set.seed(1) library('tidyverse') n <- 1000000 beta <- 0.1 dat <- tibble(x = runif(n, min = -6, max = 0), slope = runif(n, 0, 0.1), lql = -3, #…
# i: row of Zahlenmauer # j: j-th entry of Zahlenmauer i_j_to_ii <- function(i, j) { ii <- 0 if(i > 1) { ii <- ii + sum(1:(i - 1)) } return(ii + j) } ii_to_i_j …
library('tidyverse')
library('tidyverse') library('reticulate') use_condaenv('sbloggel') library('JuliaCall') julia_setup(JULIA_HOME = '/Users/seb/.juliaup/bin/') set.seed(1)
R
sink
class param: def __init__(self, **kwargs): for key, value in kwargs.items(): setattr(self, key, value) p = param(a=2, b = 3) print('a ='…
import time # Start the timer start_time = time.time() # Your code block # Example: for i in range(1000000): pass # End the timer end_time = time.time() # Calculate…
# Load the deSolve package library(deSolve) # Define the SIR model sir_model <- function(time, state, parameters) { with(as.list(c(state, parameters)), { dS <- -beta *…
dat <- expand_grid(time = factor(1:3), group = factor(1:2), ID = 1:5) |> mutate(hh = paste('time', time, 'group', group, sep = '_')) X <- model.matrix(~ hh + factor(ID), …
(J_per_kWh <- 1e3 * 3.6e3)
[1] 3600000
draw_sample <- function(x, n) { ii <- sample(x, size = n, replace = TRUE) x[ii] } calc_p_value <- function(effect_size, n) { dd <- tibble(x = rnorm(n), …
stats::nls
nlme::nlme
set.seed(1) library('tidyverse')
nr_of_trials <- 100 calc_p <- Vectorize( function(effect_size, n) { y1 <- rnorm(n) y2 <- rnorm(n, effect_size) t.test(y1, y2)$p.value } ) calc_p(1, 10)
[1…
import numpy as np import pandas as pd import random
import statsmodels.api as sm cars = sm.datasets.get_rdataset("cars")["data"] from plotnine import ggplot, aes, geom_point p = ggplot(cars, aes("speed", "dist")) + geom_point…
set.seed(2) library(tidyverse) library(survival)
library('tidyverse') library('rpart') library('rpart.plot') set.seed(1)
library('tidyverse') set.seed(1)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.1.2 ✔ readr 2.1.4 ✔ forcats 1.0.0 ✔…
cars
base
set.seed(1) library('tidymodels')
── Attaching packages ────────────────────────────────────── tidymodels 1.1.0 ──
✔ broom 1.0.4 ✔ recipes 1.0.6 ✔…
library('tidyverse') library('lme4')
library('tidyverse') library('faraway') set.seed(1)
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.get_dataset_names()
['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes'…
import matplotlib.pyplot as plt import numpy as np import statsmodels.api as sm data = sm.datasets.statecrime.load_pandas().data murder = data['murder'] X = data[[…
set.seed(1) library('neuralnet') library('tidyverse') library('knitr')
dist
cars |> ggplot(aes(dist, speed)) + …
library('tibble') library('ggplot2') library('RColorBrewer') library('plotly')
Attaching package: 'plotly'
The following object is masked from 'package:ggplot2': …
format_ods_data_frame <- function(dat, colnames = TRUE, type_definition = TRUE) { result <- dat if (colnames) { result <- …
library('lattice') library('readxl') dat <- read_excel('Psychopharmaka_Data.xlsx') dat <- dat[order(dat$Jahr), ] stripplot(order(dat$Jahr) ~ Jahr, data = dat, …
