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To simplify, I have a data set which is as follows:
b <- 1:6 # > b # [1] 1 2 3 4 5 6 jnk <- c(2, 4, 5, NA, 7, 9) # > jnk # [1] 2 4 5 NA 7 9 When I try:
cor(b, jnk, na.rm=TRUE) I get:
> cor(b, jnk, na.rm=T) Error in cor(b, jnk, na.rm = T) : unused argument (na.rm = T) I've also tried na.action = na.exclude, etc. None seem to work. It'd be really helpful to know what the issue is and how I can fix it. Thanks.
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Firstly, we use brackets with complete. cases() function to exclude missing values in R. Secondly, we omit missing values with na. omit() function.
We can remove those NA values from the vector by using is.na(). is.na() is used to get the na values based on the vector index. !
A correlation matrix is simply a table which displays the correlation coefficients for different variables. The matrix depicts the correlation between all the possible pairs of values in a table. It is a powerful tool to summarize a large dataset and to identify and visualize patterns in the given data.
Use the function cor. test(x,y) to analyze the correlation coefficient between two variables and to get significance level of the correlation.
TL; DR: Use instead:
cor(b, jnk, use="complete.obs") Read ?cor:
cor(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman")) It doesn't have na.rm, it has use.
an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings
"everything","all.obs","complete.obs","na.or.complete", or"pairwise.complete.obs".
Pick one. Details of what each does is in the Details section of ?cor.
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