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report_full.Rmd
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report_full.Rmd
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---
output:
word_document:
fig_caption: true
fig_height: 10
fig_width: 10
reference_docx: bootstrap/initial/data/reportTemplate.docx
toc: true
keep_md: false
params:
report_title: ""
strata: NULL
---
```{r introduction, include = FALSE}
# INTRODUCTION ################################################################
# This markdown documents and integrated scripts analyse biological
# maturity readings.
# The output is a .docx template that includes
# the results of the analysis and should be used as a standard for
# reporting of maturity staging comparisons.
```
```{r chunk_setup, include=FALSE}
# CHUNK SETUPS #################################################################
knitr::opts_chunk$set(echo = FALSE, warning = FALSE,
message=FALSE, results = 'asis', dpi=400)
```
```{r pander_settings, include = FALSE}
# PANDER OPTIONS ##############################################################
panderOptions('table.split.table', Inf)
panderOptions('keep.trailing.zeros', TRUE)
panderOptions('table.alignment.default', "center")
```
---
title: `r config$report_title`
---
# Executive summary
# Terms of reference
# Agenda and participant list
The agenda can be found in Annex 1 and the list of participants in Annex 2.
# Introduction
This part should include a background to the species, the workshop/exchange
and what to expect to read about in the report.
# Methods
This report contains statistical analyses and comparisons of sex categorization and maturity staging’s
in the form of tables and graphical plots.
First, an overview of participating maturity stagers and the samples are presented.
Before each table or plot there is a short explanation of it. This text is
thought as a help to understand the tables/plot and can just be deleted in
the final output report. The document can be edited just like any other
.docx file. New text can be added, additional pictures can be included and
the tables edited. If some tables which are presently in the annexes need to
be moved to the body of the report this is also possible. Only the plots
cannot be changed.
In the first part of analysis some of the tables and plots from the
Guus Eltink Excel sheet 'Age Reading Comparisons' **(Eltink, A.T.G.W. 2000)** are presented, that can be used for "maturity staging comparisons". Since the sex categories and maturity stages are categorical in comparison to ages that are quantitative, some of the statistics presented in the report of the age exchange events cannot be calculated here, like the average percentage error or the coefficient of variation. The latter is being replaced by the coefficient of unlikeability (see below).
**Percentage Agreement**
The percentage agreement tells how large is the part of sex categorizations/maturity staging’s that are equal to the modal sex/maturity. The percentage agreement is estimated by modal sex or maturity and stager as the proportion (as percentage) of times that the lectures of that stager agreed with the resulting modal sex or maturity. This percentage is estimated as the number of times that a stager agreed with the modal sex or maturity divided by the total number of gonads analyzed by a stager for that modal maturity stage.
$$PA = { \frac{ {number \,of \,readings \,that \,agree \,with \,modal \,maturity}} {total \,number \,of \,readings \,by \,modal \,maturity} } \cdot {100 \%} $$
**Coefficient of unalikeability (CU)**
The concept of unalikeability (Kader and Perry, 2007) focuses on how often observations differ within a group. Specifically, for the sex/maturity staging events, the CU provides a measure of how alike, for each modal maturity stage, the stages decided by each stager were (or all stagers at once). The cu ranges between 0 and 1. The higher the cu value, the more unalike the data are.
The table presents the CU per modal maturity stage and stager. For the case of a finite number of observations (n), a finite number of categories (m) and a finite number of objects, k_i, within category i, will allow expression of the coefficient of unalikeablity as:
$$ u = {1 - ∑ p_i ^ 2} $$
where
$$ p_i = {k_i / n} $$
To the table is also added the CU of all stagers combined per modal maturity and
a weighted mean of the CU per stager.
**Maturity stage error matrix (MSEM)**
Maturity stage error matrices (AEM) were produced following the same procedures outlined
by WKSABCAL (2014) for calculating the "Age error matrix". MSEM shows the proportion of each modal
maturity stage mis-staged as other stages. The sum of each row is 1, which equals 100%.
The maturity data was analysed twice, the first time all stagers were included
and the second time only the “advanced” stagers were included. If a stager
is “advanced” then they are considered well trained and they provide maturity stages
for stock assessment or similar purposes. When the MSEM is compiled for
assessment purposes it uses only those stagers who provide maturity data for
the stock assessment in that specific area.
In this report the MSEM presented represents is in the transpose format.
The frequency bias table represents the MSEM in the format proposed by WKSABCAL (2014).
# Analysis of maturity staging calibration exercise (ToR?)
## Overview of samples and stagers
\br
```{r sample_overview}
# Table caption
set.caption(
paste(
'**Table X:** Overview of samples used for the exchange event number ',
config$event_id,
'. The number of samples (num) are shown by year, ices area, season (qtr). The range of maturity stages (mat_stages) and sex (sex_cat) assigned by the group or stagers participating in the event is also shown, as well as the range of modal maturity stages (modal_mat_stages) and modal sex (modal_sex_cat) decided for all samples.',
sep=""
)
)
# Output table
pander(style_table0(sample_data_overview), style = "simple")
```
\br
```{r participants_overview}
# PARTICIPATANTS OVERVIEW #####################################################
# Table caption
set.caption('**Table X:** Overview of stagers participating in the event, with their overall expertise (Expertise), and their ranking position based in their experience (only valid if the experience weighting protocol has been applied).')
# Output table
pander(style_table0(filter(stager_data)))
```
## Results
Text?
### All stagers
Text?
#### All samples together
Text?
**Multimodal cases**
Those writing the report put TEXT here describing the results.
If there were available histological samples for all the fish individuals in the exchange event, no multiple modes are expected, however, if there were no histology samples for some of them, multiple maturity stage modes could be found. In the next table this information is presented for the sex category determination.
<!-- Total number of samples (NSample), number (CountMM) and percentage of multimodal cases (PercMM) text -->
```{r summary_multiple_modes_sex_all}
#histN=ad_long_all %>% subset(TypeAnnotation=="eventOrganizer") %>% select(FishID) %>% unique() %>% dim()
#allN=ad_long_all %>% select(FishID) %>% unique() %>% dim()
#perc_not_Hist=100-round(100*histN[1]/allN[1], 0)
histN=ad %>% subset(DoesSampleHaveHistologyImage=="Yes") %>% select(FishID) %>% unique() %>% dim()
allN=ad_long_all %>% select(FishID) %>% unique() %>% dim()
perc_not_Hist=100-round(100*histN[1]/allN[1], 0)
summary_multiple_modes_sex_all <-
c(NSample = length(unique(ad_long_all$FishID)),
Nhist=histN[1],
Perc_not_Hist=perc_not_Hist,
PercMM_traditional=unique(ifelse(multimode_cases_tab_traditional_Sex_all$NModes_trad=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_traditional_Sex_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_linear_weight=unique(ifelse(multimode_cases_tab_linear_Sex_all$NModes_linear=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_linear_Sex_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_negexp_weight=unique(ifelse(multimode_cases_tab_negexp_Sex_all$NModes_negexp=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_negexp_Sex_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_multistage=unique(ifelse(multimode_cases_tab_multistage_Sex_all$NModes_multistage=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_multistage_Sex_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))))
# Table caption
set.caption('**Table X:** Summary of statistics for sex staging; Total number of fish individuals studied (NSample), number of fish individuals with histological samples (Nhist), percentage of fish individuals without histology (Perc_not_Hist). The percentage of cases (fish samples) with multiple modes depending on the approach to weight the experience of the stager which will be considered when defining the fish sex stage mode. PercMM_traditional shows the percentage of the total samples for which multiple modes are obtained when all the stagers are equally weighted. PercMM_linear_weight shows the percentage of the total samples for which multiple modes are obtained when the weight assigned to the different stagers decreases linearly with the experience, while in the PercMM_negexp the weight applied decreases with a negative exponential shape with the experience. The PercMM_multistage shows the percentage of multiple mode cases when a combination of the different methodologies is used, as explained in the material and methods section')
# Output table
pander(style_table0(summary_multiple_modes_sex_all), missing="")
```
If there were available histological samples for all the fish individuals in the exchange event, no multiple modes are expected, however, if there were no histology samples for some of them, multiple maturity stage modes could be found. In the next table this information is presented for the maturity staging.
<!-- Total number of samples (NSample), number (CountMM) and percentage of multimodal cases (PercMM) text -->
```{r summary_multiple_modes_maturity_all}
histN=ad %>% subset(DoesSampleHaveHistologyImage=="Yes") %>% select(FishID) %>% unique() %>% dim()
allN=ad_long_all %>% select(FishID) %>% unique() %>% dim()
perc_not_Hist=100-round(100*histN[1]/allN[1], 0)
summary_multiple_modes_maturity_all <-
c(NSample = length(unique(ad_long_all$FishID)),
Nhist=histN[1],
Perc_not_Hist=perc_not_Hist,
PercMM_traditional=unique(ifelse(multimode_cases_tab_traditional_Maturity_all$NModes_trad=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_traditional_Maturity_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_linear_weight=unique(ifelse(multimode_cases_tab_linear_Maturity_all$NModes_linear=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_linear_Maturity_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_negexp_weight=unique(ifelse(multimode_cases_tab_negexp_Maturity_all$NModes_negexp=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_negexp_Maturity_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))),
PercMM_multistage=unique(ifelse(multimode_cases_tab_multistage_Maturity_all$NModes_multistage=="zero", paste(0,"%"), paste(round((nrow(multimode_cases_tab_multistage_Maturity_all)/length(unique(ad_long_all$SampleID)))*100, digits=0),"%"))))
# Table caption
set.caption('**Table X:** Summary of statistics for maturity staging; Total number of fish individuals studied (NSample), number of fish individuals with histological samples (Nhist), percentage of fish individuals without histology (Perc_not_Hist). The percentage of cases (fish samples) with multiple modes depending on the approach to weight the experience of the stager which will be considered when defining the fish maturity stage mode. PercMM_traditional shows the percentage of the total samples for which multiple modes are obtained when all the stagers are equally weighted. PercMM_linear_weight shows the percentage of the total samples for which multiple modes are obtained when the weight assigned to the different stagers decreases linearly with the experience, while in the PercMM_negexp the weight applied decreases with a negative exponential shape with the experience. The PercMM_multistage shows the percentage of multiple mode cases when a combination of the different methodologies is used, as explained in the material and methods section')
# Output table
pander(style_table0(summary_multiple_modes_maturity_all), missing="")
```
<!-- PA and CU text -->
```{r summary_statistics_sex_all}
summary_statistics_sex_all <-
c(NSample = length(unique(ad_long_all$FishID)),
CU = cu_tab_sex_all[nrow(cu_tab_sex_all), "Total"],
PA = pa_tab_sex_all[nrow(pa_tab_sex_all), "Total"])
summary_statistics_maturity_all <-
c(NSample = length(unique(ad_long_all$FishID)),
CU = cu_tab_maturity_all[nrow(cu_tab_maturity_all), "Total"],
PA = pa_tab_maturity_all[nrow(pa_tab_maturity_all), "Total"])
```
The average percentage agreement by modal sex category for all stagers was `r summary_statistics_sex_all[3]`, with a weighted average CU of `r summary_statistics_sex_all[2]`. Regarding the maturation staging, the percentage agreement by modal maturation stage was `r summary_statistics_maturity_all[3]`, and the weight average CU was `r summary_statistics_maturity_all[2]`.
**Sex categorization table**
Text?
```{r sex_composition_tab_all}
# Sex categorization TABLE - All stagers #################################################
data=sex_composition_tab_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Sex categorization table: presents the
number of categorizations made per expert for each modal sex category.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**Maturity staging table**
Text?
```{r num_read_maturity_all}
# maturity staging’s TABLE - All stagers #################################################
#data=num_read_tab_modal_matur_by_reader_all
data=maturity_composition_tab_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Maturity staging table: presents the
number of staging’s made per expert stager for each modal maturity stage.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**Coefficient of Unalikeability (CU) table by modal sex category**
Text?
```{r cu_sex_all_table}
# CU TABLE by modal sex category - All stagers #################################################
data=cu_tab_sex_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Coefficient of unlikeability (CU) table by modal sex category: presents the
CU per modal sex category and stager, the CU of all stagers combined per sex category stage and a weighted mean of the CU
per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**Coefficient of Unalikeability (CU) table by modal maturity stage**
Text?
```{r cu_maturity_all_table}
# CU TABLE by modal maturity stages - All stagers #################################################
data=cu_tab_maturity_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Coefficient of unlikeability (CU) table by modal maturity stage: presents the
CU per modal maturity stage and stager, the CU of all stagers combined per maturity stage and a weighted mean of the CU
per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
```{r cu_tab_maturity_females_all}
# CU TABLE by modal maturity stages (Females) - All stagers #################################################
data=cu_tab_maturity_females_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Coefficient of unlikeability (CU) table by modal maturity stage (only for females): presents the CU per modal maturity stage and stager, the CU of all stagers combined per maturity stage and a weighted mean of the CU per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
```{r cu_tab_maturity_males_all}
# CU TABLE by modal maturity stages (Males) - All stagers #################################################
data=cu_tab_maturity_males_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Coefficient of unlikeability (CU) table by modal maturity stage (only for males): presents the CU per modal maturity stage and stager, the CU of all stagers combined per maturity stage and a weighted mean of the CU per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**PA table by modal sex category**
<!-- PA text -->
The percentage agreement per stager and modal maturity stage represent the proportion of the total number of staging’s that are equal to the modal maturity stage. The weighted mean including at the bottom of the table is weighted according to number of maturity staging’s.
```{r percentage_agreement_sex_all}
# PERCENTAGE AGREEMENT TABLE by modal sex category - All stagers ####################################
data=pa_tab_sex_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Percentage agreement (PA) table: shows the PA per modal sex category and stager, the PA for all stagers combined and the weighted mean of the PA per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**PA table by modal maturity stage**
Text?
```{r percentage_agreement_maturity_all}
# PERCENTAGE AGREEMENT TABLE by modal maturity stage - All stagers ####################################
data=pa_tab_maturity_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Percentage agreement (PA) table: shows the PA per modal maturity and stager, the PA for all stagers combined and the weighted mean of the PA per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
```{r percentage_agreement_maturity_females_all}
# PERCENTAGE AGREEMENT TABLE by modal maturity stage (females) - All stagers ####################################
data=pa_tab_maturity_females_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Percentage agreement (PA) table: shows the PA per modal maturity and stager (only females), the PA for all stagers combined and the weighted mean of the PA per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
```{r percentage_agreement_maturity_males_all}
# PERCENTAGE AGREEMENT TABLE by modal maturity stage (males) - All stagers ####################################
data=pa_tab_maturity_males_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Percentage agreement (PA) table: shows the PA per modal maturity and stager (only males), the PA for all stagers combined and the weighted mean of the PA per stager.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**General frequency bias table by modal sex category**
The frequency bias is shown by sex category for each modal sex category for all stagers together. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the sex determination by the stagers participating in this event.
```{r general_freq_sex_all}
# frequency bias TABLE by sex category ###########################################
set.caption('**Table X:** General frequency bias table: represents the frequency per
modal sex category for all stagers combined. V1 in case some readers have samples without sex categorization.')
pander(style_table0(general_bias_freq_tab_sex_all), missing = "-", style = "simple") #changed here to not appear the last column in bolt
```
**General Frequency Bias plot by modal sex category**
Text?
```{r bias_plots_sex_all, fig.width = 5, fig.height = 4, fig.cap = cap_in}
# BIAS PLOT by modal sex category - All stagers ################################################
# Table caption
cap_in <- '**Figure X:** Sex categorization bias plot by modal sex category for all the stagers: presents the frequency per
modal sex category and sex category for all stager together. '
# Output figure
plot_general_freq_sex(ad_long_all, strata=NULL)
```
**General frequency bias table by modal maturity stage**
The frequency bias is shown by maturity stage for each modal maturity stage for all stagers together. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the sex determination by the stagers participating in this event.
```{r general_freq_maturity_all}
# frequency bias TABLE by sex category ###########################################
set.caption('**Table X:** General frequency bias table: represents the frequency per
modal maturity stage for all stagers combined.')
pander(style_table0(general_bias_freq_tab_maturity_all), missing = "-", style = "simple") #changed here to not appear the last column in bolt
```
**General Frequency Bias plot by modal maturity stage**
Text?
```{r bias_plots_maturity_all, fig.width = 5, fig.height = 4, fig.cap = cap_in}
# BIAS PLOT by modal maturity stage - All stagers ################################################
# Table caption
cap_in <- '**Figure X:** Maturity staging bias plot by modal maturity stage for all stagers: presents the frequency per
modal maturity and maturity stage for all the stagers together. '
# Output figure
plot_general_freq_matur(ad_long_all, strata=NULL)
```
**General frequency bias table by modal maturity stage (only females)**
The frequency bias is shown by maturity stage for each modal maturity stage (only females) for all stagers together. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the sex determination by the stagers participating in this event.
```{r general_freq_maturity_females_all}
# frequency bias TABLE by females category ###########################################
set.caption('**Table X:** General frequency bias table (only females): represents the frequency per
modal maturity stage for all stagers combined.')
pander(style_table0(general_bias_freq_tab_maturity_females_all), missing = "-", style = "simple") #changed here to not appear the last column in bolt
```
**General Frequency Bias plot by modal maturity stage (only females)**
Text?
```{r bias_plots_maturity_females_all, fig.width = 5, fig.height = 4, fig.cap = cap_in}
# BIAS PLOT by modal maturity stage (only females) - All stagers ################################################
# Table caption
cap_in <- '**Figure X:** Maturity staging bias plot by modal maturity (only females) stage for all stagers: presents the frequency per
modal maturity and maturity stage for all the stagers together. '
# Output figure
plot_general_freq_matur(ad_long_all[ad_long_all$Sex=="F",], strata=NULL)
```
**General frequency bias table by modal maturity stage (only males)**
The frequency bias is shown by maturity stage for each modal maturity stage (only males) for all stagers together. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the sex determination by the stagers participating in this event.
```{r general_freq_maturity_males_all}
# frequency bias TABLE by males category ###########################################
set.caption('**Table X:** General frequency bias table (only males): represents the frequency per
modal maturity stage for all stagers combined.')
pander(style_table0(general_bias_freq_tab_maturity_males_all), missing = "-", style = "simple") #changed here to not appear the last column in bolt
```
**General Frequency Bias plot by modal maturity stage (only males)**
Text?
```{r bias_plots_maturity_males_all, fig.width = 5, fig.height = 4, fig.cap = cap_in}
# BIAS PLOT by modal maturity stage (only males) - All stagers ################################################
# Table caption
cap_in <- '**Figure X:** Maturity staging bias plot by modal maturity (only males) stage for all stagers: presents the frequency per
modal maturity and maturity stage for all the stagers together. '
# Output figure
plot_general_freq_matur(ad_long_all[ad_long_all$Sex=="M",], strata=NULL)
```
**Stager frequency bias table by modal sex category**
<!-- Frequency bias text -->
The frequency bias is shown by sex category for each modal sex category per stager. As for the previous tables, a combined frequency distribution for all stagers are calculated. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the sex determination by the stagers participating in this event.
```{r freq_sex_all}
# frequency bias TABLE by sex category- per stagers ###########################################
data=stager_bias_freq_tab_sex_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Frequency bias table: represents the frequency per
modal sex category per stager and the frequency distribution of all stagers combined
per modal sex category.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
**Stager frequency bias table by modal maturity stage**
<!-- Frequency bias text -->
The frequency bias is shown by maturity stages for each modal maturity stage per stager. As for the previous tables, a combined frequency distribution for all stagers are calculated. This frequency distribution presented in the form of a table or a bubble plot may be used to explore the bias in the maturity determination by the stagers participating in this event.
```{r freq_maturity_all}
# frequency bias TABLE by maturity stage - per stagers ###########################################
data=stager_bias_freq_tab_maturity_all
nstagers=length(unique(stager_data$'Reader code'))
ncols=dim(data[,!colnames(data) %in% c("Total")])[2]
basicols=ncols-nstagers
z=8
nfig=round((dim(data)[2]-basicols)/z)
N=min(z,ncols)
for(i in 1:nfig)
{
if(i==1){
# Table caption
set.caption('**Table X:** Frequency bias table: represents the frequency per
modal maturity stage per stager and the frequency distribution of all stagers combined
per modal maturity stage.')
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else { if(i<nfig){
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):((i+basicols+((N-1)*(i-1)))+(N-1)))]
pander(style_table0(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
} else {
selec=data[,c(1:basicols, (i+basicols+((N-1)*(i-1))):dim(data)[2])]
pander(style_table1(selec), missing = "-", style = "simple") #changed here to not appear the last column in bolt
}
}
}
```
#### With samples split by strata
```{r set_strata_all}
# initialise strata loop
istrata <- 0
group <- "all"
print_strata <- function() length(params$strata) >= istrata
```
<!-- first strata -->
```{r strata_plus_one}
# second strata
istrata <- istrata + 1
```
```{r strata_title, eval = print_strata()}
stratum <- params$strata[istrata]
# Section title
asis_output(paste0("**Results by ", stratum, "**"))
```
```{r ar_sex_title, eval = print_strata()}
# title
asis_output(paste0("**Sex categorization by ", stratum, "**"))
```
```{r ar_sex_by, eval = print_strata()}
# NUMBER OF SEX CATEGORIZATION PER Strata - Advanced stagers #########################
# Table caption
set.caption(paste0('**Table X:** Number of sex categorization per ', stratum, ' for all the stagers.'))
# Output table
pander(style_table1(get(vsname("num_read_tab_modal_sex_by"))), missing = "-")
```
```{r ar_matur_title, eval = print_strata()}
# title
asis_output(paste0("**Maturity staging by ", stratum, "**"))
```
```{r ar_matur_by, eval = print_strata()}
# NUMBER OF MATURITY STAGING PER strata - Advanced stagers #########################
# Table caption
set.caption(paste0('**Table X:** Number of maturity staging’s per ', stratum, ' for all the stagers.'))
# Output table
pander(style_table1(get(vsname("num_read_tab_modal_matur_by"))), missing = "-")
```
```{r cu_sex_title, eval = print_strata()}
# title
asis_output(paste0("**Coefficient of Unalikeability by modal sex category per", stratum, "**"))
```
```{r cu_sex_by, eval = print_strata()}
# Table caption
set.caption(paste0('**Table X:** CU by modal sex category per ', stratum, '.'))
# Output table
pander(style_table2(get(vsname("cu_tab_sex_by"))), missing = "-")
```
```{r cu_matur_title, eval = print_strata()}
# title
asis_output(paste0("**Coefficient of Unalikeability by modal maturity stage per", stratum, "**"))
```
```{r cu_matur_by, eval = print_strata()}
# Table caption
set.caption(paste0('**Table X:** CU by modal maturity stage per ', stratum, '.'))
# Output table
pander(style_table2(get(vsname("cu_tab_maturity_by"))), missing = "-")
```
```{r pa_sex_title, eval = print_strata()}
# title
asis_output(paste0("**Percentage Agreement by sex category per ", stratum, "**"))
```
```{r pa_sex_by, eval = print_strata()}
# Section title
#asis_("## PA by sex category - Advanced stagers") # # #############################################
# Table caption
set.caption(paste0('**Table X:** Percentage Agreement by sex category per ', stratum, '.'))
# Output table
pander(style_table2(get(vsname("pa_tab_sex_by"))), missing = "-")
```
```{r pa_matur_title, eval = print_strata()}
# title
asis_output(paste0("**Percentage Agreement by maturation stage per ", stratum, "**"))
```
```{r pa_matur_by, eval = print_strata()}
# Section title
#asis_("## PA by maturation stage - Advanced stagers") # # #############################################
# Table caption
set.caption(paste0('**Table X:** Percentage Agreement by maturation stage per ', stratum, '.'))
# Output table
pander(style_table2(get(vsname("pa_tab_maturity_by"))), missing = "-")
```
<!-- second strata -->
```{r ref.label='strata_plus_one'}
```
```{r ref.label='strata_title', eval = print_strata()}
```
```{r ref.label='ar_sex_title', eval = print_strata()}
```
```{r ref.label='ar_sex_by', eval = print_strata()}
```
```{r ref.label='ar_matur_title', eval = print_strata()}
```
```{r ref.label='ar_matur_by', eval = print_strata()}
```
```{r ref.label='cu_sex_title', eval = print_strata()}
```
```{r ref.label='cu_sex_by', eval = print_strata()}
```
```{r ref.label='cu_matur_title', eval = print_strata()}
```
```{r ref.label='cu_matur_by', eval = print_strata()}
```