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---
title: "PanelCheck for Mac"
output:
github_document:
toc: true
toc_depth: 4
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# FYI
This version of PanelCheck is unpolished. By this is meant that all the features
Regarding the actual data-analysis works but miscellaneous functionality such as the
_about section_ or _help section_ might not function as desired.
# Windows ?
If you are using a windows mashine, please use PanelCheck distributed here <http://www.panelcheck.com/Home/panelcheck_downloads>
# Installation
## Requirements
You will need at least iOS __High Sierra__
## Download and your first launch
* Download this repository
* Unzip and place the folder somewhere meaningful on your computer ( _Not_ in Downloads)
* When you want to open the program first time, you will _NOT_ be able to doubleclick. Use __Finder__ to direct to the folder with the program, and then cmd+click on the icon. Then you will be prompted with this window where you hit enter.
From now on you will be able to simple double-click on the program to get it running.
# A minimal example
A real toy example data [Data_Bread.xlsx](Data_Bread.xlsx) is included. Here follows a short demonstration
## Import data
Use File > Import > Excel... to locate the data.
Here you need to make sure that the coloumns representing _Assessors_, _Samples_ and _Replicates_ are correctly identified by PanelCheck, furhter you are able to de-select some of the variables in the _Import Coloumns_.
{width=300px}
When correctly mathced, hit __Accept__
## Plots
In the graphical user interface, you will find four main tabs; _Univariate_, _Multivariate_, _Consensus_ and _Overall_. In each main tab, several different plots are available.The red/orange/grey frame indicates level of signifcanse related to differences between samples for the particular attribute.
Try to click on the different plot e.g.
### Univariate - Profile plots
Profile plots show individual(coloured lines) and consensus (black bold line) scoring (Y-axis) and ranking of samples (X-axis).
{width=300px}
### Multivariate - Tucker-1 plots
Select the _Overview Plot (attributes)_.
This plot shows the consensus among panellists for the different attributes. Assessors grouping together in a cluster indicate??? good agreement between these.
![Import]
### Consensus - PCA scores
This plot shows which prodcuts are perceived Similar and different.
![Import]
### Overall- Overview plot
Several statistical analyses can be conducted in PanelCheck as an example, a two way anova with interactions is conducted.
Select _Overall_ at the top (to the right), and select _2-way ANOVA_ as analysis. This one because there are replicates.
Select the _Overview Plot (F values)_. You should get something like this:
{width=300px}
Here, the main effects (Assessor and Product) as well as their interaction, across all attributes (x-axis). The y-axis is the F-value, which indicates the level of differences with respect to the assessor or product or the combination: The higher, the larger the difference. Colors indicate the corresponding significance test p-value. A significant product/sample effect for a specific attribute tells that this attribute significantly discriminate the prodcuts/samples.
### Export a plot
On the left at the bottom of a plot there are some action icons. The _disk_ is used for saving the particular plot.
{width=200px}