1
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0answers
14 views

Problem with visualising logistic regression using the effects package in R [migrated]

I am using the effects package in R to plot the effects of categorical and numerical predictors in a binomial logistic regression estimated using the lme4 package. My dependent variable is the ...
1
vote
0answers
38 views

Stata's xtlogit (fe, re) equivalent in R?

Stata allows for fixed effects and random effects specification of the logistic regression through the xtlogit fe and xtlogit re ...
2
votes
2answers
124 views

Difference between regression methods

I have a set of data where the response is a proportion. For each event in the experiment, a system will correctly tag X of Y ...
2
votes
0answers
24 views

Plot and interpret ordinal logistic regression

I have a ordinal dependendent variable, easiness, that ranges from 1 (not easy) to 5 (very easy). Increases in the values of the independent factors are associated with an increased easiness rating. ...
2
votes
1answer
59 views

Remove effect of a factor on continuous proportion data using regression in R

I have a data set of continuous proportions which depend on a fixed-effect factor, e.g.: ...
0
votes
0answers
18 views

Popular implementations of one vs all logistic regression for mutiple classes in R

Is there any popular implementation of logistic regression in R that defaults to using a one vs all approach for a categorical dependent variable with more than two classes? I have specific reasons to ...
1
vote
0answers
31 views

Fit and predict logistic regression model for censored data?

Suppose I have a data frame such that: ...
4
votes
2answers
75 views

How do I run Ordinal Logistic Regression analysis in R with both numerical / categorical values?

Base Data: I have ~1,000 people marked with assessments: '1,' [good] '2,' [middle] or '3' [bad] -- these are the values I'm trying to predict for people in the future. In addition to that, I have some ...
0
votes
0answers
17 views

Model selection based on bic.glm function output and K-fold cross validation

I'm looking for a logistic regression model with high prediction power, and the best model returned by the bic.glm function has a posterior probability of 0.348. I ...
3
votes
1answer
57 views

Understanding coefficients in summary output of logistic regression in R

This question is about understanding the logistic regression output using R. Here is my sample data frame: ...
0
votes
0answers
46 views

What is the difference between forecasting based on ARIMA and logistic curve? R

I'm making a project connected with identifying the dynamics of sales. My database concerns 26 weeks (so equally in 26 time-series observations) after launching the product. This is what my database ...
2
votes
1answer
97 views

Detecting heteroscedasticity - Can I use Breusch-Pagan Test on binary logistic regression?

I'm currently testing a (binary) logistic regression model, which seems to have at least some issues with multicollinearity. Now I don't really trust the data anymore and would like to also test it on ...
1
vote
2answers
46 views

Should statsmodels's GLM produce the same results as R's lm?

Should Python's statsmodels.api.GLM(train_y, train_X, family=sm.families.Binomial()).fit().predict(test_X) always produce the same results as R's ...
2
votes
3answers
109 views

CDF and logistic regression

Is the probability calculated by a logistic regression model (the one that is logit transformed) the fit of cumulative distribution function of successes of original data (ordered by the X variable)? ...
1
vote
1answer
30 views

Explanatory variables in a Lin-log model

I have a data set and I want to fit a Lin-log model. Is it possible to apply the log transformation only to some of the explanatory variables or should the log ...
1
vote
0answers
22 views

Interpretation of coefficient sign change and order of terms

I have a logistic regression with data that are kind of like this: ...
1
vote
1answer
47 views

logistic regression for modelling

I have these data plotted above. The explanatory variable represents intensity levels of ground shaking at different locations in an earthquake, and the response variable represents amounts of ...
3
votes
4answers
356 views

Fitting probability distribution to data

I am trying to fit a model for the values plotted above. The explanatory variable represents amounts of compensation claim in an earthquake, and the response variable represents amounts of ...
3
votes
0answers
23 views

Using residualized predictors outside the linear model context

Can anyone point me towards a good explanation of when a residualized variable in a regression will give you the same answer as using a non-residualized variable with controls? For instance, say I ...
2
votes
0answers
74 views

Testing for overdispersion in logistic regression

R in Action (Kabacoff, 2011) suggests the following routine to test for overdispersion in a logistic regression: Fit logistic regression using binomial distribution: ...
0
votes
1answer
32 views

Predicting based upon categorical data and one Numeric datatype

I would like to determine what variables from this sample data would be best predictors for CallHandleTimeSeconds. Im thinking it would be a combination of CreditRating, EligibleForAssistance, ...
2
votes
1answer
76 views

Help interpreting logistic regression

I'm new to R and logistic regression and have to admit that I don't really know how to interpret the result. I'm trying to compute a pretty simple model with 2 predictors (A and B). When I first try ...
0
votes
0answers
37 views

Significant difference between two correlation matrices

We have two huge correlation matrices at different experimental conditions. If we want to identify the significant differences between these matrices , what would be the ideal method. I have ...
1
vote
0answers
36 views

Sample size calculation for logistic regression using simulations

I want to find the sample size for logistic regression where I have a covariate with 15 levels and the covariates interacts with time, which means that the effect of the covariates is different for ...
0
votes
0answers
60 views

Use predicted values with or without random part to plot Residuals with binnedplot of a logistic regression in glmer (lme4 package) in R?

Which binnedplot of the glmer should I use to check the model? The residuals against the predicted values without random part(REform=NA) or residuals against the predicted values with random ...
1
vote
0answers
36 views

Interpretation of p-values & reduction of model deviance for factors in anova() vs summary()

My data has 3 major inputs: BLDDAY (a factor), BLDMNT (a factor), and D_BLD_SER (days as an ...
1
vote
1answer
35 views

Variation explained in ordinal logistic regression models

I have made these three ordinal logistic regression models: ...
0
votes
0answers
36 views

Repeated measures logistic regression

I have a dataset that features a binary outcome, a binary predictor, and an unordered factor with 7 levels, and 120 subjects. Each of the 120 subjects were asked a binary question on seven issues, ...
2
votes
1answer
61 views

Interpretation of ordinal logistic regression

I ran this ordinal logistic regression in R: mtcars_ordinal <- polr(as.factor(carb) ~ mpg, mtcars) I got this summary of the model: ...
1
vote
0answers
33 views

coefficients extracted by effects package in R

Consider this logistic regression: mtcars$vs <- as.factor(mtcars$vs) log_reg_mtcars <- glm(am ~ vs*wt +vs*mpg, family = "binomial", mtcars) I tried using ...
0
votes
1answer
49 views

Cross-validation for mixed-effect logistic regression? [duplicate]

I would like to use cross-validation to test how predictive my mixed-effect logistic regression model is (model run with glmer). Is there an easy way to do this using a package in R? I've only seen ...
0
votes
1answer
47 views

Problem with building mlogit model (with no alternative specific variables)

I am confused with using mlogit package to build a multinomial logit model. In my data the only variables I have are the individual specific variables, to be ...
1
vote
1answer
75 views

Mixed-effect logistic regression in R - questions

I am new to R, and don't see these questions answered anywhere in documentation (though I could be wrong). I am using the following nomenclature to run my mixed-effects logistic regression, based on ...
6
votes
2answers
174 views

R lme4: how to apply binomial GLM to percentages rather than yes-no counts

I have a repeated-measures experiment where the dependent variable is a percentage, and I have multiple factors as independent variables. I'd like to use glmer from ...
0
votes
1answer
26 views

Scoring validation database based on estimates from test database in R

I divided my dataset into Test and Validation (50-50 split). I ran glm function (link=binomial) on Test dataset and got the parameter estimates. How do I score the Validation dataset based on these ...
2
votes
1answer
101 views

Interpretation of R's output for binomial regression

I'm quite new on this with binomial data tests, but needed to do one and now I´m not sure how to interpret the outcome. The y-variable, the response variable, is binomial and the explanatory factors ...
1
vote
1answer
56 views

How to check for linear constraints of coefficients in logistic regression in R?

I have a logistic regression model with several variables. I want to test the hypothesis of, say $a \beta_i = b \beta_j$ for constants $a,b$. I know that this can be theoretically done with an ...
1
vote
1answer
98 views

importance of each predictor in logistic regression

I recently read a paper made a logistic regression and used a table like this to summarise the model: ...
3
votes
2answers
141 views

Weighting observations in binary logistic regression

I am working in program R. I am modeling the incidence of flight in a seabird in relation to distance to the nearest ship (potential disturbance, range = 0 to 74 km from the bird). 1= flight during ...
2
votes
0answers
79 views

How does the RMS package's nomogram calculate points for continuous variables?

I have been reading a number of papers where researchers have created risk scores based on logistic regression models. Often they refer to "Sullivan's method" but I have no access to this paper and ...
1
vote
1answer
92 views

How to calculate Prob > chi2 in R to test model fit of conditional logistic regression

I used the clogit function (from the survival package) to run a conditional logistic regression in R with a big dataset of 1:M matched pairs with n=300368964 and number of events= 39995. ...
1
vote
0answers
82 views

How to estimate ICC (degree of clustering) in hierarchical logistic regression?

I am exploring hierarchical logistic regression, using glmer from the lme4 package. To my understanding, one of the first steps in multilevel modeling is to estimate the degree of clustering of ...
0
votes
0answers
39 views

Is it possible that all parameters are highly significant? [duplicate]

I just did a binary linear regression in R with a dataset that has 100000 lines. The output of the regression is, that almost every parameter is highly significant. I wouldn't expect that when I look ...
1
vote
1answer
164 views

How to explain the logistic regression model objects from the rms::lrm() function in R?

Recently I was trying to do logistic regression using the rms::lrm() function. But I had some trouble understanding the model objects from the function. Here is the ...
3
votes
1answer
185 views

Likelihood Ratio Test and Wald test provide different conclusion for glm in R

I'm reproducing an example from Generalized, Linear, and Mixed Models. My MWE is below: ...
2
votes
0answers
134 views

Mixed effect logistic regression in R: choosing random effects

I conducted an experiment which measured a binary response for each subject. The subjects were in 1 of 3 groups. There were two other fixed factors, each of which were continuums (cont1, cont2) ...
2
votes
1answer
99 views

Coefficients of bootstrapping in logistic regression

I have seen several articles and CrossValidated questions on bootstrapping ( this, this or this for example); there are a lot of theoretical and statistical explanations, however since they are so ...
0
votes
0answers
46 views

Logistic regression on 3 covariates each with unequal sample size

I have 3 covariates $(x_1,x_2,x_3)$, each covariate takes only 2 values $\{0,1\}$ but each covariates have unequal sample size. $n(x_1)=79,\;n(x_2)=80,\; n(x_3)=77$ $x_1$:(60 zeros and 19 ones) ...
0
votes
2answers
100 views

How to calculate type II error between glm fits in R?

I'm using a GLM with logistic link function to try to predict Y (0 or 1) as a function of a ton of predictor variables (A, B, C, etc.). Some of the predictor variables (A*, B*, C*, etc.) have been ...
0
votes
0answers
25 views

Is a nested log-binomial GLM appropriate for this?

Dependent variable: Death (0 or 1) Independent variables: Dose (0, 10 or 20), Timing(Early or Late), PreviousDVT(0 or 1) Ignoring the Timing variable for the moment, the model I had originally ...