mkAR {STAR} | R Documentation |
The variables added to the data frame corresponding to the first
argument of the function are the former inter spike intervals. These variables
are moreover transformed with mkM2U
so that they have an approximately uniform
distribution on their definition domain.
mkAR(df, low, high, max.order, selfName = "lN.1",...)
df |
a data frame. This data frame should contain a variable
time like data frames returned by
mkGLMdf .
|
low |
a numeric, the smallest value of variable time from
which the transformation is looked for. If missing defaults to the
smallest time. |
high |
a numeric, the largest value of variable time up to
which the transformation is looked for. If missing defaults to the
largest time. |
max.order |
a postive integer, the maximal order of the AR model. How many previous inter spike intervals should be used in order to predict the duration of the next interval? |
selfName |
a character string or an integer specifying the
variable of df containing the elapsed time since the last spike
of the considered neuron. |
... |
additional arguments passed to mkM2U |
.
When max.order
> 1 the previous inter spike intervals are all
transformed using the "map to uniform" function estimated from the
inter spike intervals at lag 1.
A data frame is returned. In addition to the variables of df the returned data frame contains
a variable est
with the transformed elapsed time since the last
spike of the neuron and i1t
, i2t
,...,i max.order t
, the
transformed previous inter spike intervals.
The returned data frame has also four attributes:
fmla |
a formula suitable for a first argument of, say, gssanova . |
m2uL |
the function returned by mkM2U transforming the elasped time since the last spike of the neuron. |
m2uI |
the function returned by mkM2U transforming the first former inter spike interval. |
call |
the matched call. |
Christophe Pouzat christophe.pouzat@gmail.com
## Not run: require(STAR) data(e060824spont) DFA <- subset(mkGLMdf(e060824spont,0.004,0,59),neuron==1) DFA <- mkAR(DFA, 0, 29, 5, maxiter=200) head(DFA) tail(DFA) ar.fit <- gssanova(attr(DFA,"fmla"), data=DFA,family="binomial",seed=20061001) plot(ar.fit %qp% "est") plot(ar.fit %qp% "i1t") plot(ar.fit %qp% "i2t") plot(ar.fit %qp% "i3t") plot(ar.fit %qp% "i4t") plot(ar.fit %qp% "i5t") ## End(Not run)