broom

all. I want to obtain the p-value from a correlation matrix using dplyr and/or broom packages and testing multiple variables at the same time . I'm aware of other methods, but dplyr seems easier and more intuitive for me. In addition, dplyr will need to correlate each variable to obtain the specific p-value, what makes the process easier and faster. I checked other links, but they did not work for this question ( example 1 , example 2 , example 3 ) When I use this code, the correlation coefficients are reported. However, the P-values are not. agreg_base_tipo_a %>% dplyr::select(S2.RT, BIS

I am trying to use augment on a loess fit, but I receive the following error: Error in data.frame(..., check.names = FALSE) : arguments imply differing number of rows: 32, 11 In the error message, 11 happens to equal the number of observations in one segment and 32 is the total number of observations. The code is below. require(broom) require(dplyr) # This example uses the lm method and it works regressions <- mtcars %>% group_by(cyl) %>% do(fit = lm(wt ~ mpg, .)) regressions %>% augment(fit) # This example uses the loess method and it generates the error regressions2 <- mtcars %>% group_by

I am using the getData function from the raster package to retrieve the map of Argentina. I would like to plot the resulting map using ggplot2, so I am converting to a dataframe using the tidy function from the broom package. This works fine, but I can't figure out how to preserve the names of the federal districts so that I can use them on the map. Here is my original code that does not preserve the district names: # Original code: ################################## # get the map data from GADM.org and then simplify it arg_map_1 <- raster::getData(country = "ARG", level = 1, path = "./data/")

I would like to run each variable in a dataset as a univariate glmer model using the lme4 package in R. I would like to prepare the data with the dplyr/tidyr packages, and organize the results from each model with the broom package (i.e. do(glance(glmer...). I would most appreciate help that stuck within that framework. I'm not that great in R, but was able to produce a dataset that throws an error and has the same structure as the data I'm using: library(lme4) library(dplyr) library(tidyr) library(broom) Bird<-c(rep(c(0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0),10)) Stop<-c(rep(seq(1,10), 20))

I want to find correlations, p-values and 95% CI between one specific column and all other columns in a dataframe. The 'broom' package provides an example how to do that between two columns using cor.test with dplyr and pipes. For mtcars and, say, mpg column we can run a correlation with another column: library(dplyr) library(broom) mtcars %>% do(tidy(cor.test(.$mpg, .$cyl))) estimate statistic p.value parameter conf.low conf.high 1 -0.852162 -8.919699 6.112687e-10 30 -0.9257694 -0.7163171 The output is a single-row dataframe. I'd like to run cor.test for mpg with each column and send the

I currently have the following R code that runs multiple regression models with different predictors, across different subsets, and returns tidied output using the broom package. library(dplyr) library(purrr) library(broom) cars <- mtcars preds<-c("disp", "drat", "wt") model_fits <- map_df(preds, function(pred) { model_formula <- sprintf("mpg ~ %s", pred) cars %>% group_by(cyl) %>% do(tidy(lm(model_formula, data = .), conf.int = T)) %>% filter(term == pred) %>% mutate(outcome = "mpg") %>% select(outcome, cyl:estimate, starts_with("conf.")) }) This results in the following data frame: > model

The tidytext book has examples with a tidier for topicmodels: library(tidyverse) library(tidytext) library(topicmodels) library(broom) year_word_counts <- tibble(year = c("2007", "2008", "2009"), + word = c("dog", "cat", "chicken"), + n = c(1753L, 1157L, 1057L)) animal_dtm <- cast_dtm(data = year_word_counts, document = year, term = word, value = n) animal_lda <- LDA(animal_dtm, k = 5, control = list( seed = 1234)) animal_lda <- tidy(animal_lda, matrix = "beta") # Console output Error in as.data.frame.default(x) : cannot coerce class "structure("LDA_VEM", package = "topicmodels")" to a data