Only Defined On A Data Frame With All Numeric-Alike Variables Update

The term “numeric-alike” variables typically refers to variables that contain numeric data or data that can be coerced into numeric data. For example, variables that contain integers or floating-point numbers are numeric-alike variables. However, variables that contain dates or times may also be considered numeric-alike if they can be represented as numeric values (such as the number of seconds since a particular date).

In R, some functions are only defined on data frames with all numeric-alike variables. For example, the cor() function, which calculates the correlation coefficient between variables, requires all variables to be numeric-alike. If there are non-numeric variables in the data frame, an error will be thrown.

To ensure that all variables in a data frame are numeric-alike, you can use the as.numeric() function to convert non-numeric variables to numeric. However, this may not always be appropriate, as it can lead to the loss of information. It is important to carefully consider the nature of the data and the goals of the analysis before converting variables to numeric.

MDS (Multidimensional Scaling) is a technique used in statistics and data analysis to visualize the similarity or dissimilarity between a set of objects or samples. One popular implementation of MDS is called “metric MDS,” which uses a distance matrix as input and outputs a set of points in a lower-dimensional space that preserves the pairwise distances between the original objects.

MetaMDS is a specific implementation of MDS that is available in the vegan package in R. It is designed to work with ecological and environmental data, where the variables can be of different types (e.g., continuous, categorical, binary) and may be correlated with each other.

However, it is true that the default implementation of MetaMDS in R requires the input data to be a data frame with all numeric variables. This is because the algorithm uses a dissimilarity matrix as input, which is typically calculated from the original data using some distance measure (e.g., Euclidean distance, Bray-Curtis dissimilarity). Distance measures generally require numeric data as input, so the data frame must contain all numeric variables for the dissimilarity matrix to be calculated.

If your data contains non-numeric variables, you may need to convert them to numeric or use a different implementation of MDS that can handle mixed data types. There are several other packages in R that provide MDS functionality, including cmdscale() and isoMDS(), which can handle mixed data types.

The error message “Error in fun(x(i), …) : is numeric x is not true” usually occurs when you are trying to apply a function to a variable that is not numeric.

Here’s an example of how this error can occur in R:

scss
x <- "hello"
mean(x)

In this example, we are trying to calculate the mean of the variable x, which is a character string. Since the mean() function only works with numeric data, R will throw an error with the message “Error in mean(x) : ‘x’ must be numeric”.

To fix this error, you need to make sure that the variable you are applying the function to is numeric. In the example above, you can convert x to a numeric variable using the as.numeric() function:

python
x <- "42"
mean(as.numeric(x))

In this case, x is a character string containing the number 42. We use the as.numeric() function to convert it to a numeric variable before applying the mean() function. This will give us the desired result without any errors.