| Title: | Miscellaneous Functions for Bioinformatics and Bayesian Statistics |
| Version: | 1.0.0 |
| Description: | A hodgepodge of hopefully helpful functions. Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances. |
| Depends: | R (≥ 3.2.0) |
| Suggests: | ggplot2, RISmed, testthat |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-05-24 13:50:20 UTC; amckenz |
| Author: | Andrew McKenzie [aut, cre] |
| Maintainer: | Andrew McKenzie <amckenz@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-05-24 16:32:42 |
bayesbio: Miscellaneous functions useful in bioinformatics and Bayesian statistics
Description
A hodgepodge of hopefully helpful functions. Two of these perform shrinkage estimation: one using a simple weighted method where the user can specify the degree of shrinkage required, and one using James-Stein shrinkage estimation for the case of unequal variances.
Likelihood function of the James-Stein shrinkage factor.
Description
To be used in MLE computation of the James-Stein shrinkage factor.
Usage
a_hat_mle(stat, vars, a_hat)
Arguments
stat |
Input statistics to be shrinkage estimated. |
vars |
Corresponding variances of equal length. |
a_hat |
Shrinkage intensity to be estimated. |
Value
The likelihood of the function given the parameters.
References
http://projecteuclid.org/euclid.ss/1331729986
Identify all duplicates values in a vector.
Description
By default the base R function duplicated only identifies the duplicated values after the first in a vector as TRUE. This function identifies all of the duplicates as true.
Usage
allDups(x)
Arguments
x |
The input vector. |
Value
A logical vector.
cbind while converting missing entries to NA.
Description
cbind usually malfunctions on vector of unequal lengths; this function allows vectors of unequal length to be combined, while filling the missing entries with NAs.
Usage
cbindFill(...)
Arguments
... |
A set of vectors separated by commas. |
Value
A matrix that combines the inputted vectors.
References
http://r.789695.n4.nabble.com/How-to-join-matrices-of-different-row-length-from-a-list-td3177212.html; http://stackoverflow.com/a/7962286/560791
Creates random, unique character strings.
Description
Makes them unique by randomly choosing the character strings; and, in case it is necessary, adding numbers to the end using make.unique.
Usage
createStrings(number, length, upper = FALSE)
Arguments
number |
Specifies the number of character strings that should be created. |
length |
Specifies the length of each character string in letters. |
upper |
Binary parameter specifying whether the character strings should be uppercase. Default = FALSE, so the character strings are all lowercase. |
References
http://stackoverflow.com/a/1439541/560791
Create a color-labeled horizontal bar plot in ggplot2.
Description
This function takes a data frame and creates a horizontal (by default) bar plot from it while ordering the values.
Usage
ggHorizBar(data_df, dataCol, namesCol, labelsCol, decreasing = TRUE)
Arguments
data_df |
Data frame with columns to specify the data values, the row names, and the fill colors of each of the bars. |
dataCol |
The column name that specifies the values to be plotted. |
namesCol |
The column name that specifies the corresponding names for each of the bar plots to be plotted. |
labelsCol |
The column name that specifies the groups of the labels. |
decreasing |
Logical specifying whether the values in dataCol should be in decreasing order. |
Value
A ggplot2 object, which can be plotted via the plot() function or saved via the ggsave() function.
Jaccard index of two character vectors.
Description
This function compares the elements in two character vectors to find the Jaccard index, i.e. the number of intersections divided by the total number of elements in both sets.
Usage
jaccardSets(set1, set2)
Arguments
set1 |
Character vector. |
set2 |
Character vector. |
Value
A number (one-element numeric vector) specifying the Jaccard index from comparing the two sets.
References
https://en.wikipedia.org/wiki/Jaccard_index
Multiple pattern gsub.
Description
An extension to gsub that handles vectors of patterns and replacements, avoiding recursion problems associated with overlap at the extense of computation time.
Usage
mgsub(pattern, replacement, x, ...)
Arguments
pattern |
Character vector of patterns to match. |
replacement |
Character vector of replacements for each pattern. |
x |
Character vector in which the gsub should be performed. |
... |
Additional arguments to grep. |
References
http://stackoverflow.com/a/15254254/560791
Merge data frames based on the nearest datetime differences.
Description
Takes two data frames each with time/date columns in date-time or date format (i.e., able to be compared using the function difftime), finds the rows of df2 that minimize the absolute value of the datetime for each of the rows in df1, and merges the corresponding rows of df2 into df1 for downstream processing.
Usage
nearestTime(df1, df2, timeCol1, timeCol2)
Arguments
df1 |
Data frame containing the dates for which the differences between the other data frame's date column should be minimized for each row. |
df2 |
Data frame containing the dates which should be compared to, as well as other values that should be merged to df1 per minimized date time. |
timeCol1 |
Character vector specifying the date/time column in df1. |
timeCol2 |
Character vector specifying the date/time column in df2. |
Value
A merged data frame that minimizes datetime differences.
Merge data frames based on the nearest datetime differences and an ID column. Also removes duplicate column names from the result.
Description
Takes two data frames each with time/date columns in date-time or date format (i.e., able to be compared using the function difftime), finds the rows of df2 that minimize the absolute value of the datetime for each of the rows in df1, and merges the corresponding rows of df2 into df1 for downstream processing.
Usage
nearestTimeandID(df1, df2, timeCol1, timeCol2, IDcol)
Arguments
df1 |
Data frame containing the dates for which the differences between the other data frame's date column should be minimized for each row. |
df2 |
Data frame containing the dates which should be compared to, as well as other values that should be merged to df1 per minimized date time. |
timeCol1 |
Character vector specifying the date/time column in df1. |
timeCol2 |
Character vector specifying the date/time column in df2. |
IDcol |
Must be unique by row in df1. Multiple versions are allowed (and expected at least in some rows, as that is the point of the function) in df2. |
Value
A merged data frame that minimizes datetime differences.
Adjust p-values where n is less than p.
Description
This function recapitulates p.adjust but allows the number of hypothesis tests n to be less than the number of p-values p. Statistical properties of the p-value adjustments may not hold.
Usage
p.adjust.nlp(p, method = p.adjust.methods, n = length(p))
Arguments
p |
Numeric vector of p-values. |
method |
Correction method. |
n |
Number of comparisons to be made. |
References
http://stackoverflow.com/a/30110186/560791
Perform PubMed queries on 2x2 combinations of term vectors.
Description
Perform PubMed queries on the intersections of two character vectors. This function is a wrapper to RISmed::EUtilsSummary with type = 'esearch', db = 'pubmed'.
Usage
pubmedQuery(rowTerms, colTerms, sleepTime = 0.01)
Arguments
rowTerms |
Character vector of terms that should make up the rows of the resulting mention count data frame. |
colTerms |
Character vector of terms for the columns. |
sleepTime |
How much time (in seconds) to sleep between successive PubMed queries. If you set this too low, PubMed may shut down your connection to prevent overloading their servers. |
Value
A data frame of the number of mentions for each combination of terms.
Add values to the super- and sub-diagonals of a matrix.
Description
Takes a matrix and adds values to the values that are one above the diagonal (ie the superdiagonal) and the values that are one below the diagonal (ie the subdiagonal).
Usage
subsupDiag(matrix, x)
Arguments
matrix |
Matrix whose super- and sub-diagonals values should be replaced. |
x |
Numeric vector used to replace values in the matrix. If the inputted vector is not of the same length as both the super- and sub-diagonals of the matrix, then short vector recycling will occur (e.g., x can be one value to replace all of the super- and sub-diagonals of the matrix with that one value). |
Value
The original matrix with the values added.
References
http://stackoverflow.com/a/9885186/560791
Perform James-Stein shrinkage estimation using unequal variances
Description
Traditional JS shrinkage estimation assumes equal variances for each of the data points, while this algorithm extends JS shrinkage estimation to entries with different variances.
Usage
unequalVarShrink(stat, vars, verbose = TRUE)
Arguments
stat |
Input statistics to be shrinkage estimated. |
vars |
Corresponding variances of equal length. |
verbose |
Whether information about the algorithm should be reported. |
Value
A data frame containing the shrinkage estimated statistics.
References
http://projecteuclid.org/euclid.ss/1331729986
Weighted shrinkage estimation.
Description
Shrink values towards the mean (in the sample or the overall cohort) to an inverse degree to the confidence you assign to that observation.
Usage
weightedShrink(x, n, m = NULL, meanVal = NULL)
Arguments
x |
Numeric vector of values to be shrunken towards the mean. |
n |
Numeric vector with corresponding entries to x, specifying the number of observations used to calculate x, or some other confidence weight to associate with x. |
m |
Number specifying weight of the shrinkage estimation, relative to the number of observations in the input vector n. Defaults to the minimum of n, but this is an arbitrary value and should be explored to find an optimal value for your use case. |
meanVal |
Number specifying the overall mean towards which the values should be shrunken. Defaults to NULL, in which case it is calculated as the (non-weighted) arithmetic mean of the values in the inputted vector x. |
Value
A numeric vector with shrunken data values.
References
http://math.stackexchange.com/a/41513