L&M_manuscript_rev2.Rmd

---
title: "R Notebook for Feliciano-Ramos et al., 2023"
output:
  html_notebook: default
  pdf_document: default
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. 

To perform statistical analysis you need to download the R functions from
https://dornsife.usc.edu/labs/rwilcox/software/ or from github
https://github.com/nicebread/WRS 

```{r}
# Make sure the code is in your path
source("Rallfun-v40.txt")
```

### supplementary Figure 2 E1
RTC AND PFC ON and OFF duration distributions
```{r}
suppFig1E1 <- read.csv("suppFig1E1.txt")
boxplot(suppFig1E1)
summary(suppFig1E1)
btrim(suppFig1E1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
linconbt(suppFig1E1, con=0, tr=0.2, alpha=0.05, nboot=599) # 20% trimmed group means with bootstrap-t method
# lincon(suppFig1E1, con=0, tr=0.2, alpha=0.05) # without bootstrap-t
```
### supplementary Figure 2 F1
RTC AND PFC ON and OFF inter event interval(IEI) distributions
```{r}
suppFig1F1 <- read.csv("suppFig1F1.txt ")
boxplot(suppFig1F1)
summary(suppFig1F1)
btrim(suppFig1F1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
linconb(suppFig1F1,con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### Figure 3A
One-Way Robust Testing see: chapter 7 Introduction to Robust Estimation and hypothesis Testing
```{r}
data_fig3A <- read.csv("fig3A.txt ")
boxplot(data_fig3A)
summary(data_fig3A)
btrim(data_fig3A, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(data_fig3A,con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
#med1way(data_fig3A1) 
```
### supplementary Figure 3A1
One-Way Robust Testing see
```{r}
supp_data_fig3A1 <- read.csv("suppFig3A1.txt ")
boxplot(supp_data_fig3A1)
summary(supp_data_fig3A1)
btrim(supp_data_fig3A1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(supp_data_fig3A1,con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```
### supplementary Figure 3B1
One-Way Robust Testing
```{r}
supp_data_fig3B1 <- read.csv("suppFig3B1.txt ")
boxplot(supp_data_fig3B1)
summary(supp_data_fig3B1)
btrim(supp_data_fig3B1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(supp_data_fig3B1,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### Figure 3B
```{r}
data_fig3B <- read.csv("fig3B.txt ")
boxplot(data_fig3B)
summary(data_fig3B)
btrim(data_fig3B, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1)) # matrix for group testing
linconb(data_fig3B,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3A2
RTC vs Shuffles for Ripples occurring during cortical ON periods
```{r}
supp_fig3A2 <- read.csv("suppFig3A2.txt ")
boxplot(supp_fig3A2)
summary(supp_fig3A2) 
btrim(supp_fig3A2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1), c(0,1,0,-1,0,0)) # matrix for group testing
linconb(supp_fig3A2,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```
### supplementary Figure 3B2
PFC vs Shuffles for Ripples occurring during cortical ON periods
```{r}
supp_data_fig3B2 <- read.csv("suppFig3B2.txt ")
boxplot(supp_data_fig3B2)
summary(supp_data_fig3B2)
btrim(supp_data_fig3B2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1), c(0,1,0,-1,0,0)) # matrix for group testing
linconb(supp_data_fig3B2,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## Figure 3C
RTC vs PFC Down-states configurations
```{r}
data_fig3C <- read.csv("fig3C.txt ")
boxplot(data_fig3C)
summary(data_fig3C)
btrim(data_fig3C, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1)) # matrix for group testing
linconb(data_fig3C,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## Supplementary Figure 3A3
```{r}
supp_data_fig3A3 <- read.csv("suppFig3A3.txt ")
boxplot(supp_data_fig3A3)
summary(supp_data_fig3A3)
btrim(supp_data_fig3A3, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1), c(0,1,0,-1,0,0)) # matrix for group testing
linconb(supp_data_fig3A3,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## Supplementary Figure 3B3
```{r}
supp_data_fig3B3 <- read.csv("suppFig3B3.txt ")
boxplot(supp_data_fig3B3)
summary(supp_data_fig3B3)
btrim(supp_data_fig3B3, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0,0,0),c(0,0,1,-1,0,0),c(0,0,0,0,1,-1), c(0,1,0,-1,0,0)) # matrix for group testing
linconb(supp_data_fig3B3,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3C1
RTC and PFC vs Shuffles Ratio
```{r}
supp_data_fig3C1 <- read.csv("suppFig3C1.txt ")
boxplot(supp_data_fig3C1)
summary(supp_data_fig3C1)
btrim(supp_data_fig3C1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,0,-1,0,0,0),c(0,1,-1,0,0,0),c(0,0,-1,1,0,0),c(0,0,-1,0,-1,0),c(0,0,-1,0,0,-1)) # matrix for group testing
linconb(supp_data_fig3C1,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3C2
RTC and PFC vs Down Shuffles Ratio
```{r}
supp_data_fig3C2 <- read.csv("suppFig3C2.txt ")
boxplot(supp_data_fig3C2)
summary(supp_data_fig3C2)
btrim(supp_data_fig3C2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,0,-1,0,0,0),c(0,1,-1,0,0,0),c(0,0,-1,1,0,0),c(0,0,-1,0,-1,0),c(0,0,-1,0,0,-1)) # matrix for group testing
linconb(supp_data_fig3C2,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3D
One-Way Robust Testing
```{r}
supp_data_fig3D <- read.csv("suppFig3D.txt")
boxplot(supp_data_fig3D)
summary(supp_data_fig3D)
btrim(supp_data_fig3D, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(supp_data_fig3D,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3E1
One-Way Robust Testing
```{r}
supp_data_fig3E1 <- read.csv("suppFig3E1.txt")
boxplot(supp_data_fig3E1)
summary(supp_data_fig3E1)
btrim(supp_data_fig3E1, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(supp_data_fig3E1,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 3E2
One-Way Robust Testing
```{r}
supp_data_fig3E2 <- read.csv("suppFig3E2.txt")
boxplot(supp_data_fig3E2)
summary(supp_data_fig3E2)
```



### Figure 4
Delta and Down-State Phase Distribution
```{r}
data_fig4B3 <- read.csv("C:\\Users\\pdrfl\\DataAnalysis\\L&M_data_&_code\\Fig4\\fig4B3.txt ")
boxplot(data_fig4B3)
summary(data_fig4B3)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(data_fig4B3[,1],data_fig4B3[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(data_fig4B3[,1],data_fig4B3[,2]) # explanatory power
trimpb2(data_fig4B3[,1],data_fig4B3[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

### supplementary Figure 5 A1
Peak Amplitude Down-onset
```{r}
suppFig4A1 <- read.csv("suppFig5A1.txt ")
boxplot(suppFig4A1)
summary(suppFig4A1)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig4A1[,1],suppFig4A1[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig4A1[,1],suppFig4A1[,2]) # explanatory power
trimpb2(suppFig4A1[,1],suppFig4A1[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```
### Supplementary Figure 5 A2
Down-state onset Variance after a SWR
```{r}
suppFig4A2 <- read.csv("suppFig5A2.txt ")
boxplot(suppFig4A2)
summary(suppFig4A2)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig4A2[,1],suppFig4A2[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig4A2[,1],suppFig4A2[,2]) # explanatory power
trimpb2(suppFig4A2[,1],suppFig4A2[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

### supplementary Figure 5 A3
Peak Timing of a Down-state onset
```{r}
suppFig4A3 <- read.csv("suppFig5A3.txt ")
boxplot(suppFig4A3)
summary(suppFig4A3)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig4A3[,1],suppFig4A3[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig4A3[,1],suppFig4A3[,2]) # explanatory power
trimpb2(suppFig4A3[,1],suppFig4A3[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

### supplementary Figure 5 A4
Peak Timing of a Down-state onset
```{r}
suppFig4A4 <- read.csv("suppFig5A4.txt ")
boxplot(suppFig4A4)
summary(suppFig4A4)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig4A4[,1],suppFig4A4[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig4A4[,1],suppFig4A4[,2]) # explanatory power
trimpb2(suppFig4A4[,1],suppFig4A4[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

## supplementary Figure 5 B1 and B2
```{r}
suppFig4B1_2 <- read.csv("suppFig5B1_&B2.txt ")
boxplot(suppFig4B1_2)
summary(suppFig4B1_2)
btrim(suppFig4B1_2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1),c(1,0,-1,0),c(0,1,0,-1)) # matrix for group testing
linconb(suppFig4B1_2,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### supplementary Figure 5 D2
Sust. UP-State MUA RTC
```{r}
suppFig5D2 <- read.csv("suppFig5D2.txt ")
boxplot(suppFig5D2)
summary(suppFig5D2)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig5D2[,1],suppFig5D2[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig5D2[,1],suppFig5D2[,2]) # explanatory power
trimpb2(suppFig5D2[,1],suppFig5D2[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```
### supplementary Figure 5 D4
Sust. UP-State MUA PFC
```{r}
suppFig5D4 <- read.csv("suppFig5D4.txt ")
boxplot(suppFig5D4)
summary(suppFig5D4)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig5D4[,1],suppFig5D4[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig5D4[,1],suppFig5D4[,2]) # explanatory power
trimpb2(suppFig5D4[,1],suppFig5D4[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```
## supplementary Figure 5 E2
Sust. UP-State Delta RTC
```{r}
suppFig5E2 <- read.csv("suppFig5E2.txt ")
boxplot(suppFig5E2)
summary(suppFig5E2)
btrim(suppFig5E2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,-1,0)) # matrix for group testing
linconb(suppFig5E2,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## supplementary Figure 5 E3
Sust. UP-State Delta PFC
```{r}
suppFig5E3 <- read.csv("suppFig5E3.txt ")
boxplot(suppFig5E3)
summary(suppFig5E3)
btrim(suppFig5E3, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(suppFig5E3,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## supplementary Figure 5 F2
Sust. UP-State Spindle RTC
```{r}
suppFig5F2 <- read.csv("suppFig5F2.txt ")
boxplot(suppFig5F2)
summary(suppFig5F2)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig5F2[,1], suppFig5F2[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig5F2[,1], suppFig5F2[,2]) # explanatory power
trimpb2(suppFig5F2[,1], suppFig5F2[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

## supplementary Figure 5 F3
Sust. UP-State Spindle PFC
```{r}
suppFig5F3 <- read.csv("suppFig5F3.txt ")
boxplot(suppFig5F3)
summary(suppFig5F3)
# Bootstrap-t Methods for Comparing trimmed means
yuenbt(suppFig5F3[,1], suppFig5F3[,2],tr=0.2,alpha=0.05,nboot=599,side=F)
ESfun(suppFig5F3[,1], suppFig5F3[,2]) # explanatory power
trimpb2(suppFig5F3[,1], suppFig5F3[,2],tr=0.2,alpha=0.05,nboot=2000,WIN=F,win=0.1)
```

### Figure 5 C1
Replays and Coordination to UP and Down States of the RTC and PFC
```{r}
data_fig5C1 <- read.csv("fig5C1.txt ")
boxplot(data_fig5C1[,1:4])
summary(data_fig5C1)
btrim(data_fig5C1[,1:4], tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,0,-1,0,0,0),c(0,1,-1,0,0,0),c(0,0,-1,1,0,0),c(0,0,-1,0,-1,0),c(0,0,-1,0,0,-1)) # matrix for group testing
linconb(data_fig5C1[,1:4],con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### Figure 5 C2
Replays and Coordination to UP-States of the RTC and PFC
```{r}
data_fig5C2 <- read.csv("fig5C2.txt ")
data_fig5C2 <- data_fig5C2[, 1:6]
boxplot(data_fig5C2)
summary(data_fig5C2)
btrim(data_fig5C2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,0,-1,0,0,0),c(0,1,-1,0,0,0),c(0,0,-1,1,0,0),c(0,0,-1,0,-1,0),c(0,0,-1,0,0,-1)) # matrix for group testing
linconb(data_fig5C2,con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

### Figure 5 C3
Replays and Coordination from Down States of the RTC and PFC
```{r}
data_fig5C2 <- read.csv("fig5C3.txt ")
data_fig5C2 <- data_fig5C2[, 1:6]
boxplot(data_fig5C2)
summary(data_fig5C2)
btrim(data_fig5C2, tr=0.2, grp=NA, g=NULL, dp=NULL, nboot=599) # trimmed group mean anova with bootstrap-t
M = cbind(c(1,0,-1,0,0,0),c(0,1,-1,0,0,0),c(0,0,-1,1,0,0),c(0,0,-1,0,-1,0),c(0,0,-1,0,0,-1)) # matrix for group testing
linconb(data_fig5C2,con=0,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## supplementary Figure 4  B1 and B2
uncategorized SWR from RTC
```{r}
suppFig3B_B <- read.csv("suppFig3B_B.txt ")
boxplot(suppFig3B_B)
summary(suppFig3B_B)
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(suppFig3B_B,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```

## supplementary Figure 4 C1 and C2
uncategorized SWR from RTC
```{r}
suppFig3C_C <- read.csv("suppFig3C_C.txt ")
boxplot(suppFig3C_C)
summary(suppFig3C_C)
M = cbind(c(1,-1,0,0),c(0,0,1,-1)) # matrix for group testing
linconb(suppFig3C_C,con=M,tr=0.2,alpha=0.05,nboot=599) # 20% trimmed group means with bootstrap-t method
```