ztobins {repfdr} | R Documentation |
For each study, the function discretizes the z-scores into bins and estimates the probabilities in each bin for the null and non-null states.
The function can plot diagnostic plots (disabled by default) for model fit. These should be monitored for misfit of model to data, before using function output in repfdr
. See description of diagnostic plots below.
ztobins(zmat, n.association.status = 3, n.bins = 120, type = 0, df = 7, central.prop = 0.5, pi0=NULL,plot.diagnostics = FALSE, trim.z=FALSE,trim.z.upper = 8,trim.z.lower = -8, force.bin.number = FALSE, pi.using.plugin = FALSE, pi.plugin.lambda = 0.05)
zmat |
Matrix of z-scores of the features (in rows) in each study (columns). |
n.association.status |
either 2 for no-association\association or 3 for no-associtation\negative-association\positive-association. |
n.bins |
Number of bins in the discretization of the z-score axis (the number of bins is |
type |
Type of fitting used for f; 0 is a natural spline, 1 is a polynomial, in either case with degrees of freedom |
df |
Degrees of freedom for fitting the estimated density f(z). |
central.prop |
Central proportion of the z-scores used like the area of zero-assumption to estimate pi0. |
pi0 |
Sets argument for estimation of proportion of null hypotheses. Default value is NULL (automatic estimation of pi0) for every study. Second option is to supply vector of values between 0 and 1 (with length of the number of studies/ columns of |
plot.diagnostics |
If set to A second plot is the Normal Q-Q plot of Zscores, converted using Misfit in these two plots should be investigated by the user, before using output in Default value is |
trim.z |
If set to |
trim.z.upper |
Upper bound for trimming Z scores. Default value is 8 |
trim.z.lower |
Lower bound for trimming Z scores. Default value is -8 |
force.bin.number |
Set to |
pi.using.plugin |
Logical flag indicating whether estimation of the number of null hypotheses should be done using the plugin estimator.(Default is |
pi.plugin.lambda |
Parameter used for estimation of proportion of null hypotheses, for one sided tests. Default value is 0.05. This should be set to the type 1 error used for hypothesis testing. |
This utility function outputs the first two arguments to be input in the main function repfdr
.
A list with:
pdf.binned.z |
A 3-dimensional array which contains for each study (first dimension), the probabilities of a z-score to fall in the bin (second dimension), under each hypothesis status (third dimension). The third dimension can be of size 2 or 3, depending on the number of association states: if the association can be either null or only in one direction, the dimension is 2; if the association can be either null, or positive, or negative, the dimension is 3. |
binned.z.mat |
A matrix of the bin numbers for each the z-scores (rows) in each study (columns). |
breaks.matrix |
A matrix with |
df |
Number of degrees of freedom, used for spline fitting of density. |
proportions |
Matrix with |
PlotWarnings |
Vector of size |
# Simulated example using both the central proportion estimator # and the plug in estimator for the proportion of null hypotheses: set.seed(1) p = 10000 p1 = 300 z1 = (rnorm(p)) z2 = (rnorm(p)) temp = rnorm(p1, 3.5,0.5) z1[1:p1] = temp + rnorm(p1,0,0.2) z2[1:p1] = temp + rnorm(p1,0,0.2) z1.abs = abs(z1) z2.abs = abs(z2) plot(z1,z2) hist(z1) hist(z2) zmat.example = cbind(z1,z2) ztobins.res = ztobins(zmat.example, plot.diagnostics = TRUE) ztobins.res$proportions ztobins.res.plugin.estimator = ztobins(zmat.example, pi.using.plugin = TRUE, plot.diagnostics = TRUE) ztobins.res.plugin.estimator$proportions ## Not run: # three association states case (H in {-1,0,1}): download.file('http://www.math.tau.ac.il/~ruheller/repfdr_RData/zmat.RData',destfile = "zmat.RData") load(file = "zmat.RData") input.to.repfdr3 <- ztobins(zmat, 3, df = 15) pbz <- input.to.repfdr3$pdf.binned.z bz <- input.to.repfdr3$binned.z.mat # two association states case (H in {0,1}): data(zmat_sim) input.to.repfdr <- ztobins(zmat_sim, 2, n.bins = 100 ,plot.diagnostics = T) pbz_sim <- input.to.repfdr$pdf.binned.z bz_sim <- input.to.repfdr$binned.z.mat ## End(Not run)