Last updated: 2022-06-21

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add_gloabal_luminance_and_global_salience_to_main_data
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age_groups
combine_perceptual_and_motion_salience
data_preparation
define_social_vid_dataset
estimate_perceptual_and_motion_salience
exclude_missing_deviating_cases
figure_mediation_moderation
func_p_correction
grand_average_PD
group_differences
load_local_luminance
load_preprocessed_data
local_luminance_of-fixated_pixel
luminance_caluclation
luminance_data_frame
match_groups
merge_motion_and_perceptual_salience_data
merge_saccade_fixation_data
missings_in_clinical_variables
PCA_pupillary_response
plot_global_salience_luminace_figure
plot_salience_luminace_figure
polynomial_fit_comparison
power_analysis
progression_of_key_variables
pupil_size_between_groups
pupillary_response_by_salience
results_motion_salience
results_panelfigure_temporal_effects
results_physical_salience
results_pupillary_response
results_social_attention_with_pupillarycomponents
results_social_attention_with_pupillaryresponse
results_social_attention_with_salience
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sample descriptive statistics
scenes_duration
screen_attention
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setup

load data

data merging

fixation data and saccade data was previously preprocessed as separate files. These are now merged by saccade before a fixation. motion salience data is also merged with perceptual salience data

sample definition

also described in flow chart (see figure folder)

exclude cases (sample flow chart)

excluded. Missing demographic data, deviating IQ, ADOS comparative severity below clinical cutoff

## EXCLUDE CASES ####
      
      print('sample with available data / before exclusion:')

[1] "sample with available data / before exclusion:"

      nrow(df_agg)

[1] 543

      with(df_agg,table(t1_diagnosis))

t1_diagnosis
    ASD Control 
    315     228

  #DEMOGRAPHIC OUTLIER:
      #table(is.na(df_agg$t1_ageyrs)) #no missings
      #table(is.na(df_agg$t1_sex)) # no missings
      
      by(is.na(df_agg$t1_piq),df_agg$t1_diagnosis,table) #missing IQ data

df_agg$t1_diagnosis: ASD

FALSE  TRUE 
  310     5 
------------------------------------------------------------ 
df_agg$t1_diagnosis: Control

FALSE  TRUE 
  226     2

      by(df_agg$t1_piq<=60 | df_agg$t1_piq>=140,df_agg$t1_diagnosis,table) #IQ outlier

df_agg$t1_diagnosis: ASD

FALSE  TRUE 
  294    16 
------------------------------------------------------------ 
df_agg$t1_diagnosis: Control

FALSE  TRUE 
  219     7

      by(df_agg$SR==60,df_agg$t1_diagnosis,table) #sampling rate = 60hz

df_agg$t1_diagnosis: ASD

FALSE  TRUE 
  311     4 
------------------------------------------------------------ 
df_agg$t1_diagnosis: Control

FALSE  TRUE 
  224     4

      ### --> n=544
      df_agg<-df_agg[df_agg$t1_piq>60 & df_agg$t1_piq<140,] #n=23
      df_agg<-df_agg[!is.na(df_agg$t1_piq),] #n=7
      df_agg<-df_agg[df_agg$SR!=60,] #n=8
      
          
  ###--> STUDY SAMPLE
      #with(df_agg[df_agg$t1_css_total_all>3 | df_agg$t1_diagnosis=='Control',],table(t1_sensory_subgroups,t1_diagnosis))
      
      table(df_agg$t1_diagnosis == 'Control' | (!is.na(df_agg$t1_css_total_all) & df_agg$t1_css_total_all>3))


FALSE  TRUE 
   99   406

      df_sample<-df_agg[df_agg$t1_diagnosis == 'Control' | (!is.na(df_agg$t1_css_total_all) & df_agg$t1_css_total_all>3),] 
          with(df_sample,table(t1_diagnosis))

t1_diagnosis
    ASD Control 
    191     215

      #n=101
      #n=99 excluded
      #df_sample<-df_agg
      
      nrow(df_sample)

[1] 406

match groups

match groups based on initial group differences

            
             ASD Control
  adolescent  84      82
  adult       59      80
  child       48      53


    Welch Two Sample t-test

data:  t1_ageyrs by t1_diagnosis
t = -1.8108, df = 402.94, p-value = 0.07091
alternative hypothesis: true difference in means between group ASD and group Control is not equal to 0
95 percent confidence interval:
 -2.15579668  0.08850771
sample estimates:
    mean in group ASD mean in group Control 
             15.94244              16.97608


    Welch Two Sample t-test

data:  t1_piq by t1_diagnosis
t = -4.3791, df = 382.03, p-value = 1.541e-05
alternative hypothesis: true difference in means between group ASD and group Control is not equal to 0
95 percent confidence interval:
 -11.098979  -4.220616
sample estimates:
    mean in group ASD mean in group Control 
             100.0422              107.7020


    Pearson's Chi-squared test with Yates' continuity correction

data:  t1_sex and t1_diagnosis
X-squared = 1.6829, df = 1, p-value = 0.1945


    Welch Two Sample t-test

data:  Accuracy by t1_diagnosis
t = 5.1002, df = 295.37, p-value = 6.078e-07
alternative hypothesis: true difference in means between group ASD and group Control is not equal to 0
95 percent confidence interval:
 0.2378880 0.5368347
sample estimates:
    mean in group ASD mean in group Control 
           0.07190785           -0.31545349


    Welch Two Sample t-test

data:  Precision by t1_diagnosis
t = 1.0249, df = 388.4, p-value = 0.3061
alternative hypothesis: true difference in means between group ASD and group Control is not equal to 0
95 percent confidence interval:
 -0.07309426  0.23227557
sample estimates:
    mean in group ASD mean in group Control 
           -0.1031796            -0.1827702

### MATCHING sample by age, iq  ####  
      #sample size (n=406, --> matching process: ASD: n=75, TD: n=52)
      nrow(df_sample)

[1] 406

      with(df_sample,table(t1_diagnosis))

t1_diagnosis
    ASD Control 
    191     215

      #balancing based on age categories
      df_cov<-df_sample[,c('id','t1_ageyrs','t1_sex','t1_piq','t1_diagnosis','Accuracy','Precision')]
      groupBoo<-with(df_cov,ifelse(t1_diagnosis=='ASD',1,0))
      df_cov<-data.frame(df_cov,groupBoo)
      df_cov$piq<-round(df_cov$t1_piq) #round
      df_cov$age<-floor(df_cov$t1_ageyrs)
      
      #ALL - matching
      set.seed(100)
      all.match<-matchit(groupBoo~age+piq+Accuracy,
                         data=df_cov,
                         method='nearest',discard='both', 
                         replace=F,caliper=0.4)
      
      all.match

A matchit object
 - method: 1:1 nearest neighbor matching without replacement
 - distance: Propensity score [caliper, common support]
             - estimated with logistic regression
 - caliper: <distance> (0.057)
 - common support: units from both groups dropped
 - number of obs.: 406 (original), 328 (matched)
 - target estimand: ATT
 - covariates: age, piq, Accuracy

      #remove cases in unaggregated data frame
      all.match<-match.data(all.match)
      df_sample<-df_sample[df_sample$id %in% all.match$id,]
      
      df_fix<-df_fix[df_fix$id %in% all.match$id,]
      df_sac<-df_sac[df_sac$id %in% all.match$id,]
      df<-df[df$id %in% all.match$id,]

additional information

define age groups

no differences occured between age groups on key demographic variables

            
             ASD Control
  adolescent  73      67
  adult       53      55
  child       38      42


Call:
lm(formula = t1_ageyrs ~ t1_diagnosis * age_group)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1670 -1.8020  0.0008  1.5851  7.6823 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         14.7828     0.2869  51.527   <2e-16 ***
t1_diagnosisControl                  0.5876     0.4147   1.417    0.157    
age_groupadult                       8.1404     0.4424  18.402   <2e-16 ***
age_groupchild                      -5.1392     0.4903 -10.481   <2e-16 ***
t1_diagnosisControl:age_groupadult  -0.2178     0.6282  -0.347    0.729    
t1_diagnosisControl:age_groupchild  -0.5881     0.6879  -0.855    0.393    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.451 on 322 degrees of freedom
Multiple R-squared:  0.8195,    Adjusted R-squared:  0.8167 
F-statistic: 292.3 on 5 and 322 DF,  p-value: < 2.2e-16


Call:
lm(formula = t1_piq ~ t1_diagnosis * age_group)

Residuals:
    Min      1Q  Median      3Q     Max 
-40.710 -11.332   1.601  12.290  36.687 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)                         100.310      2.029  49.446   <2e-16 ***
t1_diagnosisControl                   2.020      2.933   0.689   0.4913    
age_groupadult                        1.088      3.128   0.348   0.7281    
age_groupchild                        6.418      3.467   1.851   0.0651 .  
t1_diagnosisControl:age_groupadult    2.291      4.442   0.516   0.6064    
t1_diagnosisControl:age_groupchild   -2.026      4.864  -0.416   0.6774    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 17.33 on 322 degrees of freedom
Multiple R-squared:  0.02188,   Adjusted R-squared:  0.00669 
F-statistic:  1.44 on 5 and 322 DF,  p-value: 0.2093


Call:
glm(formula = as.factor(t1_sex) ~ t1_diagnosis * age_group, family = binomial)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8174  -1.4350   0.8157   0.8672   0.9400  

Coefficients:
                                   Estimate Std. Error z value Pr(>|z|)    
(Intercept)                          1.4385     0.2973   4.839 1.31e-06 ***
t1_diagnosisControl                 -0.6544     0.3972  -1.648   0.0994 .  
age_groupadult                      -0.5089     0.4259  -1.195   0.2321    
age_groupchild                      -0.7846     0.4531  -1.732   0.0834 .  
t1_diagnosisControl:age_groupadult   0.6158     0.5821   1.058   0.2901    
t1_diagnosisControl:age_groupchild   0.5882     0.6151   0.956   0.3389    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 392.99  on 327  degrees of freedom
Residual deviance: 387.74  on 322  degrees of freedom
AIC: 399.74

Number of Fisher Scoring iterations: 4


Call:
lm(formula = Accuracy ~ t1_diagnosis * age_group)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3152 -0.4231 -0.1501  0.2360  2.3604 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                        -0.12669    0.06944  -1.824    0.069 .
t1_diagnosisControl                -0.12448    0.10038  -1.240    0.216  
age_groupadult                     -0.04448    0.10707  -0.415    0.678  
age_groupchild                     -0.15934    0.11869  -1.343    0.180  
t1_diagnosisControl:age_groupadult  0.09611    0.15205   0.632    0.528  
t1_diagnosisControl:age_groupchild  0.19950    0.16650   1.198    0.232  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5933 on 322 degrees of freedom
Multiple R-squared:  0.007859,  Adjusted R-squared:  -0.007547 
F-statistic: 0.5102 on 5 and 322 DF,  p-value: 0.7686


Call:
lm(formula = Accuracy ~ t1_diagnosis * age_group)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.3152 -0.4231 -0.1501  0.2360  2.3604 

Coefficients:
                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                        -0.12669    0.06944  -1.824    0.069 .
t1_diagnosisControl                -0.12448    0.10038  -1.240    0.216  
age_groupadult                     -0.04448    0.10707  -0.415    0.678  
age_groupchild                     -0.15934    0.11869  -1.343    0.180  
t1_diagnosisControl:age_groupadult  0.09611    0.15205   0.632    0.528  
t1_diagnosisControl:age_groupchild  0.19950    0.16650   1.198    0.232  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5933 on 322 degrees of freedom
Multiple R-squared:  0.007859,  Adjusted R-squared:  -0.007547 
F-statistic: 0.5102 on 5 and 322 DF,  p-value: 0.7686

missings in clinical variables

[1] "missing ADHD inattention data:"

     TRUE 
0.1676829

[1] "missing ADHD hyperactivity data:"

     TRUE 
0.1676829

[1] "missing Beck anxiety data"

     TRUE 
0.2286585

[1] "missing Beck depression data"

     TRUE 
0.2042683

grand average PD

variable is used in CFA

data selection

define scene duration

  ##DEFINE TIME SPAN ####
  df_scenes$vid_social<-ifelse(df_scenes$vid.id %in% c('50faces.mov','dollhouse.m4v','musicbooth.mov'),T,F)
  
  hist(diff(df_scenes$scene_time_onset)[diff(df_scenes$scene_time_onset)>0 & diff(df_scenes$scene_time_onset)<20000],30,
       xlab = 'time (ms)',main = 'duration of individual scenes',col='grey')
  abline(v=5000,lty=2)

      #create figure scene duration
      tiff(file="manuscript/supplements/figure_sceneduration.tiff", # create a file in tiff format in current working directory
      width=6, height=4, units="in", res=300, compression='lzw') #define size and resolution of the resulting figure
      
      hist(diff(df_scenes$scene_time_onset)[diff(df_scenes$scene_time_onset)>0 & diff(df_scenes$scene_time_onset)<20000],30,
           xlab = 'time (ms)',col='grey',main='')
      abline(v=5000,lty=2)
      
      dev.off() #close operation and save file

png 
  2

      #human scenes
      hist(diff(df_scenes$scene_time_onset[df_scenes$vid_social==T])[diff(df_scenes$scene_time_onset[df_scenes$vid_social==T])>0 & diff(df_scenes$scene_time_onset[df_scenes$vid_social==T])<20000],30,
           xlab = 'time (ms)',main = 'duration of individual scenes (AOI scenes)',col='grey')
      abline(v=5000,col="red")

      #non human scenes
      hist(diff(df_scenes$scene_time_onset[df_scenes$vid_social==F])[diff(df_scenes$scene_time_onset[df_scenes$vid_social==F])>0 & diff(df_scenes$scene_time_onset[df_scenes$vid_social==F])<20000],30,
           xlab = 'time (ms)',main = 'duration of individual scenes (non AOI scenes)',col='grey')
      abline(v=5000,col="red")

  #-->most scenes are shorter than 6 seconds
  
  median(diff(df_scenes$scene_time_onset)[diff(df_scenes$scene_time_onset)>0])

[1] 2880

  sd(diff(df_scenes$scene_time_onset)[diff(df_scenes$scene_time_onset)>0])

[1] 3543.542

  #number of scenes:
  length(table(droplevels(interaction(df$vid_scene_nr,df$vid.id))))

[1] 85

    #human scenes
    length(table(droplevels(interaction(df$vid_scene_nr[df$vid.id %in% c('50faces.mov','dollhouse.m4v','musicbooth.mov')],df$vid.id[df$vid.id %in% c('50faces.mov','dollhouse.m4v','musicbooth.mov')]))))

[1] 36

    #scenes only presented in adolescents and adults
    length(table(droplevels(interaction(df$vid_scene_nr[df$vid.id %in% c('dollhouse.m4v','musicbooth.mov')],df$vid.id[df$vid.id %in% c('dollhouse.m4v','musicbooth.mov')]))))

[1] 21

prepare data set

calcualte additional required variables. scaling of variables

screen attention

extract pupillary components

Chi-Squared Difference Test

                  Df    AIC    BIC  Chisq Chisq diff Df diff Pr(>Chisq)    
cfa_model_three 1172 -41779 -41388 2222.5                                  
cfa_model_two   1174 -41718 -41335 2287.3     64.793       2  8.517e-15 ***
cfa_model_one   1175 -41569 -41190 2438.8    151.505       1  < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Comparison of model fits: pupillary components
model	df	CFI	RMSEA	RMSEA CI-2.5%	RMSEA CI-97.5%
one component	1175	0.957	0.057	0.054	0.06
two components	1174	0.962	0.054	0.05	0.057
three components	1172	0.964	0.052	0.049	0.056

png 
  2

RESULTS

define function p-correction

SUPPORTING ANALYSES

gaze location comparison

png 
  2

absolute pupil size between groups

png 
  2

polynomial fit comparison

Comparison of polynomial fits: pupillary response
model	parameters (n)	AIC	BIC	logLik	deviance	Chisq	df	p
first degree	15	177165.5	177302.8	-88567.73	177135.5	NA	NA	NA
second degree	16	177165.2	177311.7	-88566.62	177133.2	2.216	1	0.137
third degree	17	177161.0	177316.6	-88563.49	177127.0	6.251	1	0.012
fourth degree	18	177162.0	177326.8	-88563.01	177126.0	0.970	1	0.325

global salience estimation

perceptual and motion salience is calculated and saved to file, will not be evaluated on knit

[1] 0.1178198

global luminance estimation

based on this conversion

step one: convert RGB values to relative values (is already done)
step two: convert relative values to a linear value (RGB values are gamma-encoded with a power curve)
step three: caculate LUMINANCE by sRGB coefficient

NOTE: use of for loop which is very slow (>1 hour) but does not overflow memory

compare salience and luminance on per-frame level

null device 
          1

null device 
          1


    Pearson's product-moment correlation

data:  df_salience_metrics$perceptual_salience_totalframe and df_salience_metrics$motion_salience_totalframe
t = 12.637, df = 11344, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.09963504 0.13592582
sample estimates:
      cor 
0.1178198


    Pearson's product-moment correlation

data:  df_salience_metrics$luminance and df_salience_metrics$motion_salience_totalframe
t = 10.048, df = 10285, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.07942023 0.11769409
sample estimates:
       cor 
0.09859362


    Pearson's product-moment correlation

data:  df_salience_metrics$luminance and df_salience_metrics$perceptual_salience_totalframe
t = 2.4787, df = 9938, p-value = 0.0132
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.005199675 0.044493669
sample estimates:
       cor 
0.02485627

add global luminance and global (overall) salience to the main data frame (df_model)

local luminance of fixated pixel

applies blur algorithm to luminance data, so that fixated pixel takes surrounding into acount
maps fixations to luminance data

load local luminance data

[1] 0.4114393

power analysis

sample descriptive statistics

Sample description
	ASD	TD	p-value
n	164	164
age	16.22/5.63	16.56/5.83	0.594
gender (F/M)	42/122	52/112	0.272
non-verbal IQ	102.15/18.11	104.59/16.61	0.204
baseline pupil size (mm)	3.69/0.55	3.67/0.49	0.773
fixation duration (ms)	340.84/40.8	338.43/37.81	0.579
screen attention (s)	73.5/41.55	84.05/44.45	0.027
center deviation of gaze (z)	-0.1/1.02	0.1/0.98	0.08
data quality - accuracy (z)	0.04/1.05	-0.04/0.94	0.486
data quality - precision (z)	0.02/1.01	-0.02/0.99	0.786
data quality - available data (%)	28.63/16.19	32.74/17.31	0.027
sampling rate (120Hz/300Hz)	120/44	133/31	0.115
SRS total	97.29/29.63	28.55/20.25	<0.001
ADHD inatt	4.38/3.16	1.31/2.03	<0.001
anxiety (BAS)	15.11/11.45	7.46/6.83	<0.001
depression (BDI)	14.78/12.35	6.15/6.41	<0.001

progression of key variables

png 
  2

MAIN ANALYSES

sensory salience

                    2.5 % 97.5 %
vid_socialTRUE -0.4 -0.66  -0.13

              2.5 % 97.5 %
rpd_lag -0.21 -0.34  -0.07

 contrast     estimate     SE  df asymp.LCL asymp.UCL
 FALSE - TRUE    0.204 0.0789 Inf    0.0498     0.359

Results are averaged over the levels of: t1_diagnosis, t1_sex 
Note: contrasts are still on the scale(0.121, 0.122) scale 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

 vid_social rpd_lag.trend     SE  df asymp.LCL asymp.UCL
 FALSE            -0.2994 0.0671 Inf    -0.431   -0.1678
  TRUE            -0.0833 0.0810 Inf    -0.242    0.0754

Results are averaged over the levels of: t1_diagnosis, t1_sex 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

Linear mixed model: physical salience
	Sum Sq	df1	df2	F	p	p_adj
group	2.314	1	37020.522	2.678	0.102	0.306
pupillary response (PR)	16.532	1	53651.559	19.137	0.000	0.000
video category (VC)	12.049	1	299.616	13.948	0.000	0.000
time^1	0.438	1	67423.958	0.507	0.477	1.000
time^2	0.071	1	67445.586	0.083	0.774	1.000
time^3	0.002	1	67452.971	0.003	0.958	1.000
sex	0.000	1	236.142	0.000	0.991	1.000
age	0.701	1	263.725	0.811	0.369	1.000
perceptual IQ	1.299	1	259.547	1.503	0.221	0.663
ADHD inattention	1.954	1	266.202	2.262	0.134	0.402
anxiety	0.418	1	203.650	0.483	0.488	1.000
depression	0.039	1	217.730	0.046	0.831	1.000
accuracy	1.664	1	297.966	1.926	0.166	0.498
precision	0.062	1	364.617	0.071	0.789	1.000
center deviation	732.059	1	60610.680	847.433	0.000	0.000
luminance	573.826	1	66932.153	664.262	0.000	0.000
group x PR	1.817	1	56980.910	2.104	0.147	0.441
group x VC	0.019	1	29940.180	0.022	0.881	1.000
PR x VC	6.255	1	29550.953	7.241	0.007	0.021
group x time^1	0.158	1	67419.609	0.183	0.669	1.000
group x time^2	0.007	1	67442.774	0.008	0.930	1.000
group x time^3	0.034	1	67450.756	0.039	0.843	1.000
PR x time^1	0.075	1	67427.198	0.087	0.768	1.000
PR x time^2	0.009	1	67448.675	0.010	0.920	1.000
PR x time^3	0.073	1	67455.560	0.085	0.771	1.000
VC x time^1	0.678	1	67447.563	0.785	0.376	1.000
VC x time^2	0.222	1	67460.945	0.257	0.612	1.000
VC x time^3	0.252	1	67459.443	0.292	0.589	1.000
group x PR x VC	0.067	1	31005.862	0.078	0.780	1.000
group x PR x time^1	0.088	1	67423.880	0.102	0.749	1.000
group x PR x time^2	0.000	1	67446.835	0.000	0.994	1.000
group x PR x time^3	0.071	1	67453.892	0.082	0.774	1.000
group x VC x time^1	1.094	1	67432.367	1.266	0.261	0.783
group x VC x time^2	1.129	1	67446.405	1.307	0.253	0.759
group x VC x time^3	0.966	1	67447.217	1.118	0.290	0.870
PR x VC x time^1	0.717	1	67447.386	0.829	0.362	1.000
PR x VC x time^2	0.277	1	67460.647	0.321	0.571	1.000
PR x VC x time^3	0.312	1	67458.853	0.361	0.548	1.000
group x PR x VC x time^1	0.963	1	67432.165	1.115	0.291	0.873
group x PR x VC x time^2	0.993	1	67446.518	1.149	0.284	0.852
group x PR x VC x time^3	0.845	1	67446.717	0.978	0.323	0.969

Linear mixed model: motion salience
	Sum Sq	df1	df2	F	p	p_adj
group	1.035	1	34071.971	1.164	0.281	0.843
pupillary response (PR)	1.691	1	41090.176	1.901	0.168	0.504
video category (VC)	3.475	1	277.584	3.907	0.049	0.147
time^1	1.526	1	67504.763	1.716	0.190	0.570
time^2	0.691	1	67515.544	0.777	0.378	1.000
time^3	0.291	1	67517.297	0.327	0.567	1.000
sex	0.022	1	241.132	0.024	0.876	1.000
age	0.063	1	275.941	0.071	0.790	1.000
perceptual IQ	0.354	1	256.233	0.397	0.529	1.000
ADHD inattention	0.883	1	288.214	0.993	0.320	0.960
anxiety	0.028	1	195.988	0.031	0.861	1.000
depression	0.048	1	220.586	0.053	0.817	1.000
accuracy	1.750	1	367.650	1.967	0.162	0.486
precision	2.322	1	480.455	2.611	0.107	0.321
center deviation	116.435	1	50057.135	130.917	0.000	0.000
luminance	71.875	1	66826.961	80.814	0.000	0.000
group x PR	1.074	1	45373.364	1.207	0.272	0.816
group x VC	0.236	1	17058.036	0.265	0.607	1.000
PR x VC	1.408	1	15371.712	1.583	0.208	0.624
group x time^1	0.430	1	67501.069	0.484	0.487	1.000
group x time^2	0.102	1	67509.458	0.115	0.735	1.000
group x time^3	0.008	1	67510.630	0.010	0.922	1.000
PR x time^1	0.043	1	67506.979	0.049	0.825	1.000
PR x time^2	0.475	1	67516.948	0.534	0.465	1.000
PR x time^3	0.884	1	67518.046	0.994	0.319	0.957
VC x time^1	20.788	1	67514.912	23.373	0.000	0.000
VC x time^2	29.605	1	67519.463	33.287	0.000	0.000
VC x time^3	33.605	1	67518.870	37.785	0.000	0.000
group x PR x VC	0.156	1	17746.994	0.175	0.675	1.000
group x PR x time^1	0.309	1	67503.532	0.348	0.555	1.000
group x PR x time^2	0.065	1	67510.490	0.073	0.786	1.000
group x PR x time^3	0.003	1	67510.893	0.003	0.957	1.000
group x VC x time^1	0.026	1	67506.595	0.029	0.865	1.000
group x VC x time^2	0.376	1	67511.296	0.423	0.516	1.000
group x VC x time^3	0.891	1	67511.223	1.001	0.317	0.951
PR x VC x time^1	18.730	1	67515.466	21.060	0.000	0.000
PR x VC x time^2	27.538	1	67520.065	30.963	0.000	0.000
PR x VC x time^3	31.424	1	67519.192	35.332	0.000	0.000
group x PR x VC x time^1	0.017	1	67506.800	0.019	0.890	1.000
group x PR x VC x time^2	0.319	1	67511.523	0.359	0.549	1.000
group x PR x VC x time^3	0.770	1	67511.354	0.866	0.352	1.000

 vid_social ts.scene rpd_lag.trend     SE  df asymp.LCL asymp.UCL
 FALSE          1000        -0.360 0.0688 Inf   -0.4951   -0.2252
  TRUE          1000         0.155 0.0826 Inf   -0.0071    0.3168
 FALSE          3000         0.346 0.0909 Inf    0.1675    0.5240
  TRUE          3000        -0.231 0.1023 Inf   -0.4317   -0.0306

Results are averaged over the levels of: t1_diagnosis, t1_sex 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

pupillary response

Linear mixed model: pupillary response
	Sum Sq	df1	df2	F	p	p_adj
group	0.108	1	302.734	0.156	0.693	1.000
video category (VC)	13.741	1	81.699	19.740	0.000	0.000
time^1	1.505	1	67260.232	2.161	0.142	0.426
time^2	1.521	1	67263.056	2.185	0.139	0.417
time^3	1.117	1	67261.557	1.604	0.205	0.615
sex	0.423	1	303.953	0.607	0.436	1.000
age	0.114	1	311.600	0.164	0.686	1.000
perceptual IQ	2.074	1	311.799	2.979	0.085	0.255
ADHD inattention	0.195	1	308.816	0.280	0.597	1.000
anxiety	2.915	1	297.315	4.187	0.042	0.126
depression	1.317	1	299.380	1.892	0.170	0.510
accuracy	0.108	1	311.184	0.155	0.694	1.000
precision	0.037	1	327.778	0.054	0.817	1.000
center deviation	242.023	1	67517.991	347.695	0.000	0.000
luminance	6.165	1	67366.054	8.857	0.003	0.009
group x VC	169.472	1	67523.791	243.467	0.000	0.000
group x time^1	1.091	1	67240.219	1.567	0.211	0.633
group x time^2	1.289	1	67242.512	1.852	0.174	0.522
group x time^3	1.132	1	67242.984	1.626	0.202	0.606
VC x time^1	0.908	1	67259.431	1.305	0.253	0.759
VC x time^2	0.692	1	67261.895	0.994	0.319	0.957
VC x time^3	0.989	1	67261.596	1.421	0.233	0.699
group x VC x time^1	6.556	1	67244.361	9.418	0.002	0.006
group x VC x time^2	5.921	1	67248.655	8.506	0.004	0.012
group x VC x time^3	4.663	1	67248.380	6.699	0.010	0.030

                    2.5 % 97.5 %
vid_socialTRUE 0.38  0.17   0.59

vid_social = FALSE:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.0776 0.0329 Inf    0.0131    0.1421

vid_social =  TRUE:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.0861 0.0334 Inf   -0.1515   -0.0207

Results are averaged over the levels of: t1_sex 
Note: contrasts are still on the scale(0.995, 0.115) scale 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

vid_social = FALSE, ts.scene =    0:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.1348 0.0442 Inf    0.0481    0.2214

vid_social =  TRUE, ts.scene =    0:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.1920 0.0467 Inf   -0.2836   -0.1004

vid_social = FALSE, ts.scene = 1000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.0842 0.0330 Inf    0.0195    0.1488

vid_social =  TRUE, ts.scene = 1000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.0823 0.0335 Inf   -0.1480   -0.0167

vid_social = FALSE, ts.scene = 2000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.0785 0.0329 Inf    0.0141    0.1430

vid_social =  TRUE, ts.scene = 2000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.0915 0.0333 Inf   -0.1567   -0.0263

vid_social = FALSE, ts.scene = 3000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.0933 0.0347 Inf    0.0252    0.1613

vid_social =  TRUE, ts.scene = 3000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.1471 0.0354 Inf   -0.2166   -0.0777

vid_social = FALSE, ts.scene = 4000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.1038 0.0365 Inf    0.0323    0.1753

vid_social =  TRUE, ts.scene = 4000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.1771 0.0375 Inf   -0.2505   -0.1037

vid_social = FALSE, ts.scene = 5000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control   0.0856 0.0638 Inf   -0.0394    0.2105

vid_social =  TRUE, ts.scene = 5000:
 contrast      estimate     SE  df asymp.LCL asymp.UCL
 ASD - Control  -0.1089 0.0643 Inf   -0.2350    0.0171

Results are averaged over the levels of: t1_sex 
Note: contrasts are still on the scale(0.995, 0.115) scale 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

gaze on face (social attention)

Linear mixed model: social attention without mediators
	Sum Sq	df1	df2	F	p	p_adj
group	3.353	1	290.249	5.957	0.015	0.045
time^1	504.599	1	30820.231	896.523	0.000	0.000
time^2	407.262	1	30824.812	723.583	0.000	0.000
time^3	350.614	1	30825.622	622.936	0.000	0.000
sex	2.061	1	288.837	3.662	0.057	0.171
age	13.854	1	294.259	24.615	0.000	0.000
perceptual IQ	6.369	1	297.504	11.315	0.001	0.003
ADHD inattention	1.101	1	293.419	1.956	0.163	0.489
anxiety	1.097	1	276.818	1.949	0.164	0.492
depression	1.889	1	282.513	3.356	0.068	0.204
accuracy	1.771	1	297.155	3.147	0.077	0.231
precision	1.010	1	322.461	1.794	0.181	0.543
center deviation	0.009	1	31066.055	0.016	0.899	1.000
luminance	1.268	1	31010.879	2.252	0.133	0.399
group x time^1	0.020	1	30819.180	0.035	0.851	1.000
group x time^2	0.007	1	30824.786	0.013	0.910	1.000
group x time^3	0.008	1	30824.660	0.013	0.908	1.000

                         2.5 % 97.5 %
t1_diagnosisControl 0.08  0.02   0.15

Linear mixed model: social attention without mediators and covariates
	Sum Sq	df1	df2	F	p	p_adj
group	9.313	1	297.305	16.547	0.000	0
time^1	504.481	1	30818.419	896.368	0.000	0
time^2	407.125	1	30823.431	723.385	0.000	0
time^3	350.543	1	30824.427	622.849	0.000	0
group x time^1	0.017	1	30817.686	0.030	0.862	1
group x time^2	0.006	1	30823.036	0.010	0.920	1
group x time^3	0.006	1	30823.110	0.011	0.917	1

                         2.5 % 97.5 %
t1_diagnosisControl 0.12  0.06   0.18

Linear mixed model: social attention with physical salience as mediator
	Sum Sq	df1	df2	F	p	p_adj
group	3.235	1	291.272	5.755	0.017	0.051
physical salience (PS)	10.369	1	30866.684	18.449	0.000	0.000
time^1	455.242	1	30817.719	809.988	0.000	0.000
time^2	366.488	1	30821.275	652.073	0.000	0.000
time^3	315.719	1	30821.199	561.744	0.000	0.000
sex	2.065	1	288.731	3.673	0.056	0.168
age	13.818	1	294.144	24.585	0.000	0.000
perceptual IQ	6.405	1	297.415	11.396	0.001	0.003
ADHD inattention	1.082	1	293.314	1.925	0.166	0.498
anxiety	1.137	1	276.758	2.024	0.156	0.468
depression	1.919	1	282.422	3.415	0.066	0.198
accuracy	1.812	1	297.025	3.224	0.074	0.222
precision	1.057	1	322.322	1.881	0.171	0.513
center deviation	0.090	1	31054.597	0.160	0.689	1.000
luminance	2.457	1	31000.482	4.371	0.037	0.111
group x PS	0.033	1	30887.318	0.058	0.809	1.000
group x time^1	0.002	1	30816.033	0.004	0.950	1.000
group x time^2	0.000	1	30820.713	0.000	0.994	1.000
group x time^3	0.000	1	30819.655	0.001	0.981	1.000
PS x time^1	16.802	1	30827.021	29.895	0.000	0.000
PS x time^2	17.812	1	30820.466	31.692	0.000	0.000
PS x time^3	18.146	1	30816.844	32.287	0.000	0.000
group x PS x time^1	0.016	1	30824.393	0.028	0.868	1.000
group x PS x time^2	0.003	1	30818.775	0.005	0.941	1.000
group x PS x time^3	0.000	1	30815.570	0.000	0.985	1.000

Data: lmm_model_data
Models:
lmm_model_sa: scale(aoi_face) ~ t1_diagnosis * (scale(ts.scene) + scale(I(ts.scene^2)) + scale(I(ts.scene^3))) + t1_sex + scale(t1_ageyrs) + scale(t1_piq) + scale(adhd_inatt) + scale(anx_beck) + scale(dep_beck) + scale(Accuracy) + scale(Precision) + scale(centdev) + scale(local_luminance) + (1 | id) + (1 | vid_scene)
lmm_model_sa_salience: scale(aoi_face) ~ t1_diagnosis * lowlvl_salience_z * (scale(ts.scene) + scale(I(ts.scene^2)) + scale(I(ts.scene^3))) + t1_sex + scale(t1_ageyrs) + scale(t1_piq) + scale(adhd_inatt) + scale(anx_beck) + scale(dep_beck) + scale(Accuracy) + scale(Precision) + scale(centdev) + scale(local_luminance) + (1 | id) + (1 | vid_scene)
                      npar   AIC   BIC logLik deviance  Chisq Df Pr(>Chisq)    
lmm_model_sa            21 71394 71569 -35676    71352                         
lmm_model_sa_salience   29 71358 71601 -35650    71300 51.451  8  2.148e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

                       2.5 % 97.5 %
lowlvl_salience_z 0.02  0.01   0.04

Linear mixed model: physical salience associated with group
	Sum Sq	df1	df2	F	p	p_adj
group	5.884	1	213.453	6.726	0.010	0.030
time^1	9.909	1	31033.902	11.327	0.001	0.003
time^2	10.224	1	31045.286	11.687	0.001	0.003
time^3	8.778	1	31044.125	10.035	0.002	0.006
sex	0.082	1	217.917	0.094	0.759	1.000
age	0.151	1	237.616	0.173	0.678	1.000
perceptual IQ	5.205	1	230.683	5.950	0.015	0.045
ADHD inattention	0.101	1	241.570	0.116	0.734	1.000
anxiety	6.394	1	180.242	7.310	0.008	0.024
depression	1.284	1	199.422	1.468	0.227	0.681
accuracy	0.190	1	279.845	0.217	0.642	1.000
precision	0.270	1	342.195	0.309	0.579	1.000
center deviation	539.821	1	27757.653	617.093	0.000	0.000
luminance	555.348	1	30715.623	634.843	0.000	0.000
group x time^1	0.052	1	31020.531	0.060	0.807	1.000
group x time^2	0.037	1	31031.798	0.042	0.838	1.000
group x time^3	0.044	1	31030.718	0.050	0.823	1.000

                         2.5 % 97.5 %
t1_diagnosisControl 0.04  0.01   0.07

Linear mixed model: social attention with pupillary response as mediator
	Sum Sq	df1	df2	F	p	p_adj
group	1.831	1	12094.657	3.252	0.071	0.213
pupillary response (PR)	2.664	1	29700.403	4.733	0.030	0.090
time^1	21.723	1	30777.189	38.593	0.000	0.000
time^2	17.733	1	30782.878	31.505	0.000	0.000
time^3	15.342	1	30785.790	27.257	0.000	0.000
sex	2.069	1	289.519	3.676	0.056	0.168
age	13.584	1	294.933	24.135	0.000	0.000
perceptual IQ	6.502	1	298.283	11.551	0.001	0.003
ADHD inattention	1.156	1	293.943	2.054	0.153	0.459
anxiety	1.078	1	277.412	1.915	0.168	0.504
depression	1.823	1	283.028	3.239	0.073	0.219
accuracy	1.798	1	297.652	3.194	0.075	0.225
precision	1.098	1	322.953	1.951	0.163	0.489
center deviation	0.001	1	31016.188	0.001	0.970	1.000
luminance	1.368	1	30959.518	2.430	0.119	0.357
group x PR	4.250	1	30043.967	7.552	0.006	0.018
group x time^1	0.181	1	30775.197	0.322	0.571	1.000
group x time^2	0.114	1	30780.823	0.203	0.652	1.000
group x time^3	0.119	1	30783.984	0.211	0.646	1.000
PR x time^1	6.116	1	30777.317	10.866	0.001	0.003
PR x time^2	4.961	1	30782.316	8.814	0.003	0.009
PR x time^3	4.274	1	30784.638	7.593	0.006	0.018
group x PR x time^1	0.176	1	30775.159	0.312	0.576	1.000
group x PR x time^2	0.107	1	30780.078	0.190	0.663	1.000
group x PR x time^3	0.112	1	30782.531	0.199	0.655	1.000

Data: lmm_model_data[!is.na(lmm_model_data$rpd_lag), ]
Models:
lmm_model_sa: scale(aoi_face) ~ t1_diagnosis * (scale(ts.scene) + scale(I(ts.scene^2)) + scale(I(ts.scene^3))) + t1_sex + scale(t1_ageyrs) + scale(t1_piq) + scale(adhd_inatt) + scale(anx_beck) + scale(dep_beck) + scale(Accuracy) + scale(Precision) + scale(centdev) + scale(local_luminance) + (1 | id) + (1 | vid_scene)
lmm_model_sa_rpd: scale(aoi_face) ~ t1_diagnosis * rpd_lag * (scale(ts.scene) + scale(I(ts.scene^2)) + scale(I(ts.scene^3))) + +t1_sex + scale(t1_ageyrs) + scale(t1_piq) + scale(adhd_inatt) + scale(anx_beck) + scale(dep_beck) + scale(Accuracy) + scale(Precision) + scale(centdev) + scale(local_luminance) + (1 | id) + (1 | vid_scene)
                 npar   AIC   BIC logLik deviance  Chisq Df Pr(>Chisq)    
lmm_model_sa       21 71316 71491 -35637    71274                         
lmm_model_sa_rpd   29 71304 71546 -35623    71246 27.292  8  0.0006295 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 t1_diagnosis rpd_lag.trend    SE  df asymp.LCL asymp.UCL
 ASD                 -0.349 0.104 Inf    -0.554   -0.1452
 Control             -0.109 0.086 Inf    -0.278    0.0596

Results are averaged over the levels of: t1_sex 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95

Linear mixed model: social attention with pupillary components as mediator
	Sum Sq	df1	df2	F	p	p_adj
group	2.819	1	296.322	4.855	0.028	0.084
early pupillary comp. (PC1)	24.273	1	30919.518	41.808	0.000	0.000
late pupillary comp. (PC2)	11.815	1	31000.258	20.351	0.000	0.000
sex	2.157	1	288.545	3.715	0.055	0.165
age	13.784	1	294.165	23.742	0.000	0.000
perceptual IQ	6.693	1	297.452	11.528	0.001	0.003
ADHD inattention	1.326	1	293.203	2.284	0.132	0.396
anxiety	1.001	1	276.239	1.725	0.190	0.570
depression	1.887	1	282.021	3.250	0.072	0.216
accuracy	1.769	1	296.904	3.046	0.082	0.246
precision	1.116	1	322.570	1.923	0.167	0.501
center deviation	0.031	1	31073.530	0.053	0.817	1.000
luminance	1.424	1	31018.382	2.453	0.117	0.351
group x PC1	1.085	1	30976.252	1.869	0.172	0.516
group x PC2	1.945	1	30996.216	3.349	0.067	0.201

                2.5 % 97.5 %
rpd_RC1_z -0.04 -0.05  -0.03

               2.5 % 97.5 %
rpd_RC2_z 0.01     0   0.03

panel figure - mediation and moderation

png 
  2

panel figure - temporal effects

png 
  2

R version 4.2.0 (2022-04-22 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale:
[1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8   
[3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C                   
[5] LC_TIME=German_Germany.utf8    

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] lattice_0.20-45    rcrossref_1.1.0    simr_1.0.6         missMDA_1.18      
 [5] psych_2.2.5        lavaan_0.6-11      MuMIn_1.46.0       emmeans_1.7.4-1   
 [9] lmerTest_3.1-3     lme4_1.1-29        Matrix_1.4-1       smoothie_1.0-3    
[13] RColorBrewer_1.1-3 readr_2.1.2        gtools_3.9.2.2     readbitmap_0.1.5  
[17] hexbin_1.28.2      kableExtra_1.3.4   gridExtra_2.3      sjmisc_2.8.9      
[21] sjPlot_2.8.10      ggplot2_3.3.6      reshape2_1.4.4     R.matlab_3.6.2    
[25] MatchIt_4.4.0      Gmisc_3.0.0        htmlTable_2.4.0    Rcpp_1.0.8.3      
[29] mice_3.14.0        zoo_1.8-10         readxl_1.4.0      

loaded via a namespace (and not attached):
  [1] utf8_1.2.2           R.utils_2.11.0       tidyselect_1.1.2    
  [4] htmlwidgets_1.5.4    FactoMineR_2.4       munsell_0.5.0       
  [7] codetools_0.2-18     effectsize_0.7.0     DT_0.23             
 [10] miniUI_0.1.1.1       withr_2.5.0          colorspace_2.0-3    
 [13] highr_0.9            knitr_1.39           rstudioapi_0.13     
 [16] leaps_3.1            stats4_4.2.0         labeling_0.4.2      
 [19] git2r_0.30.1         RLRsim_3.1-8         mnormt_2.1.0        
 [22] farver_2.1.0         datawizard_0.4.1     rprojroot_2.0.3     
 [25] vctrs_0.4.1          generics_0.1.2       xfun_0.31           
 [28] R6_2.5.1             doParallel_1.0.17    promises_1.2.0.1    
 [31] scales_1.2.0         nnet_7.3-17          gtable_0.3.0        
 [34] bmp_0.3              workflowr_1.7.0      rlang_1.0.2         
 [37] systemfonts_1.0.4    scatterplot3d_0.3-41 splines_4.2.0       
 [40] broom_0.8.0          checkmate_2.1.0      yaml_2.3.5          
 [43] abind_1.4-5          modelr_0.1.8         backports_1.4.1     
 [46] httpuv_1.6.5         Hmisc_4.7-0          tools_4.2.0         
 [49] ellipsis_0.3.2       jquerylib_0.1.4      plyr_1.8.7          
 [52] base64enc_0.1-3      purrr_0.3.4          rpart_4.1.16        
 [55] ggrepel_0.9.1        cluster_2.1.3        fs_1.5.2            
 [58] crul_1.2.0           magrittr_2.0.3       data.table_1.14.2   
 [61] forestplot_2.0.1     mvtnorm_1.1-3        whisker_0.4         
 [64] hms_1.1.1            mime_0.12            evaluate_0.15       
 [67] xtable_1.8-4         pbkrtest_0.5.1       XML_3.99-0.10       
 [70] sjstats_0.18.1       jpeg_0.1-9           ggeffects_1.1.2     
 [73] compiler_4.2.0       tibble_3.1.7         crayon_1.5.1        
 [76] minqa_1.2.4          R.oo_1.25.0          htmltools_0.5.2     
 [79] mgcv_1.8-40          later_1.3.0          tzdb_0.3.0          
 [82] tiff_0.1-11          Formula_1.2-4        tidyr_1.2.0         
 [85] lubridate_1.8.0      sjlabelled_1.2.0     MASS_7.3-56         
 [88] boot_1.3-28          car_3.1-0            cli_3.3.0           
 [91] R.methodsS3_1.8.2    parallel_4.2.0       insight_0.17.1      
 [94] pkgconfig_2.0.3      flashClust_1.01-2    numDeriv_2016.8-1.1 
 [97] foreign_0.8-82       binom_1.1-1.1        xml2_1.3.3          
[100] foreach_1.5.2        pbivnorm_0.6.0       svglite_2.1.0       
[103] bslib_0.3.1          webshot_0.5.3        estimability_1.3    
[106] rvest_1.0.2          stringr_1.4.0        digest_0.6.29       
[109] parameters_0.18.1    httpcode_0.3.0       rmarkdown_2.14      
[112] cellranger_1.1.0     curl_4.3.2           shiny_1.7.1         
[115] nloptr_2.0.3         lifecycle_1.0.1      nlme_3.1-157        
[118] jsonlite_1.8.0       carData_3.0-5        viridisLite_0.4.0   
[121] fansi_1.0.3          pillar_1.7.0         fastmap_1.1.0       
[124] httr_1.4.3           plotrix_3.8-2        survival_3.3-1      
[127] glue_1.6.2           bayestestR_0.12.1    png_0.1-7           
[130] iterators_1.0.14     stringi_1.7.6        sass_0.4.1          
[133] performance_0.9.1    latticeExtra_0.6-29  dplyr_1.0.9

data_analysis_salience

Nico Bast

21 6 2022