flexible.summary: Summarizing Kernel Semi parametric Model Fits with flexible...
In KSPM: Kernel Semi-Parametric Models

Description Usage Arguments Details Value Author(s) References See Also Examples

for flexibility in summary method for an object of class "summary.kspm"

1	flexible.summary(object, method = "davies", acc = 1e-06, lim = 10000)

`object`	an object of class "summary.kspm", usually, a result of a call to `summary.kspm`.
`method`	method to approximate the chi square distribution in p-value computation, default is 'davies', another possibility is 'imhof'.
`acc, lim`	see davies and imhof functions in CompQuadForm package.

the description of the model, including coefficients for the linear part and if asked for, test(s) of variance components associated with kernel part.

Computes and returns the followimg summary statistics of the fitted kernel semi parametric model given in object

`residuals`	residuals
`coefficients`	a p x 4 matrix with columns for the estimated coefficient, its standard error, t statistic and corresponding (two sided) p value for the linear part of the model.
`sigma`	the square root of the estimated variance of the random error sigma^2 = RSS / edf where RSS is the residual sum of squares and edf is the effective degree of freedom.
`edf`	effective degrees of freedom
`r.squared`	R^2, the fraction of variance explained by the model, 1 - sum(e_i^2) / sum((y_i - y^star)^2) where y^star is the mean of y_i if there is an intercept and zero otherwise.
`adj.r.squared`	the above R^2 statistics, adjusted, penalizing for higher p.
`score.test`	a q x 3 matrix with colums for the estimated lambda, tau and p value for the q kernels for which a test should be performed.
`global.p.value`	p value from the score test for the global model.
`sample.size`	sample size (all: global sample size, inc: complete data sample size).

Catherine Schramm, Aurelie Labbe, Celia Greenwood

Liu, D., Lin, X., and Ghosh, D. (2007). Semiparametric regression of multidimensional genetic pathway data: least squares kernel machines and linear mixed models. Biometrics, 63(4), 1079:1088.

Schweiger, Regev, et al. "RL SKAT: an exact and efficient score test for heritability and set tests." Genetics (2017): genetics 300395.

Li, Shaoyu, and Yuehua Cui. "Gene centric gene gene interaction: A model based kernel machine method." The Annals of Applied Statistics 6.3 (2012): 1134:1161.

kspm for fitting model, predict.kspm for predictions, plot.kspm for diagnostics

x <- 1:15
z1 <- runif(15, 1, 6)
z2 <- rnorm(15, 1, 2)
y <- 3*x + (z1 + z2)^2 + rnorm(15, 0, 2)
fit <- kspm(y, linear = ~ x, kernel = ~ Kernel(~ z1 + z2,
kernel.function = "polynomial", d= 2, rho = 1, gamma = 0))
summary.fit <- summary(fit)
flexible.summary(summary.fit, acc = 0.000001, lim = 1000)