20220518 - TIES.Rmd

---
title: "20220518 -TIES"
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
```

# Ties -------------------------------------------------------------------------

# Small ties
```{r}
d <- d.outliers_without %>% 
  filter(tie_small == 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 -------------------------------------------------------
# min y max es lo mismo 

# Alternative predictors -------------------------------------------------------
# min and max es lo mismo que op.1 y op.2 => evaluar solo op.1
# product, sum^2 y op.1^2+op.2^2 es lo mismo también

# d$pre.min.n            <- scale(as.numeric(d$pre.min))             # min
# d$pre.max.n            <- scale(as.numeric(d$pre.max))             # max
d$pre.product.n        <- scale(as.numeric(d$pre.product))         # product (op.1*op.2)
# d$pre.sum_squared.n    <- scale(as.numeric(d$pre.sum_squared))     # sum_squared ((op.1 + op.2)^2)
# d$pre.sup_op_squared.n <- scale(as.numeric(d$pre.sum_op_squared))  # sum of squared operands (op.1^2 + op.2^2)
d$op.1.n               <- scale(as.numeric(d$op.1))                # op.1
# d$op.2.n               <- scale(as.numeric(d$op.2))                # 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 = 20)
# hist(d$pre.sup_op_squared.n, breaks = 20)
hist(d$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)
# # singular

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)
# ok
afex::mixed(log(RT) ~ sum.f.n + (1 + sum.f.n||subject) + (1|item), data = filter(d, group.cod == group_num), method = "LRT")

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
# ----------------------------------
#   -1.41 |      7.42 | [7.19, 7.66]
#   -0.71 |      7.50 | [7.32, 7.68]
#    0.00 |      7.58 | [7.42, 7.74]
#    0.71 |      7.66 | [7.46, 7.85]
#    1.42 |      7.74 | [7.48, 7.99]
```


```{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)
# # failed to converge
# summary(m1.sq)

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)
#ok
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)
# # singular
# 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)
# # singular
# 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)
# summary(m1.cu)
# # singular

m1.cu <- lmer(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1.cu)
# singular: DEBO REDUCIR LOS DATOS

d.test <- d %>% filter(group.cod == -0.5) %>% group_by(subject, sum.f.n) %>% summarize(RT = mean(log(RT)))

m1.cu <- lmer(RT ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1|subject), data = d.test, REML = FALSE)
summary(m1.cu)
# a la vez no cambia los resultados

afex::mixed(log(RT) ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1|subject) + (1|item), data = filter(d, group.cod == group_num), method = "LRT")
afex::mixed(RT ~ sum.f.n + I(sum.f.n^2) + I(sum.f.n^3) + (1|subject), data = d.test, method = "LRT")

#
#
#
# have to take out 10 from the analysis
# tiene solo 5 ítems
#
#

pre.m1.cu <- ggpredict(m1.cu, terms = "sum.f.n[all]", se = TRUE, back.transform = FALSE)
pre.m1.cu

# sum.f.n | Predicted |       95% CI
# ----------------------------------
#   -1.41 |      7.43 | [7.29, 7.58]
#   -0.71 |      7.36 | [7.24, 7.49]
#    0.00 |      7.65 | [7.54, 7.77]
#    0.71 |      7.87 | [7.75, 8.00]
#    1.42 |      7.57 | [7.43, 7.72]
   
plot(pre.m1.cu)
```

#.
#.
#.
# Aquí los modelos alternativos también tendrían que ser cůbicos

```{r}
## 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)

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 = "tie"                                                            # non-tie or tie
problem_size = "small"                                                          # very small, medium small, large, small
```

# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 3, ncol = 12)
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")

table.1[,4] <- c("Sum", 
                 # "Min(op.1|op.2)", 
                 # "Max(op.1|op.2)", 
                 "product/sum^2/op.1^2+op.2^2", 
                 # "sum_sq ~ (op.1+op.2)^2", 
                 # "sum_sq_op ~ op.1^2+op.2^2", 
                 "op.1/op.2/Min/Max" 
                 # "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] <- "cannot evaluate"
# table.1[2,6] <- "cannot evaluate"
# table.1[2,7] <- "cannot evaluate"
# table.1[2,8] <- "cannot evaluate"
# table.1[2,9] <- "cannot evaluate"
# table.1[2,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- Max
# table.1[3,5] <- "cannot evaluate"
# table.1[3,6] <- "cannot evaluate"
# table.1[3,7] <- "cannot evaluate"
# table.1[3,8] <- "cannot evaluate"
# table.1[3,9] <- "cannot evaluate"
# table.1[3,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- product
table.1[2,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
# table.1[5,5] <- "cannot evaluate"
# table.1[5,6] <- "cannot evaluate"
# table.1[5,7] <- "cannot evaluate"
# table.1[5,8] <- "cannot evaluate"
# table.1[5,9] <- "cannot evaluate"
# table.1[5,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- sum_sq_op
# table.1[6,5] <- "cannot evaluate"
# table.1[6,6] <- "cannot evaluate"
# table.1[6,7] <- "cannot evaluate"
# table.1[6,8] <- "cannot evaluate"
# table.1[6,9] <- "cannot evaluate"
# table.1[6,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- op.1
table.1[3,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
# table.1[8,5] <- "cannot evaluate"
# table.1[8,6] <- "cannot evaluate"
# table.1[8,7] <- "cannot evaluate"
# table.1[8,8] <- "cannot evaluate"
# table.1[8,9] <- "cannot evaluate"
# table.1[8,10]<- "cannot evaluate"

table.1[,11]  <- round(aov_table[,2], 2)
table.1[,12]  <- round(aov_table[,3], 2)

View(table.1)
```

# CHANGE THE NAME HERE:
```{r}
table_tie.S.G1 <- table.1 %>% as.tibble()
write.csv(table_tie.S.G1, "table_tie.S.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

View(table.2)
```

# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_tie.S.G1.poly <- table.2 %>% as.tibble
write.csv(table_tie.S.G1.poly, "table_tie.S.G1.poly.csv")
```


# .
# .
# .
# probando eliminar 10
# .
# .
# .


# Small ties
```{r}
d <- d.outliers_without %>% 
  filter(tie_small == 1 & item != 56)

xtabs(~ sum.f, data = d)
```


```{r}
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 -------------------------------------------------------
# min y max es lo mismo 

# Alternative predictors -------------------------------------------------------
# min and max es lo mismo que op.1 y op.2 => evaluar solo op.1
# product, sum^2 y op.1^2+op.2^2 es lo mismo también

# d$pre.min.n            <- scale(as.numeric(d$pre.min))             # min
# d$pre.max.n            <- scale(as.numeric(d$pre.max))             # max
d$pre.product.n        <- scale(as.numeric(d$pre.product))         # product (op.1*op.2)
# d$pre.sum_squared.n    <- scale(as.numeric(d$pre.sum_squared))     # sum_squared ((op.1 + op.2)^2)
# d$pre.sup_op_squared.n <- scale(as.numeric(d$pre.sum_op_squared))  # sum of squared operands (op.1^2 + op.2^2)
d$op.1.n               <- scale(as.numeric(d$op.1))                # op.1
# d$op.2.n               <- scale(as.numeric(d$op.2))                # 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 = 20)
# hist(d$pre.sup_op_squared.n, breaks = 20)
hist(d$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)

# 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)
# # ok

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
# ----------------------------------
#   -1.41 |      7.42 | [7.19, 7.66]
#   -0.71 |      7.50 | [7.32, 7.68]
#    0.00 |      7.58 | [7.42, 7.74]
#    0.71 |      7.66 | [7.46, 7.85]
#    1.42 |      7.74 | [7.48, 7.99]
```


```{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)
# singular
summary(m1.sq)

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)
#ok
summary(m1.sq)

anova(m1, m1.sq)

pre.m1.sq <- ggpredict(m1.sq, terms = "sum.f.n[all]", se = TRUE, back.transform = FALSE)
pre.m1.sq
plot(pre.m1.sq)
```


<!-- ```{r} -->
<!-- # 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) -->
<!-- # # singular -->
<!-- # 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) -->
<!-- # # singular -->
<!-- # 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) -->
<!-- summary(m1.cu) -->




<!-- pre.m1.cu <- ggpredict(m1.cu, terms = "sum.f.n[all]", se = TRUE, back.transform = FALSE) -->
<!-- pre.m1.cu -->


<!-- plot(pre.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) -->

<!-- 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 = "tie"                                                            # non-tie or tie -->
<!-- problem_size = "small"                                                          # very small, medium small, large, small -->
<!-- ``` -->

<!-- # TABLE 1: Models and control models ------------------------------------------- -->
<!-- ```{r} -->
<!-- table.1 <- matrix(NA, nrow = 3, ncol = 12) -->
<!-- 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") -->

<!-- table.1[,4] <- c("Sum",  -->
<!--                  # "Min(op.1|op.2)",  -->
<!--                  # "Max(op.1|op.2)",  -->
<!--                  "product/sum^2/op.1^2+op.2^2",  -->
<!--                  # "sum_sq ~ (op.1+op.2)^2",  -->
<!--                  # "sum_sq_op ~ op.1^2+op.2^2",  -->
<!--                  "op.1/op.2/Min/Max"  -->
<!--                  # "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] <- "cannot evaluate" -->
<!-- # table.1[2,6] <- "cannot evaluate" -->
<!-- # table.1[2,7] <- "cannot evaluate" -->
<!-- # table.1[2,8] <- "cannot evaluate" -->
<!-- # table.1[2,9] <- "cannot evaluate" -->
<!-- # table.1[2,10]<- "cannot evaluate" -->
<!-- # #-------------------------------.---------------------------------- Max -->
<!-- # table.1[3,5] <- "cannot evaluate" -->
<!-- # table.1[3,6] <- "cannot evaluate" -->
<!-- # table.1[3,7] <- "cannot evaluate" -->
<!-- # table.1[3,8] <- "cannot evaluate" -->
<!-- # table.1[3,9] <- "cannot evaluate" -->
<!-- # table.1[3,10]<- "cannot evaluate" -->
<!-- #-------------------------------.---------------------------------- product -->
<!-- table.1[2,5] <- round(summary(m1.e3)$coef[1,1], 2) -->
<!-- table.1[2,6] <- round(summary(m1.e3)$coef[2,1], 2) -->
<!-- table.1[2,7] <- round(summary(m1.e3)$coef[2,2], 2) -->
<!-- table.1[2,8] <- round(summary(m1.e3)$coef[2,3], 2) -->
<!-- table.1[2,9] <- round(summary(m1.e3)$coef[2,4], 2) -->
<!-- table.1[2,10]<- round(summary(m1.e3)$coef[2,5], 5) -->
<!-- #-------------------------------.---------------------------------- sum_sq -->
<!-- # table.1[5,5] <- "cannot evaluate" -->
<!-- # table.1[5,6] <- "cannot evaluate" -->
<!-- # table.1[5,7] <- "cannot evaluate" -->
<!-- # table.1[5,8] <- "cannot evaluate" -->
<!-- # table.1[5,9] <- "cannot evaluate" -->
<!-- # table.1[5,10]<- "cannot evaluate" -->
<!-- # #-------------------------------.---------------------------------- sum_sq_op -->
<!-- # table.1[6,5] <- "cannot evaluate" -->
<!-- # table.1[6,6] <- "cannot evaluate" -->
<!-- # table.1[6,7] <- "cannot evaluate" -->
<!-- # table.1[6,8] <- "cannot evaluate" -->
<!-- # table.1[6,9] <- "cannot evaluate" -->
<!-- # table.1[6,10]<- "cannot evaluate" -->
<!-- #-------------------------------.---------------------------------- op.1 -->
<!-- table.1[3,5] <- round(summary(m1.e6)$coef[1,1], 2) -->
<!-- table.1[3,6] <- round(summary(m1.e6)$coef[2,1], 2) -->
<!-- table.1[3,7] <- round(summary(m1.e6)$coef[2,2], 2) -->
<!-- table.1[3,8] <- round(summary(m1.e6)$coef[2,3], 2) -->
<!-- table.1[3,9] <- round(summary(m1.e6)$coef[2,4], 2) -->
<!-- table.1[3,10]<- round(summary(m1.e6)$coef[2,5], 5) -->
<!-- #-------------------------------.---------------------------------- op.2 -->
<!-- # table.1[8,5] <- "cannot evaluate" -->
<!-- # table.1[8,6] <- "cannot evaluate" -->
<!-- # table.1[8,7] <- "cannot evaluate" -->
<!-- # table.1[8,8] <- "cannot evaluate" -->
<!-- # table.1[8,9] <- "cannot evaluate" -->
<!-- # table.1[8,10]<- "cannot evaluate" -->

<!-- table.1[,11]  <- round(aov_table[,2], 2) -->
<!-- table.1[,12]  <- round(aov_table[,3], 2) -->

<!-- View(table.1) -->
<!-- ``` -->

<!-- # CHANGE THE NAME HERE: -->
<!-- ```{r} -->
<!-- table_tie.S.G1 <- table.1 %>% as.tibble() -->
<!-- write.csv(table_tie.S.G1, "table_tie.S.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 -->

<!-- View(table.2) -->
<!-- ``` -->

<!-- # CHANGE THE NAMES HERE -------------------------------------------------------- -->
<!-- ```{r} -->
<!-- table_tie.S.G1.poly <- table.2 %>% as.tibble -->
<!-- write.csv(table_tie.S.G1.poly, "table_tie.S.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
# ----------------------------------
#   -1.41 |      6.93 | [6.77, 7.09]
#   -0.71 |      6.95 | [6.82, 7.07]
#    0.00 |      6.96 | [6.85, 7.07]
#    0.71 |      6.98 | [6.85, 7.10]
#    1.42 |      6.99 | [6.83, 7.16]
```


```{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)
# # singular
# summary(m1.sq)

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)
# ok
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)
# # failed to converge
# # singular
# 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) + I(sum.f.n^3)||subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
# # singular
# 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)
summary(m1.cu)


pre.m1.cu <- ggpredict(m1.cu, terms = "sum.f.n[all]", se = TRUE, back.transform = FALSE)
pre.m1.cu
plot(pre.m1.cu)

# sum.f.n | Predicted |       95% CI
# ----------------------------------
#   -1.41 |      6.91 | [6.78, 7.05]
#   -0.71 |      6.89 | [6.77, 7.01]
#    0.00 |      7.02 | [6.91, 7.12]
#    0.71 |      7.09 | [6.97, 7.21]
#    1.42 |      6.89 | [6.76, 7.03]
```


```{r}
## 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)

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 6"                                                          # grade 1 or grade 6
problem_type = "tie"                                                        # non-tie or tie
problem_size = "small"                                                     # very small, medium small, large, small
```

# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 3, ncol = 12)
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")

table.1[,4] <- c("Sum", 
                 # "Min(op.1|op.2)", 
                 # "Max(op.1|op.2)", 
                 "product/sum^2/op.1^2+op.2^2", 
                 # "sum_sq ~ (op.1+op.2)^2", 
                 # "sum_sq_op ~ op.1^2+op.2^2", 
                 "op.1/op.2/Min/Max" 
                 # "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] <- "cannot evaluate"
# table.1[2,6] <- "cannot evaluate"
# table.1[2,7] <- "cannot evaluate"
# table.1[2,8] <- "cannot evaluate"
# table.1[2,9] <- "cannot evaluate"
# table.1[2,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- Max
# table.1[3,5] <- "cannot evaluate"
# table.1[3,6] <- "cannot evaluate"
# table.1[3,7] <- "cannot evaluate"
# table.1[3,8] <- "cannot evaluate"
# table.1[3,9] <- "cannot evaluate"
# table.1[3,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- product
table.1[2,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
# table.1[5,5] <- "cannot evaluate"
# table.1[5,6] <- "cannot evaluate"
# table.1[5,7] <- "cannot evaluate"
# table.1[5,8] <- "cannot evaluate"
# table.1[5,9] <- "cannot evaluate"
# table.1[5,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- sum_sq_op
# table.1[6,5] <- "cannot evaluate"
# table.1[6,6] <- "cannot evaluate"
# table.1[6,7] <- "cannot evaluate"
# table.1[6,8] <- "cannot evaluate"
# table.1[6,9] <- "cannot evaluate"
# table.1[6,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- op.1
table.1[3,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
# table.1[8,5] <- "cannot evaluate"
# table.1[8,6] <- "cannot evaluate"
# table.1[8,7] <- "cannot evaluate"
# table.1[8,8] <- "cannot evaluate"
# table.1[8,9] <- "cannot evaluate"
# table.1[8,10]<- "cannot evaluate"

table.1[,11]  <- round(aov_table[,2], 2)
table.1[,12]  <- round(aov_table[,3], 2)

View(table.1)
```

# CHANGE THE NAME HERE:
```{r}
table_tie.S.G6 <- table.1 %>% as.tibble()
write.csv(table_tie.S.G6, "table_tie.S.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

View(table.2)
```

# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_tie.S.G6.poly <- table.2 %>% as.tibble
write.csv(table_tie.S.G6.poly, "table_tie.S.G6.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)
```

# Large ties -------------------------------------------------------------------
```{r}
d <- d.outliers_without %>% 
  filter(tie_large == 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 -------------------------------------------------------
# min and max es lo mismo que op.1 y op.2 => evaluar solo op.1
# product, sum^2 y op.1^2+op.2^2 es lo mismo también

# d$pre.min.n            <- scale(as.numeric(d$pre.min))             # min
# d$pre.max.n            <- scale(as.numeric(d$pre.max))             # max
d$pre.product.n        <- scale(as.numeric(d$pre.product))         # product (op.1*op.2)
# d$pre.sum_squared.n    <- scale(as.numeric(d$pre.sum_squared))     # sum_squared ((op.1 + op.2)^2)
# d$pre.sup_op_squared.n <- scale(as.numeric(d$pre.sum_op_squared))  # sum of squared operands (op.1^2 + op.2^2)
d$op.1.n               <- scale(as.numeric(d$op.1))                # op.1
# d$op.2.n               <- scale(as.numeric(d$op.2))                # 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 = 20)
# hist(d$pre.sup_op_squared.n, breaks = 20)
hist(d$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)
# singular

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
# ----------------------------------
#   -1.28 |      8.91 | [8.58, 9.25]
#   -0.39 |      8.96 | [8.72, 9.19]
#    0.50 |      9.00 | [8.77, 9.24]
#    1.39 |      9.05 | [8.71, 9.38]
```


```{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)
# # singular por el item
# summary(m1.sq)

d.bysubj <- d %>% filter(group.cod == -0.5) %>% group_by(subject, sum.f.n) %>% summarize(RT = mean(log(RT)))

m1.sq <- lmer(RT ~ sum.f.n + I(sum.f.n^2) + (1 + sum.f.n + I(sum.f.n^2)|subject), data = d.bysubj, REML = FALSE)
# singular por el item
summary(m1.sq)

pre.m1.sq <- ggpredict(m1.sq, terms = "sum.f.n", se = TRUE, back.transform = FALSE)
pre.m1.sq
plot(pre.m1.sq)

# sum.f.n | Predicted |       95% CI
# ----------------------------------
#   -1.28 |      8.75 | [8.51, 8.99]
#   -0.39 |      9.13 | [9.00, 9.26]
#    0.50 |      9.20 | [9.09, 9.31]
#    1.39 |      8.96 | [8.72, 9.21]
```



ES SINGULAR!!!



```{r}
# 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)
# singular
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)

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 = "tie"                                                            # non-tie or tie
problem_size = "large"                                                          # very small, medium small, large, small
```

# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 3, ncol = 12)
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")

table.1[,4] <- c("Sum", 
                 # "Min(op.1|op.2)", 
                 # "Max(op.1|op.2)", 
                 "product/sum^2/op.1^2+op.2^2", 
                 # "sum_sq ~ (op.1+op.2)^2", 
                 # "sum_sq_op ~ op.1^2+op.2^2", 
                 "op.1/op.2/Min/Max" 
                 # "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] <- "cannot evaluate"
# table.1[2,6] <- "cannot evaluate"
# table.1[2,7] <- "cannot evaluate"
# table.1[2,8] <- "cannot evaluate"
# table.1[2,9] <- "cannot evaluate"
# table.1[2,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- Max
# table.1[3,5] <- "cannot evaluate"
# table.1[3,6] <- "cannot evaluate"
# table.1[3,7] <- "cannot evaluate"
# table.1[3,8] <- "cannot evaluate"
# table.1[3,9] <- "cannot evaluate"
# table.1[3,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- product
table.1[2,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
# table.1[5,5] <- "cannot evaluate"
# table.1[5,6] <- "cannot evaluate"
# table.1[5,7] <- "cannot evaluate"
# table.1[5,8] <- "cannot evaluate"
# table.1[5,9] <- "cannot evaluate"
# table.1[5,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- sum_sq_op
# table.1[6,5] <- "cannot evaluate"
# table.1[6,6] <- "cannot evaluate"
# table.1[6,7] <- "cannot evaluate"
# table.1[6,8] <- "cannot evaluate"
# table.1[6,9] <- "cannot evaluate"
# table.1[6,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- op.1
table.1[3,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
# table.1[8,5] <- "cannot evaluate"
# table.1[8,6] <- "cannot evaluate"
# table.1[8,7] <- "cannot evaluate"
# table.1[8,8] <- "cannot evaluate"
# table.1[8,9] <- "cannot evaluate"
# table.1[8,10]<- "cannot evaluate"

table.1[,11]  <- round(aov_table[,2], 2)
table.1[,12]  <- round(aov_table[,3], 2)

View(table.1)
```

# CHANGE THE NAME HERE:
```{r}
table_tie.L.G1 <- table.1 %>% as.tibble()
write.csv(table_tie.L.G1, "table_tie.L.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

View(table.2)
```

# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_tie.L.G1.poly <- table.2 %>% as.tibble
write.csv(table_tie.L.G1.poly, "table_tie.L.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
# ----------------------------------
#   -1.28 |      7.27 | [7.11, 7.43]
#   -0.39 |      7.29 | [7.14, 7.44]
#    0.50 |      7.30 | [7.15, 7.45]
#    1.39 |      7.32 | [7.16, 7.47]
```


```{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)
# singular because of items
summary(m1.sq)
```




SINGULAR: CORREGIR





```{r}
# 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)
# singular
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) + I(sum.f.n^3)||subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
# singular
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|subject) + (1|item), data = filter(d, group.cod == group_num), REML = FALSE)
summary(m1.cu)
# still singular, but cubic term not significant

## 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
# 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 6"                                                          # grade 1 or grade 6
problem_type = "tie"                                                            # non-tie or tie
problem_size = "large"                                                          # very small, medium small, large, small
```

# TABLE 1: Models and control models -------------------------------------------
```{r}
table.1 <- matrix(NA, nrow = 3, ncol = 12)
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")

table.1[,4] <- c("Sum", 
                 # "Min(op.1|op.2)", 
                 # "Max(op.1|op.2)", 
                 "product/sum^2/op.1^2+op.2^2", 
                 # "sum_sq ~ (op.1+op.2)^2", 
                 # "sum_sq_op ~ op.1^2+op.2^2", 
                 "op.1/op.2/Min/Max" 
                 # "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] <- "cannot evaluate"
# table.1[2,6] <- "cannot evaluate"
# table.1[2,7] <- "cannot evaluate"
# table.1[2,8] <- "cannot evaluate"
# table.1[2,9] <- "cannot evaluate"
# table.1[2,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- Max
# table.1[3,5] <- "cannot evaluate"
# table.1[3,6] <- "cannot evaluate"
# table.1[3,7] <- "cannot evaluate"
# table.1[3,8] <- "cannot evaluate"
# table.1[3,9] <- "cannot evaluate"
# table.1[3,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- product
table.1[2,5] <- round(summary(m1.e3)$coef[1,1], 2)
table.1[2,6] <- round(summary(m1.e3)$coef[2,1], 2)
table.1[2,7] <- round(summary(m1.e3)$coef[2,2], 2)
table.1[2,8] <- round(summary(m1.e3)$coef[2,3], 2)
table.1[2,9] <- round(summary(m1.e3)$coef[2,4], 2)
table.1[2,10]<- round(summary(m1.e3)$coef[2,5], 5)
#-------------------------------.---------------------------------- sum_sq
# table.1[5,5] <- "cannot evaluate"
# table.1[5,6] <- "cannot evaluate"
# table.1[5,7] <- "cannot evaluate"
# table.1[5,8] <- "cannot evaluate"
# table.1[5,9] <- "cannot evaluate"
# table.1[5,10]<- "cannot evaluate"
# #-------------------------------.---------------------------------- sum_sq_op
# table.1[6,5] <- "cannot evaluate"
# table.1[6,6] <- "cannot evaluate"
# table.1[6,7] <- "cannot evaluate"
# table.1[6,8] <- "cannot evaluate"
# table.1[6,9] <- "cannot evaluate"
# table.1[6,10]<- "cannot evaluate"
#-------------------------------.---------------------------------- op.1
table.1[3,5] <- round(summary(m1.e6)$coef[1,1], 2)
table.1[3,6] <- round(summary(m1.e6)$coef[2,1], 2)
table.1[3,7] <- round(summary(m1.e6)$coef[2,2], 2)
table.1[3,8] <- round(summary(m1.e6)$coef[2,3], 2)
table.1[3,9] <- round(summary(m1.e6)$coef[2,4], 2)
table.1[3,10]<- round(summary(m1.e6)$coef[2,5], 5)
#-------------------------------.---------------------------------- op.2
# table.1[8,5] <- "cannot evaluate"
# table.1[8,6] <- "cannot evaluate"
# table.1[8,7] <- "cannot evaluate"
# table.1[8,8] <- "cannot evaluate"
# table.1[8,9] <- "cannot evaluate"
# table.1[8,10]<- "cannot evaluate"

table.1[,11]  <- round(aov_table[,2], 2)
table.1[,12]  <- round(aov_table[,3], 2)

View(table.1)
```

# CHANGE THE NAME HERE:
```{r}
table_tie.L.G6 <- table.1 %>% as.tibble()
write.csv(table_tie.L.G6, "table_tie.L.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

View(table.2)
```

# CHANGE THE NAMES HERE --------------------------------------------------------
```{r}
table_tie.L.G6.poly <- table.2 %>% as.tibble
write.csv(table_tie.L.G6.poly, "table_tie.L.G6.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)
```

# TODO: