Thank you Dennis,
You identified a factor (text column) that I was concerned with.
I simplified my example to try and factor out possible causes. I
eliminated the recurring values in columns (which were not the columns
that caused problems). I produced three examples with simple data sets.
1. Correct output, 2 columns only:
test.notext = read.csv('test-notext.csv')
cor(test.notext, method='spearman')
P3 HP_tot
P3 1.0000000 -0.2182876
HP_tot -0.2182876 1.0000000
dput(test.notext)
structure(list(P3 = c(2L, 2L, 2L, 4L, 2L, 3L, 2L, 1L, 3L, 2L,
2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L),
HP_tot = c(10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 136L,
136L, 136L, 136L, 136L, 136L, 136L, 136L, 136L, 136L, 15L,
15L, 15L, 15L, 15L, 15L, 15L)), .Names = c("P3", "HP_tot"
), class = "data.frame", row.names = c(NA, -25L))
2. Incorrect output where I introduced my P7 column containing text only
the 'a' character:
test = read.csv('test.csv')
cor(test, method='spearman')
P3 P7 HP_tot
P3 1.0000000 NA -0.2502878
P7 NA 1 NA
HP_tot -0.2502878 NA 1.0000000
Warning message:
In cor(test, method = "spearman") : the standard deviation is zero
dput(test)
structure(list(P3 = c(2L, 2L, 2L, 4L, 2L, 3L, 2L, 1L, 3L, 2L,
2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L),
P7 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = "a", class = "factor"), HP_tot = c(10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 136L, 136L, 136L, 136L, 136L,
136L, 136L, 136L, 136L, 136L, 15L, 15L, 15L, 15L, 15L, 15L,
15L)), .Names = c("P3", "P7", "HP_tot"), class = "data.frame",
row.names = c(NA,
-25L))
3. Incorrect output with P7 containing a variety of alpha-numeric
characters (ascii), to factor out equal valued column issue. Notice that
the text column is interpreted as a numeric value.
test.number = read.csv('test-alpha.csv')
cor(test.number, method='spearman')
P3 P7 HP_tot
P3 1.0000000 0.4093108 -0.2502878
P7 0.4093108 1.0000000 -0.3807193
HP_tot -0.2502878 -0.3807193 1.0000000
dput(test.number)
structure(list(P3 = c(2L, 2L, 2L, 4L, 2L, 3L, 2L, 1L, 3L, 2L,
2L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L),
P7 = structure(c(11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L), .Label = c("0", "1", "2", "3", "4", "5",
"6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h",
"i", "j", "k", "l", "m", "n", "o"), class = "factor"), HP_tot = c(10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 136L, 136L, 136L, 136L,
136L, 136L, 136L, 136L, 136L, 136L, 15L, 15L, 15L, 15L, 15L,
15L, 15L)), .Names = c("P3", "P7", "HP_tot"), class = "data.frame",
row.names = c(NA,
-25L))
Correct output is obtained by avoiding matrix computation of correlation:
cor(test.number$P3, test.number$HP_tot, method='spearman')
[1] -0.2182876
It seems that a text column corrupts my correlation calculation (only in a
matrix calculation). I assumed that text columns would not influence the
result of the calculations.
Is this a correct behaviour? If not,I can submit a bug report? If it is,
is there a known workaround?
cheers,
Stephane Vaucher
On Thu, 9 Sep 2010, Dennis Murphy wrote:
Did you try taking out P7, which is text? Moreover, if you get a message
saying ' the standard deviation is zero', it means that the entire column is
constant. By definition, the covariance of a constant with a random variable
is 0, but your data consists of values, so cor() understandably throws a
warning that one or more of your columns are constant. Applying the
following to your data (which I named expd instead), we get
sapply(expd[, -12], var)
P1 P2 P3 P4 P5
P6
5.433333e-01 1.083333e+00 5.766667e-01 1.083333e+00 6.433333e-01
5.566667e-01
P8 P9 P10 P11 P12
SITE
5.733333e-01 3.193333e+00 5.066667e-01 2.500000e-01 5.500000e+00
2.493333e+00
Errors warnings Manual Total H_tot
HP1.1
9.072840e+03 2.081334e+04 7.433333e-01 3.823500e+04 3.880250e+03
2.676667e+00
HP1.2 HP1.3 HP1.4 HP_tot HO1.1
HO1.2
0.000000e+00 2.008440e+03 3.057067e+02 3.827250e+03 8.400000e-01
0.000000e+00
HO1.3 HO1.4 HO_tot HU1.1 HU1.2
HU1.3
0.000000e+00 0.000000e+00 8.400000e-01 0.000000e+00 2.100000e-01
2.266667e-01
HU_tot HR L_tot LP1.1 LP1.2
LP1.3
6.233333e-01 7.433333e-01 3.754610e+03 3.209333e+01 0.000000e+00
2.065010e+03
LP1.4 LP_tot LO1.1 LO1.2 LO1.3
LO1.4
2.246233e+02 3.590040e+03 3.684000e+01 0.000000e+00 0.000000e+00
2.840000e+00
LO_tot LU1.1 LU1.2 LU1.3 LU_tot
LR_tot
6.000000e+01 0.000000e+00 1.440000e+00 3.626667e+00 8.373333e+00
4.943333e+00
SP_tot SP1.1 SP1.2 SP1.3 SP1.4
SP_tot.1
6.911067e+02 4.225000e+01 0.000000e+00 1.009600e+02 4.161600e+02
3.071600e+02
SO1.1 SO1.2 SO1.3 SO1.4 SO_tot
SU1.1
4.543333e+00 2.500000e-01 0.000000e+00 2.100000e-01 5.250000e+00
0.000000e+00
SU1.2 SU1.3 SU_tot SR
1.556667e+00 4.225000e+01 3.504000e+01 4.225000e+01
Which columns are constant?
which(sapply(expd[, -12], var) < .Machine$double.eps)
HP1.2 HO1.2 HO1.3 HO1.4 HU1.1 LP1.2 LO1.2 LO1.3 LU1.1 SP1.2 SO1.3 SU1.1
19 24 25 26 28 35 40 41 44 51 57 60
I suspect that in your real data set, there aren't so many constant columns,
but this is one way to check.
HTH,
Dennis
On Wed, Sep 8, 2010 at 12:35 PM, Stephane Vaucher <vauch...@iro.umontreal.ca
wrote:
Hi everyone,
I'm observing what I believe is weird behaviour when attempting to do
something very simple. I want a correlation matrix, but my matrix seems to
contain correlation values that are not found when executed on pairs:
test2$P2
[1] 2 2 4 4 1 3 2 4 3 3 2 3 4 1 2 2 4 3 4 1 2 3 2 1 3
test2$HP_tot
[1] 10 10 10 10 10 10 10 10 136 136 136 136 136 136 136 136 136
136 15
[20] 15 15 15 15 15 15 c=cor(test2$P3,test2$HP_tot,method='spearman')
c
[1] -0.2182876
c=cor(test2,method='spearman')
Warning message:
In cor(test2, method = "spearman") : the standard deviation is zero
write(c,file='out.csv')
from my spreadsheet
-0.25028783918741
Most cells are correct, but not that one.
If this is expected behaviour, I apologise for bothering you, I read the
documentation, but I do not know if the calculation of matrices and pairs is
done using the same function (eg, with respect to equal value observations).
If this is not a desired behaviour, I noticed that it only occurs with a
relatively large matrix (I couldn't reproduce on a simple 2 column data
set). There might be a naming error.
names(test2)
[1] "ID" "NOMBRE" "MAIL"
[4] "Age" "SEXO" "Studies"
[7] "Hours_Internet" "Vision.Disabilities" "Other.disabilities"
[10] "Technology_Knowledge" "Start_Time" "End_Time"
[13] "Duration" "P1" "P1Book"
[16] "P1DVD" "P2" "P3"
[19] "P4" "P5" "P6"
[22] "P8" "P9" "P10"
[25] "P11" "P12" "P7"
[28] "SITE" "Errors" "warnings"
[31] "Manual" "Total" "H_tot"
[34] "HP1.1" "HP1.2" "HP1.3"
[37] "HP1.4" "HP_tot" "HO1.1"
[40] "HO1.2" "HO1.3" "HO1.4"
[43] "HO_tot" "HU1.1" "HU1.2"
[46] "HU1.3" "HU_tot" "HR"
[49] "L_tot" "LP1.1" "LP1.2"
[52] "LP1.3" "LP1.4" "LP_tot"
[55] "LO1.1" "LO1.2" "LO1.3"
[58] "LO1.4" "LO_tot" "LU1.1"
[61] "LU1.2" "LU1.3" "LU_tot"
[64] "LR_tot" "SP_tot" "SP1.1"
[67] "SP1.2" "SP1.3" "SP1.4"
[70] "SP_tot.1" "SO1.1" "SO1.2"
[73] "SO1.3" "SO1.4" "SO_tot"
[76] "SU1.1" "SU1.2" "SU1.3"
[79] "SU_tot" "SR"
Thank you in advance,
Stephane Vaucher
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