ANOVA and Multivariate Analysis
ANOVA and Multivariate Analysis
Code
Introduction Many PhotosynQ users are interested in comparing the performance of different treatments, crop varieties, etc. A common approach to separate different groups is to use Analysis of Variance (ANOVA). However, many of the parameters measured by the MultispeQ device are affected by numerous factors including: light intensity time of measurement temperature (the temperature data on the MultispeQ beta device was not accurate and can be ignored. However, if you are using the new MultispeQ v1.0, you should take temperature into account) spatial location in the field, this is usually as a identified by âblockâ or âreplicateâ leaf age and position in the canopy growth stage when data was collected instrument/user variation. This could be caused user bias in selection of which leaf to measure or by device to device variation (especially in the MultispeQ Beta instruments, the new MultispeQ v1.0 instruments are much more consistent across instruments) this is not an exhaustive list, but rather a summary of the most common factors affecting photosynthesis measurements. In this tutorial, we will compare the performance of 4 varieties of sunflower. We will use the dataset âsunâ if you want to follow along with the tutorial. In this example, four varieties of sunflower were planted in a complete block design with 4 replicates (Blocks). Between flowering and seed fill six upper canopy leaves were measured in each plot. The data collector measured each block at multiple times so that we had a wide range of light intensities and times of day. Lets get started!
Import required Liraries First, lets load the libraries that we will need. If you haven’t installed those packages already, please install them first. library(PhotosynQ) library(broom) library(stringr) library(dplyr)
Attaching package: dplyr The following objects are masked from package:stats: filter, lag The following objects are masked from package:base: intersect, setdiff, setequal, union
Get Data from PhotosynQ
Hide
Now, we need to get the Data from the PhotosynQ platform. More detailed instructions about how to import project data into R studio can be found in the “Import PhotosynQ Data into R” tutorial. Hide
# Get data from PhotosynQ ID
Code
Introduction Many PhotosynQ users are interested in comparing the performance of different treatments, crop varieties, etc. A common approach to separate different groups is to use Analysis of Variance (ANOVA). However, many of the parameters measured by the MultispeQ device are affected by numerous factors including: light intensity time of measurement temperature (the temperature data on the MultispeQ beta device was not accurate and can be ignored. However, if you are using the new MultispeQ v1.0, you should take temperature into account) spatial location in the field, this is usually as a identified by âblockâ or âreplicateâ leaf age and position in the canopy growth stage when data was collected instrument/user variation. This could be caused user bias in selection of which leaf to measure or by device to device variation (especially in the MultispeQ Beta instruments, the new MultispeQ v1.0 instruments are much more consistent across instruments) this is not an exhaustive list, but rather a summary of the most common factors affecting photosynthesis measurements. In this tutorial, we will compare the performance of 4 varieties of sunflower. We will use the dataset âsunâ if you want to follow along with the tutorial. In this example, four varieties of sunflower were planted in a complete block design with 4 replicates (Blocks). Between flowering and seed fill six upper canopy leaves were measured in each plot. The data collector measured each block at multiple times so that we had a wide range of light intensities and times of day. Lets get started!
Import required Liraries First, lets load the libraries that we will need. If you haven’t installed those packages already, please install them first. library(PhotosynQ) library(broom) library(stringr) library(dplyr)
Attaching package: dplyr The following objects are masked from package:stats: filter, lag The following objects are masked from package:base: intersect, setdiff, setequal, union
Get Data from PhotosynQ
Hide
Now, we need to get the Data from the PhotosynQ platform. More detailed instructions about how to import project data into R studio can be found in the “Import PhotosynQ Data into R” tutorial. Hide
# Get data from PhotosynQ ID
