The log-linear model is a form of Poisson regression that allows for the analysis of multi-way contingency tables.

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Linear regression with log dependent variable

I have the following regression: $log(Y) = \alpha + \beta X + \epsilon$ with $E[\epsilon] = 0$ and $var(\epsilon) = \sigma^2$. There is no assumption on the distribution of the errors $\epsilon$. In ...
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Type of inference to use with log-linear Poisson glm on contingency table frequency counts

I was doing some log-linear models to test for interactions/associations in multiway contingency tables (based on the tutorial here, http://ww2.coastal.edu/kingw/statistics/R-tutorials/loglin.html). I ...
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Log Linear Models: Interpretation when None Fit

This is question 9.6 from Categorical Data Analysis by Alan Agresti (Wiley, 2013). The question asks us to find a Log Linear with the best fit for a 2x2x2 contingency table. The following are the ...
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35 views

Exploring dependencies between variables in log-linear models

Hi there I'm using R to perform some multivariate data analysis on health data. I'm currently using the glm() function with ...
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Log-linear analysis in Excel

Is it possible to conduct a log-linear analysis in Excel? Is a log-linear analysis (as defined in SPSS) the same as a log-linear regression?
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39 views

Parameter estimation in log linear models

Can anyone explain to me how parameter estimation is computed in log linear models? I followed this paper which is quite good, however I'm a bit confused in the parameter estimation part which is ...
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Numerical stability of IWLS for Gamma models with log-link

The combination of a $\Gamma$-distribution with the log-link function in a generalized linear model can be a useful model. However, in my experience the iterative weighted least squares (IWLS) ...
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Nested analysis for categorical variables in SPSS

I have categorical data in the form of correct response (0/1 for each item respectively) and prevalence of a misconception (0/1 for each misconception on each item) from a pre and post assessment. ...
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Is it correct to measure R coefficient for LOG LIN models

I have a model where the $y$ is very skewed and I convert it to log and run a log linear model. But, I have doubts about the way to measure the error, because in the original variables the error ...
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Why use chi-square (or other) statistic over regression modeling for 2x2 contingency tables?

I have a question regarding the use of logistic/log-linear regression vs. contingency test statistics, such as chi-square. Can someone explain to me why it would ever be preferable to use test ...
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1answer
51 views

Regression model for $f(x_1, x_2) = a + b x_1\log x_2$

Which regression algorithm do I need to use to fit the coefficients of $f(x_1, x_2) = a + b x_1\log x_2$? Will linear regression with an independent variable $x_1 \log x_2$ work?
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How to scale data in a log-lin regression

I am currently doing exam revision, and I am stuck on a question in one of the past exams. It asks us to data scale the following equation: $\ln W$ = 2.54 + 0.4...
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Equivalence of log linear and logistic regression for categorical variables

This question is similar to Does every log-linear model have a perfectly equivalent logistic regression? but my problem is understanding the proof.Every proof is very similar to the one found on: ...
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154 views

Using log-linear models for presence/absence data in wildlife

I'm working on a project wherein I compare the presence/absence of a number of bird and herptile species between wetlands that have received three different treatments. The populations were surveyed ...
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modeling rates with machine learning tools (svm, gbm, nnet)

I have a numeric integer variable that is knowly proportional to an exposure measure plus other continuous / categorical covariates. If I were to use classical log-linear glms i would model ...
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1answer
70 views

Ambiguity with multinomial logit models

I have always thought that, when dealing with multinomial logistic regression, the idea was to linearly model the "logistic" functions of the probability densities of the different response categories ...
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Volume contribution decomposition in Log-log models

I am currently working on pricing analysis : the effect of competitor SKU pricing on the number of units sold of my SKU. The model was built on the log(units) sold. I want to measure the contribution ...
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Log-linear regression vs. logistic regression

Can anyone provide a clear list of differences between log-linear regression and logistic regression? I understand the former is a simple linear regression model but I am not clear on when each ...
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How to model zero-inflated continuous response using categorical predictors - preferably resulting in multiplicative parameters

I'm having trouble finding a suitable model for predicting the AVG value (revenue in cents) of a single click on a product on a large e-commerce site. (assuming a click leading directly to a purchase ...
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Log-linear or poisson model with R [closed]

I have a data.frame (myData) with 6 variables which are: ...
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Correct interpretation of coefficient when dependent variable is change in log of y and independent variable is change in x? [duplicate]

Say I have a model such that the dependent variable is the change in the log of $y$, $\Delta ln(y)$ and the independent variable is the change in $x$, $\Delta x$. Let's say the coefficient is equal ...
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Use of log-linear analysis with percentage data

Although my question sounds like I have data suitable for log-linear analysis, I may not. I have a large number of students (about 1000) of two levels who solved a test of 8 different types of ...
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1answer
213 views

Correlation between a linear and a log series

I need to identify a relationship between two variables. I have two sets of measures performed by two competing systems, and I would like to compare how close the two systems are to each other. Here ...
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Penalized methods for categorical data: combining levels in a factor

Penalized models can be used to estimate models where the number of parameters is equal to or even greater than the sample size. This situation can arise in log-linear models of large sparse tables of ...
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Log-linear analysis vs Breslow-day test

I would like to test independence of several categorical variables for a few datasets. I believe Breslow-day is possible for some of the analyses I want to do, and log-linear may be possible for all, ...
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533 views

Does every log-linear model have a perfectly equivalent logistic regression?

I am trying to fit a log-linear model to a large number of variables from survey data. There are some reasons that it might be preferable to fit logistic regressions to that data instead. Several ...
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Is there a reliable algorithm to identify a low-dimensional log-linear model that fit high-dimensional data (if one exists)?

I am working with data from a complex survey, with over 100 variables per observation. From this I will be selecting 30 to 40 variables. All variables are categorical, with number of levels ranging ...
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Which statistical model should I apply when the DV is a count variable? [duplicate]

What is best statistical model to assess effect of habitat type and year (categorical factors), and ...
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How to test (in Stata) whether the gender distribution of employees to jobs differs across two companies?

I have data on several companies where some are headed by a male CEO while others by a female CEO. As you can imagine, the jobs within these companies have different gender compositions. What I am ...
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Replication in multivariate logit in R

I am new to both logit models and GLMs, but I think one of those two model classes might be the correct analysis for my data set: I am interested in comparing the composition of the diet of fish ...
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4answers
347 views

Log Linear Models

Can someone please explain why do we use Log Linear Models in very lay-man terms? I come from Engineering background, and this is really turning out to be a difficult subject for me, statistics that ...
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170 views

Using interaction term for describing non-linear relationship in regression model

I have created a Rscript for illustrating my question: ...
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189 views

Why can log-linear models for binary data be trained using Poisson regression

Any arbitrary distribution of a multivariate binary variable can be represented as a log-linear model. That is, for $X = (X_1,\dots,X_d)$ a $d$ dimensional binary rv, the distribution can be written ...
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How to analyze residuals of Poisson log-linear model?

I have bird count data and use classical poisson loglinear model, i.e. we have counts obs(i,j) - observed count for site i and ...
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Do zero counts need to be adjusted for a likelihood ratio test of poisson/loglinear models?

If there are 0's in the contingency table and we're fitting nested poisson/loglinear models (using R's glm function) for a likelihood ratio test, do we need to ...
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Using an OLS coefficient to estimate a non-linear coefficient

Using OLS, I've estimated the following equation: $y_i = \alpha_0 + \alpha_1 X_i + \epsilon_i$ I know that theoretically, the following should be true: $y_i = a + (1-e^{-\lambda 60}) X_i$ Is ...
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Appropriate way to deal with a 3-level contingency table

I have a three level contingency table, with count data for several species, the host plant from which they were collected and whether that collection happened on a rainy day (this actually matters!). ...
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Mixed effects log-linear models

There are occasions where I would like to fit log-linear models where the independence assumption between observations is violated. It is the normal case that I have multiple observations from each ...