cn | R Documentation |
Generate the control net for a uni-variable B-spline
cn(x, ...)
## S3 method for class 'cpr_bt'
cn(x, theta, ...)
## S3 method for class 'formula'
cn(
formula,
data,
method = stats::lm,
method.args = list(),
keep_fit = TRUE,
check_rank = TRUE,
...
)
x |
a |
... |
pass through |
theta |
a vector of (regression) coefficients, the ordinates of the control net. |
formula |
a formula that is appropriate for regression method being used. |
data |
a required |
method |
the regression method such as |
method.args |
a list of additional arguments to pass to the regression method. |
keep_fit |
(logical, defaults to |
check_rank |
(logical, defaults to |
cn
generates the control net for the given B-spline function. There
are several methods for building a control net.
a cpr_cn
object. This is a list with the following elements.
Some of the elements are omitted when the using the cn.cpr_bt
method.
the control net, data.frame
with each row defining a vertex
of the control net
A list of the marginal B-splines
the call
logical, indicates if the regression models was retained
if isTRUE(keep_fit)
then the regression model is here,
else NA
.
regression coefficients, only the fixed effects if a mixed effects model was used.
The variance-covariance matrix for the coefficients
The log-likelihood for the regression model
the residual standard error for the regression models
summary.cpr_cn
, cnr
,
plot.cpr_cn
for plotting control nets
acn <- cn(log10(pdg) ~
btensor( x = list(day, age)
, df = list(30, 4)
, bknots = list(c(-1, 1), c(44, 53))
)
, data = spdg)
str(acn, max.level = 1)
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