cn: Control Nets
In cpr: Control Polygon Reduction

Description Usage Arguments Details Value Author(s)

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, ...,
  keep_fit = FALSE, check_rank = TRUE)

## S3 method for class 'cpr_cn'
print(x, ...)

## S3 method for class 'cpr_cn'
summary(object, ...)

`x`	a `cpr_bs` object
`...`	arguments passed to the regression method
`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 `data.frame`
`method`	the regression method such as `lm`, `glm`, `lmer`, `geeglm`, ...
`keep_fit`	(logical, defaults to `FALSE`). If `TRUE` the regression model fit is retained and returned in the the `fit` element. If `FALSE` the regression model is not saved and the `fit` element will be `NA`.
`check_rank`	(logical, defaults to `TRUE`) if TRUE check that the design matrix is full rank.
`object`	a `cpr_cn` object

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.

cn: the control net, data.frame with each row defining a vertex of the control net
bspline_list: A list of the marginal B-splines
call: the call
keep_fit: logical, indicates if the regression models was retained
fit: if isTRUE(keep_fit) then the regression model is here, else NA.
coefficients: regression coefficients, only the fixed effects if a mixed effects model was used.
vcov: The variance-covariance matrix for the coefficients
loglik: The log-likelihood for the regression model
rmse: The root mean squared error for the regression models