search for: sexf

...75458 0.52484 second (Intercept) 0.014748 0.12144 Residual 4.2531 2.0623 Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 5.9147e+00 7.6795e-02 3431 77.0197 < 2e-16 *** verbal 1.5836e-01 3.7872e-03 3431 41.8136 < 2e-16 *** sexF 1.2155e-01 7.2413e-02 3431 1.6786 0.09332 . verbal:sexF 2.5929e-03 5.3885e-03 3431 0.4812 0.63041 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Correlation of Fixed Effects: (Intr) verbal sexF verbal 0.177...

...75458 0.52484 second (Intercept) 0.014748 0.12144 Residual 4.2531 2.0623 Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 5.9147e+00 7.6795e-02 3431 77.0197 < 2e-16 *** verbal 1.5836e-01 3.7872e-03 3431 41.8136 < 2e-16 *** sexF 1.2155e-01 7.2413e-02 3431 1.6786 0.09332 . verbal:sexF 2.5929e-03 5.3885e-03 3431 0.4812 0.63041 --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Correlation of Fixed Effects: (Intr) verbal sexF verbal 0.177...

...on getting confidence intervals for the ordinary (non smoothed) parameter estimates from a gam. Motivation I am studying hospital outcomes in a large data set. The outcomes of interest to me are all binary variables. The one in the example here, Dead30d, is death within 30 days of admission. Sexf is gender (M or F), Age is age in years at the start of the admission. The standard glm is a logistic regression :- glmDead.AS <- glm(Dead30d~Sexf+Age, data=HIPE,family=binomial(link="logit")) The corresponding GAM, with a smooth for age, is :- gamDead.AS <- gam(Dead30d~Sexf+s...

Hi, To be able to match cases with a benchmark I need to have a data.frame with a character id variable. however, I am surprised why this seems to be so hard. In fact I was unable to succeed. Here is what I tried: >test1 <-expand.grid(ID = 1:2, sex = c("male","female")) >is(test1[,2]) [1] "factor" "oldClass" >test2 <-expand.grid(ID =

Dear R users, Overdispersion is often a problem in binomial data. I attempt to model a binary response (sex-ratio) with three categorical explanatory variables, using GLM, which could assume the form: y<-cbind(sexf, sample-sexf) model<-glm(y ~ age+month+year, binomial) summary(model) Output: (Dispersion parameter for binomial family taken to be 1) Null deviance: 8956.7 on 582 degrees of freedom Residual deviance: 4111.9 on 555 degrees of freedom AIC: 6735.2 Following MJ Crawley (The R Book 2007)...

...696 ADMIN ITT numeric NOYESZF 0 0 0 0 696 ADMIN PP numeric NOYESZF 0 0 0 0 696 ADMIN SEX numeric SEXF 0 0 0 0 Sex 696 ADMIN AGE_C numeric 4 0 0 0 Age calc 696 ADMIN TRT numeric TRTF 0 0 0 0...

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

...pfit2 <- coxph(Surv(time, status > 0) ~ trt + log(bili) + log(protime) + age + platelet + sex, data = pbc) #however, I now get an error with survexp survexp(~ trt, ratetable = pfit2, data = pbc) Error in ratetable(trt = trt, bili = bili, protime = protime, age = age, : object 'sexf' not found Does anyone have any suggestions? Thank you, Erik Iverson sessionInfo() R version 2.11.1 (2010-05-31) x86_64-unknown-linux-gnu locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=C LC_MESSAGE...

Hi group, I have this type of data x(predictor), y(response), factor (grouping x into many groups, with 6-20 obs/group) I want to fit a linear regression with one common intercept. 'factor' should only modify the slopes, not the intercept. The intercept is expected to be >0. If I use y~ x + factor, I get a different intercept for each factor level, but one slope only if I use y~ x *

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