Dear list,
I have 50 sites where information was recorded over a 45 year time period. The
recorded data could take one of four forms: Fishing effort, Environmental, Both
or Inconclusive.
What i am aiming to do is cluster sites based on their similarity through time,
essentially i view this as being similar to making a phylogeny, where instead of
a genetic sequence i have a sequence of factors.
I was thinking of using Gower distance to create a dissimilarity matrix and go
from there but i don't think this captures what i am looking for?
Any suggestions would be gratefully received.
For space i have restricted the sample data to 4 sites
temporal_sites <- structure(list(Year = c(1959L, 1960L, 1961L, 1962L, 1963L,
1964L,
1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L,
1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L,
1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L,
1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L, 1963L,
1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L,
1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L,
1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L,
1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L,
1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L,
1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L,
1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L,
1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L,
1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L,
1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L,
1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L,
1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L,
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L), Site = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "B",
"C", "D"), class = "factor"),
Factor = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Both",
"Environmental", "Fishing Effort"), class =
"factor")), .Names = c("Year",
"Site", "Factor"), class = "data.frame", row.names
= c(NA, -184L
))
[[alternative HTML version deleted]]
Dear list,
I have 50 sites where information was recorded over a 45 year time period.
The recorded data could take one of four forms: Fishing effort,
Environmental, Both or Inconclusive.
What i am aiming to do is cluster sites based on their similarity through
time, essentially i view this as being similar to making a phylogeny, where
instead of a genetic sequence i have a sequence of factors.
I was thinking of using Gower distance to create a dissimilarity matrix and
go from there but i don't think this captures what i am looking for?
Any suggestions would be gratefully received.
For space i have restricted the sample data to 4 sites
temporal_sites <- structure(list(Year = c(1959L, 1960L, 1961L, 1962L, 1963L,
1964L,
1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L,
1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L,
1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L,
1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L,
2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L, 1963L,
1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L,
1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L,
1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L,
1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L,
1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L,
1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L,
1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L,
1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L,
1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L,
1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L,
1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L,
1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L,
1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L,
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L), Site = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "B",
"C", "D"), class "factor"),
Factor = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Both",
"Environmental", "Fishing Effort"), class =
"factor")), .Names c("Year",
"Site", "Factor"), class = "data.frame", row.names
= c(NA, -184L
))
[[alternative HTML version deleted]]
Post to R-sig-ecology , not here. -- Bert On Thu, Aug 15, 2013 at 2:20 AM, Chris McOwen <Chris.McOwen at unep-wcmc.org> wrote:> Dear list, > > I have 50 sites where information was recorded over a 45 year time period. The recorded data could take one of four forms: Fishing effort, Environmental, Both or Inconclusive. > > What i am aiming to do is cluster sites based on their similarity through time, essentially i view this as being similar to making a phylogeny, where instead of a genetic sequence i have a sequence of factors. > > I was thinking of using Gower distance to create a dissimilarity matrix and go from there but i don't think this captures what i am looking for? > > Any suggestions would be gratefully received. > > > For space i have restricted the sample data to 4 sites > > temporal_sites <- structure(list(Year = c(1959L, 1960L, 1961L, 1962L, 1963L, 1964L, > 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, 1973L, > 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, 1982L, > 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, > 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, > 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L, 1963L, > 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, 1972L, > 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, 1981L, > 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, > 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, > 2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, 1962L, > 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, 1971L, > 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, 1980L, > 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, > 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, > 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 1959L, 1960L, 1961L, > 1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L, > 1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L, > 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, > 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, > 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L), Site = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, > 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, > 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("A", "B", "C", "D"), class = "factor"), > Factor = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, > 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, > 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, > 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, > 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Both", > "Environmental", "Fishing Effort"), class = "factor")), .Names = c("Year", > "Site", "Factor"), class = "data.frame", row.names = c(NA, -184L > )) > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at 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.-- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm