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Hi Nathan, Many authors criticize stepwise variable selection, e.g., Harrell, F.E., 2001, Regression modelling strategies with applications to linear models, logistic regression and survival analysis. You can find some of his arguments and extra references in http://childrens...

Hi, Nicholas, I understand that the rule of thumb of 10 events per parameter comes from model predictive performance assessments and may not apply if you are doing inference. However, I'm not sure that the rule assumes independent predictors...

The limiting sample size in logistic regression is the minimum between the number of positive and negative cases, in Tim's data 132 positive cases (species occurrences). A minimum of 10 events per estimated parameter are recommended based on external...

Everett, If you have the original binary data that were used to calculate proportions you can use generalized linear models with logit link (i.e. logistic regression). You can find a simple explanation of this approach and some examples with...

As you use "data=Hyla_Model" in the gam function, variable names should not include the data.frame or matrix name, try: model8<- gam(AES_Peak_modified ~ s(var1) +s(var2)+ var3 + var4, data=Hyla_Model, family = poisson) Hope it...

In your example there is not categorical variables and you need at least one for ANCOVA. Try the following, is the same dataset using factor() to create a categorical variable (named CatVar): dat <- data.frame(response = rnorm(9), size = rnorm...

> I have generated a ROC plot and a calibration curve (attached) > > Also calculated the AUC 0.7201762 > > However if i am honest i am unsure where to go from here? > > 1. How does this tell me how effective the model...

Hi Tim, If you are interested in model predictions, forward, backward or stepwise predictor selection has a lot of disadvantages (see http://www.nesug.org/proceedings/nesug07/sa/sa07.pdf for a summary). My experience with logistic regression applied to...

Matthew, A more flexible model may be a better option than fitting two separate lines (e.g. restricted cubic splines, rcs function of the rms package or a generalized additive model, mgcv package). A restricted cubic spline with 4 knots...

Following your example, you have 2 positive cases and 8 negative cases, i.e. a binary response as you can code the data as 0 (not recovered) and 1 (recovered). An example of the GLM approach using simulated data: set...

As your observed data are not binary AUC (operating characteristic curve area) won't work properly. Any rank correlation coefficient may be useful, check the example, if predictions order the sites as the observations the correlation (rho) is 1. Changing...

You can change the contrasts type using contrasts. I always use the default contrast in R (treatment), but I guess that you are looking for sum contrast, running options(contrasts=c("contr.sum","contr.poly")) before you fit the model...

Chris, In your example, the intercept for CatVar1=1 is 0.7988+(-2.0183). The standar error of the CatVar1 line is the standard error of the difference You can use the multcomp package for pairwise comparisons, try this: set...

Hi Ludovico, You can compare the proportion of each life form using logistc regression, an example with simulated data for 20 plots: set.seed(100) n<-20 population<-sample(c("A","B"),n,replace=T) lifeform1count<-round(runif(n,0...