similar to: How can i fit mixed effect model in a logistic regression?

Hello, I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic from the returned from the "resid(fit,

Hi, Is there any similar command to "predict" which can be used with a logistic random effects model? I have run a random effects model using "lme()", and then use "predict.lme()" with no problems. However, I would also like to run a logistic random effects model, and then also run a predict command on the logistic random effects model. If I use "lme()",

Hi, I’ve tried to find a general approach to my problem without any success, although this might very well be due to my inexperience with R help resources (and statistics in general). My general problem is a straightforward 2 by 2 table (“Belonging to the upper quartile” vs “not-belonging to the upper quartile”, intervention vs non-intervention), but with a random effect addressing the

Hi, I am trying to fit a logistic regression using glm, but my explanatory variables are of mixed type: some are numeric, some are ordinal, some are categorical, say If x1 is numeric, x2 is ordinal, x3 is categorical, is the following formula OK? *model <- glm(y~x1+x2+x3, family=binomial(link="logit"), na.action=na.pass)* * * *Thanks,* * * *-Jack* [[alternative HTML version

I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression. The response is 0/1 and both the response and the age are the same for each pair of observations for each subject (some observations are not paired). For example: id response age 1 0 30 1 0 30 2 1 55 2 1 55 3 0 37 4 1 52 5 0 39 5 0 39 etc. I get the

Dear all, my aim is to estimate the efficacy over time of a treatment for headache prevention. Data consist of long sequences of repeated binary outcomes (1 if the subject has at least 1 episode of headache , 0 otherwise) on subjects randomized to placebo or treatment. I have fit a logistic regression model with Huber-White cluster sandwich covariance estimator. I have put in the model the

Dear friends, I hope this email finds you all well. This is the dataset I am working with: dput(random_mod12_data2) structure(list(Index = c(1L, 5L, 11L, 3L, 2L, 8L, 9L, 4L), x = c(5, 13, 25, 9, 7, 19, 21, 11), n = c(500, 500, 500, 500, 500, 500, 500, 500), r = c(100, 211, 391, 147, 122, 310, 343, 176), ratio = c(0.2, 0.422, 0.782, 0.294, 0.244, 0.62, 0.686, 0.352)), row.names = c(NA, -8L),

I am making exploratory analyses on a complex survey data by using survey package. Could you help me how to see the goodness of fit for the model below? Should I use AIC, BIC, ROC, or what? What code would let me run a goodness of fit test for the model? Here are my codes: #incorporating design effects# > mydesign <- svydesign(id=~clust, strata=~strat, weights=~sweight, > data=mydata)

Dear Colleagues, I hope to know how ordinal logistic regression with a mixed model is made in R. We (My colleague and I) are studying the behavior of a beetle. The attraction of beetles to a stimulus are recorded: the response is Slow, Mid, or Fast. They are based on the time after the presentation of the stimulus to the beetles. Because we do not observe the behavior continuously but do

Hi I know that p-values doesn't appear anymore in the summary of a linear mixed-model with lmer. However, if I do a mixed logistic regression with lmer using family=binomial, the summary includes a p-values for fixed effects. Is it normal, could I use those p-values to interpret the fixed effects or should I use mcmcsamp to obtain 95% confidence interval? Thanks Julien

Dear R help, In limma package, contrasts.fit() function is very useful. I am wondering whether there is a similar function for lme object, which means given a mixed linear model fit, compute estimated coefficients and standard errors for a given set of contrasts. Thanks, Shirley

Dear R usuer, I need to fit logistic regression with binomial response. The objective is to compare treatment groups controlling other categorical and continuous predictors. The GLM procedure with family=binomial(Logit) gives me parameters estimates as well as odd ratios. But objective is to compare if treatment groups are significantly different. I have used wald test but got error message (Plz

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

Hello, In my lab we use a four parameter logistic fit model for our ELISA data (absorbance values). We are currently testing the use of different solvents and need to find a way to add a correlation value (such as an R squared or something similar) so we can test different solvents in making this standard curve. We currently use the drc package and this is our script for the 4 parameter:

I am trying to run a mixed model logistic regression with participants nested within state with a certain amount of covariates. Here is what my model looks like: m1 <- lmer(Overweight ~ age + factor(A_RACE_G) + Prevalance + HH_Income_Dicot + Unemployment_Rate + Intensity_effect + (1 | state2) + GSD_EFFECT + FMA_EFFECT + BMI_EFFECT + DBS_EFFECT + NSM_EFFECT + NCF_EFFECT + ACF_EFFECT +

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >

Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I

Hello, I?d like to evaluate the temporal effect on the relationship between a continuous variable (e.g. size) and the probability of mate success. Initially I was trying to do a logistic regression model incorporating the temporal effect, but I don?t know if that is the best option. I simulated some data and that?s the problem:

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas