Description Usage Arguments Details Value Authors Examples
clusterSelect fits models with varying values of G to determine the appropriate number of archetype species.
1 2 3 | #clusterSelect(sp.form,sp.data,covar.data,G=1:10,\n
#em.prefit=TRUE, em.steps=4 ,em.refit=3,\n
#est.var=FALSE,trace=TRUE)
|
sp.form |
an object of class "formula" (or one that can be coerced to that class):a symbolic description of the model to be fitted |
sp.data |
a data frame containing the species information. The frame is arranged so that each row is a site and each column is a species. Species names should be included as column names otherwise numbers from 1:S are assigned. |
covar.data |
a data frame containng the covariate data for each site. Names of columns must match that given in |
G |
Vector containing the range of archetype species to fit. |
em.prefit |
obtain initial parameter estimates from EM |
em.steps |
number of EM steps to do if using em.prefit |
em.refit |
refits model so that the global maxima can be found using EM. |
est.var |
calculate the variance covariace matrix for each group |
trace |
the trace of the EM steps |
fits multiple fitMix models across the range of values for G. Most of the arguments are passed directly to fitMix
aic |
vector containing the aic value for each value of G |
bic |
bic |
fm |
a list containing all output from each vaule of G. |
Piers Dunstan and Scott Foster
1 2 3 4 5 6 7 | G <-4
S <- 20
theta <- matrix(c(-0.9,-0.6,0.5,1,-0.9,1,0.9,-0.9),4,2,byrow=TRUE)
dat <- data.frame(y=rep(1,100),x=runif(100,0,2.5),z=rnorm(100,10,2))
dat1 <- artificial.data(y~1+x,dat,theta,S)
dat <- dat[,2:3]
clusters <- clusterSelect(obs~1+x,dat1$pa,dat,G=2:5,em.refit=2)
|
Loading required package: MASS
Loading required package: numDeriv
Fitting group 2
Fitting Group 2
Iteration | LogL
1 | -1345.462
2 | -1160.237
3 | -1156.355
4 | -1156.354
Fitting Group 2
Iteration | LogL
1 | -1310.315
2 | -1167.399
3 | -1157.23
4 | -1156.354
initial value 1156.354488
final value 1156.354488
converged
Fitting group 3
Fitting Group 3
Iteration | LogL
1 | -1338.408
2 | -1141.557
3 | -1105.117
4 | -1095.105
Fitting Group 3
Iteration | LogL
1 | -1372.779
2 | -1136.457
3 | -1096.645
4 | -1094.853
initial value 1094.852987
final value 1094.848692
converged
Fitting group 4
Fitting Group 4
Iteration | LogL
1 | -1328.118
2 | -1144.365
3 | -1102.613
4 | -1092.732
Fitting Group 4
Iteration | LogL
1 | -1332.356
2 | -1119.488
3 | -1094.265
4 | -1092.278
initial value 1092.278402
iter 10 value 1078.124241
iter 20 value 1073.214849
final value 1073.214818
converged
Fitting group 5
Fitting Group 5
Iteration | LogL
1 | -1331.193
2 | -1099.792
3 | -1093.348
4 | -1091.962
Fitting Group 5
Iteration | LogL
1 | -1344.401
2 | -1111.242
3 | -1079.035
4 | -1073.228
initial value 1073.227599
iter 10 value 1073.201088
iter 20 value 1073.190275
iter 30 value 1073.102666
final value 1073.099036
converged
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