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91
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How to get weighted bootstrapped estimates and CIs from a phyloglm function of the phylolm package
I constructed a phylogenetic logistic regression model for a binary response variable using the phyloglm() function from the phylolm package.
The code looks like below (predictor and response names ...
0
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0
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56
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How to bootstrap ANOVA type 3 fixed effects of a linear mixed model in R (lme4)?
I've been trying to bootstrap a linear mixed model with three 2-level categorical fixed factors (and all two-way interactions), and one random factor.
I can get the linear mixed model to run, I can ...
1
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1
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33
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Predictor variable not found for bootstrapping
I have to bootstrap a logistic regression model on the 'birthwt' dataset from MASS. However, when I run my code for the bootstrap, I get the following error
Error in eval(predvars, data, env) : ...
1
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1
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287
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Understanding the permutation test
I'm attempting to optimize the performance of the permutation test implemented in scipy.stats. My dataset consists of 500,000 observations, each associated with 2,000 binary covariates. I've applied ...
1
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1
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50
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Adding an ifelse statement to a bootstrap function to set times model fails to converge equal to NA
rather complex question. I am running a GLMM on an experiment with repeated measurements on individuals. Due to relatively low N, and to increase transparency on the model itself, I designed a ...
2
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2
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70
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How to retrieve Bootstrap Statistics in the form of a data frame?
When I use boot() for a series of regression coefficients, I get the display of Bootstrap Statistics as shown below. Is there a way of retrieving these Bootstrap Statistics in the form of a data ...
1
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0
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42
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How to generate a bootstrap standard error, t statistics and p value for a user written dynamic panel quantile regression
rm(list=ls())
library(quantreg)
library(SparseM)
library(stats)
rq.fit.panel <- function(X,y,s,w=c(1),taus=0.30,lambda = 0){
# prototype function for panel data fitting of QR models
# the ...
1
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1
answer
68
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How to find the number of samples that are picked in each boostrap of stratified bootstrap in pROC?
Question is regarding the roc function of pROC package.
Package link: https://www.rdocumentation.org/packages/pROC/versions/1.18.5/topics/roc. Paper link https://www.ncbi.nlm.nih.gov/pmc/articles/...
1
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1
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53
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bootstrap for model estimation with a created function
I am trying to make comparisons between treatments of my heritability estimates, values that I calculated with the following function:
data <-
data.frame(treatment=c("control", "...
3
votes
1
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273
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Bootstrapped confidence intervals for predicted data shows overlap when fixed effects do not
Note: This might be more appropriate for Cross Validated, and I'm fine moving it over there if requested, but I thought I would try here first since it could be r-related.
I am comparing growth curve ...
0
votes
1
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120
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Bootstrapping a gaussian and best fit data
I have the following code which provides the best fit curve for my given data. I am not sure how to bootstrap it and use that to find the 95% confidence interval.
This is my code:
import numpy as np
...
0
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1
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289
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How to use Pearson's correlation in boostrap in python?
I have task to do in python. Here is the description:
Using the function scipy.stats.bootstrap(), perform interval estimation of the following parameters at a significance level of 1-alpha = 0.98 ...
2
votes
1
answer
511
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How to bootstrap glm regression, estimate 95% confidence interval and plot it?
I am doing glm regression with a 0-1 distribution dataset. It's going well with ggplot2::geom_smooth; here is my code:
library(ggplot2)
set.seed(123)
df <- transform(data.frame(Conc=runif(200, ...
0
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0
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126
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Error in R powerSim function for a fixed effect in a LMM
I have a LMM with 2 categorical fixed factors (ffactor1, ffactor2), a random factor (id), and a covariate of no interest (baseline). I am trying to estimate the power using R's powerSim function. I am ...
0
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0
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78
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Using FIML and bootstrapping with BCa for very simple regression model
aI need to calculate a very simple regression model outcome ~ predictor, where outcome is continuous and predictor is binary. To treat missings, I have to use FIML (full information maximum ...