search for: pred1

...atenated onto it when the code below is run and temp is returned. I've been trying to figure this out for too long. It doesn't matter when I put the FPVAL in the return statement. It happens regardless of whether it's first or last. Thanks. f.lmmultenhanced <- function(response, pred1, pred2) { regmod <- lm(response ~ pred1 + pred2) lmsum <- summary(regmod) imbcoef<-lmsum$coefficients[2,1] retcoef<-lmsum$coefficients[3,1] imbpval<-lmsum$coefficients[2,4] retpval<-lmsum$coefficients[3,4] Fstat&l...

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms in...

...achieve any value for accuracy - including unity - by re-scaling the penalty matrix. So, I'd like to ask what, if any are the contraints on the scaling process? Here is a working code that illustrates my difficulty:- set.seed(1); gldstd=round(runif(200)*4); gldstd[gldstd==0]=4; table(gldstd) pred1=gldstd*rnorm(200, mean=1, sd=2/gldstd); boxplot(pred1~gldstd) library(nonbinROC); gldstd=ordered(gldstd) ordered_penalty = matrix(c(0,0,0,0,1/3,0,0,0,2/3,1/3,0,0,1,2/3,1/3,0), nrow = 4) constant_penalty = matrix(c(0,0,0,0,1,0,0,0,1,1,0,0,1,1,1,0), nrow = 4) # first using the constant_penalty (defa...

...r too much overdispersion - mine seems to be huge......Residual deviance: 1580.1 on 123 degrees of freedom I do have some outliers - but they are legitimate (i.e., not typos)..... I included my data below....if it helps summary(glm.D93)$dispersion ## 1 (by definition) Call: glm(formula = y ~ Pred1, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max -9.940 -2.778 -0.710 2.130 10.479 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.63942 0.07205 22.753 < 2e-16 *** Pred1F -0.65228 0.11781 -5.537 3.08e...

...predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <- predict(model, newdata=data.frame(age.cat=1,male=1,lowed=1)) pred2 <- predict(model, newdata=data.frame(age.cat=2,male=0,lowed=0)) OR <- exp(pred2-pred1) Thanks [[alternative HTML version deleted]]

...ass is "dataframe". These indexed lists can be printed successfuly but are not agreeable to the plot() and lm() functions shown below as are their df$out references. Reading the documentation for plot and lm hasn't helped yet. Thanks in advance - Stan. > df=data.frame(out=1:4*3,pred1=1:4,pred2=1:4*2) > regression=function(tble,a,b) + { + plot.new() + plot(tble[a]~tble[b]) + lmm=lm(tble[a]~tble[b]) + abline(lmm) + anova(lmm) + } > df[1] out 1 3 2 6 3 9 4 12 > df out pred1 pred2 1 3 1 2 2 6...

...that, among other things, runs a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat) summary(res)$r.squared # other stuff happens here... } y <- rnorm(10) x1 <- y + runif(10) x2 <- y + runif(10) x3 <- y + runif(10) get.model.fit(y, x1, x2, x3) get.model...

Hi - How can I 'manually' reproduce the results in 'pred1' below? My attempt is pred_manual, but is not correct. Any help is much appreciated. library(splines) set.seed(12345) y <- rgamma(1000, shape =0.5) age <- rnorm(1000, 45, 10) glm1 <- glm(y ~ ns(age, 4), family=Gamma(link=log)) dd <- data.frame(age = 16:80) mm <- model.matrix( ~...

I am an R beginner and trying to run a market model using event study in R framework. First, I run a market model, that is lm(stock security~SP500 index, subset=Obs[197, 396]) ->result1 Then I get predict results for a new dataset using predict (result1, newdata=Obs[397,399]) ->pred1 Pred1 should have three numbers. Now I need to calculate abnormal return by the formula stock security [397,399] -pred1 But it does not work after trying many times. So, how to write a R code to implement the formula and get right results. Thanks, Bill [[alternative HTML ver...

Ira, obj_name<- load("arun.RData") Pred1<- get(obj_name[1]) Actual1<- get(obj_name[2]) dat2<- data.frame(S1=rep(Pred1[,1],ncol(Pred1)-1),variable=rep(colnames(Pred1)[-1],each=nrow(Pred1)),Predict=unlist(Pred1[,-1],use.names=FALSE),Actual=unlist(Actual1[,-1],use.names=FALSE),stringsAsFactors=FALSE) dat2New<- dat2[!(is.na(dat2...

Hi, I am quite new to R and I have two questions regarding ROCR. 1. I have tried to understand how to extract area-under-curve value by looking at the ROCR document and googling. Still I am not sure if I am doing the right thing. Here is my code, is "auc1" the auc value? " pred1 <- prediction(resp1,label1) perf1 <- performance(pred1,"tpr","fpr") plot( perf1, type="l",col=1 ) auc1 <- performance(pred1,"auc") auc1 <- auc1@y.values[[2]] " 2. I have to compare two models that have very close ROCs. I'd like to have...

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and "performance" are ROCR-functions.) How can I then retrieve the cutoff value fo...

Hi, May be you can try this: obj_name<- load("arun.RData") Pred1<- get(obj_name[1]) Actual1<- get(obj_name[2]) library(reshape2) dat<-cbind(melt(Pred1,id.vars="S1"),value2=melt(Actual1,id.vars="S1")[,3])? # to reshape to long form colnames(dat)[3:4]<- c("Predict","Actual") dat$variable<- as.character(dat$var...

Not sure why the data.frame function does not capture the name of the column field when its being built with only one column. Can anyone help? > data out pred1 predd2 1 1 2.0 3.0 2 2 3.5 5.5 3 3 5.5 11.0 > data1=data.frame(data[,1]) > data1 data...1. 1 1 2 2 3 3 > data1=data.frame(data[,1:2]) > data1 out pred1 1 1 2.0 2 2 3.5 3 3 5.5 > sessionInfo() R version 2.5.1 (2007-06-27) i3...

Dear all, I tried to run as.formula("x") and got an error message "Error: C stack usage 7971120 is too close to the limit" whether x exists or not. This is not the case in as.formula("y"), where "object 'y' not found" is the error message if y not exists, or "invalid formula" error or a formula depending on y. Can anyone confirm this is

I'm fairly new to R, and I'm trying to write out a population model that satisfies the following; the system consists of s species, i= 1, 2,...,s network of interactions between species is specified by a (s x s) real matrix, C[i,j] x[i] being the relative population of the "ith" species (0 =< x[i] =< 1, sum(x[i]=1) the evolution rule being considered is as follows;

...result. tab <- structure(list(dob_day = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE), dob_mon = c(FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE), dob_year = c(FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE), n = c(1489634L, 17491L, 134985L, 1639L, 47892L, 611L, 4365L, 750L), pred1 = c(1488301, 18187, 135605, 1657, 48547, 593, 4423, 54)), .Names = c("dob_day", "dob_mon", "dob_year", "n", "pred1"), row.names = c(NA, -8L), class = "data.frame") m <- glm(dob_mon ~ dob_day*dob_mon, data = tab, family = binomial())...

...t machines. data(iris) attach(iris) library(e1071) train<- iris[c(1:30,50:80,100:130),] test<- iris[-c(1:30,50:80,100:130),] y.train<- train$Species y.test<- test$Species obj<- tune.svm(train[,-5], y.train, gamma = 2^(-1:1), cost = 2^(2:4), probability=T) my.svm<- obj$best.model pred1<- predict(my.svm, test[,-5]) pred2<- predict(my.svm, test[,-5], probability=T) table(pred1, y.test) table(pred2, y.test) When I do this, the two different tables often come out different, as below: > table(pred1, y.test) y.test pred1 setosa versicolor virginica setosa...

...t machines. data(iris) attach(iris) library(e1071) train<- iris[c(1:30,50:80,100:130),] test<- iris[-c(1:30,50:80,100:130),] y.train<- train$Species y.test<- test$Species obj<- tune.svm(train[,-5], y.train, gamma = 2^(-1:1), cost = 2^(2:4), probability=T) my.svm<- obj$best.model pred1<- predict(my.svm, test[,-5]) pred2<- predict(my.svm, test[,-5], probability=T) table(pred1, y.test) table(pred2, y.test) When I do this, the two different tables often come out different, as below: > table(pred1, y.test) y.test pred1 setosa versicolor virginica setosa...

...and only allow for one iteration ? my question is how to do that ? to specify the 2x2 matrix of variance parameter values: start=list(groups=array(2,-0.5,1),dim=c(2,2)) now I need to make sure the mean structure is estimated, while the variance parameter values are kept constant ?? e.g. lmer(resp~pred1+pred2+(1 + pred1|groups),start=list(groups=array(2,-0.5,1),dim=c(2,2)),....) cheers, Wilfried Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm