summary.kspm {KSPM} | R Documentation |
summary method for an object of class "kspm"
## S3 method for class 'kspm'
summary(object, kernel.test = "all",
global.test = FALSE, ...)
object |
an object of class "kspm", usually, a result of a call to |
kernel.test |
vector of characters indicating for which kernel a test should be performed. Default is |
global.test |
logical, if |
... |
further arguments passed to or from other methods. |
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 |
sigma |
the square root of the estimated variance of the random error |
edf |
effective degrees of freedom |
r.squared |
|
adj.r.squared |
the above |
score.test |
a |
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)