R is an open source programming language and software environment for statistical computing and graphics.
11
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441 views
Quantile regression: Which standard errors?
The summary.rq function from the quantreg vignette provides a multitude of choices for standard error estimates of quantile regression coefficients. What are the ...
11
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0answers
308 views
First step for big data ($N = 10^{10}$, $p = 2000$)
Suppose you are analyzing a huge data set at the tune of billions of observations per day, where each observation has a couple thousand sparse and possibly redundant numerical and categorial ...
10
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0answers
178 views
In a multi-level model, what are the practical implications of estimating versus not-estimating random effect correlation parameters?
In a multi-level model, what are the practical and interpretation-related implications of estimating versus not-estimating random effect correlation parameters? The practical reason for asking this ...
10
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0answers
536 views
7
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0answers
1k views
How to estimate variance components with lmer for models with random effects and compare them with lme results
I performed an experiment where I raised different families coming from two different source populations, where each family was split up into a different treatments. After the experiment I measured ...
6
votes
0answers
426 views
R and EViews differences in AR(1) estimates
The main problem is: I cannot obtain similar parameter estimates with EViews and R.
For reasons I do not know myself, I need to estimate parameters for certain data using EViews. This is done by ...
6
votes
0answers
600 views
Effect Size/Mean Squared Error from Linear Mixed-Model in R
I'm trying to report an effect size for a Linear Mixed-Model we've fitted in R. Right now I'm looking at reporting partial eta squared or eta squared. However, to do so I need to calculate the Sums of ...
6
votes
0answers
177 views
Residual diagnostics in MCMC -based regression models
I've recently embarked on fitting regression mixed models in the Bayesian framework, using a MCMC algorithm (function MCMCglmm in R actually).
I believe I have understood how to diagnose convergence ...
6
votes
0answers
601 views
How to analyze longitudinal count data: accounting for temporal autocorrelation in GLMM?
Hello statistical gurus and R programming wizards,
I am interested in modeling animal captures as a function of environmental conditions and day of the year. As part of another study, I have counts ...
6
votes
0answers
304 views
Testing simultaneous and lagged effects in longitudinal mixed models with time-varying covariates
I was recently told that it was not possible to incorporate time-varying covariates in longitudinal mixed models without introducing a time lag for these covariates. Can you confirm / deny this? Do ...
5
votes
0answers
112 views
How to calculate the probability of absence for a certain category of artefacts from a sample, given prior knowledge about its abundance?
In archaeology, artefacts are commonly classified in categories according to certain criteria (those may include manufacturing technique, decoration, function, chronology, etc).
I am trying to ...
5
votes
0answers
94 views
Comparison of frequency tables over time
I have an experiment in which students are asked to solve a maze under two groups (TRT v. Ctrl) over 10 different trials. In each trial, students can try 3 times to solve the maze. If they solve it in ...
5
votes
0answers
271 views
simple repeated measures syntax - lme vs. lmer
I'm trying to look for significant effects on "similarity" (of "isinpair" and controlling for time effects) using repeated measures with an in group sample. The intervention "isinpair" occurs after a ...
5
votes
0answers
309 views
Choosing complexity parameter in CART
In the rpart() routine to create CART models, you specify the complexity parameter to which you want to prune your tree. I have seen two different recommendations for choosing the complexity ...
5
votes
0answers
370 views
How to compute confidence interval in ANOVA with repeated measures?
I made a model using repeated measures univariate ANOVA in R.
...
5
votes
0answers
135 views
Average Structural Function Calculation
EDIT: I have solved this problem myself. The problem with the simulation below is that the omitted variable should not be included in the 'true model'. I have written a blog post with a more detailed ...
5
votes
0answers
157 views
What are the options in proportional hazard regression model when Schoenfeld residuals are not good?
I am doing a Cox proportional hazards regression in R using coxph, which includes many variables. The Martingale residuals look great, and the Schoenfeld residuals ...
5
votes
0answers
183 views
Regularization $L_1$ norm and $L_2$ norm empirical study
There are many methods to perform regularization -- $L_0$, $L_1$, and $L_2$ norm based regularization for example. According to Friedman Hastie & Tibsharani, the best regularizer depends on the ...
5
votes
0answers
254 views
Testing for a significant difference between ML estimates: Likelihood ratio or Wald test?
I am trying to test whether or not there is a significant difference between maximum likelihood estimates of two genetic parameters (selection and dominance) across two environments with genotype data ...
5
votes
0answers
152 views
Regression line envelope from Census ACS data
Context:
I’m working with the Census Bureau’s American Community Survey (ACS) data which are samples (not complete enumerations) aggregated at different spatial scales. Each ACS estimate is provided ...
5
votes
0answers
981 views
Split-split-plot design and lme
I’m working on a data set in order to evaluate the impact of drying on sediment microbial activities. The objective is to determine if the impact of drying varies among sediment types and/or depth ...
5
votes
0answers
220 views
Fitting a special mixed model in R - alternatives to optim()
I would like to do something in R that SAS can do using SAS's proc mixed (there is some way to do in STATA es well), namely fitting the so called Bivariate model from Reitsma et al (2005). This model ...
5
votes
0answers
212 views
Canonical correlation analysis on a MICE data set
I am looking to do a canonical correlations analysis (CCA) in R, using the CCA package, on a multiply imputed dataset (obtained from the mice package).
I know that ...
5
votes
0answers
83 views
Is there an equivalent of ARMA for rank correlation?
Hi
I am looking at extremely non linear data for which the ARMA/ARIMA models do not work well. Though, I see some autocorrelation, and I suspect to have better results for non linear autocorrelation.
...
4
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0answers
43 views
Which model for panel data with dependent variables from [0,1]?
I'm stuck with a regression modeling problem. I have panel data where the dependent variable is a probability. Below is an excerpt from my data. The complete panel covers more countries and years, ...
4
votes
0answers
63 views
Implementation of the cross validiation
I'm attending a course in computational statistics, which should be an applied course. We study different methods, which are important in "reality". One of these topics is Cross Validation. I'm faced ...
4
votes
0answers
57 views
Residual from Functional Demographic model for fertility
I'm trying to fit a functional demographic model (fdm) to fertility rates using the demography package in R. When I've plotted ...
4
votes
0answers
117 views
Predicting count data with random forest
Can a Random Forest be trained to appropriately predict count data?
How would this proceed? I have quite a extensive range of values so classification doesn't really make sense. If I would use ...
4
votes
0answers
144 views
Stationarity tests for time series
I am currently working on time series modeling, especially on stationarity tests. For this purpose, I am extensively using Pfaff's book "Analysis of integrated and cointegrated time series with R" and ...
4
votes
0answers
166 views
The role of scale parameter in GEE
I am learning the generalized estimating equations (GEE) and the geepack R package. There are some questions that I am a little confused.
In a GEE-constructed ...
4
votes
0answers
80 views
Regarding the sampling procedure in Adaboost algorithm
The AdaBoost algorithm states that it is to train a classifier based on the training data according to a weight vector.
Assume the size of training data is N, the weight vector is of dimension N as ...
4
votes
0answers
243 views
Strange pattern in residual plot from mixed effect model
I've run a mixed effect model in R by using lme. The explanatory (Temp_Diff & Distance) and responsive (LF_Diff) factors are continued variables.
...
4
votes
0answers
71 views
How to perform exploratory factor analysis on associative network?
In an article by Teichert and Schontag ("Exploring Consumer Knowledge Structure Using Associative Network Analysis", 2010), the authors perform (page 387)
an exploratory factor analysis (EFA) on an ...
4
votes
0answers
111 views
How do you show explained variation in cforest?
I'm trying to run a cforest model in R with continuous and categorical variables. When I tried this in randomForest, the ...
4
votes
0answers
152 views
Can these data be aggregated into a proportion for a binomial glm?
We asked 60 people to list as many restaurant franchises in Atlanta as they could. The overall list included over 70 restaurants, but we eliminated those that were mentioned by fewer than 10% of the ...
4
votes
0answers
609 views
What is the correct way to calculate the explained variance of each EOF as calculated from a gappy data set?
I am trying to determine the correct amount of variance explained by each mode of an Empirical Orthogonal Function (EOF) analysis (similar to "PCA") as applied to a gappy data set. (i.e., containing ...
4
votes
0answers
188 views
Modeling a spline over time — design matrix and survey of approaches
A response variable y is a nonlinear function of a number of predictor variables X (in my real data the response is binomially distributed, but here I'm using a normally-distributed value for ...
4
votes
0answers
71 views
Implementing ANCOVA via residuals
While it is often mis-used, I'd like to provide users of ezANOVA() (from the ez package for R) with the ability to specify covariates for ANCOVA. Since
...
4
votes
0answers
236 views
Asynchronous (irregular) Time Series Analysis
I am trying to analyze the lead-lag between time series of two stock prices.
In regular time series analysis, we can do Cross Correlaton, VECM (Granger Causality). However how does one handle the ...
4
votes
0answers
178 views
Binomial mid-p value
I've been under the impression that the mid-$p$ values generally control the Type I error, and consequently confidence intervals based on mid-$p$ values control the coverage. However I have checked ...
4
votes
0answers
283 views
Propagation of uncertainty/standard deviations in biological experiments
I am trying to work out how to propagate standard deviations in a biological experiment, but I have some difficulties.
I have the following (semi)fictitious data originating from an image analysis ...
4
votes
0answers
404 views
Document classification with Bayes
I want to build a document classifier in R, using the Naive Bayes approach.
Here are steps, that I've done so far:
I have corpus with about 30 documents from 2 authors (Classes are: "target author" ...
4
votes
0answers
134 views
Tree size in gradient tree boosting
Gradient tree boosting as proposed by Friedman uses decision trees with J terminal nodes (=leaves) as base learners. There are a number of ways to grow a tree with ...
4
votes
0answers
194 views
Programming a new random effects structure in lme
I am seeking advice on programming a new random effects variance-covariance matrix/structure (pdmat) in R for use in lme()? I've checked out the lme source code (as suggested if you want to program ...
4
votes
0answers
631 views
What is the difference between scores in Princomp vs. factanal?
In R the princomp()and the factanal() are somewhat similar. At least their output looks pretty similar. I learned that this is ...
4
votes
0answers
199 views
CIR Process-Variance reduction
I'm trying to evaluate a path dependent function, $f(r_t)$, on a Cox-Ingersoll-Ross process:
$dr_t = \theta (\mu - r_t)dt + \sigma \sqrt r_t dW_t$
by Monte Carlo simulation.
Could anyone suggest ...
3
votes
0answers
23 views
Which variables are driving correlations within groups
I'm running an analysis on a few data sets that each typically have 100-200 cases measured across 120-160 variables - something similar to looking at gene expressions. Each variable is a non-centered ...
3
votes
0answers
35 views
How to build a model where variance depends on covariate?
I have what I believe is a very simple problem for anyone used to modelling with unequal variances (which I am unfortunately not). I have a dependent variable "totrich" which I want to model as a ...
3
votes
0answers
35 views
Brant test in R
In testing the parallell regression assumption in ordinal logistic regression I find there are several approaches. I've used both the graphical approach (as detailed in Harrell´s book) and the ...
3
votes
0answers
40 views
Interpreting plot(lm)
I had a question about interpreting the graphs generated by plot(lm) in R. I was wondering if you guys could tell me how to interpret the scale-location and leverage-residual plots? Any comments would ...
