JAS analyses: EMA dynamics metrics correlations + PTQ and ASI model comparisons for best EMA predictors

#load libraries
#install.packages("")
  library(glmulti)
  library(ggplot2)
  library(psycho)
  library(tidyverse)
  library(psych)
  library(magrittr) 
  library(dplyr) 
  library(apaTables)

EMA correlation table

#first create a correlation table with the various affective dynamics metrics

  #create a new mini ema dataset
  #columns 20:33 are the unweighted EMA AD metrics, 5 is number of responses, 4 is gender
  #omit rmssd and leave in mssd...
  #create two ancillary dataframes by valence...
  ema <- cog.ema[c(22,23,20,21,24,25,28:33,5,4)]
  neg.ema <- cog.ema[c(22,20,24,28,30,32,5,4)]
  pos.ema <- cog.ema[c(23,21,25,29,31,33,5,4)]
  
  #table to look at
  ema.correlations <- ema %>% 
    correlation(adjust = "none", i_am_cheating = T)
  summary(ema.correlations)

     #by valence: 
     neg.ema.correlations <- neg.ema %>% 
      correlation(adjust = "fdr")
     summary(neg.ema.correlations)

     pos.ema.correlations <- pos.ema %>% 
      correlation(adjust = "fdr")
     summary(pos.ema.correlations)

  
  #table to write out in APA format!
  #getwd()
  #apa.cor.table(neg.ema, filename="Table3_negEMAcorrs_APA.doc", table.number=1)
  #apa.cor.table(pos.ema, filename="Table2_posEMAcorrs_APA.doc", table.number=2)

#use glmulti to do some model comparisons

#create dataframe for negative model
# colnames(cog.ema)
# neg.ema <- cog.ema[c(22,20,24,28,30,32,5,4)]
# pos.ema <- cog.ema[c(23,21,25,29,21,33,5,4)]

#lets start with a model of negative affect EMA predictors of PTQ
#constrainted to 6 terms....
#neg.ema.ptq.model <- glmulti(total ~ UW.NAmean*UW.NASD*UW.NAmssd*UW.NAskew*UW.NAinertia*UW.NAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", minsize = 1, maxsize = 6)

#total~1 + UW.NAmean + UW.NAmssd:UW.NAmean + UW.NAkurt:UW.NAmean + UW.NAkurt:UW.NAinertia
#summary(neg.ema.ptq.model)
#coef(neg.ema.ptq.model)
#plot(neg.ema.ptq.model)

GLMULTI + LM to regress cognitive factors on EMA vars

model selection: PTQ on NA

#use glmulti to do some model comparisons: PTQ on NA EMA metrics: main effects only
#lets try one that does not contain interactions.... # level = 1 (versus default level = 2)
neg.ema.ptq.noX.model <- glmulti(total ~ UW.NAmean + UW.NASD + UW.NAskew + UW.NAkurt + UW.NAinertia + UW.NAmean*UW.NASD + UW.NAmean*UW.NAkurt + UW.NASD*UW.NAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: total~1+UW.NAmean+UW.NASD+UW.NAkurt+UW.NAinertia
Crit= 5493.90168684686
Mean crit= 5519.28239634064
Completed.

summary(neg.ema.ptq.noX.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 5493.902

$icvalues
 [1] 5493.902 5494.202 5495.897 5496.100 5498.535 5500.483 5501.038 5513.475 5513.838 5514.437 5515.327
[12] 5515.448 5515.836 5516.717 5516.960 5517.039 5517.811 5518.280 5518.694 5520.060 5520.071 5520.192
[23] 5533.296 5533.893 5535.747 5536.620 5536.962 5537.336 5537.759 5538.785 5539.397 5552.901

$bestmodel
[1] "total ~ 1 + UW.NAmean + UW.NASD + UW.NAkurt + UW.NAinertia"

$modelweights
 [1] 3.667344e-01 3.156046e-01 1.352388e-01 1.221674e-01 3.616685e-02 1.365193e-02 1.034418e-02
 [8] 2.061303e-05 1.718645e-05 1.274092e-05 8.163729e-06 7.684568e-06 6.330007e-06 4.075419e-06
[15] 3.608402e-06 3.468499e-06 2.357505e-06 1.864653e-06 1.516513e-06 7.659337e-07 7.616533e-07
[22] 7.167910e-07 1.023103e-09 7.591667e-10 3.004968e-10 1.941675e-10 1.636522e-10 1.357488e-10
[29] 1.098714e-10 6.578606e-11 4.842860e-11 5.661216e-14

$includeobjects
[1] TRUE

#coef(neg.ema.ptq.noX.model)
#plot(neg.ema.ptq.noX.model)

model fitting: PTQ on NA

#Now fit the best model
best.model.PTQ.NA.noX <- lm(total ~ 1 + UW.NAmean + UW.NASD + UW.NAinertia + UW.NAkurt, data = cog.ema)
summary(best.model.PTQ.NA.noX)

Call:
lm(formula = total ~ 1 + UW.NAmean + UW.NASD + UW.NAinertia + 
    UW.NAkurt, data = cog.ema)

Residuals:
    Min      1Q  Median      3Q     Max 
-26.091  -9.314   0.067   8.643  39.569 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.02023    3.83411   0.527   0.5984    
UW.NAmean     0.35497    0.07298   4.864 1.43e-06 ***
UW.NASD       0.52744    0.20505   2.572   0.0103 *  
UW.NAinertia -2.85619    2.02403  -1.411   0.1587    
UW.NAkurt    -0.43730    0.28914  -1.512   0.1309    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12.82 on 686 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.04611,   Adjusted R-squared:  0.04055 
F-statistic:  8.29 on 4 and 686 DF,  p-value: 1.561e-06

# getwd()
# apa.reg.table(best.model.PTQ.NA.noX, "/Volumes/Groups/AHeller_Lab/Administrator/Analysis/S-EMA/Structural Model/JAS additional analyses 01.2020/Table3_Reg_best.model.PTQ.NA.noX_APA.doc", table.number=3 )

Now repeat these steps for PA

model selection: PTQ on PA

# PTQ on PA (main effects only)
pos.ema.ptq.noX.model <- glmulti(total ~ UW.PAmean + UW.PASD + UW.PAskew + UW.PAkurt + UW.PAinertia + UW.PAmean*UW.PASD + UW.PAmean*UW.PAkurt + UW.PASD*UW.PAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: total~1+UW.PAmean+UW.PASD+UW.PAskew+UW.PAinertia
Crit= 5484.03949395371
Mean crit= 5514.03738445253
Completed.

summary(pos.ema.ptq.noX.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 5484.039

$icvalues
 [1] 5484.039 5484.279 5484.873 5486.247 5486.872 5488.371 5489.849 5502.613 5502.711 5503.513 5504.147
[12] 5504.698 5507.600 5507.902 5509.594 5516.112 5517.472 5517.484 5519.269 5520.267 5521.904 5522.262
[23] 5522.839 5532.855 5534.288 5534.557 5535.512 5536.248 5538.291 5539.359 5540.267 5552.901

$bestmodel
[1] "total ~ 1 + UW.PAmean + UW.PASD + UW.PAskew + UW.PAinertia"

$modelweights
 [1] 3.039566e-01 2.696010e-01 2.003291e-01 1.007765e-01 7.373669e-02 3.485777e-02 1.664125e-02
 [8] 2.815592e-05 2.681010e-05 1.795122e-05 1.307849e-05 9.929337e-06 2.326880e-06 2.000519e-06
[15] 8.585615e-07 3.298671e-08 1.671281e-08 1.661493e-08 6.806198e-09 4.131703e-09 1.822828e-09
[22] 1.523460e-09 1.141823e-09 7.631978e-12 3.727947e-12 3.259631e-12 2.021871e-12 1.398885e-12
[29] 5.038361e-13 2.952813e-13 1.875380e-13 3.387050e-16

$includeobjects
[1] TRUE

model fitting: PTQ on PA

#Now fit the best model
best.model.PTQ.PA.noX <- lm(total ~ 1 + UW.PAmean + UW.PASD + UW.PAskew + UW.PAinertia, data = cog.ema)
summary(best.model.PTQ.PA.noX)

Call:
lm(formula = total ~ 1 + UW.PAmean + UW.PASD + UW.PAskew + UW.PAinertia, 
    data = cog.ema)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.731  -9.549   0.066   8.258  37.217 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  34.22645    3.04166  11.253  < 2e-16 ***
UW.PAmean    -0.30231    0.05032  -6.008 3.05e-09 ***
UW.PASD       0.23919    0.09519   2.513   0.0122 *  
UW.PAskew    -1.27979    0.85753  -1.492   0.1361    
UW.PAinertia  0.97286    1.86740   0.521   0.6026    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12.73 on 686 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.05963,   Adjusted R-squared:  0.05415 
F-statistic: 10.87 on 4 and 686 DF,  p-value: 1.49e-08

# getwd()
# apa.reg.table(best.model.PTQ.PA.noX, "/Volumes/Groups/AHeller_Lab/Administrator/Analysis/S-EMA/Structural Model/JAS additional analyses 01.2020/Table4_Reg_best.model.PTQ.PA.noX_APA.doc", table.number=4 )

Now repeat these steps for ASI

model selection: ASI on NA

#### NA
# ASI on NA 

neg.ema.ASI.NA.noX.model <- glmulti(asi.short ~ UW.NAmean + UW.NASD + UW.NAskew + UW.NAkurt + UW.NAinertia + UW.NAmean*UW.NASD + UW.NAmean*UW.NAkurt + UW.NASD*UW.NAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: asi.short~1+UW.NAmean
Crit= 2220.83460452512
Mean crit= 2228.70107738161
Completed.

summary(neg.ema.ASI.NA.noX.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 2220.835

$icvalues
 [1] 2220.835 2222.359 2222.637 2222.782 2222.803 2224.249 2224.291 2224.312 2224.555 2224.626 2224.755
[12] 2226.157 2226.222 2226.250 2226.548 2228.138 2230.258 2230.691 2231.018 2231.902 2232.192 2232.236
[23] 2232.592 2232.687 2232.982 2233.013 2233.849 2233.901 2234.175 2234.591 2234.979 2235.849

$bestmodel
[1] "asi.short ~ 1 + UW.NAmean"

$modelweights
 [1] 0.2536224691 0.1183342991 0.1029839429 0.0957816042 0.0947919753 0.0460087380 0.0450336912
 [8] 0.0445811358 0.0394824008 0.0381056841 0.0357133265 0.0177185666 0.0171513011 0.0169116740
[15] 0.0145756750 0.0065808514 0.0022802867 0.0018359603 0.0015593645 0.0010019550 0.0008670325
[22] 0.0008479516 0.0007099080 0.0006767427 0.0005838630 0.0005750593 0.0003784844 0.0003688012
[29] 0.0003215795 0.0002612945 0.0002151361 0.0001392456

$includeobjects
[1] TRUE

model fitting: ASI on NA

best.model.ASI.NA.noX <- lm(asi.short ~ 1 + UW.NAmean, data = cog.ema)
summary(best.model.ASI.NA.noX)

Call:
lm(formula = asi.short ~ 1 + UW.NAmean, data = cog.ema)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.7024  -6.2998  -0.6354   5.1025  24.9407 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  1.82182    2.72954   0.667 0.504974    
UW.NAmean    0.20936    0.06158   3.400 0.000761 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.813 on 317 degrees of freedom
  (388 observations deleted due to missingness)
Multiple R-squared:  0.03518,   Adjusted R-squared:  0.03213 
F-statistic: 11.56 on 1 and 317 DF,  p-value: 0.0007612

# getwd()
# apa.reg.table(best.model.ASI.NA.noX, "/Volumes/Groups/AHeller_Lab/Administrator/Analysis/S-EMA/Structural Model/JAS additional analyses 01.2020/Table5_Reg_best.model.ASI.NA.noX_APA.doc", table.number=5 )

model selection: ASI on PA

#### PA
# ASI on PA 
pos.ema.ASI.PA.noX.model <- glmulti(asi.short ~ UW.PAmean + UW.PASD + UW.PAskew + UW.PAkurt + UW.PAinertia + UW.PAmean*UW.PASD + UW.PAmean*UW.PAkurt + UW.PASD*UW.PAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: asi.short~1+UW.PAmean+UW.PAinertia
Crit= 2217.74408243228
Mean crit= 2226.09760604686
Completed.

summary(pos.ema.ASI.PA.noX.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 2217.744

$icvalues
 [1] 2217.744 2218.029 2218.183 2218.209 2218.397 2218.676 2218.955 2219.314 2219.341 2219.594 2220.066
[12] 2220.183 2220.386 2220.395 2220.476 2221.207 2230.258 2231.049 2231.165 2231.741 2232.025 2232.111
[23] 2232.505 2232.835 2232.871 2232.988 2233.668 2233.849 2233.896 2234.465 2234.782 2235.760

$bestmodel
[1] "asi.short ~ 1 + UW.PAmean + UW.PAinertia"

$modelweights
 [1] 1.212971e-01 1.051701e-01 9.741169e-02 9.614200e-02 8.751771e-02 7.611337e-02 6.619191e-02
 [8] 5.532768e-02 5.459808e-02 4.809356e-02 3.798206e-02 3.583653e-02 3.237377e-02 3.223171e-02
[15] 3.095067e-02 2.147807e-02 2.325701e-04 1.565778e-04 1.477285e-04 1.107815e-04 9.610783e-05
[22] 9.205699e-05 7.561904e-05 6.411531e-05 6.296002e-05 5.937837e-05 4.226444e-05 3.861037e-05
[29] 3.770539e-05 2.837351e-05 2.421647e-05 1.484857e-05

$includeobjects
[1] TRUE

model fitting: ASI on PA

#Now fit the best model
best.model.PTQ.PA.noX <- lm(asi.short ~ 1 + UW.PAmean + UW.PAinertia, data = cog.ema)
summary(best.model.PTQ.PA.noX)

Call:
lm(formula = asi.short ~ 1 + UW.PAmean + UW.PAinertia, data = cog.ema)

Residuals:
     Min       1Q   Median       3Q      Max 
-13.6086  -6.6061  -0.7718   5.0403  27.1014 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  19.92438    2.35227   8.470 9.39e-16 ***
UW.PAmean    -0.16494    0.04185  -3.941 1.00e-04 ***
UW.PAinertia  2.80962    1.79459   1.566    0.118    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.763 on 316 degrees of freedom
  (388 observations deleted due to missingness)
Multiple R-squared:  0.05045,   Adjusted R-squared:  0.04444 
F-statistic: 8.395 on 2 and 316 DF,  p-value: 0.0002804

# getwd()
# apa.reg.table(best.model.PTQ.PA.noX, "/Volumes/Groups/AHeller_Lab/Administrator/Analysis/S-EMA/Structural Model/JAS additional analyses 01.2020/Table6_Reg_best.model.ASI.PA.noX_APA.doc", table.number=6 )

PTQ Model with the mostest (aka PA and NA): SELECTION

PTQ.NAPA.model <- glmulti(total ~ UW.NAmean + UW.NASD + UW.NAskew + UW.NAkurt + UW.NAinertia + UW.NAmean*UW.NASD + UW.NAmean*UW.NAkurt + UW.NASD*UW.NAkurt + UW.PAmean + UW.PASD + UW.PAskew + UW.PAkurt + UW.PAinertia + UW.PAmean*UW.PASD + UW.PAmean*UW.PAkurt + UW.PASD*UW.PAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: total~1+UW.NAmean+UW.NASD+UW.PAmean
Crit= 5485.85292902001
Mean crit= 5512.88338987986

After 100 models:
Best model: total~1+UW.NAmean+UW.NASD+UW.NAinertia+UW.PAmean
Crit= 5465.18833864867
Mean crit= 5507.78284250367

After 150 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5493.8353665852

After 200 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5485.35679152576

After 250 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5482.86678460949

After 300 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5479.9853187679

After 350 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5477.98878133276

After 400 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5475.54930309603

After 450 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5475.28586887797

After 500 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5473.78560253127

After 550 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5471.96415955726

After 600 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5469.71445199088

After 650 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5467.69962458636

After 700 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5466.38030398788

After 750 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5465.75242417848

After 800 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5465.02698521942

After 850 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5464.98288331395

After 900 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5464.833041867

After 950 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5464.56175781361

After 1000 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5464.47370012871

After 1050 models:
Best model: total~1+UW.NAmean+UW.NAinertia+UW.PAmean+UW.PASD
Crit= 5459.64084085942
Mean crit= 5464.19331161892

Completed.

summary(PTQ.NAPA.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 5459.641

$icvalues
  [1] 5459.641 5460.512 5460.705 5461.116 5461.253 5461.257 5461.499 5461.500 5461.515 5461.603 5461.640
 [12] 5462.037 5462.145 5462.235 5462.293 5462.467 5462.555 5462.559 5462.620 5462.929 5462.950 5463.007
 [23] 5463.017 5463.055 5463.081 5463.110 5463.111 5463.113 5463.135 5463.195 5463.207 5463.290 5463.384
 [34] 5463.442 5463.462 5463.465 5463.482 5463.552 5463.640 5463.813 5463.851 5463.930 5463.976 5463.983
 [45] 5464.053 5464.082 5464.115 5464.263 5464.435 5464.443 5464.476 5464.534 5464.699 5464.714 5464.784
 [56] 5464.830 5464.865 5464.877 5464.921 5464.923 5464.941 5464.946 5464.963 5464.979 5464.986 5464.994
 [67] 5465.012 5465.018 5465.036 5465.053 5465.090 5465.117 5465.188 5465.253 5465.332 5465.412 5465.471
 [78] 5465.551 5465.667 5465.726 5465.769 5465.812 5465.829 5465.861 5465.867 5465.889 5465.925 5466.187
 [89] 5466.205 5466.294 5466.329 5466.361 5466.437 5466.526 5466.547 5466.603 5466.641 5466.643 5466.701
[100] 5466.725

$bestmodel
[1] "total ~ 1 + UW.NAmean + UW.NAinertia + UW.PAmean + UW.PASD"

$modelweights
  [1] 0.067101317 0.043414849 0.039417058 0.032094516 0.029963719 0.029915418 0.026499225 0.026480936
  [9] 0.026281851 0.025157717 0.024694044 0.020248156 0.019184646 0.018338750 0.017812599 0.016328820
 [17] 0.015630123 0.015600717 0.015130671 0.012961377 0.012830452 0.012467280 0.012407957 0.012170313
 [25] 0.012015067 0.011843663 0.011834796 0.011824190 0.011695813 0.011346772 0.011283512 0.010821141
 [33] 0.010326764 0.010028707 0.009930440 0.009914766 0.009831525 0.009493714 0.009085075 0.008331724
 [41] 0.008174960 0.007857066 0.007678513 0.007651747 0.007391220 0.007282075 0.007165571 0.006652305
 [49] 0.006104995 0.006080744 0.005981370 0.005810210 0.005349923 0.005311283 0.005127676 0.005011897
 [57] 0.004924181 0.004894169 0.004789006 0.004784029 0.004741111 0.004728421 0.004689434 0.004652301
 [65] 0.004634444 0.004615762 0.004575162 0.004561897 0.004520258 0.004483366 0.004401097 0.004341925
 [73] 0.004188970 0.004056403 0.003898241 0.003745911 0.003636295 0.003493786 0.003297822 0.003201513
 [81] 0.003133536 0.003066433 0.003040517 0.002992429 0.002982868 0.002950507 0.002898025 0.002542399
 [89] 0.002519876 0.002410422 0.002368578 0.002330747 0.002243689 0.002145737 0.002123784 0.002064843
 [97] 0.002025853 0.002023632 0.001966324 0.001942562

$includeobjects
[1] TRUE

PTQ Model with the mostest (aka PA and NA): FITTING

summary(best.model.PTQ.NAPA)

Call:
lm(formula = total ~ 1 + UW.NAmean + UW.NAinertia + UW.PAmean + 
    UW.PASD, data = cog.ema)

Residuals:
    Min      1Q  Median      3Q     Max 
-29.817  -9.469  -0.306   8.474  35.159 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  16.68773    4.34086   3.844 0.000132 ***
UW.NAmean     0.35614    0.07047   5.054 5.56e-07 ***
UW.NAinertia -1.82007    1.95657  -0.930 0.352575    
UW.PAmean    -0.27257    0.04572  -5.962 3.99e-09 ***
UW.PASD       0.27678    0.09353   2.959 0.003191 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 12.51 on 686 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.09225,   Adjusted R-squared:  0.08696 
F-statistic: 17.43 on 4 and 686 DF,  p-value: 1.246e-13

ASI Model with the mostest (aka PA and NA): SELECTION

ASI.NAPA.model <- glmulti(asi.short ~ UW.NAmean + UW.NASD + UW.NAskew + UW.NAkurt + UW.NAinertia + UW.NAmean*UW.NASD + UW.NAmean*UW.NAkurt + UW.NASD*UW.NAkurt + UW.PAmean + UW.PASD + UW.PAskew + UW.PAkurt + UW.PAinertia + UW.PAmean*UW.PASD + UW.PAmean*UW.PAkurt + UW.PASD*UW.PAkurt, data = cog.ema, fitfunc = lm, crit = aic, method = "h", level = 1)

Initialization...
TASK: Exhaustive screening of candidate set.
Fitting...

After 50 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean
Crit= 2206.81347919884
Mean crit= 2224.035646542

After 100 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean
Crit= 2206.81347919884
Mean crit= 2224.46436851989

After 150 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2218.46491946091

After 200 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2215.96794735992

After 250 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2214.62197806637

After 300 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2213.75996645522

After 350 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2212.45293668365

After 400 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2211.00370670167

After 450 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2210.55525811289

After 500 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2210.4154097532

After 550 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2210.25728386507

After 600 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD
Crit= 2205.57606253891
Mean crit= 2209.88086159521

After 650 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2209.45100447665

After 700 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2209.28412765977

After 750 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2209.20823257763

After 800 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.98827247807

After 850 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.89689718833

After 900 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.81885175644

After 950 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.7773978808

After 1000 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.71995960851

After 1050 models:
Best model: asi.short~1+UW.NAmean+UW.PAmean+UW.PASD+UW.PAinertia
Crit= 2205.21481200334
Mean crit= 2208.67886852717

Completed.

summary(ASI.NAPA.model)

$name
[1] "glmulti.analysis"

$method
[1] "h"

$fitting
[1] "lm"

$crit
[1] "aic"

$level
[1] 1

$marginality
[1] FALSE

$confsetsize
[1] 100

$bestic
[1] 2205.215

$icvalues
  [1] 2205.215 2205.576 2206.112 2206.167 2206.358 2206.526 2206.813 2207.041 2207.077 2207.147 2207.165
 [12] 2207.184 2207.199 2207.279 2207.368 2207.408 2207.431 2207.438 2207.573 2207.601 2207.898 2207.958
 [23] 2207.975 2208.007 2208.026 2208.029 2208.070 2208.082 2208.087 2208.093 2208.099 2208.166 2208.221
 [34] 2208.266 2208.301 2208.347 2208.382 2208.396 2208.512 2208.682 2208.702 2208.757 2208.779 2208.792
 [45] 2208.833 2208.867 2208.879 2208.953 2208.990 2209.028 2209.036 2209.045 2209.049 2209.074 2209.107
 [56] 2209.129 2209.134 2209.154 2209.163 2209.190 2209.195 2209.197 2209.215 2209.244 2209.251 2209.257
 [67] 2209.260 2209.353 2209.362 2209.384 2209.397 2209.407 2209.408 2209.429 2209.446 2209.469 2209.541
 [78] 2209.657 2209.756 2209.779 2209.798 2209.802 2209.832 2209.884 2209.899 2209.924 2209.927 2209.938
 [89] 2209.948 2209.950 2209.969 2209.976 2209.981 2209.986 2209.993 2209.999 2210.022 2210.026 2210.038
[100] 2210.054

$bestmodel
[1] "asi.short ~ 1 + UW.NAmean + UW.PAmean + UW.PASD + UW.PAinertia"

$modelweights
  [1] 0.047189434 0.039391291 0.030131379 0.029316875 0.026650242 0.024494784 0.021217714 0.018932783
  [9] 0.018599123 0.017955223 0.017796498 0.017632427 0.017494978 0.016814452 0.016083767 0.015765016
 [17] 0.015583792 0.015529795 0.014513486 0.014309082 0.012338769 0.011969639 0.011873103 0.011683272
 [25] 0.011572095 0.011553965 0.011320689 0.011249714 0.011221927 0.011192154 0.011157378 0.010788676
 [33] 0.010497219 0.010262654 0.010084417 0.009857688 0.009684963 0.009616109 0.009073486 0.008336490
 [41] 0.008254971 0.008028666 0.007943124 0.007888098 0.007731433 0.007599682 0.007555412 0.007280367
 [49] 0.007147417 0.007013309 0.006985260 0.006950802 0.006940088 0.006853324 0.006740598 0.006666029
 [57] 0.006649090 0.006585187 0.006554075 0.006466196 0.006450286 0.006444925 0.006384483 0.006292755
 [65] 0.006272583 0.006252546 0.006242961 0.005959937 0.005934316 0.005869521 0.005828985 0.005800800
 [73] 0.005797735 0.005736805 0.005688247 0.005622889 0.005424188 0.005120381 0.004871627 0.004817608
 [81] 0.004771796 0.004761242 0.004690109 0.004570433 0.004536762 0.004480349 0.004472644 0.004448089
 [89] 0.004425368 0.004422499 0.004380313 0.004365891 0.004353055 0.004342265 0.004327536 0.004315230
 [97] 0.004266218 0.004257705 0.004231910 0.004197336

$includeobjects
[1] TRUE

ASI Model with the mostest (aka PA and NA): FITTING

summary(best.model.ASI.NAPA)

Call:
lm(formula = asi.short ~ 1 + UW.NAmean + UW.PAmean + UW.PASD + 
    UW.PAinertia, data = cog.ema)

Residuals:
     Min       1Q   Median       3Q      Max 
-13.7515  -6.2960  -0.3273   5.2571  24.1712 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   8.30756    3.66183   2.269 0.023966 *  
UW.NAmean     0.23407    0.06027   3.884 0.000125 ***
UW.PAmean    -0.17772    0.04107  -4.327 2.03e-05 ***
UW.PASD       0.14691    0.08680   1.693 0.091530 .  
UW.PAinertia  2.68530    1.75812   1.527 0.127675    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.588 on 314 degrees of freedom
  (388 observations deleted due to missingness)
Multiple R-squared:  0.0984,    Adjusted R-squared:  0.08691 
F-statistic: 8.567 on 4 and 314 DF,  p-value: 1.424e-06