g2l.proc | R Documentation |
This function performs customized fdr analyses tailored to each individual cases.
g2l.proc(X, z, X.target = NULL, z.target = NULL, m = c(4, 6), alpha = 0.1, nbag = NULL, nsample = length(z), lp.reg.method = "lm", null.scale = "QQ", approx.method = "direct", ngrid = 2000, centering = TRUE, coef.smooth = "BIC", fdr.method = "locfdr", plot = TRUE, rel.null = "custom", locfdr.df = 10, fdr.th.fixed = NULL, parallel = FALSE, ...)
X |
A n-by-d matrix of covariate values |
z |
A length n vector containing observations of z values. |
X.target |
A k-by-d matrix providing k sets of covariates for target cases to investigate. Set to NULL to investigate all cases and provide global inference results. |
z.target |
A vector of length k, providing the target z values to investigate |
m |
An ordered pair. First number indicates how many LP-nonparametric basis to construct for each X, second number indicates how many to construct for z. Default: |
alpha |
Confidence level for determining signals. |
nbag |
Number of bags of parametric bootstrapped samples to use for each target case, each time a new set of relevance samples will be generated for analysis, and the resulting fdr curves are aggregated together by taking the mean values. Set to |
nsample |
Number of relevance samples generated for each case. The default is the size of the input z-statistic. |
lp.reg.method |
Method for estimating the relevance function and its conditional LP-Fourier coefficients. We currently support three options: lm (inbuilt with subset selection), glmnet, and knn. |
null.scale |
Method of estimating null standard deviation from the laser samples. Available options: "IQR", "QQ" and "locfdr" |
approx.method |
Method used to approximate customized fdr curve, default is "direct".When set to "indirect", the customized fdr is computed by modifying pooled fdr using relevant density function. |
ngrid |
Number of gridpoints to use for computing customized fdr curve. |
centering |
Whether to perform regression-adjustment to center the data, default is TRUE. |
coef.smooth |
Specifies the method to use for LP coefficient smoothing (AIC or BIC). Uses BIC by default. |
fdr.method |
Method for controlling false discoveries (either "locfdr" or "BH"), default choice is "locfdr". |
plot |
Whether to include plots in the results, default is |
rel.null |
How the relevant null changes with x: "custom" denotes we allow it to vary with x, and "th" denotes fixed. |
locfdr.df |
Degrees of freedom to use for |
fdr.th.fixed |
Use fixed fdr threshold for finding signals. Default set to |
parallel |
Use parallel computing for obtaining the relevance samples, mainly used for very huge |
... |
Extra parameters to pass to other functions. Currently only supports the arguments for |
A list containing the following items:
macro |
Available when |
$result |
A list of global inference results: |
$X |
Matrix of covariates, same as input |
$z |
Vector of observations, same as input |
$probnull |
A vector of length n, indicating how likely the observed z belongs to local null. |
$signal |
A binary vector of length n, discoveries are indicated by 1. |
plots |
A list of plots for global inference: |
$signal_x |
A plot of signals discovered, marked in red |
$dps_xz |
A scatterplot of z on x, colored based on the discovery propensity scores, only available when |
$dps_x |
A scatterplot of discovery propensity scores on x, only available when |
micro |
Available when |
$result |
Customized estimates for null probabilities for target X and z |
$result$signal |
A binary vector of length k, discoveries in the target cases are indicated by 1 |
$global |
Pooled global estimates for null probabilities for target X and z |
$plots |
Customized fdr plots for the target cases. |
|
Same as input |
Subhadeep Mukhopadhyay, Kaijun Wang
Maintainer: Kaijun Wang <kaijunwang.19@gmail.com>
Mukhopadhyay, S., and Wang, K (2021) "On The Problem of Relevance in Statistical Inference". <arXiv:2004.09588>
data(funnel) X<-funnel$x z<-funnel$z ##macro-inference using locfdr and LASER: g2l_macro<-g2l.proc(X,z) g2l_macro$macro$plots #Microinference for the DTI data: case A with x=(18,55) and z=3.95 data(data.dti) X<- cbind(data.dti$coordx,data.dti$coordy) z<-data.dti$z g2l_x<-g2l.proc(X,z,X.target=c(18,55),z.target=3.95,nsample =3000) g2l_x$micro$plots$fdr.1+ggplot2::coord_cartesian(xlim=c(0,4)) g2l_x$micro$result[4]
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