A statistical model is a formalization of relationships between variables in the form of mathematical equations. The model is statistical as the variables are not deterministically but stochastically related.

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R: Interpreting mlogit coefficients

Edit: The subset of the dataframe I provided way giving a different error, so I've replaced the pastebin entry with the full data frame. Here's the top of the data frame, then read into mlogit format ...
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5 views

Test for a Difference in Two Odds Ratios [migrated]

I run a logistic model for young people and another logistic model with the same prognostic factors for old people. I would like to compare the two ORs for each prognostic factor between the two ...
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1answer
23 views

How to model written language? [on hold]

I would like to model written language and transform it to graphs. Also, the opposite, from graphs to written language. Thanks
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2answers
79 views

Should I remove non-significant variables from my regression model

I have run a multiple linear regression using stepwise regression to select the best model, however the best model returned has a non-significant variable. When I remove this the AIC value goes up ...
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18 views

Repeated measures LMM - include random slope of 'time' as well? [closed]

I have 3 treatment groups, 12 subjects per treatment, 140 repeated measures per subject (Time), which can be further factorized into 'Days' - 20 measurements per subject per day - 7 days = 140 ...
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14 views

Can linear regression model variables be constantly looped in simulations to find the perfect model? [duplicate]

Say that I have a very large dataset and have a fairly large amount of variables, let's say 30. Since I don't know which variables matter and are good predictors for regression, I construct a for-loop ...
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7 views

Parameter Tying: Using observations of one category to lift estimates of baseline ability

I am trying to model an individuals' ability to perform one of several similar tasks. We would like each individual's performance to reflect three factors: the mean ability of the general population, ...
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17 views

Multilevel regression: question about notation

I have some difficulties in understanding the notation of multilevel regression models. Let's consider, for example, a varying intercept and varying slope model with just one level-I predictor. We ...
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5 views

Split-plot design with subsampling

Do split-plot designs allow sub-plot replicates or would they technically be pseudoreplication? For example, if fertilizer were the whole plot treatment and two types were applied to four fields (so ...
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1answer
32 views

How to prove absolute lack of correlation

I have a huge dataset of 17 variables. I intended to use 15 of those to predict the 17th, and I could not find any model (ANN) to do so. I know that one of those variables definitely predicts the ...
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18 views

Multiple poisson processes ?

I'm just trying to get my head around Poisson processes, as they're fairly new to me, and I've had a thought experiment that has been annoying me a little. Imagine a volume of some mixture hit by ...
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1answer
76 views

How the effects(Q'*y) in lm.fit calculated?

In R, lm.fit return an effects variable which equals to Q'Y or Rb (X=Q*R). However, I am confused by the dimension of this variable. In my case, dim(X)=6*2, dim(Q)=6*2, dim(R)=2*2, dim(Y)=6*10, so the ...
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1answer
50 views

Find distribution of underlying feature

Let's say I have a dataset with values for the variable $W$. The distribution of $W$ doesn't follow any obvious known distribution. I have a model that gives $W$ as a functions of $E$ and $K, W = ...
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65 views

proving regression with dummy variables gives same estimates as separate models

Let ($x_{i1}$, $x_{i2}$, ..., $x_{id}$, $y_i$), $i = 1,..., n$ be an i.i.d. multivariate sample and furthermore assume each observation belongs to one of possible $K$ categories. Assume for each ...
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1answer
32 views

Calculating spline curve with custom knot positions

I want to fit a spline curve to a simple dataset in R featuring a single custom knot, and extract the resulting models. The data is: ...
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1answer
32 views

Estimates and estimators strict definition

Let's look at a simple regression model: Y = $\hat{\beta0}$ + $\hat{\beta1}$Xi + $\hat{e}$ Estimator's definition is that it's a rule for arriving at an estimate, in this example it would be a ...
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11 views

Can one fuse graphical and quantitative techniques?

The NIST Engineering Statistics Handbook provides a great compendium of graphical and quantitative techniques. Every technique (graphical or quantitative) is usually explained with a set a questions. ...
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1answer
26 views

Statistical model that finds a coordinate (x,y) that minimizes the distance from a group of coordinates

A example would be if I launched a 100 tennis balls in the air and plotted the coordinates of where each landed. I would like to be able to find the point in the center of all those coordinates. I ...
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2answers
205 views

What would be an example of a really simple model with an intractable likelihood?

Approximate Bayesian computation is a really cool technique for fitting basically any stochastic model, intended for models where the likelihood is intractable (say, you can sample from the model if ...
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33 views

Which statistical methods should I be using?

The data is about: a binary variable as response; age; some binary categorical variables (0,1); some categorical variables which have more than 2 outcomes The goal is to find which factors affect ...
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1answer
51 views

Should I just accept that data is strongly not normal when changing families for a Generalised Linear Model has not worked?

I initially tested my data with ANOVA, but upon finding it to be not normally distributed I tried a Kruskal test. This did not make any difference either, so I tried a GLM, taking into account the ...
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50 views

Model selection using AIC in Survival Analysis

As far as I know, the model with lowest AIC is said to be better. However, according to the R output below, the writer says, the model called wei is better, whose ...
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38 views

Linear regression VS linear modeling

Can I claim that linear regression and linear modeling are the same topics? If not, what is the difference? Thanks.
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31 views

What is the “pdm” stat in the “rms” R package?

I am new to the world of Regression in statistics and I have been doing a research in which I am building an ordinal logistic regression model (ORM). In order to fit my ORM model, I am using the 'orm' ...
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34 views

A better fit for sinusoidal data

I have been struggling to fit my data to a sine curve. My data looks like: frame = data.frame(hour = c(0, 1, 2, ... 24), value = (numbers between 0 and 500)) I ...
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23 views

Two-Step estimation

I am currently trying to fit a model that has the following properties: (1) data for several years (2) two decisions/equations (probably involved): (a) one that explains an initial choice of product ...
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30 views

Using A Time Series To “Scale” Another

I know the "average theoretical cost per impression" for Jan 13 - Dec 13. I have other monthly time series for "total # of impressions", "total # of clicks" and "total number of conversions" for Jan ...
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27 views

Calculate pdf of complex model

I'm trying to model the distribution of effects of mutations (let's call it s) in evolution but I'm stuck in generating the probability distribution function (pdf) for my model. So, my model is a ...
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1answer
59 views

How to choose the best logit model using step function in R

I have the data below. I was wondering how I could choose the best model fit of logit model using step function in R. Here is the data in R format: ...
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0answers
10 views

Compare model performance to distinguish two groups

I would like to compare a sample from the normal population NP to a sample from an "abnormal" population AP. I have several variables measured on both NP and AP, say age, gender, and VAR1, VAR2 and ...
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2answers
146 views

How to summarize knowledge about importance of variables?

Assume that we have 30 features (inputs) each of which can potentially influence the result (output). We try to use the available ("observed") mapping from features to targets to develop a predictive ...
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1answer
23 views

Which models for suitable for integer values of independent and dependent variables

The data set has independent and dependent measurements that are events, and can be counted. What input / output models are appropriate for integer values? So far I've tried linear regression, and ...
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0answers
18 views

geeglm not recognizing clusters

I am fitting a geeglm model and it is not recognizing the clustering. my model is ...
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1answer
49 views

graphical representation of fixed effects from lmer

I have run a lmer model in R: ...
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24 views

Adding real zeros to a dataset vs. presence-only modeling?

I have a fisheries dataset for which I have calculated the number of fishing sets in each grid cell (100 km x 100 km) for each month of every year. Fishermen in this fishery are legally required to ...
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19 views

How to decorrelate residuals in r from the covariance matrix

I fit a geeglm model with clustered data and now I would like to decorrelate the residuals of the model in order to run model diagnostics. I read that if I can obtain the covariance matrix of the ...
4
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1answer
81 views

Are these data underdispersed? If so, what mechanisms may explain this?

Say someone who is well practiced (appears to have reached a performance plateau) shoots 20 free throws on 15 different days and is successful the number of times shown in the upper histogram (...
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21 views

log-linear models and exponential models?

What is usually referred to as "log-linear models"? Is a log-linear model an exponential model where the normalization constant is 1? (since its logarithm needs to be a linear function.) Or is there ...
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31 views

How do we calculate the $R^2$ statistic for a mixed model with one random intercept only?

I have read in previous posts that for mixed models with random intercepts only, the statistic for $R^2$ is $$R^2 = \frac{\text{V of intercept only model} − \text{V of full model}}{\text{V of ...
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1answer
33 views

How do models, parameters, specifications, restrictions and assumptions relate?

So this has been something I've been struggling with for a long time: The specification of a particular model is subjective. However, there seems to be objective ('true') values of the parameters we ...
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21 views

Transformations in Simple Linear Regression [duplicate]

Suppose a linear model for Y in a single predictor var, X. If the residuals show a pattern of increasing variance (wrt X), sometimes a transformation of Y, Y'=f(Y) is considered (where f is sq rt, ...
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26 views

Assumptions on a multiple linear regression model and elastic net

I am interested in using elastic net regression in place of an multiple linear regression. I know when you perform a multiple linear regression you should check the assumptions such making sure the ...
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32 views

How to choose the best regression model?

I have entered main effect terms in Block 1 and interaction terms in Block 2. Based on their beta values, the main effect terms were not significant in Block 1 and the interaction terms were not ...
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24 views

How can I assess if a parametric model can reproduce the same curve with different parameters?

I have a parametric model that I think it is "degenerate" in the sense that I can obtain the same model with different parameters. For example, if I have a convolution of two 1D Gaussians (say, G1 ...
2
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2answers
263 views

Was this the appropriate regression model?

I was recently proof-reading a friend's thesis (for their writing, not stats usage) when I came across a usage of a regression model which I would regard as incorrect. However, I'm pretty new to the ...
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18 views

Linear model for RCBD

Hi i am told to write a linear model for my stats assignment under an RCBD. ...
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25 views

Do assumptions for estimators affect population parameters?

TL;DR: Specifying a model (a collection of restrictions over a sample space) specifies the model parameters. Specifying an estimation procedure adds additional number of restrictions (assumptions?). ...
2
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1answer
18 views

Repeated measures - sum of biomassproduction

I am student in biology. For my examination I conducted a greenhouse experiment with gras. Actually I am not sure how to develop the correct model and unfortunately I haven't found an answer in the ...
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0answers
42 views

Fitting a model to data for prediction - best choice for data

I have some data I need to fit a model to that can be used for prediction (interpolation). The data is summarized by the plot below. The black line is x=y. I want to be able to fit a model so as I ...
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26 views

Model Averaging

Good Afternoon, I am working on model averaging of data collected about bird species and habitat vegetation. I have been using the MuMIn package in R and have taken a subset of all possible models ...