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20220518 - NONTIES.Rmd
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---
title: "20220518 - NONTIES"
author: "Filip Andras"
date: "5/18/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(tidyverse)
library(MASS)
library(naniar)
library(gridExtra)
library(lme4)
library(afex)
library(lattice)
library(ggeffects)
library(harrypotter)
library(knitr)
library(kableExtra)
library(jtools)
library(gridExtra)
library(ggpubr)
library(broom.mixed)
library(sjPlot)
```
```{r}
sessionInfo()
options(scipen = 999)
getwd()
# setwd("/Users/Filip/Desktop/PhD 2020/__20210227 - Gloria/glo_2102/glo_2102")
```
```{r}
d.outliers_without <- read_csv("d_outliers_without.csv")
#para crear esto es hasta la línea 527 en el otro documento
#16138 data líneas
```
# análisis por separado --------------------------------------------------------
# Very-small non-ties ----------------------------------------------------------
```{r}
d <- d.outliers_without %>%
filter(n.tie_very_small == 1) # CHANGE IT HERE
d$sum.f.n <- scale(as.numeric(d$sum.f))
hist(d$sum.f.n, breaks = 20)
xtabs(~ sum.f + group.cod, data = d)
# Alternative predictors -------------------------------------------------------
d$pre.min.n <- scale(as.numeric(d$pre.min) , center = TRUE, scale = TRUE) # min
d$pre.max.n <- scale(as.numeric(d$pre.max) , center = TRUE, scale = TRUE) # max
d$pre.product.n <- scale(as.numeric(d$pre.product) , center = TRUE, scale = TRUE) # product (op.1*op.2)
d$pre.sum_squared.n <- scale(as.numeric(d$pre.sum_squared) , center = TRUE, scale = TRUE) # sum_squared ((op.1 + op.2)^2)
d$pre.sup_op_squared.n <- scale(as.numeric(d$pre.sum_op_squared), center = TRUE, scale = TRUE) # sum of squared operands (op.1^2 + op.2^2)
d$op.1.n <- scale(as.numeric(d$op.1) , center = TRUE, scale = TRUE) # op.1
d$op.2.n <- scale(as.numeric(d$op.2) , center = TRUE, scale = TRUE) # op.2
hist(d$pre.min.n, breaks = 10)
hist(d$pre.max.n, breaks = 10)
hist(d$pre.product.n , breaks = 10)
hist(d$pre.sum_squared.n, breaks = 10)
hist(d$pre.sup_op_squared.n, breaks = 10)
hist(d$op.1.n, breaks = 10)
hist(d$op.2.n, breaks = 10)
```
```{r}
# SPECIFY THE GROUP
group_num = -0.5 # -0.5 ~ grade 1; 0.5 ~ grade 6
# Grade 1 ----------------------------------------------------------------------
m1 <- lmer(log(RT) ~ sum.f.n + (1 + sum.f.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1)
performance::check_model(m1)
# predictions ------------------------------------------------------------------
pre.m1 <- ggpredict(m1, terms = "sum.f.n", se = TRUE, back.transform = FALSE)
pre.m1
plot(pre.m1)
# sum.f.n | Predicted | 95% CI
# ----------------------------------
# -1.22 | 7.96 | [7.77, 8.15]
# 0.01 | 8.21 | [8.04, 8.38]
# 1.24 | 8.46 | [8.24, 8.68]
```
# Probando visualizar los datos
ggplot(data = d,
aes(x = sum.f.n,
y = log(RT))) +
geom_smooth(method = "lm") +
labs(title = "",
x = "Sum",
y = "Solution time") +
scale_x_continuous(breaks = c(-1.21568, 0.00974, 1.23515),
labels = c(3, 4, 5))
<!-- # + -->
<!-- # scale_y_continuous(breaks = c(log(1000), log(3000), log(5000), log(7000), log(9000), log(11000)), -->
<!-- # labels = c("1000", "3000", "5000", "7000", "9000", "11000")) -->
labs(title = "",
x = "Sum",
y = "Solution time") +
scale_x_continuous(breaks = c(-1.21568, 0.00974, 1.23515), labels = c(3, 4, 5)) +
scale_y_continuous(breaks = c(log(1000), log(3000), log(5000), log(7000), log(9000), log(11000)),
labels = c("1000", "3000", "5000", "7000", "9000", "11000"))
ggplot(data = pre.m1,
aes(x = x,
y = predicted)) +
geom_smooth(method = "lm") +
labs(title = "",
x = "Sum",
y = "Solution time") +
scale_x_continuous(breaks = c(-1.21568, 0.00974, 1.23515), labels = c(3, 4, 5)) +
scale_y_continuous(breaks = c(log(3000)),
labels = c("3000"))
log(1000)
pre.m1$predicted
```{r}
## Control models:
# square -----------------------------------------------------------------------
m1.sq <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == -0.5), REML = FALSE)
summary(m1.sq)
# cubic ------------------------------------------------------------------------ NO puedo evaluarlo
## Alternative predictors ------------------------------------------------------
m1.e1 <- lmer(log(RT) ~ pre.min.n + (1 + pre.min.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e2 <- lmer(log(RT) ~ pre.max.n + (1 + pre.max.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e3 <- lmer(log(RT) ~ pre.product.n + (1 + pre.product.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e4 <- lmer(log(RT) ~ pre.sum_squared.n + (1 + pre.sum_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e5 <- lmer(log(RT) ~ pre.sup_op_squared.n + (1 + pre.sup_op_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e6 <- lmer(log(RT) ~ op.1.n + (1 + op.1.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e7 <- lmer(log(RT) ~ op.2.n + (1 + op.2.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
aov_table <- anova(m1,
m1.e1,
m1.e2,
m1.e3,
m1.e4,
m1.e5,
m1.e6,
m1.e7
)
```
# Change the names for each table ----------------------------------------------
```{r}
group_name = "grade 1" # grade 1 or grade 6
problem_type = "non-tie" # non-tie or tie
problem_size = "very small" # very small, medium small, large, small
```
# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 8, ncol = 13)
colnames(table.1) <- c("age_group",
"problem_type",
"problem_size",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"AIC",
"BIC",
"note")
table.1[,4] <- c("Sum", "Min(op.1|op.2)", "Max(op.1|op.2)", "product ~ (op.1*op.2)", "sum_sq ~ (op.1+op.2)^2", "sum_sq_op ~ op.1^2+op.2^2", "op.1", "op.2")
table.1[,1] <- group_name
table.1[,2] <- problem_type
table.1[,3] <- problem_size
View(table.1)
table.1[1,5] <- round(summary(m1)$coef[1,1], 2)
table.1[1,6] <- round(summary(m1)$coef[2,1], 2)
table.1[1,7] <- round(summary(m1)$coef[2,2], 2)
table.1[1,8] <- round(summary(m1)$coef[2,3], 2)
table.1[1,9] <- round(summary(m1)$coef[2,4], 2)
table.1[1,10]<- round(summary(m1)$coef[2,5], 5)
#------------------------------------------------------------------------------ Min
table.1[2,5] <- round(summary(m1.e1)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e1)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e1)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e1)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e1)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e1)$coef[2,5], 5)
#-------------------------------.---------------------------------- Max
table.1[3,5] <- round(summary(m1.e2)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e2)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e2)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e2)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e2)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e2)$coef[2,5], 5)
#-------------------------------.---------------------------------- product
table.1[4,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[4,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[4,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[4,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[4,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[4,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
table.1[5,5] <- round(summary(m1.e4)$coef[1,1], 2)
table.1[5,6] <- round(summary(m1.e4)$coef[2,1], 2)
table.1[5,7] <- round(summary(m1.e4)$coef[2,2], 2)
table.1[5,8] <- round(summary(m1.e4)$coef[2,3], 2)
table.1[5,9] <- round(summary(m1.e4)$coef[2,4], 2)
table.1[5,10]<- round(summary(m1.e4)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq_op
table.1[6,5] <- round(summary(m1.e5)$coef[1,1], 2)
table.1[6,6] <- round(summary(m1.e5)$coef[2,1], 2)
table.1[6,7] <- round(summary(m1.e5)$coef[2,2], 2)
table.1[6,8] <- round(summary(m1.e5)$coef[2,3], 2)
table.1[6,9] <- round(summary(m1.e5)$coef[2,4], 2)
table.1[6,10]<- round(summary(m1.e5)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.1
table.1[7,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[7,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[7,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[7,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[7,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[7,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
table.1[8,5] <- round(summary(m1.e7)$coef[1,1], 2)
table.1[8,6] <- round(summary(m1.e7)$coef[2,1], 2)
table.1[8,7] <- round(summary(m1.e7)$coef[2,2], 2)
table.1[8,8] <- round(summary(m1.e7)$coef[2,3], 2)
table.1[8,9] <- round(summary(m1.e7)$coef[2,4], 2)
table.1[8,10]<- round(summary(m1.e7)$coef[2,5], 5)
table.1[,11] <- round(aov_table[,2], 2)
table.1[,12] <- round(aov_table[,3], 2)
# table.1[1,13] <- "singular" # m1
# table.1[2,13] <- "singular" # m1.e1
# table.1[3,13] <- "singular" # m1.e2
# table.1[4,13] <- "singular" # m1.e3
# table.1[5,13] <- "singular" # m1.e4
# table.1[6,13] <- "singular" # m1.e5
# table.1[7,13] <- "singular" # m1.e6
# table.1[8,13] <- "singular" # m1.e7
View(table.1)
```
# CHANGE THE NAME HERE:
```{r}
table_vS.G1 <- table.1 %>% as.tibble()
write.csv(table_vS.G1, "table_vS.G1.csv")
```
# TABLE 2: higher order polynomials --------------------------------------------
```{r}
table.2 <- matrix(NA, nrow = 6, ncol = 12)
colnames(table.2) <- c("age_group",
"problem_type",
"problem_size",
"model",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"note")
table.2[,4] <- c("lineal", "cuadratic", "cuadratic", "cubic", "cubic", "cubic")
table.2[,5] <- c("Sum", "Sum", "Sum^2", "Sum", "Sum^2", "Sum^3")
table.2[,1] <- group_name
table.2[,2] <- problem_type
table.2[,3] <- problem_size
View(table.2)
# linear
table.2[1,6] <- round(summary(m1)$coef[1,1], 2) # intercept estimate
table.2[1,7] <- round(summary(m1)$coef[2,1], 2) # VI estimate
table.2[1,8] <- round(summary(m1)$coef[2,2], 2) # SE
table.2[1,9] <- round(summary(m1)$coef[2,3], 2) # df
table.2[1,10] <- round(summary(m1)$coef[2,4], 2) # t.val
table.2[1,11] <- round(summary(m1)$coef[2,5], 5) # p.val
# cuadratic --------------------------------------------------------
table.2[2,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[2,7] <- round(summary(m1.sq)$coef[2,1], 2) # VI estimate
table.2[2,8] <- round(summary(m1.sq)$coef[2,2], 2) # SE
table.2[2,9] <- round(summary(m1.sq)$coef[2,3], 2) # df
table.2[2,10] <- round(summary(m1.sq)$coef[2,4], 2) # t.val
table.2[2,11] <- round(summary(m1.sq)$coef[2,5], 5) # p.val
# ----------------------------------------------------------------
table.2[3,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[3,7] <- round(summary(m1.sq)$coef[3,1], 2) # VI estimate
table.2[3,8] <- round(summary(m1.sq)$coef[3,2], 2) # SE
table.2[3,9] <- round(summary(m1.sq)$coef[3,3], 2) # df
table.2[3,10] <- round(summary(m1.sq)$coef[3,4], 2) # t.val
table.2[3,11] <- round(summary(m1.sq)$coef[3,5], 5) # p.val
# # cubic ---------------------------------------------------------
# table.2[4,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[4,7] <- round(summary(m1.cu)$coef[2,1], 2) # VI estimate
# table.2[4,8] <- round(summary(m1.cu)$coef[2,2], 2) # SE
# table.2[4,9] <- round(summary(m1.cu)$coef[2,3], 2) # df
# table.2[4,10] <- round(summary(m1.cu)$coef[2,4], 2) # t.val
# table.2[4,11] <- round(summary(m1.cu)$coef[2,5], 5) # p.val
# # ----------------------------------------------------------------
# table.2[5,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[5,7] <- round(summary(m1.cu)$coef[3,1], 2) # VI estimate
# table.2[5,8] <- round(summary(m1.cu)$coef[3,2], 2) # SE
# table.2[5,9] <- round(summary(m1.cu)$coef[3,3], 2) # df
# table.2[5,10] <- round(summary(m1.cu)$coef[3,4], 2) # t.val
# table.2[5,11] <- round(summary(m1.cu)$coef[3,5], 5) # p.val
# # ----------------------------------------------------------------
# table.2[6,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[6,7] <- round(summary(m1.cu)$coef[4,1], 2) # VI estimate
# table.2[6,8] <- round(summary(m1.cu)$coef[4,2], 2) # SE
# table.2[6,9] <- round(summary(m1.cu)$coef[4,3], 2) # df
# table.2[6,10] <- round(summary(m1.cu)$coef[4,4], 2) # t.val
# table.2[6,11] <- round(summary(m1.cu)$coef[4,5], 5) # p.val
table.2[4,11] <- "cannot evaluate"
table.2[5,11] <- "cannot evaluate"
table.2[6,11] <- "cannot evaluate"
View(table.2)
```
# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_vS.G1.poly <- table.2 %>% as.tibble
write.csv(table_vS.G1.poly, "table_vS.G1.poly.csv")
```
# remove the objects
```{r}
rm(group_name, group_num, problem_size, problem_type, m1, pre.m1, m1.e1, m1.e2, m1.e3, m1.e4, m1.e5, m1.e6, m1.e7, m1.sq, m1.cu, aov_table, table.1, table.2)
```
```{r}
# SPECIFY THE GROUP
group_num = 0.5 # -0.5 ~ grade 1; 0.5 ~ grade 6
# Grade 6 ----------------------------------------------------------------------
m1 <- lmer(log(RT) ~ sum.f.n + (1 + sum.f.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1)
# singular por el item, tendría que agrupar los datos pero no quiero porque así no tengo en cuenta que los ítems se repiten; por otra parte son muy pocos ítems
performance::check_model(m1)
# Lo agrupo y los resultados son exactamente los mismos:
# d.bysubj <- d %>% filter(group.cod == group_num) %>% group_by(subject, sum.f.n) %>% summarize(RT = mean(log(RT)))
# m1 <- lmer(RT ~ sum.f.n + (1 + sum.f.n|subject), data = d.bysubj, REML = FALSE)
# summary(m1)
# #singular
#
# m1 <- lmer(RT ~ sum.f.n + (1 + sum.f.n||subject), data = d.bysubj, REML = FALSE)
# summary(m1)
# performance::check_model(m1)
# predictions ------------------------------------------------------------------
pre.m1 <- ggpredict(m1, terms = "sum.f.n", se = TRUE, back.transform = FALSE)
pre.m1
plot(pre.m1)
# sum.f.n | Predicted | 95% CI
# ----------------------------------
# -1.22 | 7.14 | [7.05, 7.24]
# 0.01 | 7.30 | [7.19, 7.42]
# 1.24 | 7.47 | [7.31, 7.63]
```
```{r}
## Control models:
# square -----------------------------------------------------------------------
m1.sq <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1.sq)
# singular por el item
# cubic ------------------------------------------------------------------------ NO puedo evaluarlo
## Alternative predictors ------------------------------------------------------
m1.e1 <- lmer(log(RT) ~ pre.min.n + (1 + pre.min.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e2 <- lmer(log(RT) ~ pre.max.n + (1 + pre.max.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e3 <- lmer(log(RT) ~ pre.product.n + (1 + pre.product.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
summary(m1.e3) #otra vez por el ítem
m1.e4 <- lmer(log(RT) ~ pre.sum_squared.n + (1 + pre.sum_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
summary(m1.e4) #por el ítem
m1.e5 <- lmer(log(RT) ~ pre.sup_op_squared.n + (1 + pre.sup_op_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
summary(m1.e5) #por el ítem
m1.e6 <- lmer(log(RT) ~ op.1.n + (1 + op.1.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e7 <- lmer(log(RT) ~ op.2.n + (1 + op.2.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
aov_table <- anova(m1,
m1.e1,
m1.e2,
m1.e3,
m1.e4,
m1.e5,
m1.e6,
m1.e7
)
# m0 <- lmer(log(RT) ~ 1 + (1|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
# anova(m0, m1)
#
# m0 <- lmer(log(RT) ~ 1 + (1|subject) + (1|item), data = filter(d, group.cod == group_num), REML = TRUE)
# anova(m0, m1.e1)
#
# anova(m1,m1.e1, df = "Kenward-Roger")
# anova(m1,m1.e3)
# anova(m0, m1.e1)
```
AGRUPAR O NO AGRUPAR eso es la cuestión
algunos modelos me salen singulares
# Change the names for each table ----------------------------------------------
```{r}
group_name = "grade 6" # grade 1 or grade 6
problem_type = "non-tie" # non-tie or tie
problem_size = "very small" # very small, medium small, large, small
```
# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 8, ncol = 13)
colnames(table.1) <- c("age_group",
"problem_type",
"problem_size",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"AIC",
"BIC",
"note")
table.1[,4] <- c("Sum", "Min(op.1|op.2)", "Max(op.1|op.2)", "product ~ (op.1*op.2)", "sum_sq ~ (op.1+op.2)^2", "sum_sq_op ~ op.1^2+op.2^2", "op.1", "op.2")
table.1[,1] <- group_name
table.1[,2] <- problem_type
table.1[,3] <- problem_size
View(table.1)
table.1[1,5] <- round(summary(m1)$coef[1,1], 2)
table.1[1,6] <- round(summary(m1)$coef[2,1], 2)
table.1[1,7] <- round(summary(m1)$coef[2,2], 2)
table.1[1,8] <- round(summary(m1)$coef[2,3], 2)
table.1[1,9] <- round(summary(m1)$coef[2,4], 2)
table.1[1,10]<- round(summary(m1)$coef[2,5], 5)
#------------------------------------------------------------------------------ Min
table.1[2,5] <- round(summary(m1.e1)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e1)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e1)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e1)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e1)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e1)$coef[2,5], 5)
#-------------------------------.---------------------------------- Max
table.1[3,5] <- round(summary(m1.e2)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e2)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e2)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e2)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e2)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e2)$coef[2,5], 5)
#-------------------------------.---------------------------------- product
table.1[4,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[4,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[4,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[4,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[4,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[4,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
table.1[5,5] <- round(summary(m1.e4)$coef[1,1], 2)
table.1[5,6] <- round(summary(m1.e4)$coef[2,1], 2)
table.1[5,7] <- round(summary(m1.e4)$coef[2,2], 2)
table.1[5,8] <- round(summary(m1.e4)$coef[2,3], 2)
table.1[5,9] <- round(summary(m1.e4)$coef[2,4], 2)
table.1[5,10]<- round(summary(m1.e4)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq_op
table.1[6,5] <- round(summary(m1.e5)$coef[1,1], 2)
table.1[6,6] <- round(summary(m1.e5)$coef[2,1], 2)
table.1[6,7] <- round(summary(m1.e5)$coef[2,2], 2)
table.1[6,8] <- round(summary(m1.e5)$coef[2,3], 2)
table.1[6,9] <- round(summary(m1.e5)$coef[2,4], 2)
table.1[6,10]<- round(summary(m1.e5)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.1
table.1[7,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[7,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[7,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[7,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[7,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[7,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
table.1[8,5] <- round(summary(m1.e7)$coef[1,1], 2)
table.1[8,6] <- round(summary(m1.e7)$coef[2,1], 2)
table.1[8,7] <- round(summary(m1.e7)$coef[2,2], 2)
table.1[8,8] <- round(summary(m1.e7)$coef[2,3], 2)
table.1[8,9] <- round(summary(m1.e7)$coef[2,4], 2)
table.1[8,10]<- round(summary(m1.e7)$coef[2,5], 5)
table.1[,11] <- round(aov_table[,2], 2)
table.1[,12] <- round(aov_table[,3], 2)
table.1[1,13] <- "singular" # m1
# table.1[2,13] <- "singular" # m1.e1
# table.1[3,13] <- "singular" # m1.e2
table.1[4,13] <- "singular" # m1.e3
table.1[5,13] <- "singular" # m1.e4
table.1[6,13] <- "singular" # m1.e5
# table.1[7,13] <- "singular" # m1.e6
# table.1[8,13] <- "singular" # m1.e7
View(table.1)
```
#CHANGE THE NAME HERE:
```{r}
table_vS.G6 <- table.1 %>% as.tibble()
write.csv(table_vS.G6, "table_vS.G6.csv")
```
# TABLE 2: higher order polynomials --------------------------------------------
```{r}
table.2 <- matrix(NA, nrow = 6, ncol = 12)
colnames(table.2) <- c("age_group",
"problem_type",
"problem_size",
"model",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"note")
table.2[,4] <- c("lineal", "cuadratic", "cuadratic", "cubic", "cubic", "cubic")
table.2[,5] <- c("Sum", "Sum", "Sum^2", "Sum", "Sum^2", "Sum^3")
table.2[,1] <- group_name
table.2[,2] <- problem_type
table.2[,3] <- problem_size
View(table.2)
# linear
table.2[1,6] <- round(summary(m1)$coef[1,1], 2) # intercept estimate
table.2[1,7] <- round(summary(m1)$coef[2,1], 2) # VI estimate
table.2[1,8] <- round(summary(m1)$coef[2,2], 2) # SE
table.2[1,9] <- round(summary(m1)$coef[2,3], 2) # df
table.2[1,10] <- round(summary(m1)$coef[2,4], 2) # t.val
table.2[1,11] <- round(summary(m1)$coef[2,5], 5) # p.val
# cuadratic --------------------------------------------------------
table.2[2,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[2,7] <- round(summary(m1.sq)$coef[2,1], 2) # VI estimate
table.2[2,8] <- round(summary(m1.sq)$coef[2,2], 2) # SE
table.2[2,9] <- round(summary(m1.sq)$coef[2,3], 2) # df
table.2[2,10] <- round(summary(m1.sq)$coef[2,4], 2) # t.val
table.2[2,11] <- round(summary(m1.sq)$coef[2,5], 5) # p.val
# ----------------------------------------------------------------
table.2[3,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[3,7] <- round(summary(m1.sq)$coef[3,1], 2) # VI estimate
table.2[3,8] <- round(summary(m1.sq)$coef[3,2], 2) # SE
table.2[3,9] <- round(summary(m1.sq)$coef[3,3], 2) # df
table.2[3,10] <- round(summary(m1.sq)$coef[3,4], 2) # t.val
table.2[3,11] <- round(summary(m1.sq)$coef[3,5], 5) # p.val
# # cubic ---------------------------------------------------------
# table.2[4,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[4,7] <- round(summary(m1.cu)$coef[2,1], 2) # VI estimate
# table.2[4,8] <- round(summary(m1.cu)$coef[2,2], 2) # SE
# table.2[4,9] <- round(summary(m1.cu)$coef[2,3], 2) # df
# table.2[4,10] <- round(summary(m1.cu)$coef[2,4], 2) # t.val
# table.2[4,11] <- round(summary(m1.cu)$coef[2,5], 5) # p.val
# # ----------------------------------------------------------------
# table.2[5,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[5,7] <- round(summary(m1.cu)$coef[3,1], 2) # VI estimate
# table.2[5,8] <- round(summary(m1.cu)$coef[3,2], 2) # SE
# table.2[5,9] <- round(summary(m1.cu)$coef[3,3], 2) # df
# table.2[5,10] <- round(summary(m1.cu)$coef[3,4], 2) # t.val
# table.2[5,11] <- round(summary(m1.cu)$coef[3,5], 5) # p.val
# # ----------------------------------------------------------------
# table.2[6,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
# table.2[6,7] <- round(summary(m1.cu)$coef[4,1], 2) # VI estimate
# table.2[6,8] <- round(summary(m1.cu)$coef[4,2], 2) # SE
# table.2[6,9] <- round(summary(m1.cu)$coef[4,3], 2) # df
# table.2[6,10] <- round(summary(m1.cu)$coef[4,4], 2) # t.val
# table.2[6,11] <- round(summary(m1.cu)$coef[4,5], 5) # p.val
table.2[1,11] <- "singular"
table.2[2,11] <- "singular"
table.2[3,11] <- "singular"
table.2[4,11] <- "cannot evaluate"
table.2[5,11] <- "cannot evaluate"
table.2[6,11] <- "cannot evaluate"
View(table.2)
```
# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_vS.G6.poly <- table.2 %>% as.tibble
write.csv(table_vS.G6.poly, "table_vS.G6.poly.csv")
```
#remove the objects
```{r}
rm(d, group_name, group_num, problem_size, problem_type, m1, pre.m1, m1.e1, m1.e2, m1.e3, m1.e4, m1.e5, m1.e6, m1.e7, m1.sq, m1.cu, aov_table, table.1, table.2)
```
# Medium-small non-ties --------------------------------------------------------
```{r}
d <- d.outliers_without %>%
filter(n.tie_medium == 1)
d$sum.f.n <- scale(as.numeric(d$sum.f))
hist(d$sum.f.n, breaks = 20)
xtabs(~ sum.f + group.cod, data = d)
# Alternative predictors -------------------------------------------------------
d$pre.min.n <- scale(as.numeric(d$pre.min) , center = TRUE, scale = TRUE) # min
d$pre.max.n <- scale(as.numeric(d$pre.max) , center = TRUE, scale = TRUE) # max
d$pre.product.n <- scale(as.numeric(d$pre.product) , center = TRUE, scale = TRUE) # product (op.1*op.2)
d$pre.sum_squared.n <- scale(as.numeric(d$pre.sum_squared) , center = TRUE, scale = TRUE) # sum_squared ((op.1 + op.2)^2)
d$pre.sup_op_squared.n <- scale(as.numeric(d$pre.sum_op_squared), center = TRUE, scale = TRUE) # sum of squared operands (op.1^2 + op.2^2)
d$op.1.n <- scale(as.numeric(d$op.1) , center = TRUE, scale = TRUE) # op.1
d$op.2.n <- scale(as.numeric(d$op.2) , center = TRUE, scale = TRUE) # op.2
hist(d$pre.min.n, breaks = 10)
hist(d$pre.max.n, breaks = 10)
hist(d$pre.product.n , breaks = 10)
hist(d$pre.sum_squared.n, breaks = 10)
hist(d$pre.sup_op_squared.n, breaks = 10)
hist(d$op.1.n, breaks = 10)
hist(d$op.2.n, breaks = 10)
```
```{r}
# SPECIFY THE GROUP
group_num = -0.5 # -0.5 ~ grade 1; 0.5 ~ grade 6
# Grade 1 ----------------------------------------------------------------------
m1 <- lmer(log(RT) ~ sum.f.n + (1 + sum.f.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1)
performance::check_model(m1)
# predictions ------------------------------------------------------------------
pre.m1 <- ggpredict(m1, terms = "sum.f.n", se = TRUE, back.transform = FALSE)
pre.m1
plot(pre.m1)
# sum.f.n | Predicted | 95% CI
# ----------------------------------
# -1.90 | 8.17 | [7.96, 8.37]
# -0.89 | 8.28 | [8.12, 8.45]
# 0.12 | 8.40 | [8.24, 8.55]
# 1.12 | 8.51 | [8.33, 8.70]
```
```{r}
## Control models:
# square -----------------------------------------------------------------------
m1.sq <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == -0.5), REML = FALSE)
summary(m1.sq)
# cubic ------------------------------------------------------------------------
# m1.cu <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1 + sum.f.n + I(sum.f.n^2) + I(sum.f.n^3)|subject) + (1|item), data = filter(d, group.cod == -0.5), REML = FALSE)
# # failed to converge
# summary(m1.cu)
m1.cu <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == -0.5), REML = FALSE)
summary(m1.cu)
## Alternative predictors ------------------------------------------------------
m1.e1 <- lmer(log(RT) ~ pre.min.n + (1 + pre.min.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e2 <- lmer(log(RT) ~ pre.max.n + (1 + pre.max.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e3 <- lmer(log(RT) ~ pre.product.n + (1 + pre.product.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e4 <- lmer(log(RT) ~ pre.sum_squared.n + (1 + pre.sum_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e5 <- lmer(log(RT) ~ pre.sup_op_squared.n + (1 + pre.sup_op_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e6 <- lmer(log(RT) ~ op.1.n + (1 + op.1.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
m1.e7 <- lmer(log(RT) ~ op.2.n + (1 + op.2.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
aov_table <- anova(m1,
m1.e1,
m1.e2,
m1.e3,
m1.e4,
m1.e5,
m1.e6,
m1.e7
)
```
# Change the names for each table ----------------------------------------------
```{r}
group_name = "grade 1" # grade 1 or grade 6
problem_type = "non-tie" # non-tie or tie
problem_size = "medium small" # very small, medium small, large, small
```
# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 8, ncol = 13)
colnames(table.1) <- c("age_group",
"problem_type",
"problem_size",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"AIC",
"BIC",
"note")
table.1[,4] <- c("Sum", "Min(op.1|op.2)", "Max(op.1|op.2)", "product ~ (op.1*op.2)", "sum_sq ~ (op.1+op.2)^2", "sum_sq_op ~ op.1^2+op.2^2", "op.1", "op.2")
table.1[,1] <- group_name
table.1[,2] <- problem_type
table.1[,3] <- problem_size
View(table.1)
table.1[1,5] <- round(summary(m1)$coef[1,1], 2)
table.1[1,6] <- round(summary(m1)$coef[2,1], 2)
table.1[1,7] <- round(summary(m1)$coef[2,2], 2)
table.1[1,8] <- round(summary(m1)$coef[2,3], 2)
table.1[1,9] <- round(summary(m1)$coef[2,4], 2)
table.1[1,10]<- round(summary(m1)$coef[2,5], 5)
#------------------------------------------------------------------------------ Min
table.1[2,5] <- round(summary(m1.e1)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e1)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e1)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e1)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e1)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e1)$coef[2,5], 5)
#-------------------------------.---------------------------------- Max
table.1[3,5] <- round(summary(m1.e2)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e2)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e2)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e2)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e2)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e2)$coef[2,5], 5)
#-------------------------------.---------------------------------- product
table.1[4,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[4,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[4,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[4,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[4,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[4,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
table.1[5,5] <- round(summary(m1.e4)$coef[1,1], 2)
table.1[5,6] <- round(summary(m1.e4)$coef[2,1], 2)
table.1[5,7] <- round(summary(m1.e4)$coef[2,2], 2)
table.1[5,8] <- round(summary(m1.e4)$coef[2,3], 2)
table.1[5,9] <- round(summary(m1.e4)$coef[2,4], 2)
table.1[5,10]<- round(summary(m1.e4)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq_op
table.1[6,5] <- round(summary(m1.e5)$coef[1,1], 2)
table.1[6,6] <- round(summary(m1.e5)$coef[2,1], 2)
table.1[6,7] <- round(summary(m1.e5)$coef[2,2], 2)
table.1[6,8] <- round(summary(m1.e5)$coef[2,3], 2)
table.1[6,9] <- round(summary(m1.e5)$coef[2,4], 2)
table.1[6,10]<- round(summary(m1.e5)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.1
table.1[7,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[7,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[7,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[7,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[7,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[7,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
table.1[8,5] <- round(summary(m1.e7)$coef[1,1], 2)
table.1[8,6] <- round(summary(m1.e7)$coef[2,1], 2)
table.1[8,7] <- round(summary(m1.e7)$coef[2,2], 2)
table.1[8,8] <- round(summary(m1.e7)$coef[2,3], 2)
table.1[8,9] <- round(summary(m1.e7)$coef[2,4], 2)
table.1[8,10]<- round(summary(m1.e7)$coef[2,5], 5)
table.1[,11] <- round(aov_table[,2], 2)
table.1[,12] <- round(aov_table[,3], 2)
# table.1[1,13] <- "singular" # m1
# table.1[2,13] <- "singular" # m1.e1
# table.1[3,13] <- "singular" # m1.e2
# table.1[4,13] <- "singular" # m1.e3
# table.1[5,13] <- "singular" # m1.e4
# table.1[6,13] <- "singular" # m1.e5
table.1[7,13] <- "singular" # m1.e6
table.1[8,13] <- "singular" # m1.e7
View(table.1)
```
# CHANGE THE NAME HERE:
```{r}
table_mS.G1 <- table.1 %>% as.tibble()
write.csv(table_mS.G1, "table_mS.G1.csv")
```
# TABLE 2: higher order polynomials --------------------------------------------
```{r}
table.2 <- matrix(NA, nrow = 6, ncol = 12)
colnames(table.2) <- c("age_group",
"problem_type",
"problem_size",
"model",
"VI",
"intercept estimate [log]",
"VI estimate [log]",
"SE",
"df",
"t.val",
"p.val",
"note")
table.2[,4] <- c("lineal", "cuadratic", "cuadratic", "cubic", "cubic", "cubic")
table.2[,5] <- c("Sum", "Sum", "Sum^2", "Sum", "Sum^2", "Sum^3")
table.2[,1] <- group_name
table.2[,2] <- problem_type
table.2[,3] <- problem_size
View(table.2)
# linear
table.2[1,6] <- round(summary(m1)$coef[1,1], 2) # intercept estimate
table.2[1,7] <- round(summary(m1)$coef[2,1], 2) # VI estimate
table.2[1,8] <- round(summary(m1)$coef[2,2], 2) # SE
table.2[1,9] <- round(summary(m1)$coef[2,3], 2) # df
table.2[1,10] <- round(summary(m1)$coef[2,4], 2) # t.val
table.2[1,11] <- round(summary(m1)$coef[2,5], 5) # p.val
# cuadratic --------------------------------------------------------
table.2[2,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[2,7] <- round(summary(m1.sq)$coef[2,1], 2) # VI estimate
table.2[2,8] <- round(summary(m1.sq)$coef[2,2], 2) # SE
table.2[2,9] <- round(summary(m1.sq)$coef[2,3], 2) # df
table.2[2,10] <- round(summary(m1.sq)$coef[2,4], 2) # t.val
table.2[2,11] <- round(summary(m1.sq)$coef[2,5], 5) # p.val
# ----------------------------------------------------------------
table.2[3,6] <- round(summary(m1.sq)$coef[1,1], 2) # intercept estimate
table.2[3,7] <- round(summary(m1.sq)$coef[3,1], 2) # VI estimate
table.2[3,8] <- round(summary(m1.sq)$coef[3,2], 2) # SE
table.2[3,9] <- round(summary(m1.sq)$coef[3,3], 2) # df
table.2[3,10] <- round(summary(m1.sq)$coef[3,4], 2) # t.val
table.2[3,11] <- round(summary(m1.sq)$coef[3,5], 5) # p.val
# cubic ---------------------------------------------------------
table.2[4,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
table.2[4,7] <- round(summary(m1.cu)$coef[2,1], 2) # VI estimate
table.2[4,8] <- round(summary(m1.cu)$coef[2,2], 2) # SE
table.2[4,9] <- round(summary(m1.cu)$coef[2,3], 2) # df
table.2[4,10] <- round(summary(m1.cu)$coef[2,4], 2) # t.val
table.2[4,11] <- round(summary(m1.cu)$coef[2,5], 5) # p.val
# ----------------------------------------------------------------
table.2[5,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
table.2[5,7] <- round(summary(m1.cu)$coef[3,1], 2) # VI estimate
table.2[5,8] <- round(summary(m1.cu)$coef[3,2], 2) # SE
table.2[5,9] <- round(summary(m1.cu)$coef[3,3], 2) # df
table.2[5,10] <- round(summary(m1.cu)$coef[3,4], 2) # t.val
table.2[5,11] <- round(summary(m1.cu)$coef[3,5], 5) # p.val
# ----------------------------------------------------------------
table.2[6,6] <- round(summary(m1.cu)$coef[1,1], 2) # intercept estimate
table.2[6,7] <- round(summary(m1.cu)$coef[4,1], 2) # VI estimate
table.2[6,8] <- round(summary(m1.cu)$coef[4,2], 2) # SE
table.2[6,9] <- round(summary(m1.cu)$coef[4,3], 2) # df
table.2[6,10] <- round(summary(m1.cu)$coef[4,4], 2) # t.val
table.2[6,11] <- round(summary(m1.cu)$coef[4,5], 5) # p.val
# table.2[4,11] <- "cannot evaluate"
# table.2[5,11] <- "cannot evaluate"
# table.2[6,11] <- "cannot evaluate"
View(table.2)
```
# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_mS.G1.poly <- table.2 %>% as.tibble
write.csv(table_mS.G1.poly, "table_mS.G1.poly.csv")
```
# remove the objects
```{r}
rm(group_name, group_num, problem_size, problem_type, m1, pre.m1, m1.e1, m1.e2, m1.e3, m1.e4, m1.e5, m1.e6, m1.e7, m1.sq, m1.cu, aov_table, table.1, table.2)
```
```{r}
# SPECIFY THE GROUP
group_num = 0.5 # -0.5 ~ grade 1; 0.5 ~ grade 6
# Grade 6 ----------------------------------------------------------------------
m1 <- lmer(log(RT) ~ sum.f.n + (1 + sum.f.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1)
performance::check_model(m1)
# predictions ------------------------------------------------------------------
pre.m1 <- ggpredict(m1, terms = "sum.f.n", se = TRUE, back.transform = FALSE)
pre.m1
plot(pre.m1)
# sum.f.n | Predicted | 95% CI
# ----------------------------------
# -1.90 | 7.31 | [7.17, 7.45]
# -0.89 | 7.36 | [7.23, 7.48]
# 0.12 | 7.41 | [7.27, 7.54]
# 1.12 | 7.45 | [7.30, 7.61]
pre.m1 <- ggpredict(m1, terms = "sum.f.n", se = TRUE, back.transform = TRUE)
pre.m1
plot(pre.m1)
# sum.f.n | Predicted | 95% CI
# ----------------------------------------
# -1.90 | 1491.69 | [1295.21, 1717.98]
# -0.89 | 1566.71 | [1379.86, 1778.86]
# 0.12 | 1645.50 | [1441.35, 1878.56]
# 1.12 | 1728.25 | [1479.25, 2019.17]
```
```{r}
## Control models:
# square -----------------------------------------------------------------------
m1.sq <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1.sq)
# cubic ------------------------------------------------------------------------
# m1.cu <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1 + sum.f.n + I(sum.f.n^2) + I(sum.f.n^3)|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
# summary(m1.cu)
m1.cu <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1 + sum.f.n + I(sum.f.n^2)|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
# este converge pero nada significativo
summary(m1.cu)
## Alternative predictors ------------------------------------------------------
m1.e1 <- lmer(log(RT) ~ pre.min.n + (1 + pre.min.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e2 <- lmer(log(RT) ~ pre.max.n + (1 + pre.max.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e3 <- lmer(log(RT) ~ pre.product.n + (1 + pre.product.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e4 <- lmer(log(RT) ~ pre.sum_squared.n + (1 + pre.sum_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e5 <- lmer(log(RT) ~ pre.sup_op_squared.n + (1 + pre.sup_op_squared.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
m1.e6 <- lmer(log(RT) ~ op.1.n + (1 + op.1.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
m1.e7 <- lmer(log(RT) ~ op.2.n + (1 + op.2.n|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE) #singular
aov_table <- anova(m1,
m1.e1,
m1.e2,
m1.e3,
m1.e4,