library(nlme)
#install.packages("MuMIn")
library(MuMIn)
#install.packages("reghelper")
library(reghelper)
#install.packages("car")
library(car)
#install.packages("stats")
library(stats)
#install.packages("lsr")
library(lsr)
#install.packages("psych")
library(psych)
#install.packages("devtools")
library(devtools)
#install_github("easyGgplot2", "kassambara")
library(easyGgplot2)
#install.packages("lme4")
library(lme4)
Warning message: "package 'MuMIn' was built under R version 3.5.2"Warning message: "package 'reghelper' was built under R version 3.5.2" Attaching package: 'reghelper' The following object is masked from 'package:base': beta Warning message: "package 'car' was built under R version 3.5.2"Loading required package: carData Warning message: "package 'carData' was built under R version 3.5.2"Warning message: "package 'lsr' was built under R version 3.5.2" Attaching package: 'psych' The following object is masked from 'package:car': logit The following object is masked from 'package:reghelper': ICC Loading required package: ggplot2 Attaching package: 'ggplot2' The following objects are masked from 'package:psych': %+%, alpha Warning message: "package 'lme4' was built under R version 3.5.2"Loading required package: Matrix Attaching package: 'lme4' The following object is masked from 'package:nlme': lmList
#Read csv with NoGo information (Days 1 and 2)
myNoGoData <- read.csv('Exp8_NoGo_Full.csv')
#Take subset of dataframe to analyze Day 1 data (Familiar and Novel NoFeedback)
myNoGoDay1Data <- subset(myNoGoData, FeedbackCond=="NoFeedback")
#Use ML instead of REML becuase we're concerned with comparing fixed effects between models.
nogo_nofb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + ASRS_Total*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myNoGoDay1Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(nogo_nofb_model1, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model2, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model3, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model4, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(nogo_nofb_model1))
qqnorm(resid(nogo_nofb_model2))
qqnorm(resid(nogo_nofb_model3))
qqnorm(resid(nogo_nofb_model4))
plot(nogo_nofb_model1)
plot(nogo_nofb_model2)
plot(nogo_nofb_model3)
plot(nogo_nofb_model4)
vif(nogo_nofb_model1)
vif(nogo_nofb_model2)
vif(nogo_nofb_model3)
vif(nogo_nofb_model4)
#Use beta from reghelper, otherwise beta coefs won't be standardized
beta(nogo_nofb_model1)
beta(nogo_nofb_model2)
beta(nogo_nofb_model3)
beta(nogo_nofb_model4)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 595.4846 622.1849 -289.7423 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 4.700403e-05 0.9743869 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.06855762 104 0.000000 1.0000 Age.z -0.2532351 0.17878204 98 -1.416446 0.1598 Gender.z -0.1073427 0.07216842 98 -1.487391 0.1401 Cong_Order.z 0.1745247 0.13708808 98 1.273084 0.2060 Stim_Order.z -0.2128891 0.13934073 98 -1.527831 0.1298 Drive.z 0.3186428 0.17805673 98 1.789557 0.0766 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Age.z 0.000 Gender.z 0.000 -0.206 Cong_Order.z 0.000 -0.082 0.129 Stim_Order.z 0.000 0.096 -0.212 -0.863 Drive.z 0.000 -0.922 0.178 0.090 -0.114 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.86476099 -0.61500983 0.04413854 0.66880549 2.59487867 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 604.5982 647.9862 -289.2991 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 5.855382e-05 0.9723131 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0692744 104 0.0000000 1.0000 Age.z -0.2725880 0.1864854 93 -1.4617124 0.1472 Gender.z -0.0943841 0.0763725 93 -1.2358377 0.2196 Cong_Order.z 0.1766069 0.1424845 93 1.2394818 0.2183 Stim_Order.z -0.2208710 0.1450578 93 -1.5226411 0.1312 Drive.z 0.3283236 0.1835610 93 1.7886345 0.0769 ASRS_A.z -0.0470717 0.2083050 93 -0.2259751 0.8217 ASRS_B.z 0.0169484 0.2053381 93 0.0825388 0.9344 ASRS_Total.z 0.0352486 0.3368740 93 0.1046343 0.9169 Diagnosis.z 0.0318194 0.0800553 93 0.3974681 0.6919 COHS.z -0.0556694 0.0721494 93 -0.7715842 0.4423 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B ASRS_T Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 -0.020 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 -0.014 Dgnss. Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z 0.056 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.74605833 -0.63187614 0.02859003 0.70225267 2.57491418 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 584.6008 631.3264 -278.3004 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 7.690086e-05 0.9222345 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0658739 103 0.000000 1.0000 Age.z -0.2725880 0.1773312 93 -1.537169 0.1276 Gender.z -0.0943841 0.0726236 93 -1.299634 0.1969 Cong_Order.z 0.1766069 0.1354902 93 1.303466 0.1956 Stim_Order.z -0.2208710 0.1379372 93 -1.601243 0.1127 Drive.z 0.3283236 0.1745504 93 1.880968 0.0631 ASRS_A.z -0.0470717 0.1980797 93 -0.237640 0.8127 ASRS_B.z 0.0169484 0.1952585 93 0.086800 0.9310 ASRS_Total.z 0.0352486 0.3203375 93 0.110036 0.9126 Diagnosis.z 0.0318194 0.0761256 93 0.417986 0.6769 COHS.z -0.0556694 0.0686078 93 -0.811415 0.4192 StimulusTypeNovel.z 0.3087631 0.0660328 103 4.675904 0.0000 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_T Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z -0.020 COHS.z -0.014 0.056 StimulusTypeNovel.z 0.000 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.22916619 -0.60850687 0.02559481 0.63312222 2.70553352 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 586.9089 650.3222 -274.4545 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 8.475628e-05 0.9053389 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z + ASRS_A.z * StimulusTypeNovel.z + ASRS_B.z * StimulusTypeNovel.z + ASRS_Total.z * StimulusTypeNovel.z + Diagnosis.z * StimulusTypeNovel.z + COHS.z * StimulusTypeNovel.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0655080 98 0.000000 1.0000 Age.z -0.2725880 0.1763463 93 -1.545754 0.1256 Gender.z -0.0943841 0.0722202 93 -1.306893 0.1945 Cong_Order.z 0.1766069 0.1347377 93 1.310746 0.1932 Stim_Order.z -0.2208710 0.1371711 93 -1.610186 0.1107 Drive.z 0.3283236 0.1735809 93 1.891473 0.0617 ASRS_A.z -0.0470717 0.1969796 93 -0.238968 0.8117 ASRS_B.z 0.0169484 0.1941740 93 0.087284 0.9306 ASRS_Total.z 0.0352486 0.3185583 93 0.110650 0.9121 Diagnosis.z 0.0318194 0.0757028 93 0.420321 0.6752 COHS.z -0.0556694 0.0682267 93 -0.815947 0.4166 StimulusTypeNovel.z 0.3087631 0.0656661 98 4.702019 0.0000 ASRS_A.z:StimulusTypeNovel.z 0.1714261 0.1925757 98 0.890175 0.3756 ASRS_B.z:StimulusTypeNovel.z 0.1459982 0.1916855 98 0.761655 0.4481 ASRS_Total.z:StimulusTypeNovel.z -0.3518080 0.3141462 98 -1.119886 0.2655 Diagnosis.z:StimulusTypeNovel.z 0.1038756 0.0673812 98 1.541611 0.1264 COHS.z:StimulusTypeNovel.z 0.1177012 0.0675720 98 1.741863 0.0847 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z ASRS_B.z ASRS_Tt. Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z 0.692 ASRS_Total.z -0.901 -0.895 Diagnosis.z -0.055 0.062 -0.020 COHS.z 0.077 -0.075 -0.014 0.056 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 StmTN. ASRS_A.: ASRS_B.: ASRS_T.: D.:STN Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z StimulusTypeNovel.z ASRS_A.z:StimulusTypeNovel.z 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.704 ASRS_Total.z:StimulusTypeNovel.z 0.000 -0.905 -0.900 Diagnosis.z:StimulusTypeNovel.z 0.000 -0.040 0.101 -0.022 COHS.z:StimulusTypeNovel.z 0.000 0.060 -0.091 0.004 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.25400713 -0.56730420 0.03134097 0.62224816 2.67395533 Number of Observations: 208 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(nogo_nofb_model1)
r.squaredGLMM(nogo_nofb_model2)
r.squaredGLMM(nogo_nofb_model3)
r.squaredGLMM(nogo_nofb_model4)
Warning message: "'r.squaredGLMM' now calculates a revised statistic. See the help page."
R2m | R2c |
---|---|
0.04619542 | 0.04619542 |
R2m | R2c |
---|---|
0.05026976 | 0.05026976 |
R2m | R2c |
---|---|
0.1459746 | 0.1459746 |
R2m | R2c |
---|---|
0.1771031 | 0.1771031 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(nogo_nofb_model2) - r.squaredGLMM(nogo_nofb_model1)
r.squaredGLMM(nogo_nofb_model3) - r.squaredGLMM(nogo_nofb_model2)
r.squaredGLMM(nogo_nofb_model4) - r.squaredGLMM(nogo_nofb_model3)
R2m | R2c |
---|---|
0.004074338 | 0.004074339 |
R2m | R2c |
---|---|
0.09570482 | 0.09570482 |
R2m | R2c |
---|---|
0.03112849 | 0.03112848 |
#Compare the models to each other to extract log likelihood ratio Chi^2 values and the associated p-values.
#Df is how many new variables are added to next model.
anova(nogo_nofb_model1, nogo_nofb_model2, nogo_nofb_model3, nogo_nofb_model4)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
nogo_nofb_model1 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, data = myNoGoDay1Data, random = ~1 | Subject, method = "ML") | 1 | 8 | -147.0928 | -120.39246 | 81.54638 | NA | NA | |
nogo_nofb_model2 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, data = myNoGoDay1Data, random = ~1 | Subject, method = "ML") | 2 | 13 | -137.9791 | -94.59111 | 81.98955 | 1 vs 2 | 0.8863352 | 9.712030e-01 |
nogo_nofb_model3 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType, data = myNoGoDay1Data, random = ~1 | Subject, method = "ML") | 3 | 14 | -157.9765 | -111.25096 | 92.98825 | 2 vs 3 | 21.9973927 | 2.730211e-06 |
nogo_nofb_model4 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType + ASRS_A * StimulusType + ASRS_B * StimulusType + ASRS_Total * StimulusType + Diagnosis * StimulusType + COHS * StimulusType, data = myNoGoDay1Data, random = ~1 | Subject, method = "ML") | 4 | 19 | -155.6684 | -92.25516 | 96.83419 | 3 vs 4 | 7.6918869 | 1.740538e-01 |
#Take subset of dataframe to analyze Familiar data (Feedback and NoFeedback). We'll call it "myNoGoDay2Data" for consistency.
myNoGoDay2Data <- subset(myNoGoData, StimulusType=="Familiar")
nogo_fb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + ASRS_Total*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myNoGoDay2Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(nogo_fb_model1, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model2, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model3, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model4, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(nogo_fb_model1))
qqnorm(resid(nogo_fb_model2))
qqnorm(resid(nogo_fb_model3))
qqnorm(resid(nogo_fb_model4))
plot(nogo_fb_model1)
plot(nogo_fb_model2)
plot(nogo_fb_model3)
plot(nogo_fb_model4)
vif(nogo_fb_model1)
vif(nogo_fb_model2)
vif(nogo_fb_model3)
vif(nogo_fb_model4)
beta(nogo_fb_model1)
beta(nogo_fb_model2)
beta(nogo_fb_model3)
beta(nogo_fb_model4)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 596.4015 623.1018 -290.2008 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 8.405175e-05 0.976537 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.06870891 104 0.0000000 1.0000 Age.z 0.28797508 0.17917655 98 1.6072141 0.1112 Gender.z 0.04337723 0.07232767 98 0.5997321 0.5501 Cong_Order.z 0.30199227 0.13739058 98 2.1980565 0.0303 Stim_Order.z -0.23322151 0.13964821 98 -1.6700645 0.0981 Drive.z -0.27559361 0.17844964 98 -1.5443775 0.1257 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Age.z 0.000 Gender.z 0.000 -0.206 Cong_Order.z 0.000 -0.082 0.129 Stim_Order.z 0.000 0.096 -0.212 -0.863 Drive.z 0.000 -0.922 0.178 0.090 -0.114 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.66320360 -0.62693736 0.08214435 0.65977841 2.67481999 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 602.0547 645.4427 -288.0273 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 7.560095e-05 0.9663862 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0688522 104 0.0000000 1.0000 Age.z 0.3197986 0.1853486 93 1.7253900 0.0878 Gender.z 0.0470799 0.0759070 93 0.6202313 0.5366 Cong_Order.z 0.3030269 0.1416159 93 2.1397800 0.0350 Stim_Order.z -0.2464764 0.1441736 93 -1.7095812 0.0907 Drive.z -0.2643304 0.1824421 93 -1.4488455 0.1507 ASRS_A.z 0.0501675 0.2070352 93 0.2423141 0.8091 ASRS_B.z 0.2797611 0.2040864 93 1.3707971 0.1737 ASRS_Total.z -0.2696833 0.3348205 93 -0.8054564 0.4226 Diagnosis.z -0.0424021 0.0795673 93 -0.5329083 0.5954 COHS.z -0.0849211 0.0717096 93 -1.1842360 0.2393 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B ASRS_T Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 -0.020 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 -0.014 Dgnss. Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z 0.056 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.68979349 -0.67229584 0.06596359 0.69253014 2.56578443 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 586.3289 633.0544 -279.1644 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.0001048736 0.9260734 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0661481 103 0.000000 1.0000 Age.z 0.3197986 0.1780693 93 1.795922 0.0758 Gender.z 0.0470799 0.0729259 93 0.645586 0.5201 Cong_Order.z 0.3030269 0.1360542 93 2.227252 0.0283 Stim_Order.z -0.2464764 0.1385114 93 -1.779467 0.0784 Drive.z -0.2643304 0.1752770 93 -1.508073 0.1349 ASRS_A.z 0.0501675 0.1989042 93 0.252220 0.8014 ASRS_B.z 0.2797611 0.1960712 93 1.426834 0.1570 ASRS_Total.z -0.2696833 0.3216709 93 -0.838383 0.4040 Diagnosis.z -0.0424021 0.0764425 93 -0.554693 0.5804 COHS.z -0.0849211 0.0688933 93 -1.232646 0.2208 FeedbackCondNoFeedback.z -0.2768735 0.0663077 103 -4.175587 0.0001 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B ASRS_T Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z -0.895 Diagnosis.z 0.062 -0.020 COHS.z -0.075 -0.014 0.056 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.50862643 -0.63690698 0.04735982 0.65185355 2.96649730 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 591.8593 655.2725 -276.9296 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.0001758412 0.9161766 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z + ASRS_A.z * FeedbackCondNoFeedback.z + ASRS_B.z * FeedbackCondNoFeedback.z + ASRS_Total.z * FeedbackCondNoFeedback.z + Diagnosis.z * FeedbackCondNoFeedback.z + COHS.z * FeedbackCondNoFeedback.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0662922 98 0.000000 1.0000 Age.z 0.3197986 0.1784573 93 1.792018 0.0764 Gender.z 0.0470799 0.0730848 93 0.644182 0.5210 Cong_Order.z 0.3030269 0.1363506 93 2.222410 0.0287 Stim_Order.z -0.2464764 0.1388131 93 -1.775599 0.0791 Drive.z -0.2643304 0.1756588 93 -1.504794 0.1358 ASRS_A.z 0.0501675 0.1993376 93 0.251671 0.8019 ASRS_B.z 0.2797611 0.1964984 93 1.423732 0.1579 ASRS_Total.z -0.2696833 0.3223717 93 -0.836560 0.4050 Diagnosis.z -0.0424021 0.0766090 93 -0.553487 0.5813 COHS.z -0.0849211 0.0690434 93 -1.229967 0.2218 FeedbackCondNoFeedback.z -0.2768735 0.0664521 98 -4.166510 0.0001 ASRS_A.z:FeedbackCondNoFeedback.z -0.2345784 0.1948810 98 -1.203700 0.2316 ASRS_B.z:FeedbackCondNoFeedback.z -0.2975861 0.1939802 98 -1.534105 0.1282 ASRS_Total.z:FeedbackCondNoFeedback.z 0.5161074 0.3179068 98 1.623455 0.1077 Diagnosis.z:FeedbackCondNoFeedback.z 0.0140595 0.0681878 98 0.206187 0.8371 COHS.z:FeedbackCondNoFeedback.z -0.0592250 0.0683809 98 -0.866105 0.3885 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z ASRS_B.z ASRS_Tt. Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z 0.692 ASRS_Total.z -0.901 -0.895 Diagnosis.z -0.055 0.062 -0.020 COHS.z 0.077 -0.075 -0.014 0.056 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 FdCNF. ASRS_A.: ASRS_B.: ASRS_T.: D.:FCN Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z ASRS_A.z:FeedbackCondNoFeedback.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.704 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 -0.905 -0.900 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 -0.040 0.101 -0.022 COHS.z:FeedbackCondNoFeedback.z 0.000 0.060 -0.091 0.004 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.56157612 -0.60411184 0.03389155 0.66644709 2.98005406 Number of Observations: 208 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(nogo_fb_model1)
r.squaredGLMM(nogo_fb_model2)
r.squaredGLMM(nogo_fb_model3)
r.squaredGLMM(nogo_fb_model4)
R2m | R2c |
---|---|
0.04196185 | 0.04196185 |
R2m | R2c |
---|---|
0.06186521 | 0.06186521 |
R2m | R2c |
---|---|
0.1388202 | 0.1388202 |
R2m | R2c |
---|---|
0.1572029 | 0.1572029 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(nogo_fb_model2) - r.squaredGLMM(nogo_fb_model1)
r.squaredGLMM(nogo_fb_model3) - r.squaredGLMM(nogo_fb_model2)
r.squaredGLMM(nogo_fb_model4) - r.squaredGLMM(nogo_fb_model3)
R2m | R2c |
---|---|
0.01990336 | 0.01990336 |
R2m | R2c |
---|---|
0.07695498 | 0.07695498 |
R2m | R2c |
---|---|
0.01838267 | 0.01838267 |
#Compare models
anova(nogo_fb_model1, nogo_fb_model2, nogo_fb_model3, nogo_fb_model4)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
nogo_fb_model1 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, data = myNoGoDay2Data, random = ~1 | Subject, method = "ML") | 1 | 8 | -135.0926 | -108.39226 | 75.54628 | NA | NA | |
nogo_fb_model2 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, data = myNoGoDay2Data, random = ~1 | Subject, method = "ML") | 2 | 13 | -129.4394 | -86.05141 | 77.71970 | 1 vs 2 | 4.346836 | 5.006339e-01 |
nogo_fb_model3 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond, data = myNoGoDay2Data, random = ~1 | Subject, method = "ML") | 3 | 14 | -145.1652 | -98.43966 | 86.58260 | 2 vs 3 | 17.725793 | 2.551435e-05 |
nogo_fb_model4 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond + ASRS_A * FeedbackCond + ASRS_B * FeedbackCond + ASRS_Total * FeedbackCond + Diagnosis * FeedbackCond + COHS * FeedbackCond, data = myNoGoDay2Data, random = ~1 | Subject, method = "ML") | 4 | 19 | -139.6348 | -76.22160 | 88.81741 | 3 vs 4 | 4.469627 | 4.839575e-01 |
#Read csv with Go information (Days 1 and 2)
myGoData <- read.csv('Exp8_Go_Full.csv')
#Take subset of dataframe to analyze Day 1 data (Familiar and Novel NoFeedback)
#Having inspected the day 1 standardized residuals, subjects 76, 79, 101, and 106 are outside the -3.3<x<3.3 range, thus day 1 outliers
myGoDay1Data <- subset(myGoData, FeedbackCond=="NoFeedback")
go_nofb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + ASRS_Total*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(go_nofb_model1, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model2, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model3, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model4, type="normalized")) > 3.3)
#Ran the following analyses with and without these outliers, and saw no difference. Therefore, the below outliers include complete dataset.
#myGoDay1Data <- subset(myGoDay1Data, Subject!=76 & Subject!=79 & Subject!=106)
go_nofb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + ASRS_Total*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(go_nofb_model1))
qqnorm(resid(go_nofb_model2))
qqnorm(resid(go_nofb_model3))
qqnorm(resid(go_nofb_model4))
plot(go_nofb_model1)
plot(go_nofb_model2)
plot(go_nofb_model3)
plot(go_nofb_model4)
vif(go_nofb_model1)
vif(go_nofb_model2)
vif(go_nofb_model3)
vif(go_nofb_model4)
beta(go_nofb_model1)
beta(go_nofb_model2)
beta(go_nofb_model3)
beta(go_nofb_model4)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 604.107 630.8073 -294.0535 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 5.836766e-05 0.9947938 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.06999345 104 0.0000000 1.0000 Age.z -0.07408585 0.18252633 98 -0.4058913 0.6857 Gender.z 0.02912284 0.07367987 98 0.3952618 0.6935 Cong_Order.z 0.04360765 0.13995916 98 0.3115741 0.7560 Stim_Order.z -0.01049453 0.14225899 98 -0.0737706 0.9413 Drive.z 0.11949893 0.18178583 98 0.6573611 0.5125 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Age.z 0.000 Gender.z 0.000 -0.206 Cong_Order.z 0.000 -0.082 0.129 Stim_Order.z 0.000 0.096 -0.212 -0.863 Drive.z 0.000 -0.922 0.178 0.090 -0.114 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.01171378 -0.40331764 -0.08290877 0.38128143 5.13999461 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 602.5203 645.9083 -288.2602 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 4.832084e-05 0.9674686 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0689293 104 0.0000000 1.0000 Age.z -0.1928827 0.1855562 93 -1.0394840 0.3013 Gender.z 0.0806437 0.0759920 93 1.0612132 0.2913 Cong_Order.z 0.0554230 0.1417745 93 0.3909236 0.6967 Stim_Order.z -0.0417606 0.1443351 93 -0.2893312 0.7730 Drive.z 0.1477204 0.1826464 93 0.8087781 0.4207 ASRS_A.z -0.4656776 0.2072671 93 -2.2467509 0.0270 ASRS_B.z -0.4846481 0.2043150 93 -2.3720630 0.0197 ASRS_Total.z 0.7820574 0.3351955 93 2.3331382 0.0218 Diagnosis.z 0.1474151 0.0796565 93 1.8506355 0.0674 COHS.z -0.0611066 0.0717900 93 -0.8511864 0.3969 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B ASRS_T Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 -0.020 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 -0.014 Dgnss. Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z 0.056 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.09794636 -0.45633592 -0.06737972 0.40435218 5.24372457 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 597.2374 643.9629 -284.6187 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 5.011307e-05 0.9506784 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0679056 103 0.0000000 1.0000 Age.z -0.1928827 0.1828005 93 -1.0551543 0.2941 Gender.z 0.0806437 0.0748635 93 1.0772110 0.2842 Cong_Order.z 0.0554230 0.1396690 93 0.3968168 0.6924 Stim_Order.z -0.0417606 0.1421915 93 -0.2936929 0.7696 Drive.z 0.1477204 0.1799339 93 0.8209705 0.4138 ASRS_A.z -0.4656776 0.2041890 93 -2.2806208 0.0249 ASRS_B.z -0.4846481 0.2012807 93 -2.4078219 0.0180 ASRS_Total.z 0.7820574 0.3302175 93 2.3683103 0.0199 Diagnosis.z 0.1474151 0.0784735 93 1.8785340 0.0634 COHS.z -0.0611066 0.0707238 93 -0.8640181 0.3898 StimulusTypeNovel.z 0.1798932 0.0680694 103 2.6427904 0.0095 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_T Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z -0.020 COHS.z -0.014 0.056 StimulusTypeNovel.z 0.000 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.34143067 -0.50059972 -0.04141968 0.46390871 5.14756449 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 598.7935 662.2068 -280.3968 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 5.296124e-05 0.9315763 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z + ASRS_A.z * StimulusTypeNovel.z + ASRS_B.z * StimulusTypeNovel.z + ASRS_Total.z * StimulusTypeNovel.z + Diagnosis.z * StimulusTypeNovel.z + COHS.z * StimulusTypeNovel.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0674065 98 0.0000000 1.0000 Age.z -0.1928827 0.1814569 93 -1.0629671 0.2905 Gender.z 0.0806437 0.0743132 93 1.0851871 0.2806 Cong_Order.z 0.0554230 0.1386425 93 0.3997549 0.6903 Stim_Order.z -0.0417606 0.1411464 93 -0.2958676 0.7680 Drive.z 0.1477204 0.1786114 93 0.8270493 0.4103 ASRS_A.z -0.4656776 0.2026882 93 -2.2975074 0.0238 ASRS_B.z -0.4846481 0.1998013 93 -2.4256505 0.0172 ASRS_Total.z 0.7820574 0.3277904 93 2.3858463 0.0191 Diagnosis.z 0.1474151 0.0778967 93 1.8924434 0.0615 COHS.z -0.0611066 0.0702040 93 -0.8704156 0.3863 StimulusTypeNovel.z 0.1798932 0.0675691 98 2.6623587 0.0091 ASRS_A.z:StimulusTypeNovel.z -0.2155653 0.1981567 98 -1.0878527 0.2793 ASRS_B.z:StimulusTypeNovel.z -0.0233703 0.1972407 98 -0.1184863 0.9059 ASRS_Total.z:StimulusTypeNovel.z 0.1400107 0.3232504 98 0.4331340 0.6659 Diagnosis.z:StimulusTypeNovel.z 0.1521622 0.0693340 98 2.1946269 0.0306 COHS.z:StimulusTypeNovel.z 0.0537176 0.0695303 98 0.7725784 0.4416 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z ASRS_B.z ASRS_Tt. Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z 0.692 ASRS_Total.z -0.901 -0.895 Diagnosis.z -0.055 0.062 -0.020 COHS.z 0.077 -0.075 -0.014 0.056 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 StmTN. ASRS_A.: ASRS_B.: ASRS_T.: D.:STN Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z StimulusTypeNovel.z ASRS_A.z:StimulusTypeNovel.z 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.704 ASRS_Total.z:StimulusTypeNovel.z 0.000 -0.905 -0.900 Diagnosis.z:StimulusTypeNovel.z 0.000 -0.040 0.101 -0.022 COHS.z:StimulusTypeNovel.z 0.000 0.060 -0.091 0.004 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.50738067 -0.46632070 -0.03624827 0.42873206 5.21130899 Number of Observations: 208 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(go_nofb_model1)
r.squaredGLMM(go_nofb_model2)
r.squaredGLMM(go_nofb_model3)
r.squaredGLMM(go_nofb_model4)
R2m | R2c |
---|---|
0.005631469 | 0.005631469 |
R2m | R2c |
---|---|
0.05975298 | 0.05975298 |
R2m | R2c |
---|---|
0.09224713 | 0.09224713 |
R2m | R2c |
---|---|
0.1285119 | 0.1285119 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(go_nofb_model2) - r.squaredGLMM(go_nofb_model1)
r.squaredGLMM(go_nofb_model3) - r.squaredGLMM(go_nofb_model2)
r.squaredGLMM(go_nofb_model4) - r.squaredGLMM(go_nofb_model3)
R2m | R2c |
---|---|
0.05412151 | 0.05412151 |
R2m | R2c |
---|---|
0.03249415 | 0.03249415 |
R2m | R2c |
---|---|
0.03626475 | 0.03626475 |
#Compare models
anova(go_nofb_model1, go_nofb_model2, go_nofb_model3, go_nofb_model4)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
go_nofb_model1 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, data = myGoDay1Data, random = ~1 | Subject, method = "ML") | 1 | 8 | -374.6533 | -347.9530 | 195.3266 | NA | NA | |
go_nofb_model2 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, data = myGoDay1Data, random = ~1 | Subject, method = "ML") | 2 | 13 | -376.2399 | -332.8519 | 201.1200 | 1 vs 2 | 11.586645 | 0.040912348 |
go_nofb_model3 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType, data = myGoDay1Data, random = ~1 | Subject, method = "ML") | 3 | 14 | -381.5229 | -334.7973 | 204.7614 | 2 vs 3 | 7.282949 | 0.006961216 |
go_nofb_model4 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + StimulusType + ASRS_A * StimulusType + ASRS_B * StimulusType + ASRS_Total * StimulusType + Diagnosis * StimulusType + COHS * StimulusType, data = myGoDay1Data, random = ~1 | Subject, method = "ML") | 4 | 19 | -379.9667 | -316.5535 | 208.9834 | 3 vs 4 | 8.443863 | 0.133410699 |
#Take subset of dataframe to analyze Familiar data (Feedback and NoFeedback). We'll call it "myGoDay2Data" for consistency.
myGoDay2Data <- subset(myGoData, StimulusType=="Familiar")
go_fb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + ASRS_Total*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
#Check for outliers
which(abs(residuals(go_fb_model1, type="normalized"))>3.3)
which(abs(residuals(go_fb_model2, type="normalized"))>3.3)
which(abs(residuals(go_fb_model3, type="normalized"))>3.3)
which(abs(residuals(go_fb_model4, type="normalized"))>3.3)
#Outliers make no difference - include in model
#myGoDay2Data <- subset(myGoDay2Data, Subject!=72 & Subject!=79)
go_fb_model1 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model2 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model3 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model4 <- lme(Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + ASRS_Total*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(go_fb_model1))
qqnorm(resid(go_fb_model2))
qqnorm(resid(go_fb_model3))
qqnorm(resid(go_fb_model4))
plot(go_fb_model1)
plot(go_fb_model2)
plot(go_fb_model3)
plot(go_fb_model4)
vif(go_nofb_model1)
vif(go_nofb_model2)
vif(go_nofb_model3)
vif(go_nofb_model4)
beta(go_fb_model1)
beta(go_fb_model2)
beta(go_fb_model3)
beta(go_fb_model4)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 597.6792 624.3795 -290.8396 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.1707384 0.9647754 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.06997506 104 0.0000000 1.0000 Age.z -0.0613929 0.18247836 98 -0.3364392 0.7373 Gender.z 0.0066434 0.07366050 98 0.0901888 0.9283 Cong_Order.z 0.3560088 0.13992238 98 2.5443307 0.0125 Stim_Order.z -0.2599243 0.14222161 98 -1.8276003 0.0707 Drive.z 0.0332854 0.18173806 98 0.1831503 0.8551 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Age.z 0.000 Gender.z 0.000 -0.206 Cong_Order.z 0.000 -0.082 0.129 Stim_Order.z 0.000 0.096 -0.212 -0.863 Drive.z 0.000 -0.922 0.178 0.090 -0.114 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.99623818 -0.47395858 0.08169038 0.58812210 4.44836472 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 600.6138 644.0017 -287.3069 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.0003616944 0.9630446 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0686141 104 0.0000000 1.0000 Age.z -0.1639613 0.1847077 93 -0.8876799 0.3770 Gender.z 0.0148724 0.0756445 93 0.1966094 0.8446 Cong_Order.z 0.3445313 0.1411263 93 2.4412979 0.0165 Stim_Order.z -0.2491802 0.1436751 93 -1.7343318 0.0862 Drive.z 0.1090113 0.1818113 93 0.5995849 0.5502 ASRS_A.z -0.2333807 0.2063194 93 -1.1311622 0.2609 ASRS_B.z -0.4210830 0.2033808 93 -2.0704168 0.0412 ASRS_Total.z 0.5230298 0.3336628 93 1.5675400 0.1204 Diagnosis.z -0.0236383 0.0792922 93 -0.2981167 0.7663 COHS.z -0.0836844 0.0714617 93 -1.1710385 0.2446 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A ASRS_B ASRS_T Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 -0.895 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 0.062 -0.020 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 -0.075 -0.014 Dgnss. Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z 0.056 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -5.131198387 -0.560063813 0.008018203 0.588561848 4.771032780 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 588.9856 635.7111 -280.4928 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.2319847 0.9035954 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0686651 103 0.000000 1.0000 Age.z -0.1639613 0.1848449 93 -0.887021 0.3774 Gender.z 0.0148724 0.0757007 93 0.196463 0.8447 Cong_Order.z 0.3445313 0.1412311 93 2.439486 0.0166 Stim_Order.z -0.2491802 0.1437818 93 -1.733045 0.0864 Drive.z 0.1090113 0.1819463 93 0.599140 0.5505 ASRS_A.z -0.2333807 0.2064726 93 -1.130323 0.2612 ASRS_B.z -0.4210830 0.2035318 93 -2.068880 0.0413 ASRS_Total.z 0.5230298 0.3339106 93 1.566377 0.1207 Diagnosis.z -0.0236383 0.0793511 93 -0.297895 0.7664 COHS.z -0.0836844 0.0715148 93 -1.170169 0.2449 FeedbackCondNoFeedback.z -0.2396447 0.0646982 103 -3.704037 0.0003 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z ASRS_A Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 0.692 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 -0.901 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 -0.055 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 0.077 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B ASRS_T Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z -0.895 Diagnosis.z 0.062 -0.020 COHS.z -0.075 -0.014 0.056 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.94074809 -0.46778961 0.03488434 0.47408152 4.96207009 Number of Observations: 208 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 588.8555 652.2687 -275.4277 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.3028307 0.8606425 Fixed effects: Acc_Diff.z ~ Age.z + Gender.z + Cong_Order.z + Stim_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + ASRS_Total.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z + ASRS_A.z * FeedbackCondNoFeedback.z + ASRS_B.z * FeedbackCondNoFeedback.z + ASRS_Total.z * FeedbackCondNoFeedback.z + Diagnosis.z * FeedbackCondNoFeedback.z + COHS.z * FeedbackCondNoFeedback.z Value Std.Error DF t-value p-value (Intercept) 0.0000000 0.0695580 98 0.000000 1.0000 Age.z -0.1639613 0.1872487 93 -0.875634 0.3835 Gender.z 0.0148724 0.0766852 93 0.193941 0.8466 Cong_Order.z 0.3445313 0.1430677 93 2.408169 0.0180 Stim_Order.z -0.2491802 0.1456516 93 -1.710797 0.0905 Drive.z 0.1090113 0.1843124 93 0.591448 0.5557 ASRS_A.z -0.2333807 0.2091577 93 -1.115812 0.2674 ASRS_B.z -0.4210830 0.2061786 93 -2.042321 0.0440 ASRS_Total.z 0.5230298 0.3382529 93 1.546268 0.1254 Diagnosis.z -0.0236383 0.0803830 93 -0.294071 0.7694 COHS.z -0.0836844 0.0724448 93 -1.155147 0.2510 FeedbackCondNoFeedback.z -0.2396447 0.0624241 98 -3.838974 0.0002 ASRS_A.z:FeedbackCondNoFeedback.z -0.1250251 0.1830683 98 -0.682942 0.4963 ASRS_B.z:FeedbackCondNoFeedback.z -0.3398210 0.1822221 98 -1.864873 0.0652 ASRS_Total.z:FeedbackCondNoFeedback.z 0.4852807 0.2986369 98 1.624986 0.1074 Diagnosis.z:FeedbackCondNoFeedback.z -0.0003312 0.0640546 98 -0.005171 0.9959 COHS.z:FeedbackCondNoFeedback.z -0.1007172 0.0642360 98 -1.567925 0.1201 Correlation: (Intr) Age.z Gndr.z Cng_O. Stm_O. Driv.z Age.z 0.000 Gender.z 0.000 -0.224 Cong_Order.z 0.000 -0.094 0.163 Stim_Order.z 0.000 0.103 -0.256 -0.864 Drive.z 0.000 -0.902 0.157 0.070 -0.094 ASRS_A.z 0.000 0.048 -0.135 0.055 0.045 -0.046 ASRS_B.z 0.000 0.126 -0.015 0.082 -0.055 -0.086 ASRS_Total.z 0.000 -0.069 0.051 -0.116 0.045 0.068 Diagnosis.z 0.000 -0.078 0.216 0.113 -0.115 -0.110 COHS.z 0.000 0.124 -0.078 0.015 0.006 -0.138 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 0.000 ASRS_A.z ASRS_B.z ASRS_Tt. Dgnss. COHS.z Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z 0.692 ASRS_Total.z -0.901 -0.895 Diagnosis.z -0.055 0.062 -0.020 COHS.z 0.077 -0.075 -0.014 0.056 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 0.000 FdCNF. ASRS_A.: ASRS_B.: ASRS_T.: D.:FCN Age.z Gender.z Cong_Order.z Stim_Order.z Drive.z ASRS_A.z ASRS_B.z ASRS_Total.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z ASRS_A.z:FeedbackCondNoFeedback.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.704 ASRS_Total.z:FeedbackCondNoFeedback.z 0.000 -0.905 -0.900 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 -0.040 0.101 -0.022 COHS.z:FeedbackCondNoFeedback.z 0.000 0.060 -0.091 0.004 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.9262141 -0.4826979 0.0380961 0.4935479 4.8900729 Number of Observations: 208 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(go_fb_model1)
r.squaredGLMM(go_fb_model2)
r.squaredGLMM(go_fb_model3)
r.squaredGLMM(go_fb_model4)
R2m | R2c |
---|---|
0.03558451 | 0.06487185 |
R2m | R2c |
---|---|
0.06837082 | 0.06837083 |
R2m | R2c |
---|---|
0.1260239 | 0.180068 |
R2m | R2c |
---|---|
0.1642271 | 0.2563038 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(go_fb_model2) - r.squaredGLMM(go_fb_model1)
r.squaredGLMM(go_fb_model3) - r.squaredGLMM(go_fb_model2)
r.squaredGLMM(go_fb_model4) - r.squaredGLMM(go_fb_model3)
R2m | R2c |
---|---|
0.03278631 | 0.003498984 |
R2m | R2c |
---|---|
0.0576531 | 0.1116972 |
R2m | R2c |
---|---|
0.03820317 | 0.07623578 |
#Compare models
anova(go_fb_model1, go_fb_model2, go_fb_model3, go_fb_model4)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
go_fb_model1 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive, data = myGoDay2Data, random = ~1 | Subject, method = "ML") | 1 | 8 | -620.4539 | -593.7536 | 318.2269 | NA | NA | |
go_fb_model2 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS, data = myGoDay2Data, random = ~1 | Subject, method = "ML") | 2 | 13 | -617.5193 | -574.1313 | 321.7596 | 1 vs 2 | 7.065421 | 0.2158201829 |
go_fb_model3 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond, data = myGoDay2Data, random = ~1 | Subject, method = "ML") | 3 | 14 | -629.1475 | -582.4219 | 328.5737 | 2 vs 3 | 13.628184 | 0.0002228153 |
go_fb_model4 | lme.formula(fixed = Acc_Diff ~ Age + Gender + Cong_Order + Stim_Order + Drive + ASRS_A + ASRS_B + ASRS_Total + Diagnosis + COHS + FeedbackCond + ASRS_A * FeedbackCond + ASRS_B * FeedbackCond + ASRS_Total * FeedbackCond + Diagnosis * FeedbackCond + COHS * FeedbackCond, data = myGoDay2Data, random = ~1 | Subject, method = "ML") | 4 | 19 | -629.2776 | -565.8644 | 333.6388 | 3 vs 4 | 10.130118 | 0.0716312450 |