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dvmisc (version 1.1.4)

Convenience Functions, Moving Window Statistics, and Graphics

Description

Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).

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Version

Install

install.packages('dvmisc')

Monthly Downloads

654

Version

1.1.4

License

GPL-3

Maintainer

Dane Van Domelen

Last Published

December 16th, 2019

Functions in dvmisc (1.1.4)

Convert Numeric to Factor with Convenient Interface

create_qgroups_svy

Create Quantile Groups (Complex Survey Data)

Create Quantile Groups

Convert Continuous BMI Values into 3-Level Factor

Similar to expand.grid but with Sequences Reversed and Ability to Treat Variables as Sets

Convert Continuous BMI Values into 4-Level Factor

Cut with Decreasing Factor Levels

Create a Clean Summary Table from a glm Object

Convenience Functions, Moving Window Statistics, and Graphics

Plot Points +/- Error Bars

Iterate Function Over All Combinations of User-Specified Inputs, Potentially Multiple Times

Return the First and Last Part of an Object

Check Whether Numeric Value Falls Inside Two Other Numeric Values

Constant-Scale Gamma Model for Y vs. Covariates with Y Potentially Subject to Multiplicative Lognormal Errors

Extract Mean Squared Error (MSE) from Fitted Regression Model

Convert Logit to Probability

Calculate Per-Group Sample Size for Two-Sample Equal Variance T-Test

Mean of Integer Values

interval_groups

Split Continuous Variable into Equal-Width Groups

Calculate Per-Group Sample Size for Two-Sample Unequal Variance T-Test

Graph Means Across a Grouping Variable

Graph Log-Odds of Binary Variable Across A Grouping Variable

Add Elements of Second List to First List, Replacing Elements with Same Name

mle_lnorm_lnorm

Maximum Likelihood Estimation for X[1], ..., X[n] ~ Lognormal(mu1, sigsq1) Lognormal(mu2, sigsq2)

Maximum of Numeric Values

Moving Averages

Linear Regression of log(Y) vs. Covariates with Y Potentially Subject to Multiplicative Lognormal Errors

mle_gamma_lnorm

Maximum Likelihood Estimation for X[1], ..., X[n] ~ Gamma(alpha, beta) Lognormal(mu, sigsq)

Pooled Sample Variance

Convert Probability to Odds

Minimum of Numeric Values

Return Index of (First) Maximum of a Vector

Histogram with Added Options

Convert Odds to Probability

Maximum Likelihood Estimation for X[1], ..., X[n] ~ Lognormal(mu, sigsq)

Plot Log-Likelihood vs. Values of One Parameter

quant_groups_svy

Split Continuous Variable into Quantile Groups (Survey Version)

power_2t_unequal

Calculate Power for Two-Sample Unequal Variance T-Test

Trim Tail Values off of a Vector

Return Index of (First) Minimum of a Vector

Return (Row, Column) Index of (First) Minimum of a Numeric Matrix

Range of a Vector (Not Min/Max!)

Moving Covariance as Short Vector Slides Across Long Vector

Split Continuous Variable into Quantile Groups

Maximum Likelihood Estimation for X[1], ..., X[n] ~ Gamma(alpha, beta)

Calculate Power for Two-Sample Equal Variance T-Test

Convert Probability to Logit

Summarize Simulation Results

Sum of Integer Values

Moving Correlations as Short Vector Slides Across Long Vector

Return Index of (First) Maximum of a Numeric Vector

Return Index of (First) Minimum of a Numeric Vector

Return (Row, Column) Index of (First) Minimum of an Integer Matrix

Return (Row, Column) Index of (First) Maximum of a Numeric Matrix

Return Index of (First) Maximum of an Integer Vector

Return (Row, Column) Index of (First) Maximum of an Integer Matrix

Return Index of (First) Minimum of an Integer Vector