**SOLVED**

Hello! I'm working on my Msc dissertation and have been using linear mixed effect modeling in R for my project. As of 8/31 everything was working great, the Rstudio portion of my project was done, and I just had to go on to write up my analysis. As of 9/1 when I opened and ran the program to keep writing, the output entirely change. I don't know why, I've duplicated the file and gone through it doing any edits I can think of and nothing has changed it. I started from scratch and rewrote only the LME portion of the analysis. I even went through and re-cleaned my data file thinking maybe that got corrupted. My only hint is that a few lines of code that were unrelated to the output that's not working weren't there when I opened the file (a few lines plotting the density of the data). I've looked up documentation and seen that Rstudio has a file for the history of the file but I can't seem to figure out how to restore it? Or if I have, it's not doing anything. Additionally, I've reinstalled and reupdated the lme4 and lmerTest packages. My dissertation deadline is very close, please help!

relevant packages: lme4 and lmerTest

relevant code (I made it into a function, but the actual lme stuff was written per my supervisor's direction):

```
myoptim <- "bobyqa"
cd_opt <- FALSE
control1 =glmerControl(optimizer=myoptim, optCtrl=list(maxfun=2e5), calc.derivs=cd_opt)
InteractionModel <- glmer(DV ~ IV1 * IV2 + (1 | DF$P_ID) + (1 | face_stimulus), data = DF, control = control1,
family = inverse.gaussian(link = 'identity')
NonInteractionModel <- glmer(DV ~ IV1 + IV2 + (1 | DF$P_ID) + (1 | face_stimulus), data = DF,
control = control1, family = inverse.gaussian(link = 'identity')
header_func("Interaction")
print(summary(InteractionModel))
header_func("No Interaction")
print(summary(NonInteractionModel))
header_func("Chi-squared comparing models")
anova(InteractionModel, NonInteractionModel)
```

All of this originally worked and had significant outputs. I've gone through several versions of this code and had the same output:

```
============================================================================"
[1] " "
[1] "Interaction"
[1] " "
[1] "----------------------------------------------------------------------------"
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: inverse.gaussian ( identity )
Formula: DV ~ IV1 * IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
Data: DF
Control: control
AIC BIC logLik deviance df.resid
14231.2 14273.2 -7106.6 14213.2 781
Scaled residuals:
Min 1Q Median 3Q Max
-1.1650 -0.4817 -0.1722 0.3721 8.4469
Random effects:
Groups Name Variance Std.Dev.
DF$P_ID (Intercept) 2.931e+06 1.712e+03
DF$face_stimulus (Intercept) 1.981e+04 1.407e+02
Residual 2.110e-04 1.453e-02
Number of obs: 790, groups: DF$P_ID, 33; DF$face_stimulus, 24
Fixed effects:
Estimate Std. Error t value Pr(>|z|)
(Intercept) 3833.34 329.73 11.626 < 2e-16 ***
IV11 -519.12 79.03 -6.568 5.08e-11 ***
IV21 623.46 138.28 4.509 6.52e-06 ***
IV22 -189.10 112.01 -1.688 0.0914 .
IV11:IV21 -20.91 131.62 -0.159 0.8738
IV11:IV22 -182.57 104.45 -1.748 0.0805 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) IV11 IV21 IV22 IV11:IV21
IV11 -0.116
IV21 0.089 -0.210
IV22 -0.028 -0.027 -0.693
IV11:IV21 -0.051 0.484 -0.542 0.427
IV11:IV22 0.001 -0.155 0.432 -0.687 -0.718
[1] "============================================================================"
[1] " "
[1] "No Interaction"
[1] " "
[1] "----------------------------------------------------------------------------"
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: inverse.gaussian ( identity )
Formula: DV ~ IV1 + IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
Data: DF
Control: control
AIC BIC logLik deviance df.resid
14235.8 14268.5 -7110.9 14221.8 783
Scaled residuals:
Min 1Q Median 3Q Max
-1.1477 -0.4852 -0.1755 0.3675 8.3921
Random effects:
Groups Name Variance Std.Dev.
DF$P_ID (Intercept) 2.901e+06 1.703e+03
DF$face_stimulus (Intercept) 2.064e+04 1.437e+02
Residual 2.175e-04 1.475e-02
Number of obs: 790, groups: DF$P_ID, 33; DF$face_stimulus, 24
Fixed effects:
Estimate Std. Error t value Pr(>|z|)
(Intercept) 3814.24 328.17 11.623 < 2e-16 ***
IV11 -492.57 67.71 -7.275 3.47e-13 ***
IV21 617.34 118.79 5.197 2.03e-07 ***
IV22 -343.15 82.17 -4.176 2.97e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) IV11 IV21
IV11 -0.096
IV21 0.077 0.073
IV22 -0.057 -0.060 -0.711
[1] "============================================================================"
[1] " "
[1] "Chi-squared comparing models"
[1] " "
[1] "----------------------------------------------------------------------------"
Data: DF
Models:
NonInteractionModel: DV ~ IV1 + IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
InteractionModel: DV ~ IV1 * IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
NonInteractionModel 7 14236 14268 -7110.9 14222
InteractionModel 9 14231 14273 -7106.6 14213 8.5829 2 0.01369 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```

But since opening it the other day, I keep getting stuck getting this output when I pass the same values to it:

```
[1] "============================================================================"
[1] " "
[1] "Interaction"
[1] " "
[1] "----------------------------------------------------------------------------"
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: inverse.gaussian ( identity )
Formula: DV ~ IV1 * IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
Data: DF
Control: control1
AIC BIC logLik deviance df.resid
14238.9 14280.9 -7110.4 14220.9 781
Scaled residuals:
Min 1Q Median 3Q Max
-1.1235 -0.4676 -0.1599 0.3710 7.9442
Random effects:
Groups Name Variance Std.Dev.
DF$P_ID (Intercept) 1.554e+06 1.247e+03
DF$face_stimulus (Intercept) 2.896e+04 1.702e+02
Residual 2.270e-04 1.507e-02
Number of obs: 790, groups: DF$P_ID, 33; DF$face_stimulus, 24
Fixed effects:
Estimate Std. Error t value Pr(>|z|)
(Intercept) 3699.10 254.51 14.534 < 2e-16 ***
IV11 -565.10 85.52 -6.608 3.89e-11 ***
IV21 638.72 150.46 4.245 2.19e-05 ***
IV22 -186.13 123.91 -1.502 0.1331
IV11:IV21 -11.47 141.67 -0.081 0.9355
IV11:IV22 -201.66 113.83 -1.772 0.0765 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) IV11 IV21 IV22 IV11:IV21
IV11 -0.175
IV21 0.125 -0.202
IV22 -0.034 -0.037 -0.686
IV11:IV21 -0.071 0.469 -0.550 0.429
IV11:IV22 -0.003 -0.135 0.437 -0.694 -0.715
[1] "============================================================================"
[1] " "
[1] "No Interaction"
[1] " "
[1] "----------------------------------------------------------------------------"
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
Family: inverse.gaussian ( identity )
Formula: DV ~ IV1 + IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
Data: DF
Control: control
AIC BIC logLik deviance df.resid
14235.8 14268.5 -7110.9 14221.8 783
Scaled residuals:
Min 1Q Median 3Q Max
-1.1477 -0.4852 -0.1755 0.3675 8.3921
Random effects:
Groups Name Variance Std.Dev.
DF$P_ID (Intercept) 2.901e+06 1.703e+03
DF$face_stimulus (Intercept) 2.064e+04 1.437e+02
Residual 2.175e-04 1.475e-02
Number of obs: 790, groups: DF$P_ID, 33; DF$face_stimulus, 24
Fixed effects:
Estimate Std. Error t value Pr(>|z|)
(Intercept) 3814.24 328.17 11.623 < 2e-16 ***
IV11 -492.57 67.71 -7.275 3.47e-13 ***
IV21 617.33 118.79 5.197 2.03e-07 ***
IV22 -343.15 82.17 -4.176 2.97e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) IV11 IV21
IV11 -0.096
IV21 0.077 0.073
IV22 -0.057 -0.060 -0.711
[1] "============================================================================"
[1] " "
[1] "Chi-squared comparing models"
[1] " "
[1] "----------------------------------------------------------------------------"
Data: DF
Models:
NonInteractionModel: DV ~ IV1 + IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
InteractionModel: DV ~ IV1 * IV2 + (1 | DF$P_ID) + (1 | DF$face_stimulus)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
NonInteractionModel 7 14236 14268 -7110.9 14222
InteractionModel 9 14239 14281 -7110.4 14221 0.8953 2 0.6391
```

What the heck?? Any help would be appreciated.

Edit: I should probably mention that this continued happening even when I used a version of the project from the 23rd with the current data.