fixbounds.predict.smooth.spline {LPE} R Documentation

## Makes the predicted variance non negative

### Description

Makes the predicted variance non negative

### Usage

```fixbounds.predict.smooth.spline(object,x, deriv=0)
```

### Arguments

 `object` variance from baseOlig.error function `x` vector for which variance needs to be predicted `deriv` derivative of the vetor required, default =0

### Value

Returns the predicted variance for the given vector based on the baseline error distribution. Maximum and minimum predicted values for the vetor are same as those of baseline error distribution

### References

J.K. Lee and M.O.Connell(2003). An S-Plus library for the analysis of differential expression. In The Analysis of Gene Expression Data: Methods and Software. Edited by G. Parmigiani, ES Garrett, RA Irizarry ad SL Zegar. Springer, NewYork.

Jain et. al. (2003) Local pooled error test for identifying differentially expressed genes with a small number of replicated microarrays, Bioinformatics, 1945-1951.

### Examples

```# Loading the library and the data
library(LPE)
data(Ley)

dim(Ley)
# Gives 12488*7
# First column is ID.

# Subsetting the data
subset.Ley <- Ley[1:1000,]

subset.Ley[,2:7] <- preprocess(subset.Ley[,2:7],data.type="MAS5")
# preprocess the data

# Finding the baseline distribution of condition 1 and 2.
var.1 <- baseOlig.error(subset.Ley[,2:4], q=0.01)
median.x <- apply(subset.Ley[,2:4], 1, median)

sf.x <- smooth.spline(var.1[, 1], var.1[, 2], df = 10)

var.test <- fixbounds.predict.smooth.spline(sf.x, median.x)\$y

```

[Package LPE version 1.1.5 Index]