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

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HMMs with feature vectors (block HMMs?)

I'm quite new to HMMs, but still couldn't find an approach to fit HMMs in R, where we have feature vectors instead of single values. Or perhaps I simply didn't understand some of the proposed ...
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32 views

Can I use log linear to analyse qualitative data?

I have large qualitative biological data I want to analyse (eg High/medium/low abundance; present/not present; burned/unburned). I also have a qualitative dependent variable. Can I use log linear to ...
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Loglinear modeling of a contingency table AxA' imposing marginal distributions other than observed

Anybody knows if it is possible to adjust loglinear models to a AxA' contingency table, using marginal distributions of A & A' other than observed? A & A' are the same variable measured in ...
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Sample Sizes Based on Cost Efficiency

I am planning a biology experiment that is testing the predictors of biological markers.These predictors are: Gender.(Binary) Smoking status.(Binary) Alcohol consumption.(Binary) Hiv infection ...
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Interpretation of coefficient in log-linear model with share predictor

There are several questions on the interpretation of coefficients in log-linear models such as Interpreting regression coefficients of log(y+1) transformed responses Log linear model interpretation - ...
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54 views

How to interpret conditional odds ratios from a three-way contingency table?

I am trying to understand how to correctly interpret the estimated conditional odds ratios from a loglinear model on a three-way contingency table. This is an example from Agresti 2013 (p. 346, ...
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What is the null model for a four-way contingency table?

I'm working through this tutorial on the Titanic data in R, a four-way contingency table, and I'm unclear as to what is the NULL ...
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Interpretation of Log-Lin models - % Contribution? [duplicate]

My question is as folows: I know that for log-lin models the coef express % ΔY = 100*(b*ΔX) The question is, could it also be interpreted as the % Contribution of X gave to Y ? For example, if y = ...
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1answer
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Expected values vs. fitted values from log-linear model

Let's consider, for instance, a table like this one, with two independent categorical variables: x <- Titanic[,,2,1] I want to model this contingency table ...
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How to interpret a two-dimensional contingency table?

I am trying to understand how to interpret log-linear models for contingency tables, fitted by way of Poisson GLMs. Consider this example from CAR (Fox and Weisberg, 2011, p. 252). ...
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25 views

Cell count distribution assumption in loglinear modelling

In relation to loglinear analysis, SPSS gives two options for "Distribution of Cell Counts". The SPSS User Manual states: Assumptions. Two distributions are available in General Loglinear ...
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log-linear vs log-bilinear

I have seen these two terms used often. I can not really tell the difference between them they seem Identical to me. Can someone point me to a resource or give a simple explanation of what the ...
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1answer
32 views

Ensuring parameters of log linear model sum to 1

I am training a log-linear model with parameters $\theta$ using SGD. I want to ensure that my parameters will end up being probabilities i.e. $\sum_i \theta_i = 1$. One way to do this is by using ...
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Log-Linear Dependent Variable Ratio beyond 1: how to interpret regressors

I do run a regression model with a dependent variable = ratio calculated G=y/x which provides values between 0 - 9 for extreme cases. I use a log-linear model for my calculus with various continuous ...
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Contributions of variables in Log-Log models [duplicate]

I have built a model on log(units) sold and want to measure the contribution of each independent variable in the model (which are also in ...
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1answer
66 views

loglinear analysis, assumptions met?

We've data from a large ongoing project at a big science museum. We are showing people plates of food where we vary the plate shape (round or square; 0,1), food arrangement (polygonal or vertical ...
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27 views

Sample size log linear analysis?

Is there a rule of thumb for the sample size for a log linear analysis? For example, would it be inappropriate to use this analysis for a sample size of 50 with 3 predictors?
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69 views

$\chi^2 $ of multidimensional data

I want to test if two observations of nominal data accord to the same distribution. I am using the chi squared statistics to perform a chi squared homogeneity test and normalize the result with ...
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1answer
266 views

What's the right interpretation for parameter estimates in loglinear modelling?

I'm doing a loglinear analysis of the following data. Male is coded as 1, Female as 2. Senior workers are coded as 1, middle level as 2, and shopfloor as 3. A is coded as 1 and is the most ...
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23 views

How to obtain design matrix for log-linear models?

I need to solve the question below by hand. I think the appropriate log-linear model is $Y_{ij} = \mu + \alpha_i + \tau_j + \epsilon_{ij}$. So, after researching, I realized that I should ...
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Interpreting dummy variable in semi-log model

There are numerous theories on how to interpret the coefficients of dummy variables in a semi-log model but I still am not sure about it. Do we multiply the coefficient by 100 to get the change like ...
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36 views

Advice for test on categorical variables

I am working with two variables - variable 'A' is an independent categorical variable and has three levels 'a' 'b' and 'c'; variable 'B' is continuous response variable data I have classified into ...
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1answer
80 views

Using contingency tables for testing multiple dependencies

I know that I can determine the correlation / association / dependency between two variables (X= smoker, and Y=cancer) using chi-squared with a 2x2 contingency table: ...
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How do I find the odds ratio using the output of a loglinear model?

This was a homework problem from Agresti (7.2) that I didn't get, even after looking at my class' solutions. Help? So we are given the output of a loglinear regression ($λ_{ij}^{XY}$). The output is: ...
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1answer
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Regression Model for Binary Matched or Paired Contingency Table Results

This is a follow-up question on a prior thread about the same setup as follows: We have two "Methods" ("A" and "B") to diagnose a medical condition. We are not trying to determine which one is better ...
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Hilog-linear analysis for Categorical data

I found myself in a statistical predicament, and received a few suggestions from friends that didn't convince me a lot. I'm looking for advice on this predicament. I'd really appreciate it in advance. ...
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Correlated Proportions shown in a Mosaic Plot. What is the fitted model that generates residuals?

I have a 2-by-2 table of correlated proportions where I am plotting the positivity of two diagnostic methods applied on a sample of 216 individuals: ...
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Comparing Proportion of Positive Tests

A condition or disease (D) is measured using two different methods (A and B) in a sample of 1,000 individuals from a population. Using method A, the percentage of positive cases is 25%, whereas method ...
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1answer
78 views

Antilog of a semilog regression model with dummy variables

I have a semi-log regression model, with two continuous predictors, two categorical predictors (0 or 1 dummy variables) and a non-zero intercept. The response variable is log10 transformed, none of ...
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1answer
58 views

Selecting the best subset of features in binary logistic regression [duplicate]

I am using a binary logistic regression (a type of probabilistic statistical classification model, is used to predict a likelihood of belonging to a class (True, False)). I have 4 features and I want ...
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1answer
316 views

Fitting a heteroscedastic generalized linear model for binomial responses

I have data from the following experimental design: my observations are counts of the numbers of successes (K) out of corresponding number of trials (...
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94 views

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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2answers
266 views

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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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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Are log-linear models 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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330 views

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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1answer
64 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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1answer
238 views

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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31 views

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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1answer
154 views

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
55 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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369 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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1answer
129 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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161 views

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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2answers
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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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Log-linear or poisson model with R [closed]

I have a data.frame (myData) with 6 variables which are: ...