Works for me: > x <- rnorm(10) > y <- rnorm(10) > cor.test(x, y, method = 'spearman')$p.value [1] 0.166058
What are the classes of your inputs? A reproducible example would be helpful. From the help page of cor.test(): x, y numeric vectors of data values. x and y must have the same length. Do your two inputs meet those criteria? HTH, Dennis On Tue, Aug 9, 2011 at 5:58 AM, ScottM <scott.mcgr...@abdn.ac.uk> wrote: > Cheers Jorge, > > I've tried this, but keep getting error messages, relating to either: > > Error: unexpected '$' in "$" > > or > > Error in cor(data, method = "spearman")$p.value : > $ operator is invalid for atomic vectors > > Very annoying! > > S > > Scott McGrane MA (Hons), MRes > SAGES Theme 1 PhD Student > Northern Rivers Institute > St Mary's Building > University of Aberdeen > > http://www.abdn.ac.uk/nri > ________________________________________ > From: Jorge Ivan Velez [via R] > [ml-node+3729864-606556654-252...@n4.nabble.com] > Sent: 09 August 2011 01:55 PM > To: Mcgrane, Scott > Subject: Re: Correlation Matrix - p value? > > ?cor.test > cor.test(x, y, method = "spearman")$p.value > > HTH, > Jorge > > > On Tue, Aug 9, 2011 at 8:44 AM, ScottM <> wrote: > >> Hello all, >> >> I've run a Spearman's Rank test to discern relationships between landscape >> characteristics and a specific aspect of river behaviour. >> >> I've executed a correlation matrix between the one dependent variable and >> all of the predictors, which gives me a nice output of Spearman's Rho >> values. >> >> However, I also need to somehow find the "p" value, to assess the strength >> of relationship. >> >> Conducting a simple correlation between 2 variables using the: >> >> cor(var1, var2, method="spearman") >> >> command gives me an output with both the Rho and P value, but the default >> output of the matrix is simply the Rho value. I've tried using >> summary(result), but it just produces the percentile/max/min values. >> >> Any help appreciated. >> >> Cheers, >> >> S. >> >> -- >> View this message in context: >> http://r.789695.n4.nabble.com/Correlation-Matrix-p-value-tp3729838p3729838.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> [hidden email]</user/SendEmail.jtp?type=node&node=3729864&i=0> mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > [[alternative HTML version deleted]] > > ______________________________________________ > [hidden email]</user/SendEmail.jtp?type=node&node=3729864&i=1> mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > > ________________________________ > If you reply to this email, your message will be added to the discussion > below: > http://r.789695.n4.nabble.com/Correlation-Matrix-p-value-tp3729838p3729864.html > To unsubscribe from Correlation Matrix - p value?, click > here<http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=unsubscribe_by_code&node=3729838&code=c2NvdHQubWNncmFuZUBhYmRuLmFjLnVrfDM3Mjk4Mzh8OTM1MjEwNDY5>. > > > The University of Aberdeen is a charity registered in Scotland, No SC013683. > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Correlation-Matrix-p-value-tp3729838p3729868.html > Sent from the R help mailing list archive at Nabble.com. > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
