Date: July 1, 2016
I use TeX only to create PDFs in RMarkdown (using RStudio). After clean install, I wanted to save some space and include only the essential TeX packages. I chose BasicTeX 2016 (slim version of MacTeX). I created a new .Rmd document and installed following packages until it compiled successfully.
I installed the packages using TeX Live Utility, maybe it installed also some additional dependencies.
| # dplyr ==================== | |
| # descriptive stats per group, some columns omitted | |
| selvars <- c(2, 3, 4, 5, 8, 9, 13) | |
| d %>% do(describe(.[["varname"]])[, selvars]) | |
| # ggplot2 ================== | |
| # set default theme to bw | |
| theme_set(theme_bw()) | |
| # other ==================== |
| # Power analysis examples to answer quick questions. | |
| # - using pwr package in R | |
| # - see also http://www.statmethods.net/stats/power.html | |
| # -------------------------------------------------- | |
| # setup | |
| library(pwr) | |
| p <- 0.05 | |
| power <- 0.80 |
| library(ggplot2) | |
| library(animation) | |
| library(circular) | |
| # utility functions for trajectory generation ------------------- | |
| # - these function came from motAnalysis package | |
| random.positions <- function(n, xlim = c(-10, +10), ylim = xlim, | |
| dot.radius = 0.5, min.dist = 1) { | |
| pos <- list( |
| import numpy as np | |
| from scipy.optimize import curve_fit | |
| import scipy as sy | |
| import matplotlib.pyplot as plt | |
| d = np.array([75, 80, 90, 95, 100, 105, 110, 115, 120, 125], dtype=float) | |
| p1 = np.array([6, 13, 25, 29, 29, 29, 30, 29, 30, 30], dtype=float) / 30. # scale to 0..1 | |
| # psychometric function | |
| def pf(x, alpha, beta): |
| # data from | |
| # https://en.wikipedia.org/wiki/Simpson's_paradox#Kidney_stone_treatment | |
| # in all tabs: row 1 successful, row 2 failed treatment | |
| # small stones | |
| mk = matrix(c(81, 87-81, 234, 270-234), ncol=2) | |
| chisq.test(mk) | |
| # large stones | |
| vk = matrix(c(192, 263-192, 55, 80-55), ncol=2) | |
| chisq.test(vk) |
| # How to report effect size in Wilcoxon signed-rank test | |
| # links | |
| # [1] https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test | |
| # [2] https://stats.stackexchange.com/questions/229760/wilcoxon-signed-rank-test-in-r/229761 | |
| # [3] https://stats.stackexchange.com/questions/41620/output-of-one-tailed-wilcoxon-sign-rank-test-in-r | |
| # [4] https://stats.stackexchange.com/questions/133077/effect-size-to-wilcoxon-signed-rank-test | |
| # [5] Acion, L., Peterson, J. J., Temple, S., & Arndt, S. (2006). Probabilistic index: an intuitive non-parametric approach to measuring the size of treatment effects. Statistics in Medicine, 25(4), 591–602. https://doi.org/10.1002/sim.2256 | |
| # How Wilcoxon signed-rank test works + what it reports |
| # --------- RStudio ------------------- | |
| # knit from CLI | |
| rmarkdown::render("test.Rmd", "html_document") | |
| # --------- data manipulation --------- | |
| # wide-to-long = gather, long-to-wide = spread | |
| wide %>% | |
| gather(key = column_name_of_column_names, | |
| value = column_name_of_column_data, | |
| -allvariables, -whichwewant, -ineachrow) |
| # example of fitting psychophysic curve to data are from one subject | |
| # data from Muller-Lyer illusion, subjects are supposed to compare stimulus to | |
| # standard (length = 100) | |
| # columns: | |
| # - length - length of stimulus line | |
| # - pp - proportion of saying "longer" | |
| # - id | |
| # - N - is 10 for each value of length | |
| # - k - count of saying "longer" (k = pp * N) |
| --- | |
| title: "Sample estimation for correlation coefficients" | |
| author: "Jiri Lukavsky" | |
| date: "12/20/2019" | |
| output: html_document | |
| --- | |
| ```{r setup, include=FALSE} | |
| knitr::opts_chunk$set(echo = TRUE) |