Please don't cross post [1]. Please post minimal example data only. You should not be trying to (or appearing to) use us to get your work done.
[1] http://www.r-project.org/mail.html --------------------------------------------------------------------------- Jeff Newmiller The ..... ..... Go Live... DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/Batteries O.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --------------------------------------------------------------------------- Sent from my phone. Please excuse my brevity. Moshiur Rahman <mrahmankuf...@gmail.com> wrote: >Dear all, > >I'm writing my manuscript to publish after analysis my final data with >ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of >my >data (2 categirical factors with 2 levels: Quantity & Quality; 2 >dependent >var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here >Irid.area) are significantly influenced by the covariate (standard >length, >SL), I need to use the partial correlation. I know how to calculate it >with >JMP, but as I used R to analyse all of my data (first time in my life) >for >this manuscript, can anyone help me to find a solution for this >problem? I >got some libraries to calculate it (e.g. ppcor, ggm, etc.), but none of >them fit to my required analysis (fitting covariate and subset group) >in >the model. > >Any help will be very much appreciated. > > > ### Datafrmame> data1 <- read.csv(file.choose(),header=TRUE) >#Partial correlation.csv> data1 Quantity Quality SL Irid.area > Casa.PC1 >1 High Low 16.38 10.31 1.711739555 >2 High High 15.95 16.52 0.013383537 >3 High High 15.69 12.74 2.228490878 >4 High Low 14.76 9.80 1.554975833 >5 High Low 14.63 12.95 1.823767970 >6 High High 14.32 14.21 3.152059841 >7 High High 14.95 12.57 2.069265040 >8 High Low 15.37 13.55 1.886027422 >9 High Low 14.73 14.18 1.127440602 >10 High High 16.08 15.98 1.435563307 >11 High High 15.78 16.76 2.433261686 >12 High Low 15.22 12.12 0.927454986 >13 High Low 14.22 10.91 2.328899576 >14 High High 14.47 11.03 1.522923487 >15 High Low 13.98 10.03 2.342535074 >16 High Low 14.99 11.44 0.749529924 >17 High High 16.51 20.16 2.993905677 >18 High High 14.83 16.82 2.227315597 >19 High Low 15.17 19.21 1.685063793 >20 High Low 16.29 20.31 1.551704440 >21 High High 16.23 15.03 1.982319336 >22 High High 14.18 14.80 1.839910851 >23 High Low 16.11 12.92 1.443240647 >24 High Low 13.95 7.60 2.034192171 >25 High High 17.54 17.80 2.188306237 >26 High Low 16.24 19.29 1.531264746 >27 High High 14.79 12.98 1.465644134 >28 High Low 15.87 14.85 1.372494892 >29 High High 16.09 13.71 1.462037152 >30 High Low 14.34 13.53 1.365588960 >31 High High 14.93 12.91 0.729212386 >32 High High 15.89 16.98 0.136175317 >33 High Low 16.11 11.93 1.442761666 >34 High Low 15.25 15.49 0.834442777 >35 High High 15.84 17.65 1.471713978 >36 High High 15.61 18.00 1.949457500 >37 High Low 15.42 13.87 0.200098471 >38 High Low 14.91 11.23 0.981988071 >39 High High 15.69 5.74 -0.445941360 >40 High High 15.13 9.07 1.387947896 >41 High Low 15.04 15.87 1.480980400 >42 High Low 17.08 17.24 2.620029423 >43 High High 15.85 12.47 0.027278890 >44 High High 15.35 10.44 2.597373230 >45 High Low 15.62 12.11 0.030653396 >46 High High 17.96 17.50 1.544922124 >47 High Low 17.25 17.87 1.705053951 >48 High Low 15.56 19.72 1.688867665 >49 High High 16.27 13.15 0.111371757 >50 High Low 16.68 15.43 1.538012366 >51 High High 15.78 15.07 0.744555741 >52 Low High 14.72 13.34 -0.682505420 >53 Low Low 14.93 14.07 -1.641494605 >54 Low High 13.94 13.22 -1.172268647 >55 Low High 14.01 18.65 -0.996656064 >56 Low Low 14.33 17.16 -1.789728167 >57 Low Low 14.57 12.43 -0.827526343 >58 Low High 14.01 15.29 -1.350691602 >59 Low Low 14.22 16.98 -1.688278221 >60 Low High 13.45 14.40 -1.182117327 >61 Low High 13.44 16.57 -1.358976542 >62 Low Low 14.76 15.58 0.334534454 >63 Low Low 14.85 17.65 0.251766383 >64 Low High 13.42 10.99 -0.526634460 >65 Low High 14.07 16.88 -1.112579922 >66 Low Low 14.15 16.41 -0.971918177 >67 Low Low 14.78 11.95 -1.179074800 >68 Low High 14.84 17.62 -0.777057705 >69 Low High 15.16 14.09 -1.224388816 >70 Low Low 14.60 15.03 -0.775478528 >71 Low High 13.74 10.01 -0.917153842 >72 Low High 13.54 12.34 -0.822895877 >73 Low Low 14.04 11.86 0.002789116 >74 Low High 15.73 18.50 -1.209469875 >75 Low Low 15.14 16.85 -0.479090055 >76 Low Low 14.86 17.32 -1.897204235 >77 Low High 14.43 11.20 0.469569392 >78 Low Low 14.01 15.55 -1.025059269 >79 Low High 14.20 11.67 -0.770451072 >80 Low High 16.16 17.34 -0.274527631 >81 Low Low 14.63 13.52 -1.070187945 >82 Low Low 15.83 14.85 -1.627211162 >83 Low High 14.70 14.81 -1.694118608 >84 Low High 13.91 14.48 -1.635459183 >85 Low Low 13.95 16.05 -1.449612666 >86 Low Low 14.03 12.58 -1.685968841 >87 Low High 14.82 13.57 -0.097426417 >88 Low High 14.32 12.16 -1.403512009 >89 Low Low 14.33 7.66 -1.336654713 >90 Low Low 15.01 10.15 -1.257019268 >91 Low High 14.01 9.79 -0.715404495 >92 Low Low 14.25 17.38 -1.296954022 >93 Low High 14.55 16.11 -0.616895943 >94 Low High 13.98 11.49 -0.654017365 >95 Low Low 15.59 8.43 -1.708330027 >96 Low Low 15.02 16.88 -1.352913634 >97 Low High 13.99 9.64 -0.499793618 >98 Low Low 13.98 12.25 -1.265336955 >99 Low High 13.94 13.79 -0.263925513 >100 Low Low 15.03 20.39 -0.720121308 >101 Low Low 13.93 14.63 -0.908570400> ### COrrelation >test according to group> library("MASS")> with(data1, cor.test(~ >Irid.area + Casa.PC1, subset=(Quantity=="High")))# gives cor, df+2, >p-values > Pearson's product-moment correlation > >data: Irid.area and Casa.PC1 >t = 1.5795, df = 49, p-value = 0.1206 >alternative hypothesis: true correlation is not equal to 0 >95 percent confidence interval: > -0.05905155 0.46734855 >sample estimates: > cor >0.2201142 >> with(data1, cor.test(~ Irid.area + Casa.PC1, >subset=(Quantity=="Low")))# gives cor, df+2, p-values > Pearson's product-moment correlation > >data: Irid.area and Casa.PC1 >t = -0.4275, df = 48, p-value = 0.6709 >alternative hypothesis: true correlation is not equal to 0 >95 percent confidence interval: > -0.3342116 0.2205349 >sample estimates: > cor >-0.06159377 >> #### Effect size from two-way ANOVA ####> anova<- aov(Irid.area ~ >Quantity*Quality+SL, data=data1)> summary(anova) Df Sum >Sq Mean Sq F value Pr(>F) >Quantity 1 0.0 0.04 0.004 0.947 >Quality 1 0.3 0.26 0.032 0.859 >SL 1 149.5 149.49 18.027 5.03e-05 *** >Quantity:Quality 1 0.2 0.18 0.022 0.883 >Residuals 96 796.1 8.29 >--- >Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1 > >etaSquared( anova ) # effect size eta.sq >eta.sq.part >Quantity 5.682119e-02 0.0632542041 >Quality 6.577118e-05 0.0000781554 >SL 1.510031e-01 0.1521470868 >Quantity:Quality 1.922552e-04 0.0002284211> ### partial correlation >(pcor) tests:> library(ggm)Loading required package: graphError in >loadNamespace(i[[1L]], c(lib.loc, .libPaths())) : > there is no package called �BiocGenerics�In addition: Warning >messages:1: package �ggm� was built under R version 2.15.3 2: package >�graph� was built under R version 3.0.1 Error: package �graph� could >not be loaded> data2<- data1[, c("Irid.area", "Casa.PC1", "SL"), >Quantity == "High"]> pcor(c("Irid.area", "Casa.PC1", >"SL"),var(data2))Error in match.arg(method) : 'arg' must be NULL or a >character vector> pc<-pcor(data2)> pc$estimate > Irid.area Casa.PC1 SL >Irid.area 1.0000000 -0.1313475 0.3387663 >Casa.PC1 -0.1313475 1.0000000 0.5061438 >SL 0.3387663 0.5061438 1.0000000 > >$p.value > Irid.area Casa.PC1 SL >Irid.area 0.0000000000 1.896426e-01 3.647247e-04 >Casa.PC1 0.1896425857 0.000000e+00 6.258573e-09 >SL 0.0003647247 6.258573e-09 0.000000e+00 > >$statistic > Irid.area Casa.PC1 SL >Irid.area 0.000000 -1.311637 3.564375 >Casa.PC1 -1.311637 0.000000 5.809698 >SL 3.564375 5.809698 0.000000 > >$n >[1] 101 > >$gp >[1] 1 > >$method >[1] "pearson" > > > > >Cheers, ______________________________________________ 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.