jvcasillas

library(tidyverse)

# DDM simulation function
sim_ddm <- function(drift_rate, boundary_separation, bias, ndt, n_sims, seed = NULL) {
  set.seed(seed)
  sim_df = NULL
  for (sim in 1:n_sims){
    step = 1
    value = bias
    n = 1

jvcasillas

task_comp_plot <- task_comp %>%
  mutate(., phon = fct_recode(phon, "/b/" = "b", "/d/" = "d", "/g/" = "g",
                                    "/p/" = "p", "/t/" = "t", "/k/" = "k"),
            phon = fct_relevel(phon, '/b/', '/d/', '/g/',
                                     '/p/', '/t/', '/k/')) %>%
  ggplot(., aes(x = phon, y = vot_scaled, group = task_sum)) +
  geom_hline(yintercept = 0, lty = 3) +
  stat_interval(aes(y = .prediction), data = task_preds,
                position = position_dodge(0.5)) +
  stat_pointinterval(aes(y = .value, fill = factor(task_sum)),

jvcasillas

% !TEX encoding = UTF-8 Unicode
\documentclass[convert={density=400,size=500x500,outext=.png}]{standalone}
\usepackage{tikz}
\usetikzlibrary{backgrounds}

% \usepackage[active,tightpage,psfixbb]{preview}
\usepackage{tipa}
% \PreviewEnvironment{pgfpicture}
% \setlength\PreviewBorder{2pt}

jvcasillas

df_pm %>% 
  mutate(., grade = fct_relevel(grade, "pk3", "pk4", "kin", "1st")) %>% 
  ggplot(., aes(x = grade, y = response, color = group)) + 
  facet_grid(. ~ language) +
  stat_summary(fun.data = mean_cl_boot, geom = 'pointrange', size = 1.5, 
               position = position_dodge(0.5), pch = 21, fill = "white") + 
  ylim(0, 1)

jvcasillas

# GAM analysis -----------------------------------------------------------------
#
# Resources: 
# - https://arxiv.org/abs/1703.05339
# - https://github.com/soskuthy/gamm_intro


# set modality to ordered variable and relevel so production is reference
prodShadow_gamm <- prodShadow %>% 
  filter(., group == 'midd', picName == 0, 

jvcasillas

# Load libraries
library("tidyverse")
library("TOSTER")

# Set seed for reproducibility
set.seed(10)

# Working memory means for 3 groups
wm_n   <- rnorm(mean = 8.67, sd = 1.15, n = 12)
wm_nin <- rnorm(mean = 9.04, sd = 2.11, n = 26)

jvcasillas

# Plot vowel formants
# - Include elipses, 
# - group/vowel means
# - greyscale

# Load tidyverse and phonR package (I choose this because you said you 
# had alrady used it)
library(phonR)
library(tidyverse)

jvcasillas

euc.dist <- function(x1, x2){
  x <- sqrt(sum((x1 - x2)^2))
  return(x)
}

jvcasillas

#####################################
# Functions for linguistic research #
# Joseph V. Casillas                #
# Last update: 12-10-2014           #
#####################################

# Calculate d prime in discrimination experiments. 
# Takes a df with the columns:
#    - stimDiff (actual stimuli are different)
#    - stimSame (actual stimuli are the same)

jvcasillas

# Plot Spectrum Proportion 
my_data$group <- factor(my_data$group, levels = c("EL", "NE", "LL"))
df<-with(my_data, aggregate(fpro, list(group=group, fstim=fstim), mean))
df$se<-with(my_data, aggregate(fpro, list(group=group, fstim=fstim), function(x) sd(x)/sqrt(10)))[,3]
gp<-ggplot(df, aes(x=fstim, y=x, colour=group, ymin=x-se, ymax=x+se))
gp +geom_line(aes(linetype=group), size = .4) + 
    geom_point(aes(shape=group)) + 
    geom_ribbon(alpha = 0.15, linetype=0) + 
    #geom_errorbar(aes(ymax=x+se, ymin=x-se)) +
    ylim(0, 1) +