Format results from a linear mixed model
Arguments
- object
A list of one (or more) models estimated from lmer
- format
A list giving the formatting style to be used for the fixed effecvts, random effects, and effect sizes. For the random effects, must be two options, one for when the random effects do not have confidence intervals and one when the random effects do have confidence intervals.
- digits
A numeric value indicating the number of digits to print. This is still in early implementation stages and currently does not change all parts of the output (which default to 2 decimals per APA style).
- pcontrol
A list controlling how p values are formatted.
- ...
Additional arguments passed to
confint. Notablynsimandboot.typeif the bootstrap method is used.
Examples
library(JWileymisc)
data(sleepstudy, package = "lme4")
m1 <- lme4::lmer(Reaction ~ Days + (1 + Days | Subject),
data = sleepstudy)
m2 <- lme4::lmer(Reaction ~ Days + I(Days^2) + (1 + Days | Subject),
data = sleepstudy)
testm1 <- modelTest(m1)
#> Parameters and CIs are based on REML,
#> but modelTests requires ML not REML fit for comparisons,
#> and these are used in effect sizes. Refitting.
testm2 <- modelTest(m2)
#> Parameters and CIs are based on REML,
#> but modelTests requires ML not REML fit for comparisons,
#> and these are used in effect sizes. Refitting.
APAStyler(testm1)
#> Term Est Type
#> <char> <char> <char>
#> 1: (Intercept) 251.41*** [238.03, 264.78] Fixed Effects
#> 2: Days 10.47*** [ 7.44, 13.50] Fixed Effects
#> 3: cor_Days.(Intercept)|Subject 0.07 Random Effects
#> 4: sd_(Intercept)|Subject 24.74 Random Effects
#> 5: sd_Days|Subject 5.92 Random Effects
#> 6: sigma 25.59 Random Effects
#> 7: Model DF 6 Overall Model
#> 8: N (Groups) Subject (18) Overall Model
#> 9: N (Observations) 180 Overall Model
#> 10: logLik -875.97 Overall Model
#> 11: AIC 1763.94 Overall Model
#> 12: BIC 1783.10 Overall Model
#> 13: Marginal R2 0.29 Overall Model
#> 14: Marginal F2 0.40 Overall Model
#> 15: Conditional R2 0.79 Overall Model
#> 16: Conditional F2 3.82 Overall Model
#> 17: Days (Fixed + Random) 0.40/1.99, p < .001 Effect Sizes
#> 18: Days (Random) 0.00/0.46, p < .001 Effect Sizes
APAStyler(list(Linear = testm1, Quadratic = testm2))
#> Term Linear
#> <char> <char>
#> 1: (Intercept) 251.41*** [238.03, 264.78]
#> 2: Days 10.47*** [ 7.44, 13.50]
#> 3: I(Days^2)
#> 4: cor_Days.(Intercept)|Subject 0.07
#> 5: sd_(Intercept)|Subject 24.74
#> 6: sd_Days|Subject 5.92
#> 7: sigma 25.59
#> 8: Model DF 6
#> 9: N (Groups) Subject (18)
#> 10: N (Observations) 180
#> 11: logLik -875.97
#> 12: AIC 1763.94
#> 13: BIC 1783.10
#> 14: Marginal R2 0.29
#> 15: Marginal F2 0.40
#> 16: Conditional R2 0.79
#> 17: Conditional F2 3.82
#> 18: Days (Fixed + Random) 0.40/1.99, p < .001
#> 19: Days (Random) 0.00/0.46, p < .001
#> 20: I(Days^2) (Fixed)
#> Term Linear
#> Quadratic Type
#> <char> <char>
#> 1: 255.45*** [240.72, 270.18] Fixed Effects
#> 2: 7.43** [ 1.91, 12.96] Fixed Effects
#> 3: 0.34 [ -0.18, 0.85] Fixed Effects
#> 4: 0.06 Random Effects
#> 5: 24.76 Random Effects
#> 6: 5.93 Random Effects
#> 7: 25.53 Random Effects
#> 8: 7 Overall Model
#> 9: Subject (18) Overall Model
#> 10: 180 Overall Model
#> 11: -875.14 Overall Model
#> 12: 1764.28 Overall Model
#> 13: 1786.63 Overall Model
#> 14: 0.29 Overall Model
#> 15: 0.41 Overall Model
#> 16: 0.79 Overall Model
#> 17: 3.87 Overall Model
#> 18: 0.01/0.52, p < .001 Effect Sizes
#> 19: 0.00/0.46, p < .001 Effect Sizes
#> 20: 0.00/0.01, p = .198 Effect Sizes
#> Quadratic Type
APAStyler(testm1,
format = list(
FixedEffects = "%s, %s (%s, %s)",
RandomEffects = c("%s", "%s (%s, %s)"),
EffectSizes = "%s, %s; %s"),
pcontrol = list(digits = 3, stars = FALSE,
includeP = TRUE, includeSign = TRUE,
dropLeadingZero = TRUE))
#> Term Est
#> <char> <char>
#> 1: (Intercept) 251.41, p < .001 (238.03, 264.78)
#> 2: Days 10.47, p < .001 ( 7.44, 13.50)
#> 3: cor_Days.(Intercept)|Subject 0.07
#> 4: sd_(Intercept)|Subject 24.74
#> 5: sd_Days|Subject 5.92
#> 6: sigma 25.59
#> 7: Model DF 6
#> 8: N (Groups) Subject (18)
#> 9: N (Observations) 180
#> 10: logLik -875.97
#> 11: AIC 1763.94
#> 12: BIC 1783.10
#> 13: Marginal R2 0.29
#> 14: Marginal F2 0.40
#> 15: Conditional R2 0.79
#> 16: Conditional F2 3.82
#> 17: Days (Fixed + Random) 0.40, 1.99; p < .001
#> 18: Days (Random) 0.00, 0.46; p < .001
#> Type
#> <char>
#> 1: Fixed Effects
#> 2: Fixed Effects
#> 3: Random Effects
#> 4: Random Effects
#> 5: Random Effects
#> 6: Random Effects
#> 7: Overall Model
#> 8: Overall Model
#> 9: Overall Model
#> 10: Overall Model
#> 11: Overall Model
#> 12: Overall Model
#> 13: Overall Model
#> 14: Overall Model
#> 15: Overall Model
#> 16: Overall Model
#> 17: Effect Sizes
#> 18: Effect Sizes
# \donttest{
testm1 <- modelTest(m1, method = "profile")
#> Computing profile confidence intervals ...
#> Parameters and CIs are based on REML,
#> but modelTests requires ML not REML fit for comparisons,
#> and these are used in effect sizes. Refitting.
testm2 <- modelTest(m2, method = "profile")
#> Computing profile confidence intervals ...
#> Parameters and CIs are based on REML,
#> but modelTests requires ML not REML fit for comparisons,
#> and these are used in effect sizes. Refitting.
APAStyler(testm1)
#> Term Est Type
#> <char> <char> <char>
#> 1: (Intercept) 251.41*** [237.68, 265.13] Fixed Effects
#> 2: Days 10.47*** [ 7.36, 13.58] Fixed Effects
#> 3: cor_Days.(Intercept)|Subject 0.07 [-0.48, 0.68] Random Effects
#> 4: sd_(Intercept)|Subject 24.74 [14.38, 37.72] Random Effects
#> 5: sd_Days|Subject 5.92 [ 3.80, 8.75] Random Effects
#> 6: sigma 25.59 [22.90, 28.86] Random Effects
#> 7: Model DF 6 Overall Model
#> 8: N (Groups) Subject (18) Overall Model
#> 9: N (Observations) 180 Overall Model
#> 10: logLik -875.97 Overall Model
#> 11: AIC 1763.94 Overall Model
#> 12: BIC 1783.10 Overall Model
#> 13: Marginal R2 0.29 Overall Model
#> 14: Marginal F2 0.40 Overall Model
#> 15: Conditional R2 0.79 Overall Model
#> 16: Conditional F2 3.82 Overall Model
#> 17: Days (Fixed + Random) 0.40/1.99, p < .001 Effect Sizes
#> 18: Days (Random) 0.00/0.46, p < .001 Effect Sizes
APAStyler(list(Linear = testm1, Quadratic = testm2))
#> Term Linear
#> <char> <char>
#> 1: (Intercept) 251.41*** [237.68, 265.13]
#> 2: Days 10.47*** [ 7.36, 13.58]
#> 3: I(Days^2)
#> 4: cor_Days.(Intercept)|Subject 0.07 [-0.48, 0.68]
#> 5: sd_(Intercept)|Subject 24.74 [14.38, 37.72]
#> 6: sd_Days|Subject 5.92 [ 3.80, 8.75]
#> 7: sigma 25.59 [22.90, 28.86]
#> 8: Model DF 6
#> 9: N (Groups) Subject (18)
#> 10: N (Observations) 180
#> 11: logLik -875.97
#> 12: AIC 1763.94
#> 13: BIC 1783.10
#> 14: Marginal R2 0.29
#> 15: Marginal F2 0.40
#> 16: Conditional R2 0.79
#> 17: Conditional F2 3.82
#> 18: Days (Fixed + Random) 0.40/1.99, p < .001
#> 19: Days (Random) 0.00/0.46, p < .001
#> 20: I(Days^2) (Fixed)
#> Term Linear
#> Quadratic Type
#> <char> <char>
#> 1: 255.45*** [240.52, 270.38] Fixed Effects
#> 2: 7.43** [ 1.93, 12.94] Fixed Effects
#> 3: 0.34 [ -0.18, 0.85] Fixed Effects
#> 4: 0.06 [-0.48, 0.68] Random Effects
#> 5: 24.76 [14.48, 37.75] Random Effects
#> 6: 5.93 [ 3.81, 8.76] Random Effects
#> 7: 25.53 [22.77, 28.69] Random Effects
#> 8: 7 Overall Model
#> 9: Subject (18) Overall Model
#> 10: 180 Overall Model
#> 11: -875.14 Overall Model
#> 12: 1764.28 Overall Model
#> 13: 1786.63 Overall Model
#> 14: 0.29 Overall Model
#> 15: 0.41 Overall Model
#> 16: 0.79 Overall Model
#> 17: 3.87 Overall Model
#> 18: 0.01/0.52, p < .001 Effect Sizes
#> 19: 0.00/0.46, p < .001 Effect Sizes
#> 20: 0.00/0.01, p = .198 Effect Sizes
#> Quadratic Type
APAStyler(testm1,
format = list(
FixedEffects = "%s, %s (%s, %s)",
RandomEffects = c("%s", "%s (%s, %s)"),
EffectSizes = "%s, %s; %s"),
pcontrol = list(digits = 3, stars = FALSE,
includeP = TRUE, includeSign = TRUE,
dropLeadingZero = TRUE))
#> Term Est
#> <char> <char>
#> 1: (Intercept) 251.41, p < .001 (237.68, 265.13)
#> 2: Days 10.47, p < .001 ( 7.36, 13.58)
#> 3: cor_Days.(Intercept)|Subject 0.07 (-0.48, 0.68)
#> 4: sd_(Intercept)|Subject 24.74 (14.38, 37.72)
#> 5: sd_Days|Subject 5.92 ( 3.80, 8.75)
#> 6: sigma 25.59 (22.90, 28.86)
#> 7: Model DF 6
#> 8: N (Groups) Subject (18)
#> 9: N (Observations) 180
#> 10: logLik -875.97
#> 11: AIC 1763.94
#> 12: BIC 1783.10
#> 13: Marginal R2 0.29
#> 14: Marginal F2 0.40
#> 15: Conditional R2 0.79
#> 16: Conditional F2 3.82
#> 17: Days (Fixed + Random) 0.40, 1.99; p < .001
#> 18: Days (Random) 0.00, 0.46; p < .001
#> Type
#> <char>
#> 1: Fixed Effects
#> 2: Fixed Effects
#> 3: Random Effects
#> 4: Random Effects
#> 5: Random Effects
#> 6: Random Effects
#> 7: Overall Model
#> 8: Overall Model
#> 9: Overall Model
#> 10: Overall Model
#> 11: Overall Model
#> 12: Overall Model
#> 13: Overall Model
#> 14: Overall Model
#> 15: Overall Model
#> 16: Overall Model
#> 17: Effect Sizes
#> 18: Effect Sizes
# }
rm(m1, m2, testm1, testm2)