baran95 {ade4}R Documentation

African Estuary Fishes

Description

This data set is a list containing relations between sites and fish species linked to dates.

Usage

data(baran95)

Format

This list contains the following objects:

fau
is a data frame 95 seinings and 33 fish species.
plan
is a data frame 2 factors : date and site. The date has 6 levels (april 1993, june 1993, august 1993, october 1993, december 1993 and february 1994) and the sites are defined by 4 distances to the Atlantic Ocean (km03, km17, km33 and km46).
species.names
is a vector of species latin names.

Source

Baran, E. (1995) Dynamique spatio-temporelle des peuplements de Poissons estuariens en Guinée (Afrique de l'Ouest). Thèse de Doctorat, Université de Bretagne Occidentale. Data collected by net fishing sampling in the Fatala river estuary.

References

See a data description at http://pbil.univ-lyon1.fr/R/pps/pps027.pdf (in French).

Examples

data(baran95)
w <- dudi.pca(log(baran95$fau+1), scal = FALSE, scann = FALSE, 
    nf = 3)
w1 <- within(w, baran95$plan$date, scann = FALSE)
fatala <- ktab.within(w1)
stat1 <- statis(fatala, scan = FALSE, nf = 3)
w1 <- split(stat1$C.Co, baran95$plan$date)
w2 <- split(baran95$plan$site, baran95$plan$date)
par(mfrow = c(3,2))
for (j in 1:6) {
    s.label(stat1$C.Co[,1:2], clab = 0,
    sub = tab.names(fatala)[j], csub = 3)
    s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese = FALSE,
    add.plot = TRUE)
}
par(mfrow = c(1,1))

kplot(stat1, arrow = FALSE, traj = FALSE, clab = 2, uni = TRUE, 
    class = baran95$plan$site) #simpler

mfa1 <- mfa(fatala, scan = FALSE, nf = 3)
w1 <- split(mfa1$co, baran95$plan$date)
w2 <- split(baran95$plan$site, baran95$plan$date)
par(mfrow = c(3,2))
for (j in 1:6) {
    s.label(mfa1$co[,1:2], clab = 0,
    sub = tab.names(fatala)[j], csub = 3)
    s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese=FALSE,
    add.plot = TRUE)
}
par(mfrow = c(1,1))

Worked out examples


> library(ade4)
> ### Name: baran95
> ### Title: African Estuary Fishes
> ### Aliases: baran95
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(baran95)
> w <- dudi.pca(log(baran95$fau+1), scal = FALSE, scann = FALSE, 
+     nf = 3)
> w1 <- within(w, baran95$plan$date, scann = FALSE)
> fatala <- ktab.within(w1)
> stat1 <- statis(fatala, scan = FALSE, nf = 3)
> w1 <- split(stat1$C.Co, baran95$plan$date)
> w2 <- split(baran95$plan$site, baran95$plan$date)
> par(mfrow = c(3,2))
> for (j in 1:6) {
+     s.label(stat1$C.Co[,1:2], clab = 0,
+     sub = tab.names(fatala)[j], csub = 3)
+     s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese = FALSE,
+     add.plot = TRUE)
+ }
> par(mfrow = c(1,1))
> 
> kplot(stat1, arrow = FALSE, traj = FALSE, clab = 2, uni = TRUE, 
+     class = baran95$plan$site) #simpler
> 
> mfa1 <- mfa(fatala, scan = FALSE, nf = 3)
> w1 <- split(mfa1$co, baran95$plan$date)
> w2 <- split(baran95$plan$site, baran95$plan$date)
> par(mfrow = c(3,2))
> for (j in 1:6) {
+     s.label(mfa1$co[,1:2], clab = 0,
+     sub = tab.names(fatala)[j], csub = 3)
+     s.class(w1[[j]][,1:2], w2[[j]], clab = 2, axese=FALSE,
+     add.plot = TRUE)
+ }
> par(mfrow = c(1,1))
> 
> 
> 
> 

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