similar to: Standard Error for difference in predicted probabilities

Dear list, I'm a bit perplexed why the 95% confidence bands for the predicted probabilities for units where x=0 and x=1 overlap in the following instance. I've simulated binary data to which I've then fitted a simple logistic regression model, with one covariate, and the coefficient on x is statistically significant at the 0.05 level. I've then used two different methods to

I'm trying to get predicted probabilities out of a regression model, but am having trouble with the "newdata" option in the predict() function. Suppose I have a model with two independent variables, like this: y=rbinom(100, 1, .3) x1=rbinom(100, 1, .5) x2=rnorm(100, 3, 2) mod=glm(y ~ x1 + x2, family=binomial) I can then get the predicted probabilities for the two values of

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y which does have the original labels. How should I change the probabilities into values of zero and 1 and then compare it with my vector y to find out about the accuracy of my

Dear R Help, I read through the archives pretty extensively before sending this email, as it seemed there were several threads on using predict with GLM. However, while my issue is similar to previous posts (cannot get it to predict using new data), none of the suggested fixes are working. The important bits of my code: set.seed(644) n0=200 #number of observations

Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm): logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y))

hello, I want to simulate 200 times the mean of a joint probability (y1) and 200 times the mean of another joint distribution (y2), that is I'm expecting to get 200 means of y1 and 200 means of y2. y1 and y2 are probabilities that I calculate from the marginal prob. (z1 and z2 respectively) multiple by the conditional prob. (x1 and x2 respectively), which I generaterd from the binomial

Dear R-users, i try to generate missing values in a matrix X according to a given missingnes pattern R with the probabilities p per row. X<-matrix(rnorm(3*100),ncol=3) ## indicator matrix for missingnes (1 observed, 0 missing) R<-matrix(c(1,1,1, 0,0,1, 1,1,0, 0,1,1),ncol=3,byrow=TRUE) ## probabilities for row 1, row 2, row 3 and row 4

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Hi, I have used package quantreg to estimate a non-linear fit to the lowest part of my data points. It works great, by the way. But I'd like to extract the predicted values. The help for predict.qss1 indicates this: predict.qss1(object, newdata, ...) and states that newdata is a data frame describing the observations at which prediction is to be made. I used the same technique I used

Hello R-helpers, I would like to calculate the standard error for the predicted value from geeglm. As an example, I would like to calculate the GEE mean of treatments and their standard error. I first specified the model as mod <- geeglm(resp ~ trt, data=dat,id=id,family=Gaussian,corstr="ar1",weights=weight) Then I predicted the GEE mean and se using the following code

I am new to R and I am trying to do a monte carlo simulation where I generate data and interject error then test various cut points; however, my output was garbage (at x equal zero, I did not get .50) I am basically testing the performance of classifiers. Here is the code: n <- 1000; # Sample size fitglm <- function(sigma,tau){ x <- rnorm(n,0,sigma) intercept <- 0 beta

Dear list. I using the SVM-methods from the e1071, but I can't get the probabilities when predicting. Code: x <- matrix(rbinom(100, 10, 0.3), ncol=2) y <- apply(x, 1, sum) fit <- svm(y ~ x, method = "C-classification", kernel = "radial", probability = TRUE) predict(fit, x, probability=TRUE) Here predict doesn't containing any probabilities (not as attributes

Dear list members, How can I replace the nested for loops at then end of the script below with more efficient code? # Begin script__________________________________________________ # Dichotomous scores for 100 respondents on 3 items with # probabilities of a correct response = .6, .4, and .7, # respectively x1 <- rbinom(100,1,.6) x2 <- rbinom(100,1,.4) x3 <- rbinom(100,1,.7) #

I am aware this has been asked before but I could not find a resolution. I am doing a logit lg <- glm(y[1:200] ~ x[1:200,1],family=binomial) Then I want to predict a new set pred <- predict(lg,x[201:250,1],type="response") But I get varying error messages or warnings about the different number of rows. I have tried data/newdata and also to wrap in data.frame() but cannot get

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

Dear lattice wizards, I am trying to figure out how to plot predicted values in xyplot, where the intercept, but not the slope, varies among conditioning factor levels. I am sure it involves the groups, but I have been unsuccessful in my search in Pinhiero and Bate, in the help files, or in the archive, or in my attempts on my own. My example follows: FACT is a factor with levels a,b,c

I am writing a function to assess the out of sample predictive capabilities of a time series regression model. However lm.predict isn't behaving as I expect it to. What I am trying to do is give it a set of explanatory variables and have it give me a single predicted value using the lm fitted model. > model = lm(y~x) > newdata=matrix(1,1,6) > pred =

Maybe a useful addition to the predict functions would be to return the values of the predictor variables. It just (unless there are problems) requires an extra line. I have inserted an example below. "predict.glm" <- function (object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE,