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

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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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59 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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11 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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$\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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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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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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27 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
48 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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1answer
54 views

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
59 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
39 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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260 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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85 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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1answer
145 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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1answer
25 views

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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1answer
56 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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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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234 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
54 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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206 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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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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134 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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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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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
101 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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132 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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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: ...
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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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186 views

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
346 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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1answer
630 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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2answers
182 views

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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2answers
2k views

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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447 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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174 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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1answer
220 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 ...