Package 'dvmisc'

Description

Converts a continuous BMI variable into a 3-level factor variable: Normal weight if [-Inf, 25), Overweight if [25, 30), and Obese if [30, Inf).

Usage

bmi3(x, labels = TRUE)

Arguments

Value

Factor variable with 3 levels.

Description

Converts a continuous BMI variable into a 4-level factor variable: Underweight if [-Inf, 18.5), Normal weight if [18.5, 25), Overweight if [25, 30), and Obese if [30, Inf).

Usage

bmi4(x, labels = TRUE)

Arguments

Value

Factor variable with 4 levels.

Description

User selects response variable and predictors, app displays optimal tree in terms of cross-validation error along with a summary table and importance plot. Relies on rpart for fitting and fancyRpartPlot for plotting.

Usage

cart_app(data)

Arguments

Value

Shiny app.

Description

Formats a glm object for printing to console or inputting to kable.

Usage

clean_glm(
  fit,
  columns = NULL,
  expand_factors = TRUE,
  variable_labels = NULL,
  prep_kable = FALSE,
  decimals = 2,
  formatp_list = NULL
)

Arguments

Value

Data frame.

Examples

fit <- glm(mpg ~ wt + as.factor(cyl) + hp, data = mtcars)
clean_glm(fit)
fit %>% clean_glm(prep_kable = TRUE) %>% knitr::kable()

Description

So you can stop guess-and-checking with cut.

Usage

cleancut(x, breaks, labels = NULL)

Arguments

Value

Factor or integer vector.

Examples

x <- rnorm(100)
y <- cleancut(x, "(-Inf, -1), [-1, 1], (1, Inf)")
tapply(x, y, range)

y <- cleancut(x, "(-Inf, -1), [-1, 1], (1, Inf)", c("<-1", "-1 to 1", ">1"))
tapply(x, y, range)

Description

Combines quantile and cut into a single function, with strata-specific quantiles possible. For example, you could create sex-specific height tertiles with create_qgroups(height, groups = 3, strata = sex). Compatible with dplyr functions like mutate and transmute.

Usage

create_qgroups(
  x,
  groups = 4,
  probs = seq(1/groups, 1 - 1/groups, 1/groups),
  strata = NULL,
  quantile_list = list(na.rm = TRUE),
  cut_list = list(include.lowest = TRUE)
)

Arguments

Value

Factor variable.

Examples

# In mtcars dataset, create tertiles for mpg
mtcars$mpg_tertiles <- create_qgroups(mtcars$mpg, groups = 3)
table(mtcars$mpg_tertiles)

# Define tertile cutpoints separately for 4-, 6-, and 8-cylinder vehicles
mtcars$mpg_tertiles <- create_qgroups(mtcars$mpg, groups = 3, strata = mtcars$cyl)
table(mtcars$mpg_tertiles)

# Works with dplyr functions like mutate
mtcars <- mtcars %>% 
  dplyr::mutate(mpg_tertiles = create_qgroups(mpg, groups = 3, strata = cyl))
table(mtcars$mpg_tertiles)

# Can embed in lm, glm, etc.
summary(lm(mpg ~ create_qgroups(wt), data = mtcars))

Description

Complex survey version of create_qgroups. Relies heavily on the survey package [1,2].

Usage

create_qgroups_svy(
  x,
  groups = 4,
  probs = seq(1/groups, 1 - 1/groups, 1/groups),
  strata = NULL,
  design,
  svyquantile_list = list(na.rm = TRUE),
  cut_list = list(include.lowest = TRUE)
)

Arguments

Value

Factor variable.

References

1. Therneau, T. (2015). A Package for Survival Analysis in S. R package version 2.38. https://cran.r-project.org/package=survival.

2. Therneau, T.M. and Grambsch, P.M. (2000). Modeling Survival Data: Extending the Cox Model. Springer, New York. ISBN 0-387-98784-3.

Description

Convenience function to get decreasing factor levels from cut. Currently requires specifying breaks as vector of cutpoints rather than number of desired intervals.

Usage

cut_decreasing(x, breaks, include.lowest = FALSE, right = TRUE, ...)

Arguments

Value

Factor variable.

Examples

# In mtcars dataset, create 3 mpg groups
table(cut(mtcars$mpg, breaks = c(-Inf, 15, 20, Inf)))

# Repeat with cut_decreasing to get factor levels ordered from high to low. 
# To match cut here, need to specify right = FALSE
table(cut_decreasing(mtcars$mpg, breaks = c(Inf, 20, 15, -Inf), right = FALSE))

# You can specify breaks from low to high, but then include.lowest and right 
# arguments get confusing
table(cut_decreasing(mtcars$mpg, breaks = c(-Inf, 15, 20, Inf), right = TRUE))

Description

Creates plot showing user-specified points (e.g. means, medians, regression coefficients) along with user-specified error bars (e.g. standard deviations, min/max, 95% confidence intervals).

Usage

dots_bars(
  y = NULL,
  bars = NULL,
  bars.lower = y - bars,
  bars.upper = y + bars,
  truth = NULL,
  group.labels = NULL,
  group.dividers = TRUE,
  subgroup.spacing = 1,
  subgroup.labels = NULL,
  subgroup.pch = NULL,
  subgroup.col = NULL,
  points.list = NULL,
  arrows.list = NULL,
  xaxis.list = NULL,
  yaxis.list = xaxis.list,
  abline.dividers.list = NULL,
  abline.truth.list = NULL,
  legend.list = NULL,
  ...
)

Arguments

Value

Plot showing points +/- error bars across groups/subgroups.

Examples

# Generate 100 values from normal distributions with different means, and 
# graph mean +/- standard deviation across groups 
dat <- cbind(rnorm(100, 2), rnorm(100, 2.5), rnorm(100, 1.75))
means <- apply(dat, 2, mean)
sds <- apply(dat, 2, sd)
fig1 <- dots_bars(y = means, bars = sds, main = "Mean +/- SD by Group",
                  ylab = "Mean +/- SD")
                  
# Simulate BMI values for males and females in 3 different age groups, and 
# graph mean +/- 95% CI
sex <- as.factor(c(rep("Male", 300), rep("Female", 300)))
age <- as.factor(rep(c("Young", "Middle", "Old"), 2))
bmi <- c(rnorm(100, 25, 4), rnorm(100, 26, 4.25), rnorm(100, 27, 4.5),
         rnorm(100, 26.5, 4.5), rnorm(100, 27.25, 4.75), rnorm(100, 28, 5))
dat <- data.frame(sex = sex, age = age, bmi = bmi)
means <- tapply(dat$bmi, dat[, c("sex", "age")], mean)
ci.lower <- tapply(dat$bmi, dat[, c("sex", "age")],
                   function(x) t.test(x)$conf.int[1])
ci.upper <- tapply(dat$bmi, dat[, c("sex", "age")],
                   function(x) t.test(x)$conf.int[2])
fig2 <- dots_bars(y = means, bars.lower = ci.lower, bars.upper = ci.upper,
                  main = "BMI by Sex and Age",
                  ylab = "BMI (mean +/- CI)",
                  xlab = "Age group")

Description

Collection of functions for running and summarizing statistical simulation studies, creating visualizations (e.g. CART Shiny app, histograms with fitted probability mass/density functions), calculating moving-window statistics efficiently, and performing common computations.

Details

See CRAN documentation for full list of functions.

Author(s)

Dane R. Van Domelen
[email protected]

References

Eddelbuettel, D. and Francois, R. (2011) Rcpp: Seamless R and C++ Integration. Journal of Statistical Software, 40(8), 1-18. http://www.jstatsoft.org/v40/i08/.

Eddelbuettel, D. (2013) Seamless R and C++ Integration with Rcpp. Springer, New York. ISBN 978-1-4614-6867-7.

Eddelbuettel, D. and Balamuta, J.J. (2017). Extending R with C++: A Brief Introduction to Rcpp. PeerJ Preprints 5:e3188v1. https://doi.org/10.7287/peerj.preprints.3188v1.

Acknowledgment: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-0940903.

Description

Loops over the last argument, then the second-last, and so on. It should be faster than expand.grid.

Usage

expand_grid(..., together = NULL)

Arguments

Value

Data table.

Examples

# Simple example of expand.grid vs. expand_grid
expand.grid(x = c("a", "b", "c"), y = c(1, 2), z = c(TRUE, FALSE))
expand_grid(x = c("a", "b", "c"), y = c(1, 2), z = c(TRUE, FALSE))

# How to keep certain variables together
expand_grid(x = 1: 5, 
            together = data.frame(y = c("1a", "2a"), z = c("1b", "2b")))

Description

Uses maximum likelihood to fit Y|X ~ Gamma(exp(beta_0 + beta_x^T X), b), with the shape-scale (as opposed to shape-rate) parameterization described in GammaDist. Y can be precisely measured or subject to multiplicative mean-1 lognormal errors, in which case replicates can be incorporated by specifying y as a list.

Usage

gammareg(
  y,
  x = NULL,
  merror = FALSE,
  integrate_tol = 1e-08,
  integrate_tol_hessian = integrate_tol,
  estimate_var = TRUE,
  fix_posdef = FALSE,
  ...
)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Description

The MSE, defined as the sum of the squared residuals divided by n-p (n = number of observations, p = number of regression coefficients), is an unbiased estimator for the error variance in a linear regression model. This is a convenience function that extracts the MSE from a fitted lm or glm object. The code is rev(anova(model.fit)$"Mean Sq")[1] if model.fit is a lm object and sum(model.fit$residuals^2) / model.fit$df.residual if model.fit is a glm object.

Usage

get_mse(model.fit, var.estimate = FALSE)

Arguments

Value

If var.estimate = FALSE, numeric value indicating the MSE; if var.estimate = TRUE, named numeric vector indicating both the MSE and a variance estimate for the error variance.

Examples

# Generate 100 values: Y = 0.5 + 1.25 X + e, e ~ N(0, 1)
set.seed(123)
x <- rnorm(100)
y <- 0.5 + 1.25 * x + rnorm(100, sd = 1)

# Fit regression model using lm and using glm
lm.fit <- lm(y ~ x)
glm.fit <- glm(y ~ x)

# Extract MSE from lm.fit and glm.fit
get_mse(lm.fit)
get_mse(glm.fit)

Description

Optionally resizes title and axis labels and saves a 1000 dpi png 5 inches tall and ~9.5 inches wide via ggsave. Values are adjustable.

Usage

gg_facebook(
  filename = paste("fig1-", Sys.Date(), ".png", sep = ""),
  path = getwd(),
  plot = last_plot(),
  resize_labels = TRUE,
  title_size = 16,
  axistitle_size = 13,
  axistext_size = 9,
  height = 5,
  width = height/0.525,
  dpi = 1000
)

Arguments

Description

Optionally resizes title and axis labels and saves a 1000 dpi png 6 inches tall and 8 inches wide via ggsave. Values are adjustable.

Usage

gg_quicksave(
  filename = paste("fig1-", Sys.Date(), ".png", sep = ""),
  path = getwd(),
  plot = last_plot(),
  resize_labels = TRUE,
  title_size = 16,
  axistitle_size = 13,
  axistext_size = 9,
  height = 6,
  width = height/0.75,
  dpi = 1000
)

Arguments

Description

Optionally resizes title and axis labels and saves a 600 dpi png 5 inches tall and ~8.9 inches wide via ggsave. Values are adjustable.

Usage

gg_twitter(
  filename = paste("fig1-", Sys.Date(), ".png", sep = ""),
  path = getwd(),
  plot = last_plot(),
  resize_labels = TRUE,
  title_size = 16,
  axistitle_size = 13,
  axistext_size = 9,
  height = 5,
  width = height/0.5625,
  dpi = 600
)

Arguments

Description

Simply head and tail combined.

Usage

headtail(x, ...)

Arguments

Value

Same class as x.

Examples

# Generate data from N(0, 1), sort, and look at smallest and largest 3 values
x <- rnorm(1000)
x.sorted <- sort(x)
headtail(x.sorted, 3)

Description

Similar to base R function hist, but with two added features: (1) Can overlay one or more fitted probability density/mass functions (PDFs/PMFs) for any univariate distribution supported in R (see Distributions); and (2) Can generate more of a barplot type histogram, where each possible value gets its own bin centered over its value (useful for discrete variables with not too many possible values).

Usage

histo(
  x,
  dis = "none",
  dis_shift = NULL,
  integer_breaks = NULL,
  colors = rep("black", length(dis)),
  lty = 1:length(dis),
  legend_form = ifelse(length(dis) == 1, 0, 1),
  aic_decimals = 1,
  points_list = NULL,
  axis_list = NULL,
  legend_list = NULL,
  ...
)

Arguments

Details

When x takes on whole numbers, you typically want to set dis_shift = -0.5 if right = TRUE (hist's default) and dis_shift = 0.5 if right = FALSE. The function will do this internally by default.

To illustrate, suppose a particular bin represents (7, 10]. Its midpoint will be at x = 8.5 on the graph. But if input values are whole numbers, this bin really only includes values of 8, 9, and 10, which have a mean of 9. So you really want f(9) to appear at x = 8.5. This requires shifting the curve to the left 0.5 units, i.e. setting dis_shift = -0.5.

When x takes on whole numbers with not too many unique values, you may want the histogram to show one bin for each integer. You can do this by setting integer_breaks = TRUE. By default, the function sets integer_breaks = TRUE if x contains whole numbers with 10 or fewer unique values.

Value

Histogram with fitted PDFs/PMFs if requested.

Examples

# Sample 10,000 Poisson(2) values and commpare default hist vs. histo
set.seed(123)
x <- rpois(n = 10000, lambda = 2)
par(mfrow = c(1, 2))
hist(x, main = "hist function")
histo(x, main = "histo function")

# Sample 10,000 lognormal(0, 0.35) values. Create histogram with curves
# showing fitted lognormal, normal, and Gamma PDFs.
set.seed(123)
x <- rlnorm(n = 10000, meanlog = 0, sdlog = 0.35)
par(mfrow = c(1, 1))
histo(x, c("lnorm", "norm", "gamma"), main = "X ~ Lognormal(0, 0.35)")

# Generate 10,000 Binomial(8, 0.25) values. Create histogram, specifying
# size = 5, with blue line/points showing fitted PMF.
set.seed(123)
x <- rbinom(n = 10000, size = 5, prob = 0.25)
par(mfrow = c(1, 1))
histo(x, dis = "binom", size = 5, colors = "blue", 
      points_list = list(type = "b"))

Description

Returns TRUE if x falls inside range defined by ends and FALSE otherwise. Also works for multiple sets of values and/or endpoints.

Usage

inside(x, ends, inclusive = TRUE)

Arguments

Value

Logical value or vector.

Examples

# Check whether 2 is inside [0, 2.5]
inside(1, c(0, 2.5))

# Check whether 2 and 3 are inside (0, 3)
inside(c(2, 3), c(0, 3), inclusive = FALSE)

# Check whether 1 is inside [1, 2] and [3, 4]
inside(1, rbind(c(1, 2), c(3, 4)))

Description

Splits a continuous variable into equal-width groups. Useful for assessing linearity in regression models.

Usage

interval_groups(x, groups = 5, ...)

Arguments

Value

Factor variable.

See Also

cut

Examples

# Convert values from N(0, 1) into 6 equal-width groups
x <- rnorm(1000)
groups <- interval_groups(x, 6)
table(groups)

# Use interval_groups to detect non-linearity
set.seed(123)
x <- rnorm(1000)
y <- 1.5 + 1.25 * x + 0.25 * x^2 + rnorm(1000)
plot(tapply(y, interval_groups(x), mean))

Description

Same idea as purrr::pmap, but with some different functionality. It can runs all combinations of vector-valued arguments in ... or the 1st set, 2nd set, and so forth, and multiple trials can be run for each scenario, which can be useful for simulations.

Usage

iterate(
  f,
  ...,
  all_combinations = TRUE,
  fix = NULL,
  trials = 1,
  varnames = NULL
)

Arguments

Value

Data frame.

Examples

# Define function to generate data from N(mu, sigsq) and perform t-test.
f <- function(n = 100, mu = 0, sigsq = 1, alpha = 0.05) {
  x <- rnorm(n = n, mean = mu, sd = sqrt(sigsq))
  fit <- t.test(x = x, alpha = alpha)
  return(list(t = fit$statistic, p = fit$p.value))
}

# Call f once for various sample sizes and means
f %>% iterate(n = c(100, 500), mu = c(0.1, 0.25))

# Run 100 trials for each scenario and calculate empirical power
f %>% iterate(n = c(100, 500), mu = c(0.1, 0.25), trials = 100) %>%
  group_by(n, mu) %>%
  summarise(mean(p < 0.05))

Description

Adds each element of list2 to list1, overriding any elements of the same name. Similar to modifyList function in utils package, but either list can be NULL. Useful for do.call statements, when you want to combine a list of default inputs with a list of user-specified inputs.

Usage

list_override(list1, list2)

Arguments

Value

List containing the named elements initially in list1 and not in list2, any additional named elements in list2, and any named elements in list1 that were replaced by elements of the same name in list2.

Examples

# Create list that has default inputs to the plot function
list.defaults <- list(x = 1: 5, y = 1: 5, type = "l", lty = 1)

# Create list of user-specified inputs to the plot function
list.user <- list(main = "A Straight Line", lty = 2, lwd = 1.25)

# Combine the two lists into one, giving priority to list.user
list.combined <- list_override(list.defaults, list.user)

# Plot data using do.call
do.call(plot, list.combined)

Description

Defined as: exp_x <- exp(x); out <- exp_x / (1 + exp_x). This 2-step approach is faster than exp(x) / (1 + exp(x)) because the exponentials only have to be calculated once.

Usage

logit_prob(x)

Arguments

Value

Numeric vector.

Description

Uses maximum likelihood to fit Y|X ~ Lognormal(beta_0 + beta_x^T X, sigsq). Y can be precisely measured or subject to multiplicative mean-1 lognormal errors, in which case replicates can be incorporated by specifying y as a list).

Usage

lognormalreg(
  y,
  x = NULL,
  merror = FALSE,
  estimate_var = TRUE,
  fix_posdef = FALSE,
  ...
)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Description

Creates plot showing sample log-odds of binary Y variable across levels of a grouping variable, with customizable error bars. Observations with missing values for y and/or group are dropped.

Usage

logodds_graph(
  y,
  group,
  error.bars = "none",
  alpha = 0.05,
  p.legend = "chi",
  plot.list = NULL,
  lines.list = NULL,
  axis.list = NULL,
  legend.list = NULL,
  ...
)

Arguments

Value

Plot showing log-odds of y across levels of group.

Description

Written in C++, this function tends to run faster than max for large numeric vectors/matrices.

Usage

max_n(x)

Arguments

Value

Numeric value.

Examples

# For large objects, max_n is faster than max
x <- rnorm(100000)
max(x) == max_n(x)
benchmark(max(x), max_n(x), replications = 1000)

# For smaller objects, max_n is slower than max
x <- rnorm(100)
max(x) == max_n(x)
benchmark(max(x), max_n(x), replications = 1000)

Description

Written in C++, this function runs faster than mean for large integer vectors/matrices.

Usage

mean_i(x)

Arguments

Value

Numeric value.

Examples

# For very large integer objects, sum_i is faster than sum
x <- rpois(100000, lambda = 5)
mean(x) == mean_i(x)
benchmark(mean(x), mean_i(x), replications = 1000)

# For smaller integer objects, sum_i is slower than sum 
x <- rpois(1000, lambda = 5)
mean(x) == mean_i(x)
benchmark(mean(x), mean_i(x), replications = 1000)

Description

Creates plot showing mean of Y variable across levels of a grouping variable, with customizable error bars. Observations with missing values for y and/or group are dropped.

Usage

means_graph(
  y,
  group,
  error.bars = "t.ci",
  alpha = 0.05,
  p.legend = TRUE,
  plot.list = NULL,
  lines.list = NULL,
  axis.list = NULL,
  legend.list = NULL,
  ...
)

Arguments

Value

Plot showing mean of y across levels of group.

Description

Written in C++, this function tends to run faster than min for large numeric vectors/matrices.

Usage

min_n(x)

Arguments

Value

Numeric value.

Examples

# For large objects, min_n is faster than min
x <- rnorm(100000)
min(x) == min_n(x)
benchmark(min(x), min_n(x), replications = 1000)

# For smaller objects, min_n is slower than min
x <- rnorm(100)
min(x) == min_n(x)
benchmark(min(x), min_n(x), replications = 20000)

Description

Performs maximization via nlminb. alpha and beta correspond to the shape and scale (not shape and rate) parameters described in GammaDist.

Usage

mle_gamma(x, alpha = NULL, beta = NULL, estimate_var = FALSE, ...)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Examples

# Generate 1,000 values from Gamma(0.5, 1) and estimate alpha and beta
set.seed(123)
x <- rgamma(1000, shape = 0.5, scale = 1)
mle_gamma(x)

Description

Each observation is assumed to be the product of a Gamma(alpha, beta) and Lognormal(mu, sigsq) random variable. Performs maximization via nlminb. alpha and beta correspond to the shape and scale (not shape and rate) parameters described in GammaDist, and mu and sigsq correspond to meanlog and sdlog^2 in Lognormal.

Usage

mle_gamma_lnorm(
  x,
  gamma_mean1 = FALSE,
  lnorm_mean1 = TRUE,
  integrate_tol = 1e-08,
  estimate_var = FALSE,
  ...
)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Examples

# Generate 1,000 values from Gamma(0.5, 1) x Lognormal(-1.5/2, 1.5) and 
# estimate parameters
## Not run: 
set.seed(123)
x <- rgamma(1000, 0.5, 1) * rlnorm(1000, -1.5/2, sqrt(1.5))
mle_gamma_lnorm(x, control = list(trace = 1))

## End(Not run)

Description

Performs maximization via nlminb. mu and sigsq correspond to meanlog and sdlog^2 in Lognormal.

Usage

mle_lnorm(x, mu = NULL, sigsq = NULL, estimate_var = FALSE, ...)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Examples

# Generate 1,000 values from Lognormal(0.5, 1) and estimate mu and sigsq
set.seed(123)
x <- rlnorm(1000, meanlog = 0.5, sdlog = sqrt(1))
mle_lnorm(x)

Description

Each observation is assumed to be the product of a Lognormal(mu1, sigsq1) and Lognormal(mu2, sigsq2) random variable, with mu2 and sigsq2 known. Performs maximization via nlminb. mu and sigsq correspond to meanlog and sdlog^2 in Lognormal.

Usage

mle_lnorm_lnorm(x, mu2 = NULL, sigsq2 = NULL, estimate_var = FALSE, ...)

Arguments

Value

List containing:

1. Numeric vector of parameter estimates.

2. Variance-covariance matrix (if estimate_var = TRUE).

3. Returned nlminb object from maximizing the log-likelihood function.

4. Akaike information criterion (AIC).

Examples

# Generate 1,000 values from Lognormal(0.5, 1) x Lognormal(0.75, 1.5) and 
# estimate parameters based on known mu and sigsq for one of them
set.seed(123)
x <- rlnorm(1000, 0.5, sqrt(1)) * rlnorm(1000, 0.75, sqrt(1.5))
mle_lnorm_lnorm(x, mu2 = 0.75, sigsq2 = 1.5)

Description

Calculates moving averages or maximum moving average. For optimal speed, use integer = TRUE if x is an integer vector and integer = FALSE otherwise.

Usage

moving_mean(x, window, integer = FALSE, max = FALSE)

Arguments

Value

Numeric value or vector depending on max.

Examples

# 5-unit moving average for integer vector of length 10
x <- rpois(10, lambda = 3)
moving_mean(x, 5)

Description

Same idea as power.t.test. Less flexible, but faster.

Usage

n_2t_equal(d, sigsq, alpha = 0.05, beta = 0.2)

Arguments

Value

Numeric value indicating per-group sample size, rounded up to the nearest whole number.

Examples

# Per-group sample size for 90% power to detect difference of 0.2 with
# sigsq = 1
n_2t_equal(d = 0.2, sigsq = 1, beta = 0.1)

Description

Unequal variance version of n_2t_equal. Assumes an equal sample size for both groups, which is actually not optimal.

Usage

n_2t_unequal(d, sigsq1, sigsq2, alpha = 0.05, beta = 0.2)

Arguments

Value

Numeric value indicating per-group sample size, rounded up to the nearest whole number.

Examples

# Per-group sample size for 90% power to detect difference of 0.2 with
# sigsq's of 1 and 1.25
n_2t_unequal(d = 0.2, sigsq1 = 1, sigsq2 = 1.25, beta = 0.1)

Description

Defined simply as log(x / (x + 1)).

Usage

odds_prob(x)

Arguments

Value

Numeric vector.

Description

Generates plot of log-likelihood vs. one parameter of interest while other parameters are held fixed at certain values (e.g. MLEs). This is not a profile likelihood, and is mainly intended for use with a Shiny app.

Usage

plot_ll(
  start,
  objective,
  lower = -Inf,
  upper = Inf,
  xaxis_param = 1,
  xaxis_range = NULL,
  param_values = NULL,
  mles = NULL,
  return_info = FALSE
)

Arguments

Details

Note that objective should be the negative log-likelihood function, since internal optimization uses (nlminb), which does minimization.

Value

Plot of log-likelihood vs. value of parameter of interest, generated by ggplot.

Examples

# Generate normal data, define log-likelihood function, and plot likelihood
set.seed(123)
x <- rnorm(100, mean = 0.5, sd = sqrt(0.25))
ll.f <- function(theta) {
  return(-sum(dnorm(x, log = TRUE, mean = theta[1], sd = sqrt(theta[2]))))
}
plot_ll(start = c(0, 1), objective = ll.f, lower = c(-Inf, 1e-6))

Description

Calculates pooled sample variance used in equal variance two-sample t-test.

Usage

pooled_var(x, y, integer = FALSE)

Arguments

Value

Numeric value.

Description

Same idea as power.t.test. Less flexible, but faster.

Usage

power_2t_equal(n = 100, d, sigsq, alpha = 0.05)

Arguments

Value

Numeric value.

Examples

# Power to detect difference of 0.2 with 100 subjects per group and sigsq = 1
power_2t_equal(n = 100, d = 0.2, sigsq = 1)

Description

Unequal variance version of power_2t_equal. Assumes an equal sample size for both groups, which is actually not optimal.

Usage

power_2t_unequal(n = 100, d, sigsq1, sigsq2, alpha = 0.05)

Arguments

Value

Numeric value.

Examples

# Power to detect difference of 0.2 with 100 subjects per group and sigsq's
# of 1 and 1.25
power_2t_unequal(n = 100, d = 0.2, sigsq1 = 1, sigsq2 = 1.25)

Description

Defined simply as log(x / (1 - x)).

Usage

prob_logit(x)

Arguments

Value

Numeric vector.

Description

Defined simply as x / (1 - x).

Usage

prob_odds(x)

Arguments

Value

Numeric vector.

Description

Splits a continuous variable into quantiles groups. Basically combines quantile and cut into a single function. Note that create_qgroups will likely supersede this function in future versions of dvmisc.

Usage

quant_groups(
  x,
  groups = 4,
  probs = NULL,
  quantile.list = NULL,
  cut.list = NULL
)

Arguments

Value

Factor variable.

Examples

# Convert values from N(0, 1) into quintiles (i.e. 5 groups)
x <- rnorm(1000)
groups <- quant_groups(x, 5)
table(groups)

Description

Complex survey version of quant_groups. Speeds up process of creating quantile groups based on survey weighted percentiles.

Usage

quant_groups_svy(x, by = NULL, groups = 4, probs = NULL, design)

Arguments

Value

Factor variable.

Description

Calls remove_all_labels and then restores special variable names.

Usage

ral(x)

Arguments

Value

Vector or data frame.

Description

Convenience function to get reversed factor levels from cut. Currently requires specifying breaks as vector of cutpoints rather than number of desired intervals.

Usage

reverse_cut(x, breaks, include.lowest = FALSE, right = TRUE, ...)

Arguments

Value

Factor variable.

Examples

# In mtcars dataset, create 3 mpg groups
table(cut(mtcars$mpg, breaks = c(-Inf, 15, 20, Inf)))

# Repeat with reverse_cut to get factor levels ordered from high to low
table(reverse_cut(mtcars$mpg, breaks = c(Inf, 20, 15, -Inf)))

# You can specify breaks from low to high, but then include.lowest and right 
# arguments get confusing
table(reverse_cut(mtcars$mpg, breaks = c(-Inf, 15, 20, Inf), right = TRUE))

Description

Uses C++ code for efficiency.

Usage

sliding_cor(short, long)

Arguments

Value

Numeric vector.

Examples

short <- rnorm(4)
long <- rnorm(10)
sliding_cor(short, long)

Description

Uses C++ code for efficiency.

Usage

sliding_cov(short, long)

Arguments

Value

Numeric vector.

Examples

short <- rnorm(4)
long <- rnorm(10)
sliding_cov(short, long)

Description

Calls kable and kable_styling as appropriate based on class of input object and creates object ready to snip or copy/paste out of R. Currently works for data frames, matrices, and tables.

Usage

snip(x)

Arguments

Examples

# Print 4-cylinder cars
data(mtcars)
mtcars %>%
  dplyr::filter(cyl == 4) %>%
  snip()

# Crosstab of gears and cylinder using mtcars dataset
snip(table(mtcars$cyl, mtcars$gear))

Description

Written in C++, this function runs faster than sum for large integer vectors/matrices.

Usage

sum_i(x)

Arguments

Value

Numeric value.

Examples

# For very large integer objects, sum_i is faster than sum
x <- rpois(100000, lambda = 5)
sum(x) == sum_i(x)
benchmark(sum(x), sum_i(x), replications = 1000)

# For smaller integer objects, sum_i is slower than sum 
x <- rpois(1000, lambda = 5)
sum(x) == sum_i(x)
benchmark(sum(x), sum_i(x), replications = 1000)

Description

Creates table summarizing results of statistical simulations, providing common metrics of performance like mean bias, standard deviation, mean standard error, mean squared error, and confidence interval coverage.

Usage

sumsim(
  estimates,
  ses = NULL,
  truth = NULL,
  theta_0 = 0,
  statistics = c("mean_bias", "sd", "mean_se", "mse", "coverage"),
  alpha = 0.05,
  digits = 3,
  listwise_deletion = TRUE
)

Arguments

Value

Numeric matrix.

Examples

# For X ~ N(mu, sigma^2), the MLE for sigma^2 is the sample variance with n 
# in the denominator, but the unbiased version with (n - 1) is typically used 
# for its unbiasedness. Compare these estimators in 1,000 trials with n = 25. 
MLE <- c()
Unbiased <- c()
for (ii in 1: 1000) {
   x <- rnorm(n = 25)
   MLE[ii] <- sum((x - mean(x))^2) / 25
   Unbiased[ii] <- sum((x - mean(x))^2) / 24
 }
sumsim(estimates = cbind(MLE, Unbiased), truth = 1)

Description

Returns input vector with tail values trimmed off of it. User can specify tail probability to trim or lower and upper cutpoints for values to retain.

Usage

trim(x, p = NULL, tails = "both", cutpoints = NULL, keep.edge = TRUE)

Arguments

Value

Numeric vector.

See Also

inside

Examples

# Generate data from N(0, 1) and then trim the lower and upper 1\%
x <- rnorm(1000)
y <- trim(x, p = 0.01)

# Generate data from N(0, 1) and then trim values outside of (-1.5, 1.5)
x <- rnorm(100000)
y <- trim(x, cutpoints = c(-1.5, 1.5))

Description

The base R function range returns the minimum and maximum of a vector, but the "range" is actually defined as the difference between the minimum and maximum. This function calculates the actual range. It is equivalent to the base R code diff(range(x)), but a bit simpler and much faster.

Usage

truerange(x, integer = FALSE)

Arguments

Value

Integer or numeric value.

Examples

# truerange vs. diff(range()) for integer vector
x <- rpois(1000, lambda = 5)
all.equal(diff(range(x)), truerange(x, TRUE))
benchmark(diff(range(x)), truerange(x, TRUE), replications = 2000)

# truerange vs. diff(range()) for numeric vector
x <- rnorm(1000)
all.equal(diff(range(x)), truerange(x))
benchmark(diff(range(x)), truerange(x), replications = 2000)

Description

Written in C++, this function tends to run much faster than the equivalent (if maximum is unique) base R solution which(x == max(x), arr.ind = TRUE).

Usage

which_max_im(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_max_nv for numeric vector.
which_max_iv for integer vector.
which_max_nm for numeric matrix.
which_max_im for integer matrix.

Value

Integer vector.

Examples

# which_max_im is typically much faster than 
# which(x == max(x), arr.ind = TRUE)
x <- matrix(rpois(100, lambda = 15), ncol = 10)
all(which(x == max(x), arr.ind = TRUE) == which_max_im(x))
benchmark(which(x == max(x), arr.ind = TRUE), which_max_im(x), 
          replications = 5000)

Description

Written in C++, this function tends to run faster than which.max for large integer vectors.

Usage

which_max_iv(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_max_nv for numeric vector.
which_max_iv for integer vector.
which_max_nm for numeric matrix.
which_max_im for integer matrix.

Value

Integer value.

Examples

# For long vectors, which_max_iv is faster than which.max
x <- rpois(10000, lambda = 15)
which.max(x) == which_max_iv(x)
benchmark(which.max(x), which_max_iv(x), replications = 5000)

# For shorter vectors, which_max_iv is slower than which.max
x <- rpois(100, lambda = 15)
which.max(x) == which_max_iv(x)
benchmark(which.max(x), which_max_iv(x), replications = 20000)

Description

Written in C++, this function tends to run much faster than the equivalent (if maximum is unique) base R solution which(x == max(x), arr.ind = TRUE).

Usage

which_max_nm(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_max_nv for numeric vector.
which_max_iv for integer vector.
which_max_nm for numeric matrix.
which_max_im for integer matrix.

Value

Integer vector.

Examples

# which_max_nm is typically much faster than 
# which(x == max(x), arr.ind = TRUE)
x <- matrix(rnorm(100), ncol = 10)
all(which(x == max(x), arr.ind = TRUE) == which_max_nm(x))
benchmark(which(x == max(x), arr.ind = TRUE), which_max_nm(x),
          replications = 5000)

Description

Written in C++, this function tends to run faster than which.max for large numeric vectors.

Usage

which_max_nv(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_max_nv for numeric vector.
which_max_iv for integer vector.
which_max_nm for numeric matrix.
which_max_im for integer matrix.

Value

Integer value.

Examples

# For long vectors, which_max_nv is faster than which.max
x <- rnorm(100000)
which.max(x) == which_max_nv(x)
benchmark(which.max(x), which_max_nv(x), replications = 500)

# For shorter vectors, which_max_nv is slower than which.max
x <- rnorm(100)
which.max(x) == which_max_nv(x)
benchmark(which.max(x), which_max_nv(x), replications = 10000)

Description

Written in C++, this function tends to run much faster than the equivalent (if minimum is unique) base R solution which(x == min(x), arr.ind = TRUE).

Usage

which_min_im(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_min_nv for numeric vector.
which_min_iv for integer vector.
which_min_nm for numeric matrix.
which_min_im for integer matrix.

Value

Integer vector.

Examples

# which_min_im is typically much faster than 
# which(x == min(x), arr.ind = TRUE)
x <- matrix(rpois(100, lambda = 10), ncol = 10)
all(which(x == min(x), arr.ind = TRUE) == which_min_im(x))
benchmark(which(x == min(x), arr.ind = TRUE), which_min_im(x),
          replications = 5000)

Description

Written in C++, this function tends to run faster than which.min for large integer vectors.

Usage

which_min_iv(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_min_nv for numeric vector.
which_min_iv for integer vector.
which_min_nm for numeric matrix.
which_min_im for integer matrix.

Value

Integer value.

Examples

# For long vectors, which_min_iv is faster than which.min 
x <- rpois(10000, lambda = 15)
which.min(x) == which_min_iv(x)
benchmark(which.min(x), which_min_iv(x), replications = 5000)

# For shorter vectors, which_min_iv is slower than which.min
x <- rpois(100, lambda = 15)
which.min(x) == which_min_iv(x)
benchmark(which.min(x), which_min_iv(x), replications = 20000)

Description

Written in C++, this function tends to run much faster than the equivalent (if minimum is unique) base R solution which(x == min(x), arr.ind = TRUE).

Usage

which_min_nm(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_min_nv for numeric vector.
which_min_iv for integer vector.
which_min_nm for numeric matrix.
which_min_im for integer matrix.

Value

Integer vector.

Examples

# which_min_nm is typically much faster than 
# which(x == min(x), arr.ind = TRUE)
x <- matrix(rnorm(100), ncol = 10)
all(which(x == min(x), arr.ind = TRUE) == which_min_nm(x))
benchmark(which(x == min(x), arr.ind = TRUE), which_min_nm(x), 
          replications = 5000)

Description

Written in C++, this function tends to run faster than which.min for large numeric vectors.

Usage

which_min_nv(x)

Arguments

Details

For optimal speed, choose the version of this function that matches the class of your x:

which_min_nv for numeric vector.
which_min_iv for integer vector.
which_min_nm for numeric matrix.
which_min_im for integer matrix.

Value

Integer value.

Examples

# For long vectors, which_min_nv is faster than which.min
x <- rnorm(100000)
which.min(x) == which_min_nv(x)
benchmark(which.min(x), which_min_nv(x), replications = 1000)

# For shorter vectors, which_min_nv is slower than which.min
x <- rnorm(100)
which.min(x) == which_min_nv(x)
benchmark(which.min(x), which_min_nv(x), replications = 10000)

Description

Returns index of maximum for vectors and index or (row, column) position for matrices. For optimal speed, use integer = TRUE if x is an integer vector/matrix and integer = FALSE otherwise. Typically faster than which.max for matrices and for large vectors.

Usage

which.max2(x, arr.ind = FALSE, integer = FALSE)

Arguments

Value

Numeric value.

Examples

# which.max2 vs. which.max for integer vector
x <- rpois(10000, lambda = 5)
all.equal(which.max(x), which.max2(x, integer = TRUE))
benchmark(which.max(x), which.max2(x, integer = TRUE), replications = 10000)

# which.max2 vs. which.max for numeric vector
x <- rnorm(10000)
all.equal(which.max(x), which.max2(x))
benchmark(which.max(x), which.max2(x), replications = 10000)

Description

Returns index of minimum for vectors and index or (row, column) position for matrices. For optimal speed, use integer = TRUE if x is an integer vector/matrix and integer = FALSE otherwise. Typically faster than which.min for matrices and for large vectors.

Usage

which.min2(x, arr.ind = FALSE, integer = FALSE)

Arguments

Value

Numeric value.

Examples

# which.min2 vs. which.min for integer vector
x <- rpois(10000, lambda = 10)
all.equal(which.min(x), which.min2(x, integer = TRUE))
benchmark(which.min(x), which.min2(x, integer = TRUE), replications = 10000)

# which.min2 vs. which.min for numeric vector
x <- rnorm(10000)
all.equal(which.min(x), which.min2(x))
benchmark(which.min(x), which.min2(x), replications = 10000)