Search results for 'zeynepcagis polr'

MASS: Support Functions and Datasets for Venables and Ripley's MASS

is proportional odds
logistic regression, after which the function is named.
Usage

Polr: Ordered Categorical Regression

tram: Transformation Models

regression models for ordered categorical responses
Usage
Polr(formula, data, subset, weights, offset, cluster

Polr: Ordered Categorical Regression

tram: Transformation Models

regression models for ordered categorical responses
Usage
Polr(formula, data, subset, weights, offset, cluster

extract-polr-method: 'extract' method for 'polr' objects

texreg: Conversion of R Regression Output to LaTeX or HTML Tables

R: 'extract' method for 'polr' objects
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

extract-polr-method: 'extract' method for 'polr' objects

leifeld/texreg: Conversion of R Regression Output to LaTeX or HTML Tables

R: 'extract' method for 'polr' objects
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

sumitrahman/polr-back2raw: fits an ordered logistic regression model to make marginal fitted probabilities

sumitrahman/polr-back2raw: fits an ordered logistic regression model to make marginal fitted probabilities close to observed ones

Package: polr-back2raw
Type: Package
Title: fits an ordered logistic regression model to make marginal fitted

R/polr-back2raw.R
man/polr-back2raw-package.Rd

R/mass-polr-tidiers.R:

dgrtwo/broom: Convert Statistical Objects into Tidy Tibbles

#' @templateVar class polr
#' @template title_desc_tidy
#'

polr-back2raw-package: fits an ordered logistic regression model to make marginal

sumitrahman/polr-back2raw: fits an ordered logistic regression model to make marginal fitted probabilities close to observed ones

R: fits an ordered logistic regression model to make marginal...
polr-back2raw-packageR Documentation
fits

tests/Polr-Ex.R:

tram: Transformation Models

<- function(x, y)
stopifnot(isTRUE(all.equal(x, y, tolerance = tol)))
(house.plr <- polr(Sat ~ Infl + Type + Cont

tests/polr-Ex.R:

mlt: Most Likely Transformations

library("MASS")
options(digits = 4)
mp <- polr(Sat ~ Infl, weights = Freq, data = housing)

tests/polr-Ex.R:

mlt: Most Likely Transformations

library("MASS")
mp <- polr(Sat ~ Infl, weights = Freq, data = housing)
library("mlt")

inst/prompts/models/polr/role_specific.md:

statlingua: Explain Statistical Output with Large Language Models

You are particularly skilled with **Proportional Odds Logistic Regression models** created using the `polr()` function

inst/prompts/models/polr/instructions.md:

statlingua: Explain Statistical Output with Large Language Models

You are explaining a **Proportional Odds Logistic Regression Model** (from `MASS::polr()`).
**Core Concepts & Purpose

get_polr: Get polr()

icj/mecfun: MEC Microbiome Helpers

R: Get polr()
get_polrR Documentation
Get polr()

glance.polr: Glance at a(n) polr object

broom: Convert Statistical Objects into Tidy Tibbles

R: Glance at a(n) polr object
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

get_polr_data: Get Data for polr()

icj/mecfun: MEC Microbiome Helpers

R: Get Data for polr()
get_polr_dataR Documentation
Get Data for polr()

predict.regrpolr: Predict and Fitted for polr Models

plgraphics: User Oriented Plotting Functions

R: Predict and Fitted for polr Models
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

predict.regrpolr: Predict and Fitted for polr Models

plgraphics: User Oriented Plotting Functions

R: Predict and Fitted for polr Models
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

predictdf.polr: Prediction data frame for polr