Convenience Functions, Moving Window Statistics, and Graphics

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Documentation for package ‘dvmisc’ version 1.1.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

dvmisc-package	Convenience Functions, Moving Window Statistics, and Graphics
bmi3	Convert Continuous BMI Values into 3-Level Factor
bmi4	Convert Continuous BMI Values into 4-Level Factor
cleancut	Convert Numeric to Factor with Convenient Interface
clean_glm	Create a Clean Summary Table from a glm Object
create_qgroups	Create Quantile Groups
create_qgroups_svy	Create Quantile Groups (Complex Survey Data)
cut_decreasing	Cut with Decreasing Factor Levels
dots_bars	Plot Points +/- Error Bars
dvmisc	Convenience Functions, Moving Window Statistics, and Graphics
expand_grid	Similar to expand.grid but with Sequences Reversed and Ability to Treat Variables as Sets
gammareg	Constant-Scale Gamma Model for Y vs. Covariates with Y Potentially Subject to Multiplicative Lognormal Errors
get_mse	Extract Mean Squared Error (MSE) from Fitted Regression Model
headtail	Return the First and Last Part of an Object
histo	Histogram with Added Options
inside	Check Whether Numeric Value Falls Inside Two Other Numeric Values
interval_groups	Split Continuous Variable into Equal-Width Groups
iterate	Iterate Function Over All Combinations of User-Specified Inputs, Potentially Multiple Times
list_override	Add Elements of Second List to First List, Replacing Elements with Same Name
logit_prob	Convert Logit to Probability
lognormalreg	Linear Regression of log(Y) vs. Covariates with Y Potentially Subject to Multiplicative Lognormal Errors
logodds_graph	Graph Log-Odds of Binary Variable Across A Grouping Variable
max_n	Maximum of Numeric Values
means_graph	Graph Means Across a Grouping Variable
mean_i	Mean of Integer Values
min_n	Minimum of Numeric Values
mle_gamma	Maximum Likelihood Estimation for X[1], ..., X[n] ~ Gamma(alpha, beta)
mle_gamma_lnorm	Maximum Likelihood Estimation for X[1], ..., X[n] ~ Gamma(alpha, beta) Lognormal(mu, sigsq)
mle_lnorm	Maximum Likelihood Estimation for X[1], ..., X[n] ~ Lognormal(mu, sigsq)
mle_lnorm_lnorm	Maximum Likelihood Estimation for X[1], ..., X[n] ~ Lognormal(mu1, sigsq1) Lognormal(mu2, sigsq2)
moving_mean	Moving Averages
n_2t_equal	Calculate Per-Group Sample Size for Two-Sample Equal Variance T-Test
n_2t_unequal	Calculate Per-Group Sample Size for Two-Sample Unequal Variance T-Test
odds_prob	Convert Odds to Probability
plot_ll	Plot Log-Likelihood vs. Values of One Parameter
pooled_var	Pooled Sample Variance
power_2t_equal	Calculate Power for Two-Sample Equal Variance T-Test
power_2t_unequal	Calculate Power for Two-Sample Unequal Variance T-Test
prob_logit	Convert Probability to Logit
prob_odds	Convert Probability to Odds
quant_groups	Split Continuous Variable into Quantile Groups
quant_groups_svy	Split Continuous Variable into Quantile Groups (Survey Version)
reverse_cut	Reverse Cut
sliding_cor	Moving Correlations as Short Vector Slides Across Long Vector
sliding_cov	Moving Covariance as Short Vector Slides Across Long Vector
sumsim	Summarize Simulation Results
sum_i	Sum of Integer Values
trim	Trim Tail Values off of a Vector
truerange	Range of a Vector (Not Min/Max!)
which.max2	Return Index of (First) Maximum of a Vector
which.min2	Return Index of (First) Minimum of a Vector
which_max_im	Return (Row, Column) Index of (First) Maximum of an Integer Matrix
which_max_iv	Return Index of (First) Maximum of an Integer Vector
which_max_nm	Return (Row, Column) Index of (First) Maximum of a Numeric Matrix
which_max_nv	Return Index of (First) Maximum of a Numeric Vector
which_min_im	Return (Row, Column) Index of (First) Minimum of an Integer Matrix
which_min_iv	Return Index of (First) Minimum of an Integer Vector
which_min_nm	Return (Row, Column) Index of (First) Minimum of a Numeric Matrix
which_min_nv	Return Index of (First) Minimum of a Numeric Vector