I have run a Mann Whitney test in R. The output is….

I’m a total noob at R trying to complete a Stats II project. I keep getting the following error for the code below: [1] “Confusion Matrix: TRAINING set based on random forest model built using 2 trees”
Error in table(train.data$heart_data, train.data.predict): all arguments must have the same length
Traceback:

I have collected and continue to collect data on an informal sports league. The league has approximately 60 players. Prior to each match, two players are selected as captains and select players for their team from a limited pool of 10 assigned to play that day in a structure: (Team 1 picks 1, Team 2 picks 2, Team 1 picks 2, Team 2 picks 2, Team 1 picks 2, Team 2 picks 1). The levels of the players differ substantially, and therefore pick order differs depending upon who the 10 players are who are assigned to play that day. A player picked 1st one day might be picked 5th on a different day, depending upon who is assigned to play that day.

What kind of statistical testing, with a corresponding example code in R, would you use to compare the nitrate concentrations of two different locations (with both many different sub-locations), at three different depths? The data is not normally distributed and therefore requires non-parametric testing.

So was making a scatter plot matrix and noticed that my scatter plots were giving a series of horizontal lines on one side and vertical lines on the other is there a way to make it look like a clump of letters like using the tvalues or something like that? If so how would i accomplish that?
here is my code so far:
CE<-read.csv(“Carbon Emission.csv”)
CE<-filter(CE, complete.cases(CE))
CE1<-select(CE,ce=CarbonEmission,body_type=Body.Type,sex=Sex, diet=Diet,shower= How.Often.Shower,heat_source=Heating.Energy.Source)

I need to estimate a Seemingly Unrelated Regression (SUR) model that accounts for the temporal dimension of the data. Specifically, I am having trouble with the equation formula and incorporating the spatial dimension of the data. For a bit more context, the SUR model allows for different coefficients for each time period and accounts for inter-temporal dependence through the covariance matrix of the system of regression equations.

I have separated a dataset between train and test like this. By selecting the 80%th index of rows and spliting it.

Hello fellow statistics lovers!

I want to generate x and y variables with a controlled correlation. Currently, I’m using the following code to generate x and y (below). However, I want create a function where I can specify the correlation between x and y when simulating them. Does anyone know how I can adjust this code to achieve a specific correlation between x and y? The error terms erorr_x and erorr_y have mean zero and are independent.

I have conducted a study observing the habitat preference of two different species of beetle. In the experiment, the beetle was placed in an arena with four habitat choices. The response variable was the proportion of time spent in each area. The explanetory variables are species, time of day, and size of beetle.