I have 1,000 data points of measurements of the length (mm) and weight (mg) of wheat grains. With this data, I want to analyze the relationship between the length and weight of the wheat grain to propose an equation model that can predict grain weight. I will draw a graph to visualize the data. If you are new to R, you can copy and paste the following code into your R script window to obtain the same graph as shown…
Previously, I scanned wheat grains to obtain the area of each grain, and then measured the weight of each grain corresponding to its area in order to develop a model equation. The following regression demonstrates the relationship between grain area and weight. # Data download https://www.kaggle.com/datasets/agronomy4future/wheat-grain-area-vs-weight I obtained the equation y = 3.3333x – 13.7155, where y is the grain weight (mg) and x is the grain area (mm2), using both Excel and R. However, this model predicts negative values…
A factorial experiment involves the simultaneous manipulation of multiple factors or independent variables (x) to study their effects on a dependent variable (y). The experiment is called factorial because it involves testing multiple factors simultaneously. In factorial experiments, the combination of the different levels of each factor being tested is called a factorial, and each factorial represents a unique combination of these levels. For instance, N0_Genotyp1, N0_Genotyp2, N1_Genotyp1, N1_Genotyp2, etc. are different factorials used to conduct the experiment and analyze…
This is my experimental data. There are 10 corn varieties, and I want to analyze the effect of nitrogen treatments (N0, N1) on grain yield for each variety. This is One-Way ANOVA analysis. Let’s assume that there are no blocks for the replicates. Therefore, the statistical model will be a One-Way ANOVA with no blocks. If we run the above analysis, we can observe the overall effect of nitrogen treatments on grain yield across all varieties, as they are pooled…
I’ll now show you how to change the order of variables in a graph. For this example, I have prepared some data and will create a graph using it. Now, let’s re-order the variables so that Calcium Nitrate comes first, followed by Urea, then Sodium Nitrate, and lastly Ammonium Sulfate. First, let’s take a look at the variables: The variables in R are currently ordered alphabetically by default. The following code will change the order of the variables as desired:…
If you need to change the text of a specific column while analyzing data in R, I will introduce how to do it. First, let’s create a simple dataset First, let’s rename the column names. We will change the ‘Nation’ column name to ‘Country’ and the ‘Sex’ column name to ‘Gender’. If you enter the following code, the column names will be updated accordingly. If the nationality of DAVID is Canada instead of Germany, you can update by entering the…
I have the following data. Nitrogen Sulphur Rep Yield 0 0 1 1.0 0 0 2 0.9 0 0 3 0.8 N1 S1 1 1.0 N1 S1 2 1.2 N1 S1 3 1.3 N1 S2 1 2.1 N1 S2 2 2.2 N1 S2 3 2.3 N2 S1 1 1.4 N2 S1 2 1.6 N2 S1 3 1.7 N2 S2 1 2.5 N2 S2 2 2.6 N2 S2 3 2.8 Let’s assume that this data is the result of investigating how…
Sometimes, I see many people managing their data as columns like the example below. It seems convenient because we can see our data all at once. However, this data format is problematic for data analysis, which fundamentally relies on variables, namely independent and dependent variables. Download data file (.csv) https://github.com/agronomy4future/raw_data_practice/blob/main/yield_per_location.csv In the given data format, each level for the independent variable (i.e., location) was not combined in one column, and therefore we need to rearrange the data format. If the…
In this session, I will introduce the method of calculating the Best Linear Unbiased Estimator (BLUE). Instead of simply listing formulas as many websites do to explain BLUE, this post aims to help readers understand the process of calculating BLUE with an actual dataset using R. I have the following data. location sulphur (kg/ha) block yield Cordoba 0 1 750 Cordoba 24 1 1250 Cordoba 36 1 1550 Cordoba 48 1 1120 Cordoba 0 2 780 Cordoba 24 2 1280…
When we draw regression lines for a group, they are usually of the same type, such as simple linear regression. Here is an example using yield data for different nitrogen rates per genotype. Then, the regression graph for each group would be shown below. I think it would be better to show the quadratic regression line for genotype A. In this case, how can we create separate linear and quadratic regression graphs for each group in the same panel? Data…
To gain a basic understanding of the topic, I recommend reading the following posts. Analysis of Covariance (ANCOVA) I have a dataset as shown below, and I would like to analyze crop yield, and height based on different fertilizer types (Control, Slow-release, and Fast-release). The experimental design is a Completely Randomized Design (CRD) with 10 replicates. Rep Fertilizer Yield Height Fertilizer Yield Height Fertilizer Yield Height 1 Control 12.2 45.0 Slow 16.6 63.0 Fast 9.5 52.0 2 Control 12.4 52.0…
Today, I will explain the meaning of the F-value in testing for significance through statistical processing. Let me give you an example. Suppose we want to determine whether there are differences in the yield according to the varieties (A, B, C). The total experimental unit is 12 (3 varieties x 4 replicates). What would happen if there is a significant difference in yield among varieties A and C? If there is a large difference in yield between these varieties, the…
Sometimes, when creating graphs using R, there may be a need to include superscripts or subscripts in axis text or titles. In this post, I will introduce about how to enter text with superscripts or subscripts. I will generate one simple data and draw a graph to demonstrate. Here, I want to add superscripts or subscripts to the axis titles of the graph. For example, for the x-axis, I want to name it as “GenotypeTM” and for the y-axis, I…
Logistic regression is a type of statistical analysis used to model the relationship between a binary (yes/no) dependent variable and independent variables. The goal of logistic regression is to find a relationship between the independent variables (x) and the probability of a particular outcome for the dependent variable (y). The logistic regression model calculates the probability of a certain outcome by applying a logistic function to the linear combination of the independent variables. Here is one example. Sulphur improves plant…
When we make graphs, the unit is great and the number would be overlapped. Here is an example. Now, I’d like to change the unit of number. For example, I want to divide each value by 1000, so that to show 5 to 30 in x-axis. We can add below codes.
In my previous post, I explained what split-plot design and the statistical model is, and also how it is different RCBD. What is split-plot design in agronomy research? I explained the main difference between split-plot design and RCBD is that in split-plot design, error is divided into two (error a and b), increasing the significance of interaction between the main plot and sub-plot. Now our interest lies in cases where we have three factors. In a split-plot design, we typically…
Split-plot design has been widely used particularly in the agronomy research. In split-plot design, the experimental units are divided into smaller units. Split-plot designs are useful when some factors are difficult or expensive to change or when the levels of the factors cannot be randomized (I’ll explain in detail later). Split-plot design consists of one whole plot and one subplot. The whole plot factor is randomly assigned to the experimental units, while the subplot factor is applied to a smaller…
Sample No. x y 1 10 30 2 20 40 3 30 50 4 40 80 5 50 90 6 60 100 7 70 120 Here is a dataset that allows us to analyze the relationship between x and y and obtain the model equation, y= β0 + β1x. Although statistical programs can provide us with results in just 10 seconds, it is more important to understand the principles behind the calculations than to simply know how to run the…
When you conduct statistical analysis, you might want to include/exclude some variables. For example, here is one data. This is data about how yield, grain number (GN) and averge grain weight (AGW) are different according to two different fertilizers (N0, N1) in five genotypes (CV1 – CV5). That is, there will be 10 treatments [Genotype (5) x Nitrogen (2) =10]. Replicates are 10 as blocks, and therefore experimental unit will be 30 [10 treatments x 3 blocks = 30]. What…
I wrote many codes in R and now I want to delete all of them. You can click the broom icon to delete all. But, you can use this code to delete all.
Previous post□ How to analyze linear plateau model in R Studio? In my previous post, I explained how to analyze linear plateau model. I simulated yield data for five different crop varieties with different sulphur applications, and suggsted the optimum sulphur application would be 23.3 kg/ha based on the linear plateau model. In this time, I’ll explain how to analyze quadratic plateau model with the same data using R studio 1) Data upload If you run the below code, the…
When we talk about regression, it’s usually about simple linear regression model. This is about the relationship between two variables. FYI□ Simple linear regression (1/5)- correlation and covariance□ Simple linear regression (2/5)- slope and intercept of linear regression model Linear plateau model is similar with simple linear model, but linear plateau model is a segmented model, and this statistical model is interested in the critical value (the x-value above which there is no further increase in y), indicating the plateau value (the statistically highest value…
In my previous post, I explained how to add extra column and row to calculate mean respectively. In R, how to add extra column and row to calculate mean respectively? Now, I’d like to substrtact the mean from each value in each column. This will be genotypic effect.
Let’s generate one data table. Now, I’d like to calculate mean of each column and row. For example, I want to calculate mean of ENV1 to ENV5, and also CV1 to CV5. First, I’ll calculate mean of each row (ENV1 to ENV 5). I discarded Environment row (dataA %>% select(-Environment)) because it’s not a numeric. Now, I’ll calculate mean of each column (CV1 to CV5). Now, mean of each column and row was calculated.
When we analyze data, we may need to show graphs depicting normal distributions. These graphs differ from density graphs as they convey various concepts that simple bar graphs cannot. While it is easy to draw these graphs in Excel, understanding the underlying concepts is crucial. In this article, I will explain what the Probability Density Function (PDF) is, and I will show how we can calculate it in both Excel and R. Here is a dataset of 1,000 individual wheat…
Let’s upload one data to R. Now, I’d like to change the name of column as field → locationgenotype → varietyblock → repstreatment → experimentshoot → branchgrain_number → GNgrain_weight → GW I introduce two ways to change column names. 1) using colnames() 2) using rename() in dplyr package In this time, I’ll use dplyr package.
I uploaded one .csv file to GitHub. Now I want to analyze this data in R. Simply I can download this file and upload to R. But let’s directly upload this file from GitHub to R. First, we need to know the URL address of this file. If you click your file name in GitHub, you can find the “Raw” button. So, let’s click this button. Then, in the address bar of your web browser, you can obtain the URL…
I’ll generate one data. Then, I’ll make a bar graph about this data. To make a bar graph, data should be summarized. Now, I want to change legend name from N0 to 0kg N/ha, and N1 to 200kg N/ha. Simply we can add more code like this; scale_fill_manual(label=c(“0kg N/ha”,”200kg N/ha”), values=c(“grey75″,”grey25″)) What if we want to change the title of legend from Fertilizer to Treatment? Just add one code like this; labs(fill=”Treatment”,x=”Genotype”, y=”Yield”)
When running statistical programs, you might come across RMSE (Root Mean Square Error). For instance, the table below displays RMSE values obtained from SAS, which indicate that it is approximately equal to 2.72 I am wondering how RMSE is calculated. The equation for RMSE is shown below. First, calculate the difference between the estimated and observed values: (ŷi – yi), and then square the difference: (ŷi – yi)². Second, calculate the sum of squares: Σ(ŷi – yi)². Third, divide the…
The genotype is dependent on environmental changes. One genotype may strongly respond to certain environmental conditions, while another genotype may weakly respond to the same conditions. If some genotypes strongly respond under better conditions, they would be adaptable to the environment. Adaptability refers to the flexibility of a genotype in its response to improved environments. If a certain genotype exhibits high performance across a wide range of environmental conditions, it would be considered to have broad adaptation. To achieve this…