search for: fittedmodell

You may recall that I was recently constructing a function to bootstrap the coefficients in a linear regression model. In S-PLUS I was using the model.matrix function applied to the fitted model, then taking the QR decomposition of that. I discovered that it was in fact easier to accomplish the bootstrapping in R because the QR decomposition of the model matrix is stored with the fitted model.

I am trying to show some techniques to my graduate regression class. The textbook mentioned using bootstrap samples of regression coefficients for assessing variability. I decided to show them reasonably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ##

When regressing by month, how do I get the coefficients out into a new data set? I'm looking for [ month, a, b, c ] from the Pastor-Stambaugh model I'm using which is: r[i+1] = a + b * r[i] + c * v[i] + e the model I'm using wants to create a new dataseries based on the coefficient in each month. I'm doing a simple linear regression on DataSet, and >

...an AUC value (for example with the pROC package) following the script: BaseRate <- table(Data$Y[[1]])/sum(table(Data$Y)) L(0)=Likelihood(Null-Model)= (BaseRate*log(BaseRate)+(1-BaseRate)*log(1-BaseRate))*sum(table(Data$Y)) LIKM <- predict(fit.glm, type="response") L(M)=Likelihood(FittedModell)=sum(Data$Y*log(LIKM)+(1-Data$Y)*log(1-LIKM)) R2 = 1-(L(0)/L(M))^2/n R2_max=1-(L(0))^2/n R2_Nagelkerke=R2/R2max library(pROC) AUC <- auc(Data$Y,LIKM) I checked this kind of caculation of R2_Nagelkerke and AUC-Value with the built-in calculation in package "Design" and got consisten...

Full_Name: Ben Bolker Version: 2.2.1 OS: Windows XP and 2000 Submission from: (NULL) (128.227.60.124) The following code, using confint() to try to get confidence intervals on an nls object that has been fitted with algorithm="port" reliably crashes R 2.2.0 and 2.2.1 with the latest version of MASS on a Windows 2000 and a Windows XP machine here. I *think* earlier versions of MASS

Does anyone know how to extract residuals in lmer? Here's the error I get: > crop.lme=lmer(response~variety*irrigation*pesticide+(1|rep)+(1|rep: pesticide)+(1|rep:pesticide:irrigation), crop.data) > qqnorm(crop.lme) Error in qqnorm.default(crop.lme) : y is empty or has only NAs > resid(crop.lme) NULL Thanks! --Jake

Since you only have 3 predictors, each categorical with a small number of categories, you can use expand.grid to make a data.frame containing all possible combinations and give that the predict method for your model to get all possible predictions. Something like the following untested code. newdata <- expand.grid( Humidity = levels(Humidity), #(High, Medium,Low)

I need the output to have groups and the probability any given record in that group then has of being in the response class. Just like my email in the beginning i need the output that looks like if A and if B and if C then %77 it will be D. The examples you provided are just simply not similar. They are different and would take interpretation to get what i need. On Apr 14, 2016 1:26 AM,