summary(mod<-rma(z, zvar, mods = ~age, method = "REML", test = 't', data= dat))
## Warning: 5 studies with NAs omitted from model fitting.
##
## Mixed-Effects Model (k = 68; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -2.1148 4.2296 10.2296 16.7986 10.6167
##
## tau^2 (estimated amount of residual heterogeneity): 0.0556 (SE = 0.0105)
## tau (square root of estimated tau^2 value): 0.2358
## I^2 (residual heterogeneity / unaccounted variability): 95.95%
## H^2 (unaccounted variability / sampling variability): 24.69
## R^2 (amount of heterogeneity accounted for): 2.98%
##
## Test for Residual Heterogeneity:
## QE(df = 66) = 1097.2556, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 66) = 2.6753, p-val = 0.1067
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt -0.1048 0.1017 -1.0308 66 0.3064 -0.3079 0.0982
## age 0.0049 0.0030 1.6356 66 0.1067 -0.0011 0.0109
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
describe(dat$age)
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 68 32.37 10.02 32.55 31.57 11.24 18.05 62.41 44.36 0.69 0.17
## se
## X1 1.22
predict(mod, 32.37+10.02)
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.1041 0.0427 0.0189 0.1893 -0.3743 0.5825
predict(mod, 32.37)
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.0547 0.0298 -0.0049 0.1143 -0.4198 0.5292
predict(mod, 32.37-10.02)
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.0053 0.0422 -0.0790 0.0896 -0.4729 0.4835
rma(z, zvar, mods= ~spirituality, method = "REML", test = 't', data= dat)
##
## Mixed-Effects Model (k = 73; tau^2 estimator: REML)
##
## tau^2 (estimated amount of residual heterogeneity): 0.0429 (SE = 0.0080)
## tau (square root of estimated tau^2 value): 0.2071
## I^2 (residual heterogeneity / unaccounted variability): 95.71%
## H^2 (unaccounted variability / sampling variability): 23.29
## R^2 (amount of heterogeneity accounted for): 22.05%
##
## Test for Residual Heterogeneity:
## QE(df = 71) = 1122.5475, p-val < .0001
##
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 71) = 18.2514, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## intrcpt 0.0062 0.0281 0.2206 71 0.8260 -0.0498 0.0622
## spirituality 0.2876 0.0673 4.2722 71 <.0001 0.1533 0.4218 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(mod2 <- rma(z, zvar, mods= ~wellbeing_type, method = "REML", test = 't', data= dat))
##
## Mixed-Effects Model (k = 73; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -2.6737 5.3474 13.3474 22.3414 13.9628
##
## tau^2 (estimated amount of residual heterogeneity): 0.0571 (SE = 0.0104)
## tau (square root of estimated tau^2 value): 0.2389
## I^2 (residual heterogeneity / unaccounted variability): 96.55%
## H^2 (unaccounted variability / sampling variability): 28.98
## R^2 (amount of heterogeneity accounted for): 6.42%
##
## Test for Residual Heterogeneity:
## QE(df = 70) = 1548.9610, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## F(df1 = 2, df2 = 70) = 3.2666, p-val = 0.0440
##
## Model Results:
##
## estimate se tval df pval ci.lb
## intrcpt 0.0536 0.0459 1.1666 70 0.2473 -0.0380
## wellbeing_type1=externalizing 0.0585 0.0675 0.8672 70 0.3888 -0.0761
## wellbeing_type1=internalizing 0.1883 0.0740 2.5456 70 0.0131 0.0408
## ci.ub
## intrcpt 0.1452
## wellbeing_type1=externalizing 0.1932
## wellbeing_type1=internalizing 0.3358 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
predict(mod2, c(1, 0))
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.1121 0.0495 0.0135 0.2108 -0.3744 0.5987
predict(mod2, c(0, 1))
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.2419 0.0580 0.1262 0.3575 -0.2484 0.7321
predict(mod2, c(0, 0))
##
## pred se ci.lb ci.ub pi.lb pi.ub
## 0.0536 0.0459 -0.0380 0.1452 -0.4316 0.5387