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UGBehaviorResults_DF.txt
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UGBehaviorResults_DF.txt
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###UG Output
#Repeated Measures, fixed effects only
#regression output
Residuals:
Min 1Q Median 3Q Max
-0.4201 -0.1123 0.0153 0.1356 0.4862
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.49618 0.07758 6.395 3.49e-09 ***
Partner 0.01764 0.03591 0.491 0.62420
Block 0.35414 0.10972 3.228 0.00162 **
Partner:Block -0.01295 0.05079 -0.255 0.79914
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2271 on 116 degrees of freedom
Multiple R-squared: 0.3516, Adjusted R-squared: 0.3348
F-statistic: 20.97 on 3 and 116 DF, p-value: 6.352e-11
######
####output as ANOVA (f-statistic)
Df Sum Sq Mean Sq F value Pr(>F)
Partner 1 0.010 0.010 0.193 0.661
Block 1 3.232 3.232 62.648 1.63e-12 ***
Partner:Block 1 0.003 0.003 0.065 0.799
Residuals 116 5.985 0.052
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
######
#Repeated Measures using lmer, modeling subject as random effect
Linear mixed model fit by REML
t-tests use Satterthwaite approximations to degrees of freedom ['lmerMod']
Formula: ProportionAccept ~ Partner * Block + (1 | SubjectID)
Data: UGBehavData_Long
REML criterion at convergence: -62.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.82893 -0.85153 0.08793 0.72923 2.10753
Random effects:
Groups Name Variance Std.Dev.
SubjectID (Intercept) 0.03186 0.1785
Residual 0.02028 0.1424
Number of obs: 120, groups: SubjectID, 20
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.49618 0.06292 73.53010 7.886 2.21e-11 ***
Partner 0.01764 0.02252 97.00010 0.783 0.435
Block 0.35414 0.06879 97.00010 5.148 1.38e-06 ***
Partner:Block -0.01295 0.03184 97.00010 -0.407 0.685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) Partnr Block
Partner -0.716
Block -0.547 0.655
Partnr:Blck 0.506 -0.707 -0.926