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Hi, if both variables have numeric values in them, and the correlation with other variables in the matrix shows up normally, why do I get missing values for some correlations in the matrix? An example of the data that returned this is Organization Type and Assets Under Management. There are only missing values for the correlation between the two, although the Organization Sub-Type (which is contained in the Organization Type) does not return missing values when correlated with Assets Under Management. Explanations for this will be really useful and appreciated as my searches of stata help and forums haven't helped. Many thanks, Sue. Sue: I fail to see any problem in the results you report in your last post (where are the missing values?).

However, two asides: - the results you posted woud increase readability if you put them in between the code delimiters (# icon), which are included among advanced editor (A icon) options; - you seemingly used -mkcorr- for your analysis. As it is not a Stata built-in command, as per FAQ you're asked to report the source you downoaded it from ( SSC?). This request is not for 'out of curiosity' purposes but stems from the consideration that sometimes different versions of the same user-written programme 'are floating around in the cyberspace'; hence, knowing the one that has been used can be helpful for those trying to reply to your query. Copying the results from Sue's post at #3 above into a code block (and fiddling the spacing afterwards) shows that missing values appear as the leftmost column of the table: all the correlations between Insurerdummy and the four AUMdummy variables are missing. If nothing else, this example wonderfully demonstrates why the FAQ requests results be presented in code blocks!

My guess is that every nonmissing value of Insurerdummy corresponds to missing values for the AUMdummy set, and vice versa. As a newbie, I'm not sure what the best way of exploring that possibility is, but -list 1/10- would seem to be a place to start. Hello all and thank you for your replies so far. I'm new to Stata and statistics in general so sorry if I'm slow to understand things. William's suggestion that every nonmissing value of Insurerdummy corresponds to missing values for the AUMdummy set, and vice versa doesn't seem to be the case here. I have checked and I do have AUM data for Insurers. So there are 1's in the Insurerdummy variable that correspond to 1's in the AUM quartile variables.

Nick: 'Missing correlations are inevitable if one of the variables takes on only a single value. In that circumstance, the corresponding variance is zero. Stata is behaving reasonably if that is so.' ' Do you mean that the variable only takes on a single value because it is a dummy variable?

But all of the other variables in this dataset are dummies and I still get a correlation value between them so why not these? Carlo: How can I check where I downloaded my version of mkcorr from? I just looked for it via stata and clicked on one of the first links that came up in the results for Stata 12 Thanks again. Hello Nick and Ben and sorry for the long break in replying. I think I understand now what you meant and I think that is indeed the case. So the only observations in the AUM column are for Insurers, that is 1s in the Insurer indicator variable. I have the same problem elsewhere in the dataset where I'm calculating correlations between two indicator variables however it gives me missing values as the correlation when for example all the 1s for indicator variable one correspond to only 0s or only 1s in indicator variable two.

So am I understanding correctly what the problem is now? I'm trying to understand statistics but I'm not a quant minded person but - it seems like if it's all 0s vs. All 1s in two indicator variables, the correlation should be just 0.? But it's not calculated as that so there must be an error in my thinking. Do you have any advice on how to handle this? I've never seen missing values in a correlation matrix in a journal before but the data I'm working with is what it is and I can't do much about it. So do any of you have any advice on how to deal with a situation like this?

Working backwards: You probably have not seen missing values reported for correlations because authors realised, on their own account or otherwise, that there is no point to reporting them. In examples like yours, the situation is that a row or column should just be omitted from the correlation matrix. Imagine that y = 0 and x = 1 with no other values. Then a scatter plot consists of a single point, repeated, No straight line can be fitted unambiguously to that display.

It is true that an infinity of straight lines could be fitted but the underlying relationship is, to put it politely, ambiguous or, to use a more mathematical term, indeterminate. Stata, like any other program, naturally does not settle the point by drawing a plot and thinking what it implies. Computationally, the issue is settled when Stata in effect tries to calculate cov(x, y) / sd(x) sd(y) and it is sufficient for either sd in the denominator to be zero (which happens whenever either variable is constant) for Stata to throw up its hands and report missing as the only thing it can say; dividing by zero is fatal to the calculation. If you are thinking that the the correlation for (y = 0, x = 1) should be 0, you are thinking perhaps that there is no relationship between the variables, so the correlation should be 0.

Close, but the statistical argument is closer to a statement that there is no relationship between the variables in the stronger sense that we can't even say what the relationship is. This differs from say (y = 0, x = 0 sometimes, 1 other times) in which there is no relationship (meaning, no linear relationship) but we can still reasonably summarise the data by a horizontal straight line. The correlation is still indeterminate, but the situation differs.

The bottom line is that there is really no problem here once you have realised what is going on. In essence, a constant is not a variable and can't be treated as such.

On this page. Using R for statistical analyses - Simple correlation This page is intended to be a help in getting to grips with the powerful statistical program called R.

It is not intended as a course in statistics (see for details about those). If you have an analysis to perform I hope that you will be able to find the commands you need here and copy/paste them into R to get going.

I run training in data management, visualisation and analysis using Excel and R: The Statistical Programming Environment. From 2013 courses will be held at The Field Centre at in Devon.

Alternatively I can come to you and provide the training at your workplace. See details on my. On this page learn how to conduct simple correlations to find as well as (e.g. Spearman Rank and Pearson). Also find an introduction to (see the for more detail). See also: My publications about R See my books about R on my Statistics for Ecologists is available now from.

Get a 20% discount using the S4E20 code! Beginning R is available from the publisher or see the entry on. The Essential R Reference is available from the publisher now (see the entry on )! Community Ecology is available now from. Managing Data Using Excel is available now from. Get £5 discount using the MDUE20 code!

I have more projects in hand - visit from time to time. You might also like my random essays on selected R topics in. See also my page, details about my latest writing project including R scripts developed for the book. R is Open Source R is Free What is R?

R is an open-source (GPL) statistical environment modeled after S. The S language was developed in the late 1980s at AT&T labs. The R project was started by Robert Gentleman and Ross Ihaka (hence the name, R) of the Statistics Department of the University of Auckland in 1995. It has quickly gained a widespread audience. It is currently maintained by the R core-development team, a hard-working, international team of volunteer developers. The is the main site for information on R. At this site are directions for obtaining the software, accompanying packages and other sources of documentation.

R is a powerful statistical program but it is first and foremost a programming language. Many routines have been written for R by people all over the world and made freely available from the as 'packages'. However, the basic installation (for Linux, Windows or Mac) contains a powerful set of tools for most purposes. Because R is a programming language it can seem a bit daunting; you have to type in commands to get it to work. However, it does have a Graphical User Interface (GUI) to make things easier. You can also copy and paste text from other applications into it (e.g.

Calculate Correlation Eviews For Mac Os

Word processors). So, if you have a library of these commands it is easy to pop in the ones you need for the task at hand. That is the purpose of this web page; to provide a library of basic commands that the user can copy and paste into R to perform a variety of statistical analyses.

Navigation index Getting started with R: More about manipulating data and entering data without using a spreadsheet: A short section on how to find more help with R Some statistical tests: Mean Variance Quantile Length Variance unequal Variance Equal Paired t-test Two sample test Paired test Paired tests Stats on multiple samples when you have non-parametric data. Getting started with correlation and a basic graph: Multiple regression analysis: Analysis of variance: Getting started with graphs, some basic types: More graphical methods: More advanced graphical methods: You can get Spearman, Kendall or Pearson correlation coefficients. You can also obtain a matrix of pairwise comparisons in a data set.

Correlation R can perform correlation with the cor function. Built-in to the base distribution of the program are three routines; for Pearson, Kendal and Spearman Rank correlations. The first stage is to. Use a column for each variable and give it a meaningful name.

Don't forget that variable names in R can contain letters and numbers but the only punctuation allowed is a period. The second stage is to and give it a sensible name.

The next stage is to so that the individual variables are read into memory. To get the correlation coefficient you type: cor( var1, var2, method = 'method') The default method is 'pearson' so you may omit this if that is what you want. If you type 'kendall' or 'spearman' then you will get the appropriate correlation coefficient. Correlation coefficients The default correlation returns the pearson correlation coefficient cor(var1, var2) If you specify 'spearman' you will get the spearman correlation coefficient cor(var1, var2, method = 'spearman') If you use a datset instead of separate variables you will return a matrix of all the pairwize correlation coefficients cor(dataset, method = 'pearson') You can test the significance of a correation using Pearson, Kendall or Spearman methods. Correlation and Significance tests Getting a correlation coefficient is generally only half the story; you will want to know if the relationship is significant. The cor function in R can be extended to provide the significance testing required. The function is cor.test As you need to read your data into R from a.CSV file and attach the factors so that they are all stored in memory.

To run a correlation test we type: cor.test(var1, var2, method = 'method') The default method is 'pearson' so you may omit this if that is what you want. If you type 'kendall' or 'spearman' then you will get the appropriate significance test. As usual with R it is a good idea to assign a variable name to your result in case you want to perfom additional operations. Correlation Significance tests The default method is 'pearson' cor.p = cor.test(var1, var2) If you specify 'spearman' you will get the spearman correlation coefficient cor.s = cor.test(var1, var2, method = 'spearman') To see a summary of your correlation test type the name of the variable e.g. cor.s Spearman's rank correlation rho data: y and x1 S = 147.713, p-value = 0.00175 alternative hypothesis: true rho is not equal to 0 sample estimates: rho 0.7362267 Find out more Graphing the Correlation You will usually want to use a scatter plot to graph your correlation.

The basic plot is plot R has various default parameters set e.g. The axes are labelled as the factor name and the plotting symbol is set as an open circle. Correlation graphs Use the basic defaults to create a scatter plot of your two variables plot(x.var, y.var) This changes the axes titles plot(x.var, y.var, xlab='X-axis', ylab='Y-axis') This changes the plotting symbol to a solid circle plot(x.var, y.var, pch=16) Adds a line of best fit to your scatter plot (don't do this for non-parametric plots). Abline(lm(y.var x.var) Correlation Step by Step Step-by-step Correlation First create your data file. Use a spreadsheet and make each column a variable.

Each row is a replicate. The first row should contain the variable names.

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Save this as a.CSV file Read the data into R and save as some name your.data = read.csv(file.choose) Allow the factors within the data to be accessible to R attach(your.data) Decide on the method, run the correlation and assign the result to a new variable. Methods are 'pearson' (default), 'kendal' and 'spearman' your.cor = cor(var1, var2, method = 'pearson') Have a look at the resulting correlation coefficient your.cor Perform a pairwize correlation on all the variables in the data set. Decide on the method ('pearson' (default), 'kendal' and 'spearman') cor.mat = cor(your.data, method = 'pearson') have a look at the resulting correlation matrix cor.mat To evaluate the statistical significance of your correlation decide on the appropriate method (pearson is the default, see above), assign a variable and run the test your.cor cor.test(var1, var2, method='spearman') Have a look at the result of yor significance test your.cor Plot a graph of the two variables from your correlation.

Pch=21 plots an open circle, pch=19 plots a solid circle. Try other values.

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Plot(x.var, y.var, xlab='x-label', ylab='y-label', pch=21)) Add a line of best fit (if appropriate) abline(lm(y.var x.var).