Hi
I am not sure what you want to do as you speak about using different
function for different part of your data.
> head(data)
city year sex V
1 1 1975 F 25.3044
2 1 1975 M 16.5711
3 1 1976 M 16.6072
4 1 1976 F 24.2841
5 1 1977 M 14.8838
6 1 1977 F 24.8124
If it was only correlation coefficients you could use lapply split
construction like
lapply(split(data[,c(4,2)], interaction(data$city, data$sex)), function(x)
summary(lm(x))$r.sq)
$`1.M`
[1] 0.923669
$`2.M`
[1] 0.5151131
$`1.F`
[1] 0.823817
$`2.F`
[1] 0.7483883
or
> lapply(split(data[,c(4,2)], interaction(data$city, data$sex)), cor)
$`1.M`
V year
V 1.000000 -0.961077
year -0.961077 1.000000
$`2.M`
V year
V 1.0000000 -0.7177138
year -0.7177138 1.0000000
Regards
Petr Pikal
[EMAIL PROTECTED]
724008364, 581252140, 581252257
[EMAIL PROTECTED] napsal dne 14.05.2008 11:46:02:
> Hallo All,
>
> I have difficulties understanding how factors work in R. Suppose a have
> data in the panel form below. I would to compute a correlation
coefficient
> (actually apply a different function of two time series) in the V
variable
> between members of the two sexes in each city over time. How can this be
> done?
>
> Thank you in advance,
> Serguei
>
> city, year, sex, V
> 1, 1975, 1, 25.3044
> 1, 1975, 0, 16.5711
> 1, 1976, 0, 16.6072
> 1, 1976, 1, 24.2841
> 1, 1977, 0, 14.8838
> 1, 1977, 1, 24.8124
> 1, 1978, 1, 23.0570
> 1, 1978, 0, 14.5627
> 1, 1979, 1, 21.2071
> 1, 1979, 0, 13.5277
> 2, 1975, 1, 62.4457
> 2, 1975, 0, 26.9745
> 2, 1976, 1, 67.3025
> 2, 1976, 0, 31.4600
> 2, 1977, 1, 53.0577
> 2, 1977, 0, 25.1941
> 2, 1978, 0, 23.3694
> 2, 1978, 1, 40.1452
> 2, 1979, 1, 44.5686
> 2, 1979, 0, 23.4042
>
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