Re: [R] Partial correlations and p-values
Your R code looks correct. Because this is a straightforward calculation, I would be surprised if there were any differences with SPSS. It may be worthwhile to check if SPSS gives partial correlations or semipartial correlations. For example, if you take the correlation between py - resid(lm(y ~ z1 + z2,data=mydat2)) and x where mydat2 has missing values removed, you get 0.47. On Tue, Dec 1, 2009 at 8:24 PM, dadrivr dadr...@gmail.com wrote: I am trying to calculate a partial correlation and p-values. Unfortunately, the results in R are different than what SPSS gives. Here is an example in R (calculating the partial correlation of x and y, controlling for z1 and z2): x - c(1,20,14,30,9,4,8) y - c(5,6,7,9,NA,10,6) z1 - c(13,8,16,14,26,13,20) z2 - c(12,NA,2,5,8,16,13) fmx - lm(x ~ z1 + z2, na.action = na.exclude) fmy - lm(y ~ z1 + z2, na.action = na.exclude) yres - resid(fmy) xres - resid(fmx) cor(xres, yres, use = p) ct - cor.test(xres, yres) ct$estimate ct$p.value R give me: r = .65, p = .23 However, SPSS calculates: r = .46, p = .70 I think something may be different with R's handling of missing data, as when I replace the NA's with values, R and SPSS give the same r-values, albeit different p-values still. I am doing pairwise case exclusion in both R and SPSS. Any ideas why I'm getting different values? Is something wrong with my formula in R? Any help would be greatly appreciated. Thanks! __ 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.
Re: [R] Partial correlations and p-values
On Sat, 5 Dec 2009, Juliet Hannah wrote: Your R code looks correct. There are a couple of hiccups. First the degrees of freedom for the partial correlation would be wrong even if there was no missing data. Because this is a straightforward calculation, I would be surprised if there were any differences with SPSS. There are differences. SPSS seems to use the correlation matrix computed with a pairwise present method and compute partial correlations from that. Following http://wiki.r-project.org/rwiki/doku.php?id=tips:data-matrices:part_corr R.pp - cor(cbind(x,y,z1,z2),use='pair') R.comp - cor(cbind(x,y,z1,z2),use='complete') Rinv - solve(R.pp) D - diag(1 / sqrt(diag(Rinv))) P - -D %*% Rinv %*% D P[1,2] [1] 0.4596122 Rinv - solve(R.comp) D - diag(1 / sqrt(diag(Rinv))) P - -D %*% Rinv %*% D P[1,2] [1] 0.657214 The pairwise present value seems to be what SPSS is reporting. The complete cases values is nearly (but not the same as) what you got. A real issue here is how to usefully compute and test partial correlations in the presence of missing data. If you want to persue that, I would suggest opening a new thread with a subject line like 'partial correlations with missing observations' HTH, Chuck It may be worthwhile to check if SPSS gives partial correlations or semipartial correlations. For example, if you take the correlation between py - resid(lm(y ~ z1 + z2,data=mydat2)) and x where mydat2 has missing values removed, you get 0.47. On Tue, Dec 1, 2009 at 8:24 PM, dadrivr dadr...@gmail.com wrote: I am trying to calculate a partial correlation and p-values. Unfortunately, the results in R are different than what SPSS gives. Here is an example in R (calculating the partial correlation of x and y, controlling for z1 and z2): x - c(1,20,14,30,9,4,8) y - c(5,6,7,9,NA,10,6) z1 - c(13,8,16,14,26,13,20) z2 - c(12,NA,2,5,8,16,13) fmx - lm(x ~ z1 + z2, na.action = na.exclude) fmy - lm(y ~ z1 + z2, na.action = na.exclude) yres - resid(fmy) xres - resid(fmx) cor(xres, yres, use = p) ct - cor.test(xres, yres) ct$estimate ct$p.value R give me: r = .65, p = .23 However, SPSS calculates: r = .46, p = .70 I think something may be different with R's handling of missing data, as when I replace the NA's with values, R and SPSS give the same r-values, albeit different p-values still. I am doing pairwise case exclusion in both R and SPSS. Any ideas why I'm getting different values? Is something wrong with my formula in R? Any help would be greatly appreciated. Thanks! __ 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. Charles C. Berry(858) 534-2098 Dept of Family/Preventive Medicine E mailto:cbe...@tajo.ucsd.edu UC San Diego http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901 __ 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.
Re: [R] Partial correlations and p-values
you might look at partial.r in the psych package dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! -- View this message in context: http://n4.nabble.com/Partial-correlations-and-p-values-tp908641p949283.html Sent from the R help mailing list archive at Nabble.com. __ 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.
Re: [R] Partial correlations and p-values
I am trying to calculate a partial correlation and p-values. Unfortunately, the results in R are different than what SPSS gives. Here is an example in R (calculating the partial correlation of x and y, controlling for z1 and z2): x - c(1,20,14,30,9,4,8) y - c(5,6,7,9,NA,10,6) z1 - c(13,8,16,14,26,13,20) z2 - c(12,NA,2,5,8,16,13) fmx - lm(x ~ z1 + z2, na.action = na.exclude) fmy - lm(y ~ z1 + z2, na.action = na.exclude) yres - resid(fmy) xres - resid(fmx) cor(xres, yres, use = p) ct - cor.test(xres, yres) ct$estimate ct$p.value R give me: r = .65, p = .23 However, SPSS calculates: r = .46, p = .70 I think something may be different with R's handling of missing data, as when I replace the NA's with values, R and SPSS give the same r-values, albeit different p-values still. I am doing pairwise case exclusion in both R and SPSS. Any ideas why I'm getting different values? Is something wrong with my formula in R? Any help would be greatly appreciated. Thanks! Peter Ehlers wrote: dadrivr wrote: The variables have the same length, but with different numbers of missing values (NA). As a result, the residuals calculations (xres yres) have different lengths, and I cannot compute the correlation between the two (error of incompatible dimensions - see example below). Is there a way, when calculating residuals, to leave the NAs in the residual calculation and output? Thanks! x - c(1,20,14,NA,9) y - c(5,6,7,9,10) z - c(13,NA,16,14,NA) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value Well, your example above just uses two points for the x on z regression and that gives zero-residuals. Let's use something a bit more realistic: set.seed(123) x - rnorm(5) y - rnorm(5) z - rnorm(5) x[sample(5,1)] - NA z[sample(5,1)] - NA fmx - lm(x ~ z, na.action = na.exclude) fmy - lm(y ~ z, na.action = na.exclude) yres - resid(fmy) xres - resid(fmx) cor(xres, yres, use = p) # -0.95978 ct - cor.test(xres, yres) ct$estimate # -0.95978 ct$p.value # 0.04021994 Check out the 'na.exclude' action in lm(): it preserves the length of the residual vector. Then the 'use=' argument to cor() uses pairwise complete observations. cor.test() will do that automatically. -Peter Ehlers Ista Zahn wrote: 1) Think about what you did wrong. It doesn't make sense to do correlation/regression with variables of different lengths. You can have missing values in one or more variables, if that's what you mean. Just code them NA. 2) Just add in the predictors, e.g. residuals(lm(y ~ z1 + z2)) -Ista On Wed, Nov 11, 2009 at 10:34 PM, dadrivr dadr...@gmail.com wrote: Awesome, that's what I was looking for.  I have two additional questions: (1) What can I do if the variables are of different lengths? (2) How do I update the formula if I want to control for more than one variable. Let's take the following example: x - c(1,20,14,7,9) y - c(5,6,7,9,10,11) z - c(13,27,16,5,4,17,20) a - c(4,6,7,1) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value How do I update the above formula to: (1) take into account that the variables are of different lengths?  I get an error when calculating the residuals. (2) control for z and a (i.e., more than one variable)? Thanks so much for your help. Peter Ehlers wrote: dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values).  I'm new to R, and I don't know how to do this.  I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate  # 0.9978857 ct$p.value  # 0.003934582  -Peter Ehlers __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26312873.html Sent from the R help mailing list archive at Nabble.com. __ 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
Re: [R] Partial correlations and p-values
1) Think about what you did wrong. It doesn't make sense to do correlation/regression with variables of different lengths. You can have missing values in one or more variables, if that's what you mean. Just code them NA. 2) Just add in the predictors, e.g. residuals(lm(y ~ z1 + z2)) -Ista On Wed, Nov 11, 2009 at 10:34 PM, dadrivr dadr...@gmail.com wrote: Awesome, that's what I was looking for. I have two additional questions: (1) What can I do if the variables are of different lengths? (2) How do I update the formula if I want to control for more than one variable. Let's take the following example: x - c(1,20,14,7,9) y - c(5,6,7,9,10,11) z - c(13,27,16,5,4,17,20) a - c(4,6,7,1) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value How do I update the above formula to: (1) take into account that the variables are of different lengths? I get an error when calculating the residuals. (2) control for z and a (i.e., more than one variable)? Thanks so much for your help. Peter Ehlers wrote: dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate # 0.9978857 ct$p.value # 0.003934582 -Peter Ehlers __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26312873.html Sent from the R help mailing list archive at Nabble.com. __ 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. -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org __ 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.
Re: [R] Partial correlations and p-values
The variables have the same length, but with different numbers of missing values (NA). As a result, the residuals calculations (xres yres) have different lengths, and I cannot compute the correlation between the two (error of incompatible dimensions - see example below). Is there a way, when calculating residuals, to leave the NAs in the residual calculation and output? Thanks! x - c(1,20,14,NA,9) y - c(5,6,7,9,10) z - c(13,NA,16,14,NA) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value Ista Zahn wrote: 1) Think about what you did wrong. It doesn't make sense to do correlation/regression with variables of different lengths. You can have missing values in one or more variables, if that's what you mean. Just code them NA. 2) Just add in the predictors, e.g. residuals(lm(y ~ z1 + z2)) -Ista On Wed, Nov 11, 2009 at 10:34 PM, dadrivr dadr...@gmail.com wrote: Awesome, that's what I was looking for. I have two additional questions: (1) What can I do if the variables are of different lengths? (2) How do I update the formula if I want to control for more than one variable. Let's take the following example: x - c(1,20,14,7,9) y - c(5,6,7,9,10,11) z - c(13,27,16,5,4,17,20) a - c(4,6,7,1) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value How do I update the above formula to: (1) take into account that the variables are of different lengths? I get an error when calculating the residuals. (2) control for z and a (i.e., more than one variable)? Thanks so much for your help. Peter Ehlers wrote: dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate # 0.9978857 ct$p.value # 0.003934582 -Peter Ehlers __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26312873.html Sent from the R help mailing list archive at Nabble.com. __ 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. -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26318276.html Sent from the R help mailing list archive at Nabble.com. __ 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.
Re: [R] Partial correlations and p-values
dadrivr wrote: The variables have the same length, but with different numbers of missing values (NA). As a result, the residuals calculations (xres yres) have different lengths, and I cannot compute the correlation between the two (error of incompatible dimensions - see example below). Is there a way, when calculating residuals, to leave the NAs in the residual calculation and output? Thanks! x - c(1,20,14,NA,9) y - c(5,6,7,9,10) z - c(13,NA,16,14,NA) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value Well, your example above just uses two points for the x on z regression and that gives zero-residuals. Let's use something a bit more realistic: set.seed(123) x - rnorm(5) y - rnorm(5) z - rnorm(5) x[sample(5,1)] - NA z[sample(5,1)] - NA fmx - lm(x ~ z, na.action = na.exclude) fmy - lm(y ~ z, na.action = na.exclude) yres - resid(fmy) xres - resid(fmx) cor(xres, yres, use = p) # -0.95978 ct - cor.test(xres, yres) ct$estimate # -0.95978 ct$p.value # 0.04021994 Check out the 'na.exclude' action in lm(): it preserves the length of the residual vector. Then the 'use=' argument to cor() uses pairwise complete observations. cor.test() will do that automatically. -Peter Ehlers Ista Zahn wrote: 1) Think about what you did wrong. It doesn't make sense to do correlation/regression with variables of different lengths. You can have missing values in one or more variables, if that's what you mean. Just code them NA. 2) Just add in the predictors, e.g. residuals(lm(y ~ z1 + z2)) -Ista On Wed, Nov 11, 2009 at 10:34 PM, dadrivr dadr...@gmail.com wrote: Awesome, that's what I was looking for.  I have two additional questions: (1) What can I do if the variables are of different lengths? (2) How do I update the formula if I want to control for more than one variable. Let's take the following example: x - c(1,20,14,7,9) y - c(5,6,7,9,10,11) z - c(13,27,16,5,4,17,20) a - c(4,6,7,1) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value How do I update the above formula to: (1) take into account that the variables are of different lengths?  I get an error when calculating the residuals. (2) control for z and a (i.e., more than one variable)? Thanks so much for your help. Peter Ehlers wrote: dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values).  I'm new to R, and I don't know how to do this.  I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate  # 0.9978857 ct$p.value  # 0.003934582  -Peter Ehlers __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26312873.html Sent from the R help mailing list archive at Nabble.com. __ 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. -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org __ 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.
[R] Partial correlations and p-values
I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26308463.html Sent from the R help mailing list archive at Nabble.com. __ 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.
Re: [R] Partial correlations and p-values
dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate # 0.9978857 ct$p.value # 0.003934582 -Peter Ehlers __ 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.
Re: [R] Partial correlations and p-values
Awesome, that's what I was looking for. I have two additional questions: (1) What can I do if the variables are of different lengths? (2) How do I update the formula if I want to control for more than one variable. Let's take the following example: x - c(1,20,14,7,9) y - c(5,6,7,9,10,11) z - c(13,27,16,5,4,17,20) a - c(4,6,7,1) xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) ct - cor.test(xres, yres) ct$estimate ct$p.value How do I update the above formula to: (1) take into account that the variables are of different lengths? I get an error when calculating the residuals. (2) control for z and a (i.e., more than one variable)? Thanks so much for your help. Peter Ehlers wrote: dadrivr wrote: I'm trying to write code to calculate partial correlations (along with p-values). I'm new to R, and I don't know how to do this. I have searched and come across different functions, but I haven't been able to get any of them to work (for example, pcor and pcor.test from the ggm package). In the following example, I am trying to compute the correlation between x and y, while controlling for z (partial correlation): x - c(1,20,14,7,9) y - c(5,6,7,9,10) z - c(13,27,16,5,4) What function can I append to this to find this partial correlation? Many thanks! I'm not sure what you need, but does this give you what you want: xres - residuals(lm(x ~ z)) yres - residuals(lm(y ~ z)) cor(xres, yres) # [1] 0.9778857 or ct - cor.test(xres, yres) ct$estimate # 0.9978857 ct$p.value # 0.003934582 -Peter Ehlers __ 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. -- View this message in context: http://old.nabble.com/Partial-correlations-and-p-values-tp26308463p26312873.html Sent from the R help mailing list archive at Nabble.com. __ 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.
