampling variability):  186.07

Test for Heterogeneity:
Q(df = 11) = 3882.0715, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4228  0.0366  11.5593  <.0001  0.3511  0.4945  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.1678 0.1177 0.2248 0.0269 0.3936 

> data3 <- subset(data, health == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 34; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0428 (SE = 0.0172)
tau (square root of estimated tau^2 value):      0.2068
I^2 (total heterogeneity / total variability):   99.82%
H^2 (total variability / sampling variability):  561.82

Test for Heterogeneity:
Q(df = 33) = 20658.5887, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5424  0.0357  15.2131  <.0001  0.4726  0.6123  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2661 0.2066 0.3301 0.0162 0.6655 

> data3 <- subset(data, method == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 6; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0103 (SE = 0.0021)
tau (square root of estimated tau^2 value):      0.1017
I^2 (total heterogeneity / total variability):   98.88%
H^2 (total variability / sampling variability):  89.07

Test for Heterogeneity:
Q(df = 5) = 1976.4421, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5052  0.0422  11.9744  <.0001  0.4225  0.5879  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2339 0.1677 0.3073 0.0808 0.4357 

> data3 <- subset(data, fieldp056 == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 24; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0283 (SE = 0.0130)
tau (square root of estimated tau^2 value):      0.1683
I^2 (total heterogeneity / total variability):   99.76%
H^2 (total variability / sampling variability):  420.88

Test for Heterogeneity:
Q(df = 23) = 12072.0404, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5327  0.0346  15.3788  <.0001  0.4648  0.6005  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2574 0.2003 0.3190 0.0370 0.5837 

> data3 <- subset(data, fieldp57more == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 18; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0130 (SE = 0.0055)
tau (square root of estimated tau^2 value):      0.1139
I^2 (total heterogeneity / total variability):   99.14%
H^2 (total variability / sampling variability):  116.91

Test for Heterogeneity:
Q(df = 17) = 2524.0732, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4965  0.0271  18.3247  <.0001  0.4434  0.5496  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2267 0.1838 0.2726 0.0692 0.4407 

> data3 <- subset(data, intRem014 == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 21; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0227 (SE = 0.0091)
tau (square root of estimated tau^2 value):      0.1507
I^2 (total heterogeneity / total variability):   99.56%
H^2 (total variability / sampling variability):  225.08

Test for Heterogeneity:
Q(df = 20) = 6739.1845, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4799  0.0331  14.4985  <.0001  0.4150  0.5448  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2128 0.1621 0.2683 0.0306 0.4968 

> data3 <- subset(data, lenghtshort == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 10; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0072 (SE = 0.0021)
tau (square root of estimated tau^2 value):      0.0850
I^2 (total heterogeneity / total variability):   96.34%
H^2 (total variability / sampling variability):  27.31

Test for Heterogeneity:
Q(df = 9) = 658.9607, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.4797  0.0280  17.1517  <.0001  0.4249  0.5345  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2123 0.1691 0.2589 0.0888 0.3708 

> data3 <- subset(data, lenghtmedmore == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 15; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0195 (SE = 0.0087)
tau (square root of estimated tau^2 value):      0.1398
I^2 (total heterogeneity / total variability):   99.53%
H^2 (total variability / sampling variability):  214.29

Test for Heterogeneity:
Q(df = 14) = 3829.0525, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5435  0.0363  14.9881  <.0001  0.4724  0.6146  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2673 0.2069 0.3324 0.0660 0.5412 

> data3 <- subset(data, reminder12 == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 30; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0448 (SE = 0.0202)
tau (square root of estimated tau^2 value):      0.2118
I^2 (total heterogeneity / total variability):   99.88%
H^2 (total variability / sampling variability):  806.12

Test for Heterogeneity:
Q(df = 29) = 27179.8867, p-val < .0001

Model Results:

estimate      se     zval    pval   ci.lb   ci.ub      
  0.5618  0.0389  14.4538  <.0001  0.4856  0.6380  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2834 0.2173 0.3544 0.0185 0.6936 

> 
> data3 <- subset(data, reminder3more == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 17; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0454 (SE = 0.0177)
tau (square root of estimated tau^2 value):      0.2130
I^2 (total heterogeneity / total variability):   99.66%
H^2 (total variability / sampling variability):  291.94

Test for Heterogeneity:
Q(df = 16) = 5108.9143, p-val < .0001

Model Results:

estimate      se    zval    pval   ci.lb   ci.ub      
  0.5127  0.0518  9.8912  <.0001  0.4111  0.6143  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2403 0.1593 0.3320 0.0063 0.6545 

> data3 <- subset(data, personalized == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 11; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0476 (SE = 0.0240)
tau (square root of estimated tau^2 value):      0.2181
I^2 (total heterogeneity / total variability):   99.83%
H^2 (total variability / sampling variability):  587.94

Test for Heterogeneity:
Q(df = 10) = 8543.5339, p-val < .0001

Model Results:

estimate      se    zval    pval   ci.lb   ci.ub      
  0.5275  0.0660  7.9932  <.0001  0.3982  0.6569  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2531 0.1499 0.3727 0.0059 0.6845 

> 
> data3 <- subset(data, incentive == 1)
> res <- rma(yi, vi, method="HS", data=data3)
> res

Random-Effects Model (k = 14; tau^2 estimator: HS)

tau^2 (estimated amount of total heterogeneity): 0.0634 (SE = 0.0283)
tau (square root of estimated tau^2 value):      0.2518
I^2 (total heterogeneity / total variability):   99.82%
H^2 (total variability / sampling variability):  562.03

Test for Heterogeneity:
Q(df = 13) = 10753.8872, p-val < .0001

Model Results:

estimate      se    zval    pval   ci.lb   ci.ub      
  0.5266  0.0676  7.7947  <.0001  0.3942  0.6590  *** 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> predict(res, transf=transf.ipft.hm, targs=list(ni=data3$ni))

   pred  ci.lb  ci.ub  pi.lb  pi.ub 
 0.2521 0.1468 0.3747 0.0000 0.7421 

> 