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,

-- 
MD. MOSHIUR RAHMAN
PhD Candidate
School of Animal Biology/Zoology (M092)
University of Western Australia
35 Stirling Hwy, Crawley, WA, 6009
Australia.
Mob.: 061-425205507

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