Logistic Regression - Science topic

for an outcome (relapse after therapy, yes or no), when univariate logistic regression was applied; p value for each variable was significant, then if multivariate logistic regression was applied with enter method or forward selection, all the significant variables P values became high as 0.9 or even 1.0.

how can these results be solved and what is the explanation?, what step did we miss?

Logistic regression can handle small datasets by using shrinkage methods such as penalized maximum likelihood or Lasso. These techniques reduce regression coefficients, improving model stability and preventing overfitting, which is common in small sample sizes (Steyerberg et al., 2000).

Steyerberg, E., Eijkemans, M., Harrell, F. and Habbema, J. (2000) ‘Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets’, Statistics in medicine, 19(8), pp. 1059-1079.

What are the simple effect and main effects in the regression model? While I'm familiar with the main and intraction effects in the multinomial logistic regression model, I have no idea what simple effects are and how they are involved with the regression model. I'd greatly appreciate it if you could explain this and recommend useful resources. Thank you.

One key limitation is multicollinearity, which affects the interpretability of results. Moreover, oversaturation in models with too many predictors can result in overfitting. Small datasets, or sparse data, can also challenge the accuracy of logistic regression models.

Logistic regression can be adapted for survival analysis by modeling grouped event times to estimate parameters similar to those in proportional hazards models. This approach helps when analyzing intervals for event occurrences (Abbott, 1985).

Logistic regression provides the odds ratio for each predictor, quantifying how a one-unit increase in the predictor variable impacts the odds of the outcome, assuming other variables are held constant. Specifically, the odds ratio measures the likelihood of an event occurring versus not occurring, given the predictor. For example, an odds ratio of 2.0 means that for each unit increase in the predictor variable, the odds of the outcome happening double.

One limitation of the chi-square test is that it does not provide information on the strength or direction of the relationship between variables; it only indicates whether an association exists. Additionally, the test can be sensitive to sample size; large samples may lead to significant results even for small, trivial associations. Therefore, researchers should complement chi-square results with additional analyses, such as logistic regression, to gain deeper insights into the data.

Hi! I'm currently conducting a study where I'm looking at an outcome variable that is ordinal, with the regressors and mediating variables being continuous in nature. Given that this is not a linear regression model, which is normally when the Baron and Kenny (1986) method is used, how would one go about testing a mediating relationship?

Interactions and confounder testing in logistic regression are essential for understanding how different variables influence the outcome of interest. what is the procedure of Interactions and cofounder testing in logistic regression?

Hello, fellow researchers! I'm hoping to find someone well familiar with Firth's logistic regression. I am trying to analyse whether certain emotions predict behaviour. My outcomes are 'approached', 'withdrew', & 'accepted' - all coded 1/0 & tested individually. However, in some conditions the outcome behaviour is a rare event, leading to extremely low cell frequencies for my 1's, so I decided to use Firth's method instead of standard logistic regression.

However, I can't get the data to converge & get warning messages (see below). I've tried to reduce predictors (from 5 to 2) and increase iterations to 300, but no change. My understanding of logistic regression is superficial so I have felt too uncertain to adjust the step size. I'm also not sure how much I can increase iterations. The warning on NAs introduced by coercion I have ignored (as per advice on the web) as all data looks fine in data view.

My skill-set is only a very 'rusty' python coding, so I can't use other systems. Any SPSS friendly help would be greatly appreciated!

***

Warning messages:

1: In dofirth(dep = "Approach_Binom", indep = list("Resent", "Anger"), :

NAs introduced by coercion

2: In options(stringsAsFactors = TRUE) :

'options(stringsAsFactors = TRUE)' is deprecated and will be disabled

3: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

logistf.fit: Maximum number of iterations for full model exceeded. Try to increase the number of iterations or alter step size by passing 'logistf.control(maxit=..., maxstep=...)' to parameter control

4: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

logistf.fit: Maximum number of iterations for null model exceeded. Try to increase the number of iterations or alter step size by passing 'logistf.control(maxit=..., maxstep=...)' to parameter control

5: In (function (formula, data, pl = TRUE, alpha = 0.05, control, plcontrol, :

Nonconverged PL confidence limits: maximum number of iterations for variables: (Intercept), Resent, Anger exceeded. Try to increase the number of iterations by passing 'logistpl.control(maxit=...)' to parameter plcontrol

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Hello!

I am performing a study to introduce a new test for a specific eye disease diagnosis. The new test has continuous values, the disease can be present in one or both eyes, and the disease severity by eye could also be different. Furthermore, the presence of the disease in one eye increases the probability of having the disease in the other eye.

Because we aim to estimate the diagnostic performance of the new test, we performed the new test and gold standard for the disease in both eyes in a sample of patients. However, the fact of repeated measurements by each patient could introduce intra-class correlation to the data, limiting analyzing the results as i.i.d. Therefore, diagnostic performance derived directly from a logistic regression model or ROC curve could not be correct.

What do you think is the best approach to calculate the AUC, sensitivity, specificity, and predictive values in this case?

I think that a mixed-effects model with the patient as a random intercept could be useful. However, I do not know if there is any method to estimate the diagnostic performance with this type of models.

Thank you in advance.

Im currently conducting a study on factors that may influence the chances of patients having delirium post-surgery. I have around 30 variables that have been collected including continous (HB, HBA1c, urea levels (pre-op, peri-operatively and urea difference), alcohol audit c score, CPB duration etc), categorical (blood transfusion - yes/no, smoking status, drinking status, surgery type etc) and demographic information (gender, age, ethnicity). The study also looks at whether our current measurements of risk of delirium are good predictors of actual delirium (the DEAR score, consists of 5 yes/no questions and a final total score).

As with many studies using data from previous patients (around 750), there are a lot of missing information in many categories. I have already been conducting assumption tests including testing the linearity of the logs and this has excluded some variables. I am using SPSS if anyone knows of anything on these systems i can use.

QUESTIONS:

1. (More of a clarification) I have not been using the pre-op, peri-op and difference between urea levels scores in the same models as i assume this violates the assumption of independency between variables - this is correct yes? If so, I assume that other variables that measure the same thing in different ways (e.g., age at time of surgery and the delirium risk quesiton that asks if patients are over 80) should also be excluded from the same model, and instead test the model with each difference and select the strongest model for prediction?

2. What should i do with my missing data? There is a big chunk (around 50% of the 750ish patients included) that dont have a delirium risk score - should i only conduct my model with patients that have a score if im investigating the validity of the DEAR score for predicting delirium or will SPSS select these case automatically for me? Other missing data includes HBA1c (because we do not test every patient), ethnicity (as the patient has not declared their ethnicity on the system), Drinking status (no audit c score made for the patient as they either dont drink or were not asked about their drinking status) etc... I've seen some chat about using a theory to generate predictions for the missing information but I feel like using this for example, for gender wouldnt be sensible as my population is heavily male centric.

3. Part of our hypothesis is identifying a model of prediction for males and females separately if they show different significant influences on chances of delirium. Can i simply split my data files by gender and conduct the regression that way to get different models for each gender? When i have done this, I have not used gender as a variable in the regression, but have tested it with all the data and found a significant influence of gender but only when tested with age and ethnicity or on its own (not in a model that includes all of my variables, or in a model that includes only the significant variables determined from testing various models). Should I just ignore gender all together?

Sorry for what may seem to be very silly or 'dealers choice' questions - I am not very experienced with studies with this many variables or cases and usually have full data sets (normally collect data in the here and now, not based on previous patients).

Any help or suggestions would be much appreciated!

I am using unit level data (IHDS round 2) & Stata 17

Seeking to clarify my understanding of determinants of minimal sample size for logistic regression.

I am looking at predictors of worry after injury. There are n=120 people in the ‘not worried’ group, and n=33 in the ‘worried’ group (I am aware some may take issue with categorising a continuous outcome!).

My understanding is that the sample size is largely dependent on the number of people in the smaller grp (so in this case, the ‘worried’ group. I also understand that the 10 events per predictor rule of thumb guide for logistic regression tends to underestimate the sample required.

Given this, do I have any grounds to run a logistic regression with even just 2 predictors (eg Age and Sex)? Or am I limited to t-tests/chi-squared as a first step / means of preliminary exploration of associations?

The Analyse for Pilot study should be based on the descriptive stat and ideally not involved inferential stat.

what about exploratory study? can I do inferential stat like cox regression or logistic regression for exploratory study?

Thanks

I have two outcome variables:

1- binary

2- repeated measure

There is only one independent variable which is time varying covariate

Iam looking for the article to write the Stat for:

1- logistic regression with time varying covariate (the outcome is binary)

2- mixed effect logistic regression (the outcome is repeated measure)

other question is there is no problem if I have only one independent variable?

Thank you!

I plan to apply multinomial logistic regression using the complex sample option of SPSS. The dependent variables have 04 categories (low, moderate, high, and very high), and 05 independent variables are classified/categorized as Yes/no. The 'low' category of dependent variable will be the reference. 'No' will be the reference category of each independent variable.

I plan to apply multinomial logistic regression using the complex sample option of SPSS. The dependent variables have 04 categories (low, moderate, high, and very high), and 05 independent variables are classified/categorized as Yes/no. The 'low' category of dependent variable will be the reference. 'No' will be the reference category of each independent variable.

Dear Colleagues

I carried out a multinomial logistic regression to predict the choice of three tenses based on the predictor variables as shown on the image. According to the SPSS output below, the predictor variable "ReportingVoice" appears to have the same result as the intercept. I wonder why this issue happens and how I should deal with this problem. Thank you for your help. Please note that I'm not good at statistics, so your detailed explanation is very much appreciated.

Hi,

How do I interpret the p-values of the goodness-of-fit test results in ordinal logistic regression? I'm looking for literature in the internet but I couldn't find any.

The tests that I need to know the interpretations are:

1) Lipsitz test

2) Hosmer-Lemeshow test

3) Pulkstenis-Robinson test

Also, any recommendations on the literature of the interpretations?

Thank you very much.

Hello every one,

I run binary logistic regression in SPSS but i did not have results because of complete separation. How can i solve this proplem?

Thanks in advance.

Hi,

I am trying to do an Ordinal Logistic Regression (OLR) in R since this is the regression analysis that I need to use for my research.

I followed the tutorial video I found in YouTube in setting up the two sample models. Here are the 2 codes for the models:

modelnull <- clm(as.factor(PRODPRESENT)~1,

data = Ordinaldf,

link = "logit")

model1 <- clm(as.factor(PRODPRESENT)~Age+ as.factor(Gender)+Civil Status,

data = Ordinaldf,

link = "logit")

Next, I followed what the instructor did in doing anova and an error message prompted. It says, Error in UseMethod("anova") : no applicable method for 'anova' applied to an object of class "c('double', 'numeric')"

Is there something wrong in setting up the two sample models, hence, an error message in prompting? What needs to be done to fix the error?

Please help.

Thank you in advance.

I have fit a Multinomial Logistic Regression (NOMREG) model in SPSS, and have a table of parameter estimates. How are these parameter estimates used to compute the predicted probabilities for each category (including the reference value) of the dependent variable?

I want to estimate the half-life value for the virus as a function of strain and concentration, and as a continuous function of temperature.

Could anybody tell me, how to calculate the half-life value in R programming?

I have attached a CSV file of the data

I'm applying multinomial logistic regression to examine the influence of various predictors on three outcomes. The issue I'm facing now is that the SPSS outputs returned extremely large positive/negative values of some predictor values, that is, "quasi-complete seperation". I want to deal with this problem using penalized logistic regression since I do not want to remove the variables. I would greatly appreciate it if you could give me advice on how to perform penalized logistic regression or recommend statistics services. Thank you.

Hello everyone,

I have research including two objectives,

one of them is to assess the relationship using logistic regression.

another one is comparing two groups using Mann-Whitney U Test.

if I want to apply sample size formulation need to calculate separately for each objective?

also what is the Minium sample size for logistic?

Thanks.

Hi there, I am currently struggling with running analysis on my data set due to missing data caused by the research design.

The IV is binary -- 0 = don't use, 1 = use

Participants were asked to rate 2 selection procedures they use, and 2 they don't use, and were provided with 5 option. So, for every participant there are ratings for 4/5 procedures.

Previous studies used a multilevel logistic regression, and analysed the data in HLM as it can cope with missing data.

Would R be able to handle this kind of missing data? I currently only have access to either SPSS and R. Or is there a particular way to handle this kind of missing data?

Hi! My hypothesis have 2 Likert scaled variables to check the effect on one dichotomous dependent variable. Which test to put in SPSS? Can the dichotomous variable later be checked as a DV in mediation analyses?

Hi all,

I'm currently working on a logistic regression model in which I've included year as a random variable, so in the end I am working with a Generalized Linear Mixed Model (GLMM). I've built the model, I got an output and I've checked the residuals with a very handy package called 'Dharma' and everything is ok.

But looking for bibliography and documentation on GLMMs, I found out that a good practice for evaluating logistic regression models is the k-fold cross-validation (CV). I would like to perform the CV on my model to check how good it is, but I can't find a way to implement it in a GLMM. Everything I found is oriented for GLM only.

Anyone could help me? I would be very thankful!!

Iraida

An emblem model for fraud detection in credit cards using logistic regression and random forest algorithms.

I really want to learn about I really want to learn about Linear Mixed-Effects Modeling

in SPSS or Mixed Models for Logistic Regression in SPSS. Can you show me:

1. Theory of those two models

2. How to run in SPSS

3. Is there a way to select variables into mixed models and random effects models?

Thank you

in SPSS or Mixed Models for Logistic Regression in SPSS. Can you show me:

1. Theory of those two models

2. How to run in SPSS

3. Is there a way to select variables into mixed models and random effects models?

Thank you

I have a problem with running my logistic regression. When I run my analysis, I get really strange values and I cannot find anywhere how I can fix it. I already changed my reference category and that led to less strange values but they are still there. Also, this only happens to two of my eight predictors. These two predictors have multiple levels/categories.

Can someone explain to me what's wrong and how I can fix it?

I am curr research at phising detection Using URL . Using logistic Regression model . I have data set 1:10 ratio 20k legitimate and 2 k phishing .

I am doing a conditional logistic regression in RStudio using the clogit()-function from the survival-package. I want to assess linearity on the log-odds scale and I prefer not to use Box-Tidwell test, instead I am looking for a quicker way.

I wonder if anyone here has a tip on how to quickly and easily assess this?

How to interpret negative total and direct effects and positive indirect effect? all are significant in mediation analysis

X --- M --- Y

Total Effect: Negative (-0.42)

Indirect Effect 1: Positive (0.03)

Indirect Effect 2: negative (-0.22)

Indirect Effect 3: positive (0.06) - Not significant

Direct Effect: Negative (-0.29)

Is the Hosmer and Lemeshow test in binary logistic regression mandatory? because I read that this test has many flaws, especially in data with repeated values.

in my research with data that has a lot of repeating values ​​it always doesn't pass the test. can I skip this test?

Can someone suggest a R package for Blinder Oaxaca decomposition for logistic regression models?

I have molecular data (0,1) and a trait with continuous variables. My goal is to detect the significance of markers associated with the trait. Which statistical analysis should I perform? Should I use a t-test, logistic regression, or something else?

In most of the studies tobit regression is used but in tobit model my independent variable is not significant. Whether fractional logistic regression is also an appropriate technique to explore determinants of efficiency?

I have data from a questionnaire study structured like so:

I am using SPSS to run an ordinal logistic regression with 'response' as my dependent variable and the other 5 as my independent variables.

When putting the data into SPSS I have coded it as follows:

When I run the regression, this is my output with a significant result highlighted in yellow (attached).

From what I've read and understood about interpreting the results of an ordinal logistic regression, this is saying that:

"The odds ratio of being in a higher category of the dependent variable for males versus females is 2.244" which is saying that males are more likely to agree more strongly than females.

However, when I create a graph looking at the split of responses between males and females it shows that females are actually more likely to agree more strongly than males (see attached).

I would be grateful if anyone could help me to understand what I'm doing wrong - either in my modelling or my interpretation.

I am trying to examine the effects of studentification on private residents in a studentified area, either it is positive or negative (which is coded as 1 or 0 respectively) as the dependent variable.

The independent variables are effects of studentification (across literatures) on 5-likert scale.

The question is am I to also dichotomies the likert scale responses from (strongly disagree, disagree, neutral, agree and disagree) to (1: positive, 0: negative)?

Firth logistic regression is a special version of usual logistic regression which handles separation or quasi-separation issues. To understand the Firth logistic regression, we have to go one step back.

What is logistic regression?

Logistic regression is a statistical technique used to model the relationship between a categorical outcome/predicted variable, y(usually, binary - yes/no, 1/0) and one or more independent/predictor or x variables.

What is maximum likelihood estimation?

Maximum likelihood estimation is a statistical technique to find the best representative model that represents the relationship between the outcome and the independent/predictor variables of the underlying data (your dataset). The estimation process calculates the probability of different models to represent the dataset and then selects the model that maximizes this probability.

What is separation?

Separation means empty bucket for a side! Suppose, you are trying to predict meeting physical activity recommendations (outcome - 1/yes and 0/no) and you have three independent or predictor variables like gender (male/female), socio-economic condition (rich/poor), and incentive for physical activity (yes/no). Suppose, you have a combination, gender = male, socio-economic condition = rich, incentive for physical activity = no, which always predict not meeting physical activity recommendation (outcome - 0/no). This is an example of complete separation.

What is quasi-separation?

Reconsider the above example. We have 50 adolescents for the combination- gender = male, socio-economic condition = rich, incentive for physical activity = no. For 49/48 (not exactly 50, near about 50) of them, outcome is "not meeting physical activity recommendation" (outcome - 0/no). This is the instance of quasi-separation.

How separation or quasi-separation may impact your night sleep?

When separation or quasi-separation is present in your data, the traditional logistic regression will keep increasing the co-efficient of predictors/independent variables to infinite level (to be honest, not infinite, the wording should be without limit) to establish the bucket theory - one of the outcomes is completely or nearly empty. When the anomaly happens, it is actually suggesting that the traditional logistic regression model is outdated here.

There is a bookish name of the issue - convergence issue. But how to know convergence issues have occurred with the model?

- Very large co-efficient estimates. The estimates could be near infinite too!

- Along with large co-efficient estimates, you may see large standard errors too!

- It may also happen that logistic regression tried several times (known as iterations) but failed to get the best model or in bookish language, failed to converge.

What to do if such convergence issues have occurred?

Forget all the hard works you have done so far! You have to start your new journey with an alternative logistic regression, which is known as Firth logistic regression. But what Firth logistic regression actually does? Without using much technical terms, Firth logistic regression actually leads to more reliable co-efficients, which helps to choose best representative model for your data ultimately.

How to conduct Firth logistic regression?

First install the package "logistf" and load it in your R-environment.

install.packages("logistf")

library(logistf)

Now, assume you have a dataset "physical_activity" with a binary outcome variable "meeting physical activity recommendation" and three predictor/independent variables: gender (male/female), socio-economic condition (rich/poor), and incentive for physical activity (yes/no).

pa_model <- logistf(meet_PA ~ gender + sec + incentive, data = physical_activity)

Now, display the result.

summary(pa_model)

You got log odds. Now, we have to convert it into odds.

odds_ratios_pa <- exp(coef(pa_model))

print(odds_ratios_pa)

Game over! Now, how to explain the result?

Don't worry! There is nothing special. The explanation of Firth logistic regression's result is same as traditional logistic regression model. However, if you are struggling with the explanation, let me know in the comment. I will try my best to reduce your stress!

Note: If you find any serious methodological issue here, my inbox is open!

Would anyone happen to know how the percentages are calculated in SPSS for the predicted and observed categories? Is it something you can do by hand or does SPSS use some kind of internal calculation? The type of output I'm referring to is the screenshot below. Huge thanks in advance for any help!

I have a choice model using multi-nominal logistic regression. Now I want to expand the model and consider unobserved heterogeneity using R

What is the best package to find random parameters using R?

I want to look into heterogeneity in mean and variance on the random parameter as well. Which package and command should I use to find heterogeneity in mean and variance using R?

I would like to create a forest plot for a study we are conducting, in order to represent data from a logistic regression model with OR and CI for each variable included. However, I'm struggling to do it with Meta-Essentials resources. Is it possible or does it work exclusively for meta-analysis? Thank you.

Hi,

I have some confusion as to which one is better for outcomes as a model using binomial regression or logistic regression. currently i am working on judicial decisions, outcomes in tax courts where the cases go either in favour of assessee or the taxman. The factors influencing the judges as reflected in the cases are duly represented by presence(1) or absence (0) of the same. If a factor is not considered in final judgment it takes '0' else '1'. if outcome is favourable to assessee - it is '1' else'0' - now which would be the best approach to put this into a regression model showing relationhip between outcome (dependant) and independent ( factors - may be 5-6 variables). I need some guidance on this . can i use any other better model for forecast after i can perform a bootstrap run for say 1000 simulations and then arrive average outcomes and related statistics.

Hi Everyone, 

I am working with data of SNPs, I want to do logistic regression analysis. 

In multinomial logistic regression, is it compulsory to choose most common genotype as reference? or I can choose any genotype as reference? 

In my one SNP (Genotypes: II, ID, DD), when I choose most common II genotype as reference than Odds ratio come out like 0.57, but on choosing ID genotype Odds ratio change to 1.67 with p <0.05. Is it fine to choose heterozygous genotype as reference?

Thanks,  

I have retrieved a study that reports a logistic regression, the OR for the dichotomous outcome is 1.4 for the continuous variable ln(troponin) . This means that the Odds increase 0.4 every 2.7-fold in the troponin variable; but, is there any way of calculating the OR for a 1 unit increase in the Troponin variable?

I want to meta-analyze many logistic regressions, for which i need them to be in the same format (i.e some use the variable ln(troponin) and others (troponin). (no individual patient data is available)

When conducting a logistic regression analysis in SPSS, a default threshold of 0.5 is used for the classification table. Consequently, individuals with a predicted probability < 0.5 are assigned to Group "0", while those with a predicted probability > 0.5 are assigned to Group "1". However, this threshold may not be the one that maximizes sensitivity and specificity. In other words, adjusting the threshold could potentially increase the overall accuracy of the model.

To explore this, I generated a ROC curve, which provides both the curve itself and the coordinates. I can choose a specific point on this curve.

My question now is, how do I translate from this ROC curve or its coordinates to the probability that I need to specify as the classification cutoff in SPSS (default: 0.50)? The value must naturally fall between 0 and 1.

I have the OR of a logistic regresion that used the independent variable as continuous. I also have the ORs of 2x2 tables that dichotomized the variable (high if >0.1, low if < 0.1).

Is there anyway i can merge them for a meta-analysis. i.e. can the OR of the regression (OR for 1 unit increase) be converted to OR for High vs Low?

Hello, I am looking for some guidance on how to calculate p-values for mixed-effects multinomial logistic regression using the npmlt() function from the mixcat package in R. I have fitted a model using this function and obtained the estimates and standard errors for each parameter. However, I am not sure how to derive the p-values from these values. I have tried to compute the Z values by dividing the estimates by the standard errors, and then compare them with the critical values from a standard normal distribution. Is this a valid method? I have yet found any documentation or examples on how to calculate p-values using npmlt() in R. I would appreciate any discussion or suggestion on this topic. Thank you very much.

Hello everyone! I'm seeking a comprehensive understanding of how to handle confounding variables when comparing two groups based on the presence of a specific variable. Should I use propensity score matching or multivariable logistic regression for this purpose?

Hello,

Please I need to perform a logistic regression analysis using 2 independent variables, each has multiple indicators using SPSS. For example, the independent variable perceived behavioral control (PBC) is measured using two indicators, which are self-efficacy and easy-to-start (each is binary). The other independent variable is the subjective norm, measured by 2 indicators (respect and motivation), each of which is also binary.

My question is: how to deal with the multiple indicators for one independent variable when performing the analysis?

In case that I want the outcome to appear like in the attached table, in which it includes only the independent variables (not each indicator individually). I assume that I need to compute each variable by summing its indicators but I am not sure if this is correct. So, I need the assistance of experts.

I hope that I am able to communicate my inquiry properly.

Thank you.

Hello,

I estimated a mixed-effect logistic regression model (GLMM) and I need to evaluate it. Specifically, I tried a few combinations of the independent variables in the model and I need to compare between them.

I know that for a regular logistic regression model (GLM), the Nagelkerke R-squared fits (a pseudo R-squared measure). But does it fit also for a mixed-effect model? If not, what is the correct way for evaluating a mixed-effect logistic regression model?

Thanks!

I want to know whether i should include the non significant categorical variables in multinomial logistic regression.

Regards.

I am using SPSS to perform binary logistic regression. One of the parameters generated is the prediction probability. Is there a simple mathematical formula that could be used to calculate it manually? e.g. based on the B values generated for each variable in model?

I have measured Irisin levels in plasma and now I'm trying to analyze the results. As far as I have read, I need to perform a 4 parameter logistic regression, should I use logarithm for absorbances and for concentrations?

Hi everybody! :)

I wish to investigate a possible prediction between two variables I'm currently studying, and wanted to use a logistic regression model.

Is it possible to run a multinomial logistic regression having just one nominal independent variable with four categories? My dependent variable is also nominal with four categories.

Many thanks for your attention :)

Can someone suggest me the best method for meta-analysis of proportions where there is a high heterogeneity. I am using random effects model to estimate the pooled proportion. I have done the pooled proportion and subgroup analysis with both logistic regression and dersimonian Laird method. Both yielded a varying result. Which one should I take into consideration?

I have been performed the multinomial logistic regression model in SPSS. The goodness of fit shows that the model fits the data. Based on my literature study, there are several methods can be performed to validate the model, but SPSS 23's window of performing Logistic Regression doesn't show the options. Kindly help me to inform that what particular method I can use to validate the model in SPSS.

I run a multinomial logistic regression. In the SPSS output, under the table "Parameter Estimates", there is a message "Floating point overflow occurred while computing this statistic. Its value is therefore set to system missing." How should I deal with this problem? Thank you.

Seeking insights from the research community: Does the imbalance of textual genres within corpora, when used as an explanatory variable rather than the response variable, affect the performance of logistic regression and classification models? I'm interested in understanding how the unequal distribution of word counts across genres might introduce biases and influence the accuracy of these machine learning algorithms. Any explanations or relevant details on mitigating strategies are appreciated. Thank you!

Hi. I'm planning to conduct a multinomial logistic regression analysis for my predictive model (3 outcome categories). How can I estimate the sample size? I believe the EPV rule of thumb is suitable only for binary outcomes. Is there any formula/software that I can use?

Logistic Regression type

I have conducted some ordinal logistic regressions, however, some of my tests have not met the proportional odds assumptions so I need to run multinomial regressions. What would I have to do to the ordinal DV to use it in this model? I'm doing this in SPSS by the way.

if there would be any literature on BLR Coefficient, that would be very helpful to understand.

I am running 6 separate binomial logistic regression models with all dependent variables having two categories, either 'no' which is coded as 0 and 'yes' coded as 1.

4/6 models are running fine, however, 2 of them have this error message.

I am not sure what is going wrong as each dependent variable have the same 2 values on the cases being processed, either 0 or 1.

Any suggestions what to do?