# Summarizes Fit Information for bigsplines Model This function summarizes basic model fit information for a fit `bigsplines` model. ```r ## S3 method for class 'bigspline' summary(object, fitresid = TRUE, chunksize = 10000, ...) ## S3 method for class 'bigssa' summary(object, fitresid = TRUE, chunksize = 10000, diagnostics = FALSE,...) ## S3 method for class 'bigssg' summary(object, fitresid = TRUE, chunksize = 10000, diagnostics = FALSE,...) ## S3 method for class 'bigssp' summary(object, fitresid = TRUE, chunksize = 10000, diagnostics = FALSE,...) ## S3 method for class 'bigtps' summary(object, fitresid = TRUE, chunksize = 10000, ...) ``` ## Arguments - `object`: Object of class "bigspline" (output from `bigspline`), class "bigssa" (output from `bigssa`), class "bigssg" (output from `bigssg`), class "bigssp" (output from `bigssp`), or class "bigtps" (output from `bigtps`). - `fitresid`: Logical indicating whether the fitted values and residuals should be calculated for all data points in input `object`. - `chunksize`: If `fitresid=TRUE`, fitted values are calculated in chunks of size `chunksize`. - `diagnostics`: If `diagnostics=TRUE`, cosine diagnostics are calculated for each term in the model. These give an approximate break-down of the model R-squared into that accounted for by each term in the model. - ``: Ignored. ## Returns - **call**: Called model in input `formula`. - **type**: Type of smoothing spline that was used for each predictor. - **fitted.values**: Vector of fitted values (if `fitresid=TRUE`). - **linear.predictors**: Vector of linear predictors (only for class "bigssg" with `fitresid=TRUE`). - **residuals**: Vector of residuals (if `fitresid=TRUE`). For class "bigssg" these are deviance residuals. - **sigma**: Estimated error standard deviation. - **deviance**: Model deviance (only for class "bigssg"). - **dispersion**: Estimated dispersion parameter (only for class "bigssg"). - **n**: Total sample size. - **df**: Effective degrees of freedom of the model. - **info**: Model fit information: vector containing the GCV, multiple R-squared, AIC, and BIC of fit model. - **converged**: Convergence status: `converged=TRUE` if the iterative theta update converged, `converged=FALSE` if the iterative theta update failed to converge, and `converged=NA` if option `skip.iter=TRUE` was used. - **iter**: Number of iterative updates (`iter=NA` if option `skip.iter=TRUE` was used). - **rparm**: Rounding parameters used for model fitting. - **lambda**: Global smoothing parameter used for model fitting. - **gammas**: Vector of additional smoothing parameters (only for class "bigssa"). - **thetas**: Vector of additional smoothing parameters (only for class "bigssp"). - **pi**: Vector of cosine diagnostics. - **family**: Distribution family (only for class "bigssg"). - **gcvtype**: Smoothing parameter selection criterion (only for class "bigssg"). ## Details See `bigspline`, `bigssa`, `bigssg`, `bigssp`, and `bigtps` for more details. ## Author(s) Nathaniel E. Helwig ## Note For "bigspline" and "bigtps" objects, the outputs `call`, `converged`, and `iter` are NA. ## Examples ```r ########## EXAMPLE 1 ########## # define relatively smooth function set.seed(773) myfun <- function(x){ sin(2*pi*x) } x <- runif(10^4) y <- myfun(x) + rnorm(10^4) # cubic spline cubmod <- bigspline(x,y) summary(cubmod) ########## EXAMPLE 2 ########## # function with two continuous predictors set.seed(773) myfun <- function(x1v,x2v){ sin(2*pi*x1v) + log(x2v+.1) + cos(pi*(x1v-x2v)) } x1v <- runif(10^4) x2v <- runif(10^4) y <- myfun(x1v,x2v) + rnorm(10^4) # cubic splines with 100 randomly selected knots (efficient parameterization) cubmod <- bigssa(y~x1v*x2v,type=list(x1v="cub",x2v="cub"),nknots=100) summary(cubmod) ########## EXAMPLE 3 ########## # function with two continuous predictors set.seed(1) myfun <- function(x1v,x2v){ sin(2*pi*x1v) + log(x2v+.1) + cos(pi*(x1v-x2v)) } ndpts <- 1000 x1v <- runif(ndpts) x2v <- runif(ndpts) # poisson response set.seed(773) lp <- myfun(x1v,x2v) mu <- exp(lp) y <- rpois(n=ndpts,lambda=mu) # generalized smoothing spline anova genmod <- bigssg(y~x1v*x2v,family="poisson",type=list(x1v="cub",x2v="cub"),nknots=50) summary(genmod) ########## EXAMPLE 4 ########## # function with two continuous predictors set.seed(773) myfun <- function(x1v,x2v){ sin(2*pi*x1v) + log(x2v+.1) + cos(pi*(x1v-x2v)) } x1v <- runif(10^4) x2v <- runif(10^4) y <- myfun(x1v,x2v) + rnorm(10^4) # cubic splines with 100 randomly selected knots (classic parameterization) cubmod <- bigssp(y~x1v*x2v,type=list(x1v="cub",x2v="cub"),nknots=100) summary(cubmod) ########## EXAMPLE 5 ########## # define relatively smooth function set.seed(773) myfun <- function(x){ sin(2*pi*x) } x <- runif(10^4) y <- myfun(x) + rnorm(10^4) # thin-plate with default (30 knots) tpsmod <- bigtps(x,y) summary(tpsmod) ```

summary function