2006. Error Adjusted SE Lower CI Upper CI, global.mod<-glm(count~area+distance+elev+ slope, data = dater, family = poisson), # calculate c-hat to evaluate model assumptions, chat > 1 means overdispersion, chat<-sum(residuals(glob.mod,"pearson")^2)/glob.mod$df.residual, # fit global quasipoisson regression model to global model, global.mod<-glm(count~area+distance+elev+ slope, data = dater, family = quasipoisson), modl2<-glm(count~area+slope, data = dater, family = quasipoisson), modl3<-glm(count~area+distance, data = dater, family = quasipoisson), modl4<-glm(count~area+elev, data = dater, family = quasipoisson), # try model selection with quasi AICc, QAICc, be sure to supply the, # chat of the global model using the rank.args inside the model.set function, quasi.MS<-model.sel(global.mod,modl2,modl3,modl4, rank = QAICc, rank.args = alist(chat = chat)), (Intercept) area distance elev slope df logLik, # This is needed to get the likelihood and calculate QAICc, # update the models so you can get the log likelihood, global.mod<-update(global.mod,family = "x.quasipoisson"), modl2<-update(modl2,family = "x.quasipoisson"), modl3<-update(modl3,family = "x.quasipoisson"), modl4<-update(modl4,family = "x.quasipoisson"), (Intercept) area distance elev slope df logLik, # yes, dredge works but only on updated model, dredge(global.mod, rank = "QAICc", chat = chat), Global model call: glm(formula = count ~ area + distance + elev + slope, family = "x.quasipoisson", data = dater), trout<-read.csv("http://sites.google.com/site/rforfishandwildlifegrads/home/week-8/Westslope.csv?attredirects=0&d=1"), ## fit logistic regression with random effect output to model1, model1 <-glmer(PRESENCE ~ SOIL_PROD + GRADIENT + WIDTH + (1|WSHD),data = trout, family = binomial), Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) [glmerMod], model2 <-glmer(PRESENCE ~ GRADIENT + WIDTH + (1|WSHD),data = trout, family = binomial), model3 <-glmer(PRESENCE ~ SOIL_PROD + (1|WSHD),data = trout, family = binomial), model4 <-glmer(PRESENCE ~ SOIL_PROD + WIDTH + (1 + SOIL_PROD|WSHD),data = trout, family = binomial), model5 <-glmer(PRESENCE ~ SOIL_PROD + WIDTH + (1 |WSHD),data = trout, family = binomial), my.models<-model.sel(model1,model2,model3,model4,model5,rank=BIC), Anderson, D. R., K. P. Burnham and W. L. Thompson. Notice above that every parameter is in the same number of models. (Photo courtesy Jim West Jr.). NEVER EVER DO THIS FOR A REAL, all.parms<-lm(density~slope+distance+elev+ pct.cover, data = dater), # the dredge function fits all combinations, # of the variables in the all.parms model fit above, Global model call: lm(formula = density ~ slope + distance + elev + pct.cover, data = dater). Fine coal slurry is reclaimed from onsite impoundments, processed and mixed with coal shipped by rail to the plant from various locations in the western United States. How to use dredge in a sentence. Journal of Wildlife Management 64:912-923. Result: Error: could not find function "isGeneric" Execution halted Throughout the weekend, heavy waves and high winds hampered search efforts to locate two men reported missing after a gold mining dredge overturned Thursday night. The Coast Guard searched the area by air but West said heavy winds and elevated surf prevented them from searching by boat. Sign in|Recent Site Activity|Report Abuse|Print Page|Powered By Google Sites, # download the file directly from the website, dater<-read.csv("https://sites.google.com/site/rforfishandwildlifegrads/home/mumin_usage_examples/Example%20data.csv?attredirects=0&d=1"), # make sure all numeric values and no missing, #First, fit 4 candidate linear models to explain variation in density, mod1<-lm(density~distance+elev, data = dater), mod2<-lm(density~slope+pct.cover, data = dater), mod3<-lm(density~slope+distance, data = dater), mod4<-lm(density~slope+distance+elev, data = dater), # use the mod.sel function to conduct model selection, # what's it look like, hmm AIC with small sample bias adjustment AICc, # create a confidence set of models using the subset function, # select models with delta AICc less than 5. The search for a second missing man will continue throughout the week, said Nome Fire Department Chief Jim West Jr., who also runs the search and rescue. and J.T. Tracking COVID-19 in Alaska: 4 deaths and 563 new cases reported Thursday, Anchorage inmate dies at hospital after medical emergency, Anchorage will return to a monthlong limited ‘hunker down’ in December, Anchorage’s hospitals filling with COVID-19 patients amid surging cases and ‘no sign that is slowing down’. Searchers found one … # select models using Royall's 1/8 rule for strength of evidence, subset(out.put, 1/8 < weight/max(out.put$weight)), # select models 95% cumulative weight criteria, # coerce the object out.put into a data frame, # elements 6-10 in out.put have what we want, df logLik AICc delta weight, # a little clean-up, lets round things a bit, sel.table[,2:3]<- round(sel.table[,2:3],2), sel.table[,4:5]<- round(sel.table[,4:5],3), sel.table df logLik AICc delta weight, # how about a little renaming columns to fit proper conventions, ## lets be sure to put the model names in a column, # replace Model name with formulas little tricky so be careful, for(i in 1:nrow(sel.table)) sel.table$Model[i]<- as.character(formula(paste(sel.table$Model[i])))[3], df logLik AICc delta weight Model, Model K logLik AICc delta weight, # write to a file, here a comma separated values format, # make sure your working directory is properly specified, write.csv(sel.table,"My model selection table.csv", row.names = F), mod.sel(mod1,mod2,mod3,mod4, rank = BIC) Model selection table, (Int) dst elv pct.cvr slp df logLik BIC delta, #consistent AIC with Fishers information matrix, mod.sel(mod1,mod2,mod3,mod4, rank = CAICF) Model selection table, (Int) dst elv pct.cvr slp df logLik CAICF delta, # Importance weights for individual predictor variables, # calculated using the importance function, importance(out.put) distance elev slope pct.cover, # Model average using all candidate models, always use revised.var = TRUE, MA.ests<-model.avg(out.put, revised.var = TRUE), model.avg.model.selection(object = out.put, revised.var = TRUE), #Here are the beta tilda bar MA estimates, (Intercept) distance elev slope pct.cover, # you can also obtain importance weights for individual params, #create model averaged estimates for parameters in confidence set of, MA.ests<-model.avg(out.put, subset= delta < 5, revised.var = TRUE), # lets clean up a bit and write the table to a file, MA.est.table<-round(MA.ests$avg.model[,c(1,3:5)],6), write.csv(MA.est.table, "My model averaged estimates.csv"), #extract parameters and weights from confidence model set, pred.parms<-get.models(out.put, subset= delta < 5), # predict values using each model, here were just using the, # the example dataset, you could use a new dataset, model.preds = sapply(pred.parms, predict, newdata = dater), # weight the prediction from each model by its AIC weight, # the "Weights" function extracts the weights, # we also are using matrix multiplication %*%, mod.ave.preds<-model.preds %*% Weights(out.put), # elevation ranges from the observed minimum to maximum, # create plotdata data frame with mean values, plotdata<-as.data.frame(lapply(lapply(dater[5:8],mean),rep,length(elev)))), # now predict density for the plot data with each model, model.preds = sapply(pred.parms, predict, newdata = plotdata), mod.ave4plot<-model.preds %*% Weights(out.put), # plot the model averaged predicted densities vs elevation, plot(mod.ave4plot~ elev, type = 'l', xlab="Elevation (m)", ylab="Model averaged predicted density"), ## FOR EXPLORATORY PURPOSES ONLY!!!
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