sim | R Documentation |
Data on life history traits for four years and five fitness components
data(sim)
Loads nine objects.
The objects beta.true
, mu.true
, phi.true
, and
theta.true
are the simulation truth parameter values in
different parametrizations.
Regression coefficient vector for model
resp ~ varb + 0 + z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2)
.
Unconditional mean value parameter vector for same model.
Unconditional canonical value parameter vector for same model.
Conditional canonical value parameter vector for same model.
The objects fam
, pred
, and vars
specify the aster model graphical and probabilistic structure.
Integer vector giving the families of the variables in the graph.
Integer vector giving the predecessors of the variables in the graph.
Character vector giving the names of the variables in the graph.
The objects ladata
and redata
are the simulated data
in two forms "wide"
and "long"
in the terminology
of the reshape
function.
Data frame with variables y
, z1
,
z2
used for Lande-Arnold type estimation of fitness landscape.
y
is the response, fitness, and z1
and z1
are
predictor variables, phenotypes.
Data frame with variables resp
, z1
,
z2
, varb
, id
, root
used for aster type estimation of fitness landscape.
resp
is the response, containing all components of fitness,
and z1
and z1
are predictor variables, phenotypes.
varb
is a factor whose levels are are elements of vars
indicating which elements of resp
go with which nodes of the
aster model graphical structure. The variables z1
and z2
have been set equal to zero except when grep("nseed", varb)
is
TRUE
. For the rationale see Section 3.2 of TR 669 referenced
below.
Geyer, C. J and Shaw, R. G. (2008) Supporting Data Analysis for a talk to be given at Evolution 2008. Technical Report No. 669. School of Statistics, University of Minnesota. http://hdl.handle.net/11299/56204.
Geyer, C. J and Shaw, R. G. (2009) Hypothesis Tests and Confidence Intervals Involving Fitness Landscapes fit by Aster Models. Technical Report No. 671. School of Statistics, University of Minnesota. http://hdl.handle.net/11299/56219.
data(sim)
## Not run:
### CRAN policy says examples must take < 5 sec. This doesn't.
out6 <- aster(resp ~ varb + 0 + z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2),
pred, fam, varb, id, root, data = redata)
summary(out6)
## End(Not run)
lout <- lm(y ~ z1 + z2 + I(z1^2) + I(z1*z2) + I(z2^2), data = ladata)
summary(lout)
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