Datasets:

drndr

/

statcodesearch

like 1

Linear mixed effect model (RTs) with ART_English

Nword_LME_AR = lmer(-1000/RT ~ primec*AR_English + (1|item) + (1+primec|subject), data = nbyTrial, contrasts = list(primec = cc1), control=lmerControl(optimizer="bobyqa", optCtrl=list(maxfun=2e5))) summary(Nword_LME_AR) plot(allEffects(Nword_LME_AR), x.var = "AR_English")

Statistical Modeling

https://osf.io/gztxa/

Vowel_Harmony_LDT_Naming.R

exclude features with zero or nearzero variance

exclude_zero.var = nearZeroVar(data[, which(!colnames(data) %in% no.features)], freqCut = 95/5, uniqueCut = 10, saveMetrics = FALSE, allowParallel = TRUE) length(exclude_zero.var) exclude_zero.var = colnames(data[, which(!colnames(data) %in% no.features)])[exclude_zero.var] data = data %>% dplyr::select(-all_of(exclude_zero.var))

Data Variable

https://osf.io/b7krz/

ML_target_independent_preprocessing.R

Add the posterior_samples values as a column in the dataframe. Repeat it as many times as there are cell in the factorial design, namely 2. factor estimates

post.data$b_GramGender2 <- rep(posterior_samples(model.study2)[['b_GramGender2']], 2)

Data Variable

https://osf.io/5xdbu/

study2-rscript.R

create dataset with only correct trials and only trials above 100 ms

data.3SD <- data[data$corr == 1, ] data.3SD <- data.3SD[data.3SD$min2000 == 1, ] data.3SD <- data.3SD[data.3SD$plus100 == 1, ]

Data Variable

https://osf.io/5yvnb/

analyse_final_Exp6_OSF.R

Function for showing parameter estimats with pvalues for polr models

showCoefWithPValue <- function(model) { coef <- coef(summary(model)) pvalue <- pnorm(abs(coef[, "t value"]), lower.tail = FALSE) * 2 coef <- cbind(coef, "pvalue" = pvalue) return(round(coef, 4)) }

Statistical Modeling

https://osf.io/aczx5/

220423_FinalModelEstimation.R

Correlation between bulletin user type and avalanche awareness training

cor.test(as.numeric(PartBkgr$BullUseType), as.numeric(PartBkgr$BackgrAvTraining), method = "spearman")

Data Variable

https://osf.io/aczx5/

220423_FinalModelEstimation.R

This additional analysis test the influence of trialbytrial blink duration on response error by computing individual Bayes factors the Bayes factor is calculated by examining the residuals of a null model and how they correlate with blinkdurations the procedure use a 'default' prior for calculating bayes factors for correlations code here:

source("BF_correlations.R") bf_null <- rep(NA, length(unique(d$Subject))) dur_slope <- rep(NA, length(unique(d$Subject))) for(i in 1:length(bf_null)){ d_i <- d[d$Subject==unique(d$Subject)[i] & d$cond1==1,] m0 <- lm(ResponseError~vel1, d_i) m1 <- lm(ResponseError~vel1 + cond1:bdur, d_i) dur_slope[i] <- coef(m1)[3] bf_null[i] <- 1/bf10JeffreysIntegrate(n=nrow(d_i), r=cor(residuals(m0), d_i$bdur)) }

Statistical Test

https://osf.io/f6qsk/

analysis_exp1.R

median and range of BF supporting the null

round(median(bf_null[bf_null > 10^(1/2)]),digits=2) round(range(bf_null[bf_null > 10^(1/2)]),digits=2)

Statistical Test

https://osf.io/f6qsk/

analysis_exp1.R

calculate number of days between symptoms

mutate( ill_where_n=ifelse(sym_temp==1, 1, 0), ill_where_n=cumsum(ill_where_n), ill_where_n=ifelse(sym_temp==1, ill_where_n, NA), prev_day_ill=lag(sym_temp, 1), day_lag=lag(day_date, 1),

Data Variable

https://osf.io/n7sep/

code.R

recode illness vars as NA if no specific symptoms during episode

mutate_at(vars(ill_where_id, ill_where_n, ill_where_start, ill_where_end, ill_where_new), funs(ifelse(any_specif_sym==FALSE, NA,. ))) %>%

Data Variable

https://osf.io/n7sep/

code.R

remove spaces, brackets, periods in column names

names(df) <- gsub("\\s+", "", names(df)) names(df) <- gsub("\\(|\\)", "", names(df)) names(df) <- gsub("\\.", "", names(df)) cat(" 💅 Column names fixed (no spaces, brackets, periods)\n")

Data Variable

https://osf.io/mp9td/

preflight.r

initialization of the vectors with the names of the modueles/variables

names.modules.df.w <- names.var.modules <- NULL

Data Variable

https://osf.io/kgtx6/

f_readRShare.R

list of the directories included in the datadir directory

my.dirlist <- list.dirs(path = datadir) if (length(my.dirlist) == 0) stop("There are no subdirectories in the directory that you selected.")

Data Variable

https://osf.io/kgtx6/

f_readRShare.R

creates a list with the names of the variables included for each module

names.var.modules <- vector("list", num.waves) names(names.var.modules) <- paste("Wave", waves) if(verbose) cat("Reading data from the selected modules within the waves. \n")

Data Variable

https://osf.io/kgtx6/

f_readRShare.R

check which of the specified variables are actually present in the downloaded data

which.var.ok <- variables.in.modules[[i.mod]] %in% names(new.data) if (any(!which.var.ok)) warning( "Some of the variables that you selected from module ", modules[i.mod], " in wave ", waves[i.wave], " are not included in the data set. \n The variables that could not be imported were: ", variables.in.modules[[i.mod]][!which.var.ok], ". Please check these variables, the other variables were exported. \n" ) new.data <- select(new.data, variables.in.modules[[i.mod]][which.var.ok]) } names(new.data)[-1] <- paste(names(new.data)[-1], modules[i.mod], sep = "...")

Data Variable

https://osf.io/kgtx6/

f_readRShare.R

Trim outliers Perform winsorizing (replacefor each individual seperatelyvalues below 5th percentile and values above 95th percentile with 5th and 95th percentile values respectively)

for(i in min(all.df$person):max(all.df$person)){ all.df$zw.HR[all.df$person == i] <- Winsorize(all.df$z.HR[all.df$person==i]) }

Data Variable

https://osf.io/qj86m/

2_data_prep_merge.R

Create a lag variable the data is lag within person and within days

lag.Y = function(data){ Y_lag = rep(0,nrow(data)) subjno.i = unique(data$subjno) for (i in subjno.i){ n.i = which(data$subjno==i) Y_lag[n.i] = shift(data$Y[n.i],1) } return(Y_lag) }

Data Variable

https://osf.io/vguey/

lag.Y.R

Personality models Extract standardized parameters and N from personality models

Values_Analysis1_Model1_Pers <- Values_Personality[which(Values_Personality$analysis == "analysis1" & ((Values_Personality$study %in% c("S1", "S2") & Values_Personality$DV == "well_being") | (Values_Personality$study == "S3" & Values_Personality$DV == "affect_balance")) & Values_Personality$output %in% c("standardized", "N")), ] Values_Analysis1_Model2_Pers <- Values_Personality[which(Values_Personality$analysis == "analysis2" & ((Values_Personality$study %in% c("S1", "S2") & Values_Personality$DV == "well_being") | (Values_Personality$study == "S3" & Values_Personality$DV == "affect_balance")) & Values_Personality$output %in% c("standardized", "N")), ] Values_Analysis1_Model3_Pers <- Values_Personality[which(Values_Personality$analysis == "analysis3" & ((Values_Personality$study %in% c("S1", "S2") & Values_Personality$DV == "well_being") | (Values_Personality$study == "S3" & Values_Personality$DV == "affect_balance")) & Values_Personality$output %in% c("standardized", "N")), ]

Data Variable

https://osf.io/nxyh3/

MainTables.R

Extract withinperson variances of interactions with close peers, family, and weak ties from unstandardized models

variances <- Values_Base$est[which(Values_Base$analysis == "analysis3" & ((Values_Base$study %in% c("S1", "S2") & Values_Base$DV == "well_being") | (Values_Base$study == "S3" & Values_Base$DV == "affect_balance")) & Values_Base$output == "unstandardized" & Values_Base$paramHeader == "Variances" & Values_Base$param %in% c("PEERS", "FAMILY", "WEAK_TIES") & Values_Base$BetweenWithin == "Within")] rows <- which(Values_Analysis1_Model3$paramHeader %in% c("S1|WB.ON", "S2|WB.ON", "S3|WB.ON") & Values_Analysis1_Model3$param %in% c("PEERS", "FAMILY", "WEAK_TIES")) # within-person effects variances <- Values_Personality$est[which(Values_Personality$analysis == "analysis3" & ((Values_Personality$study %in% c("S1", "S2") & Values_Personality$DV == "well_being") | (Values_Personality$study == "S3" & Values_Personality$DV == "affect_balance")) & Values_Personality$output == "unstandardized" & Values_Personality$paramHeader == "Variances" & Values_Personality$param %in% c("PEERS", "FAMILY", "WEAK_TIES") & Values_Personality$BetweenWithin == "Within")] rows <- which(Values_Analysis1_Model3_Pers$paramHeader %in% c("S1|WB.ON", "S2|WB.ON", "S3|WB.ON") & Values_Analysis1_Model3_Pers$param %in% c("PEERS", "FAMILY", "WEAK_TIES")) # within-person effects

Statistical Modeling

https://osf.io/nxyh3/

MainTables.R

Model selection for four data sets First find bestfitting random effect model, then test for fixed effect predictors Process is backward by elimintating weakest terms sequentially, starting with full model, until only significant effects remain Nested model comparisons (likelihood ratio tests using anova command) are used to select best fitting models _ Pair Experiment 1 Random effects: selecting empty model

pair1_rand_full <- lmer(distance ~ (1 | group) + (1 + session0 | pair), data = pair_data1) # full random model;; fails to converge pair1_rand_full <- lmer(distance ~ (1 | group) + (1 + session0 | pair), data = pair_data1, control = lmerControl(optimizer = "bobyqa")) # full random model;; uses bobyqa optimizer pair1_rand2 <- lmer(distance ~ (1 + session0 | pair), data = pair_data1) # drops group ran.intercept;; fails to converge pair1_rand2 <- lmer(distance ~ (1 + session0 | pair), data = pair_data1, control = lmerControl(optimizer = "bobyqa")) # drops group ran.intercept;; uses bobyqa optimizer;; BEST pair1_rand3 <- lmer(distance ~ (1 | pair), data = pair_data1) # drops random slope

Statistical Modeling

https://osf.io/67ncp/

duque_etal_2020_rcode.R

Pearson's correlation calculation

calc_r <- function(model_vector, data_vector, S_0, D_0, M_0, E_0, filename) { sst_mean <- mean(data_vector) sst_vector <- (data_vector - sst_mean) ^ 2 sst <- sum(sst_vector) ssr_vector <- (data_vector - model_vector) ^ 2 ssr <- sum(ssr_vector) r_sq <- 1 - (ssr / sst) cat(r_sq, file = paste(format(Sys.time(),"%Y_%m_%d_%H_%M"), filename, "r_sq", "S", S_0, "D", D_0, "M", M_0, "E", E_0, ".txt", sep = "_")) return(r_sq) } r_sq <- calc_r(CO2_flux_ratios_hat_median, data_vector, S_0, D_0, M_0, E_0, filename)

Statistical Test

https://osf.io/7mey8/

stan_AWB_adriana_pools5i_vary_mic.r

Data wrangling recode values for missing data (9, 1) in whole dataset as NA

data <- data %>% mutate_all(~na_if(., -9)) data <- data %>% mutate_all(~na_if(., -1))

Data Variable

https://osf.io/r4wg2/

elsa_analyses.R

age recode values of 99 as missing

data$indager.w2[data$indager.w2 == 99] <- NA data$age <- data$indager.w2

Data Variable

https://osf.io/r4wg2/

elsa_analyses.R

Fit models with trimmed weights Weighted regression model for SciPop Score (Goertz approach)

m3.scipopgoertz.trim.svyglm.fit <- svyglm(scipopgoertz ~ age + gender + education.comp + education.uni + sciprox.score + urbanity.log + languageregion.ger + languageregion.ita + polorientation + religiosity + interestscience + sciliteracy + trustscience + trustscientists, design = bar.design.scipopgoertz.trim, family = gaussian, na.action = na.omit)

Statistical Modeling

https://osf.io/qj4xr/

06_sensitivity-tests.R

for each row assign fixation probability (1 or 0) to each picture based on the activation !! need a better decision criteria when the activations are the same;; !! right now it assigns '1' to both

dt.act$max.act = apply(dt.act[,4:7], 1, max) dt.act = subset(dt.act, max.act != 0)

Data Variable

https://osf.io/fsbzw/

plot.fix.e1.R

4.4.4) Create data where u(x) is at sample means to get residuals based on rest of models to act as yobs Recall: columns 1 & 2 have y and u(x) in obs.data

data.xufixed =data.obs data.xufixed[,2]=mean(data.obs[,2]) #Note, the 1st predictor, 2nd columns, is always the one hypothesized to be u-shaped

Data Variable

https://osf.io/hu2n8/

Simonsohn_twolines_tweaked.R

6 RUN TWO LINE REGRESSIONS 6.1 First an interrupted regression at the midpoint of the flat region

rmid=reg2(f,xc=median(xflat),graph=0, axislabels=axislabels)

Statistical Modeling

https://osf.io/hu2n8/

Simonsohn_twolines_tweaked.R

Pairwise comparison of optimism scores for each cue and table Wilcoxon signedrank test

cue_diff_wilcox_ts = wilcox.paired.multcomp( opt_score ~ cue | id, subset(jbt_all, jbt_all$cjb_test == "ts"), p.method = "holm") cue_diff_wilcox_tunnel = wilcox.paired.multcomp( opt_score ~ cue | id, subset(jbt_all, jbt_all$cjb_test == "tunnel"), p.method = "holm")

Statistical Test

https://osf.io/z6nm8/

Stats_figures_JBT.R

round the numeric values (p that is not < 0.001)

cue_diff_wilcox_ts$p.value[numeric_values] <- round(as.numeric(cue_diff_wilcox_ts$p.value[numeric_values]), 3) names(cue_diff_wilcox_ts) <- c("Cues compared","p-value")

Data Variable

https://osf.io/z6nm8/

Stats_figures_JBT.R

new tibble with IDs and emails

wos_emails <- tibble( "responseID" = wos$response_ID, "email" = wos$email ) aaas_emails <- tibble( "responseID" = aaas$response_ID, "email" = aaas$email )

Data Variable

https://osf.io/3bn9u/

4_1_deidentification.R

drop rows with empty email fields

wos_emails <- drop_na( wos_emails, email ) aaas_emails <- drop_na( aaas_emails, email )

Data Variable

https://osf.io/3bn9u/

4_1_deidentification.R

getmode() Get mode of a variable Input: numeric vector Output: single number

getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] }

Data Variable

https://osf.io/8fzns/

2H_Recode_helper.R

For studyProgramme remove countries with only one programme

if(varname == "studyProgram" & length(lev) == 1){ return(NA) }

Data Variable

https://osf.io/8fzns/

2H_Recode_helper.R

Model for Frequency DV

mod1.1 <- lmer(frequency ~ ruleType + (1|sceneType) + (1|Participant), contrasts = my.contrasts, data = PD3a.Long) icc(mod1.1) summary(mod1.1) emmeans(mod1.1,list(pairwise~ruleType),adjust="satterthwaite") confint(mod1.1)

Data Variable

https://osf.io/dhmjx/

ExperimentS1-Analyses.R

Define functions Means, SDs, and two sample ttests comparing Swiss census data and the panel sample

census.msd <- function(v) { v <- reformulate(v) cbind( as.data.frame(svyby(v, ~t, dslong_dsgn, svymean, na.rm = T))[-c(1,3)], # M (exclude SE) sqrt(as.data.frame(svyby(v, ~t, dslong_dsgn, svyvar, na.rm = T)[[2]]))) %>% # SD (with a workaround) set_colnames(c("M", "SD")) %>% set_rownames(c("2019 (census)", "2019 (panel)")) }

Statistical Test

https://osf.io/3hgpe/

02_analysis.R

RQ1: Examine predictors of change in SciPop Scores and subscale scores .. Visual inspection with Sankey plot Visualize withinsubject change in SciPop Score in Sankey plot

dslongpnl %>% group_by(t, scipopgoertz) %>% summarise(scipopgoertz_freq = n()) %>% merge(dslongpnl, by = c("scipopgoertz", "t")) %>% transform(scipopgoertz_lvls = factor(scipopgoertz, levels = c("5", "4.5", "4", "3.5", "3", "2.5", "2", "1.5", "1", "NA"))) %>% replace_na(list(scipopgoertz_lvls = "NA")) %>% ggplot(aes(x = t, stratum = scipopgoertz_lvls, alluvium = id, y = scipopgoertz_freq, fill = scipopgoertz_lvls, label = scipopgoertz_lvls)) + scale_x_discrete(expand = c(.1, .1)) + scale_fill_viridis_d() + geom_flow(width = .1) + geom_stratum(alpha = .5, width = .1) + geom_text(stat = "stratum", size = 4) + ggtitle("Sankey plot of within-subject change in SciPop Score") + theme_minimal() + theme(legend.position = "none", panel.grid = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank())

Visualization

https://osf.io/3hgpe/

02_analysis.R

H1 analyses .. Descriptive analyses Plot means, SDs, skewness, kurtosis of alternative SciPop Scores

meantbl <- as.data.frame(matrix(0, nrow = 2)) groups <- quos(scipopgoertz, scipopbollenm, scipopbollencfa, scipopsartori75, scipopsartoricat) for (j in seq_along(groups)) { meantbl <- cbind(meantbl, group_by(dslongpnl, t) %>% summarise(mean = mean(!!groups[[j]], na.rm = T), sd = sd(!!groups[[j]], na.rm = T), skewness = skewness(!!groups[[j]], na.rm = T), kurtosis = kurtosis(!!groups[[j]], na.rm = T))) names(meantbl)[names(meantbl) == "mean"] <- paste0(quo_text(groups[[j]]), " (M)") names(meantbl)[names(meantbl) == "sd"] <- paste0(quo_text(groups[[j]]), " (SD)") names(meantbl)[names(meantbl) == "skewness"] <- paste0(quo_text(groups[[j]]), " (Skewness)") names(meantbl)[names(meantbl) == "kurtosis"] <- paste0(quo_text(groups[[j]]), " (Kurtosis)") meantbl <- select(meantbl, contains("scipop")) %>% set_rownames(c("2019", "2020")) }

Visualization

https://osf.io/3hgpe/

02_analysis.R

save means and compute their higher order terms

dfs$x.mean <- mean( dfs$x, na.rm = TRUE ) dfs$y.mean <- mean( dfs$y, na.rm = TRUE ) dfs$x2.mean <- dfs$x.mean^2 dfs$xy.mean <- dfs$x.mean*dfs$y.mean dfs$y2.mean <- dfs$y.mean^2 dfs }

Statistical Modeling

https://osf.io/jhyu9/

PrepareData.R

add column indicating if individual gave birth during testing (this will help with age classification)

birthsanc <- read_excel("motherssanctuary.xlsx", col_types=c("text", "date")) bigdatasanctuary$Individual<-as.factor(bigdatasanctuary$Individual) birthsanc$Mother <- as.factor(birthsanc$Mother) bigdatasanctuary$mother <- ifelse(bigdatasanctuary$Individual %in% birthsanc$Mother, "yes", "no") bigdatasanctuary$birth <- birthsanc$Date[match(bigdatasanctuary$Individual, birthsanc$Mother)] bigdatasanctuary$birth <- as.Date(bigdatasanctuary$birth, format="YY-mm-dd") bigdatasanctuary$testbeforebirth <- ifelse(!is.na(bigdatasanctuary$birth) & bigdatasanctuary$birth > bigdatasanctuary$TestDate, "yes", "no") bigdatasanctuary$AgeAtTesting <- as.numeric(bigdatasanctuary$AgeAtTesting)

Data Variable

https://osf.io/p2xgq/

Analyses for revision

Step 1 compare the rising ridge asymmetric congruence model (RRCA) and the full thirdorder model (cubic)

rrca_comp <- compare2(rrca_myrsa, "RRCA", "cubic") rrca_comp

Statistical Modeling

https://osf.io/drv3a/

illustration.R

How much variance in the outcome can be explained by the full thirdorder polynomial model/the rising ridge leveldependent congruence model?

getPar(rrcl_myrsa, model="cubic", type="R2") getPar(rrcl_myrsa, model="RRCL", type="R2")

Statistical Modeling

https://osf.io/drv3a/

illustration.R

get subsequent fixation index (+1) and retrieve row number using fi_pars and assign

scope_start[i] <- fi_pairs$fistart[scope_start_fis[i] + 1] } return(scope_start) }

Data Variable

https://osf.io/mp9td/

get_first_free_fi.R

Exclude participants who completed less than 10 surveys

dat <- dat[-which(dat$N < 10), ] dim(dat) # 36074 assessments length(unique(dat$id)) # 1177 participants (1109 participants excluded)

Data Variable

https://osf.io/nxyh3/

01b_DataPrep_Study2.R

define function to compute colors and point sizes that yield 3d effect

points3d <- function(data, xname, yname, zname, cex.limit.close = 1.7, cex.limit.far = 1, col.limit.close = 0.1, col.limit.far = 0.8, adapted_eye = list(x=-12, y=-16, z=2) ){

Visualization

https://osf.io/fbshg/

ComF_helpers.R

for each row of the data, compute euclidean distance between the person's 3dim point and the plane

distance <- abs( (data[,xname] - a$x) * n$x + (data[,yname] - a$y) * n$y + (data[,zname] - a$z) * n$z ) / sqrt(n$x^2 + n$y^2 + n$z^2)

Data Variable

https://osf.io/fbshg/

ComF_helpers.R

Calculate delta change in testosterone pivot data sets for delta values

hormones_wide <-hormones %>% pivot_wider(id_cols = c("id", "sex"), names_from = condition, values_from = t_conc_corr) %>% mutate(delta_t = back_home - baseline) delta_t <- left_join(hormones, hormones_wide) delta_t <- delta_t %>% filter(condition == "baseline")%>% filter(!is.na(delta_t))

Data Variable

https://osf.io/3bpn6/

af_testosterone_analysis.R

Plot sex and time point Boxplot for sex differences

t_condition <- hormones %>% ggplot(aes(x = condition, y = log(t_conc_corr), fill = sex)) + geom_boxplot(outlier.shape = NA, width = 0.7) +

Visualization

https://osf.io/3bpn6/

af_testosterone_analysis.R

Network Estimation Ising Model (Binary Data) can be estimated via estimateNetwork variables automatically binarized at median estimate regularized logistic nodewise regression network define where to binarize variables eLASSO (LASSO with EBIC model selection) listwise deletion of missing values (pairwise not possible for regressions)

Ising_net <- estimateNetwork(data, default = "IsingFit", missing = "listwise", rule = "OR")

Statistical Modeling

https://osf.io/b4gc7/

workshop_example.R

compare GGM and Ising model correlating weights matrices

cor.test(Wmat_GGM[upper.tri(Wmat_GGM)], Wmat_Ising[upper.tri(Wmat_Ising)]) #.80

Statistical Modeling

https://osf.io/b4gc7/

workshop_example.R

create a storage container (i.e. a empty lists) for all hit_names ... ... that track looking times over all trials (e.g., looking_times$left)

looking_times <- setNames(vector("list", length(hit_names)), hit_names)

Data Variable

https://osf.io/yfegm/

getLooks.r

create a storage container for all looking frequencies (i.e., counting the number of looks within an AOI)

looking_frequencies <- setNames(vector("list", length(hit_names)), hit_names)

Data Variable

https://osf.io/yfegm/

getLooks.r

init storage containers for looking frequencies

current_trial_total_looks <- setNames(vector("list", length(hit_names)), hit_names) for (hn in hit_names) {

Data Variable

https://osf.io/yfegm/

getLooks.r

get first and last FixationIndex (remove NAs), which define the boundaries of a single trial

min_FixationIndex <- min(inter_trial_FixationIndexes, na.rm = TRUE) max_FixationIndex <- max(inter_trial_FixationIndexes, na.rm = TRUE)

Data Variable

https://osf.io/yfegm/

getLooks.r

space between axis label and tick mark labels

my_settings$layout.widths$ylab.axis.padding <- 0.2 my_settings$layout.heights$axis.xlab.padding <- 0.2 my_settings$box.rectangle$col = 1 my_settings$box.umbrella$col = 1 my_settings$box.dot$col = 1 my_settings$plot.symbol$col = 1

Visualization

https://osf.io/mc26t/

my_utils.R

Overlay the basic grid with colourful grid corresponding to the limits of sensitive period rectangles. These colourful lines should start in the points indicating the change values.

cols<-colscale[sapply(result$p1,function(x){which.min(abs(x-scaleseq))})] cols<-colscale[sapply(result$p1.rand[,exrun],function(x){which.min(abs(x-scaleseq))})]

Visualization

https://osf.io/greqt/

06_extrapermut_comaprison_with_random.R

Skew effect

neg <- apply(chains[,c(1,3)], 1, sum) pos <- apply(chains[,c(1,2)], 1, sum) quantile(neg-pos, prob=c(.025, .975)) neg <- apply(chains[,c(1,3)], 1, sum) pos <- apply(chains[,c(1,2)], 1, sum) quantile(pos-neg, prob=c(.025, .975))

Visualization

https://osf.io/8abj4/

Exp1.R

Fit hierarchical Bayesian model

fit.HBM <- stan(file='CAM_full_1.stan', data=c('Nobs','Nind','stim','bias','RM','AS','B1','id','mid','stim_sizes','Nstim'), chains=4, iter=2500, cores=no_cores, control=list(adapt_delta=.90, max_treedepth=15)) fit.HBM sum(summary(fit.HBM)$summary[,'Rhat'] > 1.01) pars <- extract(fit.HBM)

Statistical Modeling

https://osf.io/8abj4/

Exp1.R

Compute probabilities using MNL model

P[['choice']] = apollo_mnl(mnl_settings1, functionality) P[["indic_cost_tap"]] = apollo_mnl(mnl_settings2, functionality) P[["indic_na1"]] = apollo_mnl(mnl_settings3, functionality) P[["indic_na2"]] = apollo_mnl(mnl_settings4, functionality) P[["indic_na3"]] = apollo_mnl(mnl_settings5, functionality) P[["indic_cost_bottle"]] = apollo_ol(ol_settings5, functionality) P[["indic_bill"]]= apollo_normalDensity(normalDensity_settings1,functionality) P[["indic_qual"]] = apollo_ol(ol_settingsB1, functionality) P[["indic_qual_f"]] = apollo_ol(ol_settingsB3, functionality) P = apollo_combineModels(P, apollo_inputs, functionality)

Statistical Modeling

https://osf.io/6pq9e/

Model.R

d) Ftest (Stephan Gries 2013, p. 218) [only for normally distributed variables!] use var.test() Output interpretation: 1) Is p > .05?;; 2) Does the CI include 1?

F.lextale <- var.test(pp.explicit.incidental$lextale~pp.explicit.incidental$learningtype);; F.lextale

Statistical Test

https://osf.io/938ye/

Apriori_group_differences.R

c) Wilcoxon ranksum test ( MannWhitney U test) [Nonparametric alternative if normality is violated, or if you have ordinal data] Continuous variables: ratioscaled lextale

wilcox.lextale <- wilcox.test(lextale~learningtype, data=pp.explicit.incidental, paired=FALSE);; wilcox.lextale

Statistical Test

https://osf.io/938ye/

Apriori_group_differences.R

Selfrated variables: ordinal proficiency_overall

wilcox.proficiency_overall <- wilcox.test(proficiency_overall~learningtype, data=pp.explicit.incidental, paired=FALSE);; wilcox.proficiency_overall

Data Variable

https://osf.io/938ye/

Apriori_group_differences.R

Effect size (approximate) for Wilcoxon ranksum test Write the function (from Field, Miles & Fiels 2012, p.665)

rWilcox <- function(wilcoxModel, N){ z <- qnorm(wilcoxModel$p.value/2) r <- z/sqrt(N) cat(wilcoxModel$data.name, "Effect size, r = ", r) }

Statistical Modeling

https://osf.io/938ye/

Apriori_group_differences.R

create new variable for the IRV intraindividual response variability

data$irv <- irv( dplyr::select( data, belonging, control, meaningful_existence, self_esteem ), na.rm = TRUE, split = FALSE)

Data Variable

https://osf.io/bhrwx/

script_for_the_analysis_of_game_data.R

display the correlations as a histogram and heatmap

cor_matrix_half <- cor_matrix[upper.tri(cor_matrix)] mean(cor_matrix_half) sd(cor_matrix_half) hist(as.vector(cor_matrix_half), breaks=24, cex.axis=2) # Note: Novich et al. suppressed correlations of r<.4 in their visualisation heatmap(x = cor_matrix, symm = TRUE)

Visualization

https://osf.io/r24vb/

clustering_syn_types.R

Create stringent dataset Exclude People with Strange Response Patterns These are identified as subclusters in the inclusive dendogram that group together

inclusive_data$weird_responses = inclusive_data$body_postures_shape + inclusive_data$punctuation_shape + inclusive_data$letter_shape + inclusive_data$number_shape + inclusive_data$people_name_shape + inclusive_data$english_word_shape + inclusive_data$foreign_word_shape + inclusive_data$tastes_taste + inclusive_data$smells_smell + inclusive_data$noises_noise + inclusive_data$music_music + inclusive_data$colour_colour + inclusive_data$shapes_shape + inclusive_data$smells_taste + inclusive_data$tastes_smell + inclusive_data$voices_noise + inclusive_data$voices_music + inclusive_data$noises_music + inclusive_data$music_noise hist(inclusive_data$weird_responses) table(inclusive_data$weird_responses)

Data Variable

https://osf.io/r24vb/

clustering_syn_types.R

gives the prevalence of each cluster of data

colMeans(short_data) N_types <- matrix(apply(short_data[,1:i], 1, sum, na.rm=TRUE)) stringent_N_clusters <- cbind(stringent_N_clusters,N_types) }

Data Variable

https://osf.io/r24vb/

clustering_syn_types.R

create a continuoius time metric (seconds since midnight of day0)

dat$sec_midnight.day0 <- dat$start.secmidnight + dat$day * 86400

Data Variable

https://osf.io/6krj7/

01_addlagvars.R

create a variable indicating, if the current observation is the first observation of the day ("morning") this variable is 1, if the current observation was obtained on a different day than the previous observation

dat[!is.na(dat$lagday) & dat$day!=dat$lagday, "morning"] <- 1 dat[!is.na(dat$lagday) & dat$day==dat$lagday, "morning"] <- 0

Data Variable

https://osf.io/6krj7/

01_addlagvars.R

create continuous time variable in hours and seconds

data$time_to_hours = lubridate::hour(data$timestamp.corrected) + lubridate::minute(data$timestamp.corrected)/60 + lubridate::second(data$timestamp.corrected)/3600 data$time_to_sec = data$time_to_hours*60*60 return(data) }

Data Variable

https://osf.io/b7krz/

timestamp_correction.R

set font size for facet labels

strip.text.x = element_text(size = font_size_facets_x), strip.text.y = element_text(size = font_size_facets_y), strip.text.x = element_text(size = font_size_facets_x), strip.text.y = element_text(size = font_size_facets_y),

Visualization

https://osf.io/dpkyb/

my_ggplot_themes.R