| License: | GPL-3 |
| Title: | A Simulation Tool to Determine the Required Sample Size for Repertory Grid Studies |
| Type: | Package |
| LazyLoad: | yes |
| Description: | Simulation tool to facilitate determination of required sample size to achieve category saturation for studies using multiple repertory grids in conjunction with content analysis. |
| Version: | 0.6 |
| Date: | 2016-11-23 |
| Imports: | shiny, ggplot2, reshape2, plyr, shinythemes, BiasedUrn, shinyBS |
| Suggests: | knitr, testthat, rmarkdown |
| Encoding: | UTF-8 |
| URL: | https://github.com/markheckmann/gridsampler |
| BugReports: | https://github.com/markheckmann/gridsampler/issues |
| VignetteBuilder: | knitr |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-11-23 14:57:38 UTC; mark |
| Author: | Mark Heckmann [aut, cre], Lukas Burk [aut] |
| Maintainer: | Mark Heckmann <heckmann.mark@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2016-11-23 17:24:13 |
gridsampler - A sample size simulation software for repertory grid studies
Description
gridsampler - A sample size simulation software for repertory grid studies
References
Green, B. (2004). Personal construct psychology and content analysis. Personal Construct Theory & Practice, 1(3), 82-91.
Jankowicz, D. (2004). The easy guide to repertory grids. Chichester, England: John Wiley & Sons.
Probability for certain degree of saturation
Description
Calculate probability for getting certain proportion of categories with at least m constructs
Usage
calc_probabilities(r, n, ms, min.props = c(0.9, 0.95, 0.99))
Arguments
r |
A dataframe. The result returned from |
n |
Vector of n for which to calculate probabilities. |
ms |
minimal number of constructs in each category |
min.props |
Proportion of categores to contain at least m constructs. |
See Also
Other Utilities: expected_frequencies,
prob_categories
Examples
prob <- dexp(1:30, .05)
n <- seq(10, 80, by = 20)
r <- sim_n_persons_x_times_many_n(prob, n, a = 7, times = 100)
dd <- calc_probabilities(r, n, ms=1:5, min.props = c(0.9, .95, 1))
head(dd)
Draw and redraw results of simulation
Description
Draw and redraw results of simulation
Usage
draw_multiple_n_persons_x_times(d)
Arguments
d |
A dataframe as returned by |
See Also
Other Plotting: draw_n_person_sample
Examples
## simulate
prob <- dexp(1:30, .05) # probabilities for categories
N <- seq(10, 80, by = 10) # smaple sizes to simulate
r <- sim_n_persons_x_times_many_n(prob, n = N, a = 7, times = 100, progress = "none")
# calculate and draw
M <- 1:5 # minimal number of categories to evaluate
p <- c(0.9, .95, 1) # proportion of categories for which minimal m holds
d <- calc_probabilities(r, n = N, ms = M, min.props = p)
draw_multiple_n_persons_x_times(d)
Produce graphic for a single sample of n persons
Description
Produce graphic for a single sample of n persons
Usage
draw_n_person_sample(prob, n, a = 10, ap = rep(1/length(a), length(a)))
Arguments
prob |
Probability to draw a construct from a certain category. |
n |
Number of persons, i.e. grids to be sampled. |
a |
Possible number of attributes sampled from. |
ap |
Attribute probabilities, i.e. for each number of attributes given
in |
See Also
Other Plotting: draw_multiple_n_persons_x_times
Examples
draw_n_person_sample(dexp(1:30, rate = .05), n = 100, a = 10)
draw_n_person_sample(dexp(1:30, rate = .05), n = 100, a = 1:5, ap = 5:1)
Produce ggplot of percentiles for simulated frequencies
Description
Produce ggplot of percentiles for simulated frequencies
Usage
expected_frequencies(r)
Arguments
r |
A dataframe. The result returned from |
Value
Draws a ggplot
See Also
Other Utilities: calc_probabilities,
prob_categories
Examples
r <- sim_n_persons_x_times(dexp(1:30, rate = .05), n = 50, a = 5:7, ap = 1:3, 100)
expected_frequencies(r)
Run gridsampler app
Description
This function starts the gridsampler shiny app.
Usage
gridsampler(display.mode = "auto",
launch.browser = getOption("shiny.launch.browser", interactive()))
Arguments
display.mode |
|
launch.browser |
Boolean, set |
Examples
## Not run:
gridsampler()
## End(Not run)
Probability for certain degree of saturation
Description
Calculate probability for getting certain proportion of categories with at least m constructs
Usage
prob_categories(r, m, min.prop = 1)
Arguments
r |
A dataframe. The result returned from |
m |
minimal number of constructs in each category |
min.prop |
Proportion of categores to contain at least m constructs. |
See Also
Other Utilities: calc_probabilities,
expected_frequencies
Examples
r <- sim_n_persons_x_times(dexp(1:30, rate = .05), n = 50, a = 5:7, times = 100, progress = "none")
prob_categories(r, 4, min.prop = .9)
Adjusted sampling function
Description
Adjusted sampling function
Usage
sample_new(x, size, replace = FALSE, prob = NULL)
Simulate n persons
Description
Function is a simple replicate wrapper around sim_one_person
Usage
sim_n_persons(prob, n, a = 10, ap = rep(1/length(a), length(a)))
Arguments
prob |
Probability to draw a construct from a certain category. |
n |
Number of persons, i.e. grids to be sampled. |
a |
Possible number of attributes sampled from. |
ap |
Attribute probabilities, i.e. for each number of attributes given
in |
See Also
Other Simulations: sim_n_persons_x_times_many_n,
sim_n_persons_x_times,
sim_one_person
Examples
sim_n_persons(dexp(1:30, .05), n = 2, a = 10)
sim_n_persons(dexp(1:30, .05), n = 2, a = c(1, 30))
sim_n_persons(dexp(1:30, .05), n = 2, a = c(1, 30), ap = c(1,4))
sim_n_persons(dexp(1:30, .05), n = 2, a = 1:5, ap = c(1,1,2,2,3))
Complete simulation
Description
Complete simulation
Usage
sim_n_persons_x_times(prob, n, a, ap = rep(1/length(a), length(a)),
times = 100, progress = "text")
Arguments
prob |
Probability to draw a construct from a certain category. Length of vector determines number of categories. |
n |
Number of persons, i.e. grids to sample. |
a |
Number of constructs to be sampled. |
ap |
Probabilities for each number of attributes to be sampled. |
times |
Number of times to repeat each simulation. |
progress |
Type of progress bar shown during simulation. |
See Also
Other Simulations: sim_n_persons_x_times_many_n,
sim_n_persons, sim_one_person
Examples
## Not run:
sim_n_persons_x_times(dexp(1:30, .05), n = 2, a = c(1,30), ap = 1:2, times = 100)
sim_n_persons_x_times(dexp(1:30, .05), n = 2, a = c(1,30), times = 200, progress = "tk")
## End(Not run)
Simulate for different n
Description
Creates simulation results for different n. Runs
sim_n_persons_x_times for different n.
Usage
sim_n_persons_x_times_many_n(prob, n = seq(10, 80, by = 10), a = 7,
ap = rep(1/length(a), length(a)), times = 100, progress = "text")
Arguments
prob |
Probability to draw a construct from a certain category. Length of vector determines number of categories. |
n |
Number of persons, i.e. grids to sample. |
a |
Number of constructs to be sampled. |
ap |
Probabilities for each number of attributes to be sampled. |
times |
Number of times to repeat each simulation. |
progress |
Type of progress bar shown during simulation. |
Value
A result dataframe.
See Also
Other Simulations: sim_n_persons_x_times,
sim_n_persons, sim_one_person
Examples
## Not run:
r <- sim_n_persons_x_times_many_n(dexp(1:30, .05), a = 7, times = 100)
r <- sim_n_persons_x_times_many_n(dexp(1:30, .05), a = 5:7, ap = 1:3, times = 100)
## End(Not run)
Simulate a single grid
Description
Simulate a single grid
Usage
sim_one_person(prob, a = 10)
Arguments
prob |
Probability to draw a construct from a certain category. |
a |
Number of constructs to be sampled. |
See Also
Other Simulations: sim_n_persons_x_times_many_n,
sim_n_persons_x_times,
sim_n_persons
Examples
# draw from exponential distribution
p <- dexp(1:20, rate = .1)
sim_one_person(p, a = 10)