---
title: "Spacek et al., 2021, Figure 1-Supplement 4"
output: pdf_document
---

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(arm)
library(lmerTest)
library(tidyverse)

source('get_data.R')
```

```{r read_data_1_S4_e-h, include=FALSE}

tib = get_data("../csv/fig1S4mvi.csv")
```

```{r tidy_for_1_S4ef, include = FALSE}

# Turn feedback into a binary variable
tb <- tib %>% mutate(feedback = ifelse(opto == TRUE, 0, 1))
```

# Figure 1-Supplement 4e
## Effect of suppression on firing rate - movies 

```{r fit_model_1_S4e}

# Random intercept, random slope for neurons,
# random intercept for experiments
lmer.1_S4e = lmer(rates ~ feedback + (1 + feedback | uid) + (1 | eid), 
                  data = tb %>% drop_na(rates))

display(lmer.1_S4e)
anova(lmer.1_S4e)
```

```{r get_predicted_average_effect_1_S4e, include=F}

mSuppr = fixef(lmer.1_S4e)[1]
diffRate = fixef(lmer.1_S4e)[2]
mActive  = fixef(lmer.1_S4e)[1] + diffRate
```

Feedback: `r format(mActive, digits=2, nsmall=2)` spikes/s \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` spikes/s  \newline
n = `r nrow(tb %>% drop_na(rates) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(rates) %>% count(mid))` mice

\newpage
# Figure 1-Supplement 4f
## Effect of suppression on burst ratio - movies

```{r fit_model_1_S4f}

# Random intercept, random slope for neurons,
# random intercept for experiments, nested in series
lmer.1_S4f = lmer(burstratios ~ feedback + (1 + feedback | uid) + (1 | sid/eid), 
                  data = tb %>% drop_na(burstratios))

display(lmer.1_S4f)
anova(lmer.1_S4f)
```

```{r get_predicted_average_effect_1_S4f, include=F}

mSuppr = fixef(lmer.1_S4f)[1]
diffRate = fixef(lmer.1_S4f)[2]
mActive  = fixef(lmer.1_S4f)[1] + diffRate
```

Feedback: `r format(mActive, digits=2, nsmall=2)` \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` \newline
n = `r nrow(tb %>% drop_na(burstratios) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(burstratios) %>% count(mid))` mice

\newpage
# Figure 1-Supplement 4g
## Effect of suppression on sparseness - movies

```{r tidy_for_1_S4gh, include=FALSE}

# 'Sparseness', and 'reliability' are not computed on a trial-by-trial basis. In the csv-file, 
# these two measures are therefore identical across trials, so we simply pull out the first trial of each neuron

tbgh = tb %>% select(mid, sid, eid, uid, mseu, feedback, spars, rel) %>% distinct(mseu, feedback, .keep_all = TRUE)

```

```{r fit_model_1_S4g}

# Random intercept, random slope for single neurons,
# random intercept for series
lmer.1_S4g = lmer(spars ~ feedback + (1 + feedback | uid) + (1 | sid), 
                  data = tbgh %>% drop_na(spars))

display(lmer.1_S4g)
anova(lmer.1_S4g)
```

```{r get_predicted_average_change_1_S4g, include=F}

mSuppr = fixef(lmer.1_S4g)[1]
mDiff = fixef(lmer.1_S4g)[2]
mFeedback  = fixef(lmer.1_S4g)[1] + mDiff
```

Feedback: `r format(mFeedback, digits=2, nsmall=2)` \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` \newline
n = `r nrow(tbgh %>% drop_na(spars) %>% count(uid))` neurons from `r nrow(tbgh %>% drop_na(spars) %>% count(mid))` mice

\newpage
# Figure 1-Supplement 4h
## Effect of suppression on reliability - movies


```{r fit_model_1_S4h}

# Random intercept, random slope for neurons,
# random intercept for experiments, nested in mice
lmer.1_S4h = lmer(rel ~ feedback + (1 +feedback | uid) + (1 | mid/eid), 
                  data = tbgh %>% drop_na(rel))

display(lmer.1_S4h)
anova(lmer.1_S4h)
```

```{r get_predicted_average_effect_1_S4h, include=F}

mSuppr = fixef(lmer.1_S4h)[1]
mDiff = fixef(lmer.1_S4h)[2]
mFeedback  = fixef(lmer.1_S4h)[1] + mDiff
```

Feedback: `r format(mFeedback, digits=2, nsmall=2)` \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` \newline
n = `r nrow(tbgh %>% drop_na(rel) %>% count(uid))` neurons from `r nrow(tbgh %>% drop_na(rel) %>% count(mid))` mice


```{r read_data_1_S4_l-o, include=FALSE}

tib = get_data("../csv/fig1S4grt.csv")
```

```{r tidy_for_1_S4_l-o, include = FALSE}

# Turn feedback into a binary variable
tb <- tib %>% mutate(feedback = ifelse(opto == TRUE, 0, 1))
```

\newpage
# Figure 1-Supplement 4l
## Effect of suppression on firing rate - gratings

```{r fit_model_1_S4l}

# Random intercept, random slope for neurons,
# random intercept for experiments nested in series
lmer.1_S4l = lmer(rates ~ feedback + (1 + feedback | uid) + (1 | sid/eid), 
                  data = tb %>% drop_na(rates))

display(lmer.1_S4l)
anova(lmer.1_S4l)
```

```{r get_predicted_average_effect_1_S4l, include=F}

mSuppr = fixef(lmer.1_S4l)[1]
diffRate = fixef(lmer.1_S4l)[2]
mActive  = fixef(lmer.1_S4l)[1] + diffRate
```

Feedback: `r format(mActive, digits=2, nsmall=2)` spikes/s \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` spikes/s \newline
n = `r nrow(tb %>% drop_na(rates) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(rates) %>% count(mid))` mice

\newpage
# Figure 1-Supplement 4m
## Effect of suppression on burst ratio - gratings

```{r fit_model_1_S4m}

# Random intercept, random slope for neurons,
# random intercept for experiments
lmer.1_S4m = lmer(burstratios ~ feedback + (1 + feedback | uid) + (1 | eid), 
                  data = tb %>% drop_na(burstratios))

display(lmer.1_S4m)
anova(lmer.1_S4m)
```

```{r predicted_average_effect_1_S4m, include=F}

mSuppr = fixef(lmer.1_S4m)[1]
diffRate = fixef(lmer.1_S4m)[2]
mActive  = fixef(lmer.1_S4m)[1] + diffRate
```

Feedback: `r format(mActive, digits=2, nsmall=2)` \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` \newline
n = `r nrow(tb %>% drop_na(burstratios) %>% count(uid))` neurons from `r nrow(tb %>% drop_na(burstratios) %>% count(mid))` mice

\newpage
# Figure 1-Supplement 4n
## Effect of suppression on F1/F0 - gratings

```{r tidy_for_1_S4no, include=FALSE}

# 'F1/F0 ratio' is not computed on a trial-by-trial basis. In the csv-file, 
# this measure is therefore identical across trials, so we simply pull out the first trial of each neuron

tbn = tb %>% select(mid, sid, eid, uid, mseu, feedback, f1f0) %>% distinct(mseu, feedback, .keep_all = TRUE)

```

```{r fit_model_1_S4n}

# Random intercept for neurons,
# random intercept for experiments, nested in series
lmer.1_S4n = lmer(f1f0 ~ feedback + (1 | uid) + (1 | sid/eid), 
                  data = tbn %>% drop_na(f1f0))

display(lmer.1_S4n)
anova(lmer.1_S4n)

```

```{r get_predicted_average_effect_1_S4n, include=F}

mSuppr = fixef(lmer.1_S4n)[1]
mDiff = fixef(lmer.1_S4n)[2]
mFeedback  = fixef(lmer.1_S4n)[1] + mDiff
```

Feedback: `r format(mFeedback, digits=2, nsmall=2)` \newline
Suppression: `r format(mSuppr, digits=2, nsmall=2)` \newline
n = `r nrow(tbn %>% drop_na(f1f0) %>% count(uid))` neurons from `r nrow(tbn %>% drop_na(f1f0) %>% count(mid))` mice