Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [log-linear]

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

1
vote
0answers
15 views

Log-linear regression: interpretation of RSE

AIM: To really, really understand what the model outputs mean in a log-linear regression, specifically, how to interpret the residual standard error (RSE). I have read this post but it does not ...
2
votes
1answer
35 views

Confidence interval of a log-linear regression

AIM: Make a confidence interval statement on a log-linear regression I have read posts like: Interpreting Standard Deviation of Natural Log Transformed Data Lognormal Regression? But they do not ...
0
votes
0answers
15 views

INTERPRET A REGRESSION MODEL WHEN OUTCOME VARIABLE IS LOG TRANSFORMED [duplicate]

I have used a linear model between a log-transformed outcome variable and a group of predictor variables. In this model, the dependent variable is in its log-transformed state, and the independent ...
0
votes
0answers
7 views

Log-linear BIAS adjustment

I have a loglinear model of: $log(\mu(S_{ij|gij}))=\alpha_0+\alpha_1g_{ij}$ where gij is distance, and Sij is connectivity There is a bias in the distribution of count values for the outcome ...
0
votes
0answers
29 views

Poisson log linear Regression: using either R or python

I was hoping someone can help me with this problem. I posted a similar question earlier but it's not the same. I have the following: A 2x2 matrix of structural connectivity values between brain ...
2
votes
2answers
104 views

Interpretation of β in case of log-lin model for relationship between X and Y

In many papers, the dependent variable is transformed by taking natural log. For instance, consider the following model: $$\newcommand{\Cov}{{\rm Cov}} \ln(\text{Y}) = \alpha + \beta\, X_1 + \epsilon ...
1
vote
0answers
27 views

Logistic regression and log-inear

I do not know why log-linear is non-sensitive margins and logistic regression is sensitive margins. Both are using odds ratios Can anyone give some explanation.
1
vote
1answer
224 views

Predicting probabilities after log-linear regression

I would like to estimate a log-linear regression and examine the results with Stata's marginsplot command. I have transformed my dependent variable into natural logarithm (to make a highly skewed ...
0
votes
1answer
141 views

Check log-linearity of a continuous variable to predict survival

I have the following survival data: cases with a continuous variable risk_factor wich higher values having a higher HR (1.026, CI: [1.017-1.034], p = 2.03e-09) for ...
1
vote
0answers
19 views

What is the appropriate analysis for this type of repeated measures multi-binary data?

There is a popular theory within psychology that certain emotions will trigger "prototypical" facial expressions defined by the simultaneous contraction of specific facial muscles. For example, if a ...
3
votes
1answer
64 views

Log-linear regression preferred over logistic regression for categorical vars?

I read here that, when all variables are categorical, log-linear is preferred over logistic regression "because log-linear is merely an extension of the chi-square test." I don't have a stats ...
0
votes
0answers
27 views

Conceptual question about log-linear analysis

I have measured the binary occurrence of three types of events on multiple days. Theory suggests that these events are not independent and should occur on the same days more than would be expected ...
0
votes
0answers
71 views

Difference between PROC CATMOD and PROC GENMOD for log-linear regression

I am reading "An Introduction to Categorical Data Analysis, Second Edition" by Alan Agresti. Data I am trying to fit is presented in Table 7.3 in the book. The log-linear regression by PROC GENMOD is ...
5
votes
1answer
60 views

How do I interpret the coefficients of a log-linear regression with quadratic terms?

I have a regression equation of this kind: $$\log {y} = a + bx + cx^2 + \epsilon$$ where $a$ is the intercept, $b$ and $c$ are the coefficients of $x$ and $x^2,$ and $\epsilon$ is the error. How do ...
0
votes
0answers
49 views

Log-linear analysis versus independent chi-squares result

I am analyzing a number of categorical variables, and used log-linear modeling in SPSS to see whether there is association between any of them. I have some variables, however, which turn out to be non-...
0
votes
2answers
169 views

How can I interpret intercept when my dependent variable is in log form?

My model consists of both log independent and dependent variables, and percentage share, as well as number of people and etc. My dependent variable is log(GPD per capita in USD), my statistic package ...
0
votes
1answer
43 views

Newton-Raphson Error

According to Agresti(2013) pg 364-365, iterative methods such as Newton-Raphson methods, $ \begin{aligned} \beta^\text{new} &= \beta^\text{old} + (X^{T}WX)^{-1}X^{T}(V) \end{aligned} $ help to ...
2
votes
0answers
93 views

Log-normal vs. log-linear vs. logging the response variable

I've been reading a lot of Wikipedia pages and StackExchange/CrossValidated posts, and I have come to a point where I realize I do not understand some of the terminology I have been using. What's ...
0
votes
0answers
54 views

calculate P-value loglinear coefficient marginal models fitted with maximum likelihood

i am using the cmm package in R to fit graph models between 4 categorical dummy variables of interest such that A is marginal independent from B B is conditional independent from A and B given D. I ...
3
votes
1answer
183 views

How are graphical models “a subset of log-linear models”?

Caveat lector: I am not sure what is meant by a "log-linear model". The Wikipedia page makes it seem as if log-linear model is an alternative term for exponential family. This description of a book ...
1
vote
1answer
65 views

Log-linear model vs Multinomial-Dirichlet model for contingency table

Given a 1x3 contingency table of the form $$[y_1,y_2,y_3]$$ where each $y_i$ represents a count on some random variable $$X\sim Multinomial(n, \pi_1,\pi_2,\pi_3)$$ We can estimate the vector of ...
1
vote
2answers
684 views

Poisson regression vs log-linear model

I'm confused about the difference between log-linear model and poisson regression and I am not sure which one to use to answer my research question. In the experiment, participants were grouped into ...
2
votes
1answer
110 views

What does it mean if the residuals for a log-linear model are all zero?

Probably a very basic question, but I have a contingency table formed of 3 categorical variables (one with 3 levels, the rest with 2) and have been asked to try to test the association between them ...
0
votes
0answers
90 views

How is a covariate added to a log-linear model?

There are a number of good Q/As about log-linear models (i.e., here). The data I am analyzing is off shifts between categories, 0, ...
1
vote
0answers
125 views

Bootstrapping in contingency table

I have categorical data represented by a two-way contingency table (say $Y_{1}$ and $Y_{2}$, each with three levels). The data consists of fully observed counts (where data on both $Y_{1}$ and $Y_{2}$...
1
vote
0answers
36 views

Present difference in estimates for positively-skewed continuous outcomes

I'm working with a dataset containing results from two types of cognitive tests: Pairs-matching test (say, $Y_1$), which recorded as number of incorrect matches (discrete count of mistakes, ranging ...
0
votes
0answers
664 views

semi-log (log linear) regression model

I know there are different reasons for choosing a particular model (e.g. log log, semi log, lin log). I read somewhere that the semi log (where only the log of Y is taken) corresponds to a ...
-1
votes
0answers
51 views

Real-world implications of categorical interactions

I'm studying the interaction of three categorical variables but I'm struggling to explain what the interactions mean in real-world terms. The three variables each have two levels (yes/no). They are: ...
1
vote
1answer
44 views

Is there a multiple regression model with both percentage and unit changes in $Y$?

In a standard linear model, $Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2$, a unit increase in $X_1$ leads to a $\beta_1$ increase in $Y$ (likewise for $X_2$). In a log-level model, $\ln(Y) = \beta_0 + \...
1
vote
0answers
52 views

Distinguish 2-way and 3-way interactions in binary data, independent of reference category

$\newcommand{\P}{\mathbb{P}}$I have three variables $X_a, X_b, X_c \in \{0, 1\}$ and I define a joint distribution: $$ \P(X_a, X_b, X_c) \propto \exp \left \{ \theta_{abc} \mathbb{I}_{\{X_a = 0 \...
2
votes
1answer
109 views

Effect Size log linear model when predictor is in percent

When performing a linear regression with a log-transformed dependent variable, one has to exponentiate the estimated coefficient of a predictor, subtract it by one and multiply it by 100 in order to ...
1
vote
0answers
81 views

Analysing count data for within-subjects design

I have the following experimental design but I'm not certain which analysis I should use to test my claim: I have a binomial outcome (correct, false = 1, 0). Each participant takes three questions, ...
1
vote
1answer
568 views

Log-linear regression vs. Poission regression

In this post, OP asked the difference between log linear regression and logistic regression. Two answers in the post are very clear and directly address OP's question. I understand Log-linear ...
1
vote
0answers
123 views

On the (wrong?) interpretation of log-linear models

For simplicity, let $X$ be a continuous random variable and assume that ${E}[\ln(Y)|X] = \beta X$, i.e., the conditional expectation of $Y$ given $X$ is proportional to $X$. According to many ...
6
votes
1answer
154 views

What distribution to fit if the log of log is still convex?

I am trying to fit a model with variable x, and y. plot(x, y) shows that it is convex (downward) and decaying which makes me think I need to make a log ...
1
vote
0answers
148 views

Log-linear analysis of transition matrices using R

I have some demographic transition matrices that I want to compare using a log linear analysis, but am having some trouble with model construction in R. I have read through several tutorials on log-...
1
vote
2answers
249 views

Check for endogeneity

I run a log linear model, as $log(y) = b_0 + b_1x_1 + b_2x_2 + e$. I think $x_1$ may be endogenous and I would like to test it, so that I can consequently run a two-stage model. I would like to know ...
3
votes
0answers
160 views

Analysing categorical variables across several categories (LogLinear analysis?)

I'm editing this question in hopes of improving it, and thus receiving some advice on it. I have collected info on diet from several populations. There are different groups (e.g. ElMolo) made of ...
3
votes
1answer
197 views

Nonlinear regression for a log-linear model

I have data points $(x_i, y_i), i = 1, \dots, N$, and a model (log-linear) with parameters $w_j, j = 0, \dots, m$ such that $y_i=\alpha_i e^{w_0+w_1x_i+w_2x_i^2+\dots+w_mx_i^m}+ n_i$, where $n_i$ are ...
2
votes
0answers
36 views

Bacteria counts in petri dish confusion

I'm currently (attempting) to help a fellow student with some statistical analysis. Basically she is looking at the effect of a sugar treatment on bacterial growth within a petri dish. She has three ...
2
votes
1answer
228 views

What's the difference in growth of Y in a linear regression model when using a log-lin model or a lin-log model

Description I'm currently studying a chapter on linear regression analysis. I have come to a section where we study the interpretation of the coefficients with logarithmically transformed variables. ...
1
vote
0answers
31 views

I want to find contributions of each independent variables

I would like to get contributions of independent variables (factors driving sales) in absolute dollar values. The log linear equation looks like this: $$\ln(\text{sales})=b_1 \times \ln(\text{...
2
votes
1answer
110 views

Multivariate Log-linear Model

I know from here that a log-linear model can be used to estimate a conditional probability of class $c$ given the feature representation $d$ of datapoint $x$. $p(c|d;\theta) = \frac{exp(\theta.d_c)}{\...
1
vote
2answers
174 views

Applied interpretation of coefficients of log linear regression model

I'm in the process of using a log-linear transformed OLS model to predict the impact of temperature changes on sales. I know that the interpretation of a log-linear model loosely follows as: for each ...
0
votes
1answer
1k views

What is the difference between Poisson Regression and Log-linear Models in terms of their usage and which should I use?

I'm currently analyzing a data set from an experiment, where participants could give one of three types of answers: 1) Correct, 2) incorrect-congruent, 3) incorrect-incongruent. The priming ...
2
votes
1answer
1k views

offset in glm for poisson regression

The offset argument in the glm() quite troubles me. As below, m3 is the usage of offset that I have seen. m4 is a manually calculated analog. But the result obtained is completely different, with m3 ...
0
votes
1answer
421 views

Log-linear model, Poisson regression, categorical variable with 100 levels

I want to compare the incidence rate(asum) of 100 different cities(cityID) to see if there are significant differences among them. Given that the incidence rate is following Poisson, so it is a log-...
0
votes
1answer
75 views

problem with the relationship between log linear and logistic regression models

I am supposed to fit a logistic regression model and the find the log- linear model which correspond to it, fit that model and show the correspondence between parameters. But it is not working, I am ...
1
vote
0answers
111 views

Converting Dirichlet distribution to distribution on the log-linear parameters

Dirichlet prior/posterior provides a probability density on distributions over a multinomial variable. It has the form : $P(P) \varpropto \prod_i{P_i^{\alpha_i-1}}$ I can also describe the ...