search for: sexmale

..., 132; Socialgroup:Location1, 22; Location1, 4; Year, 2 Fixed effects: Estimate Std. Error t value (Intercept) 8.83179 0.52961 16.676 AGECAT2OLD -0.44101 0.41081 -1.074 AGECAT2YEARLING 0.01805 0.38698 0.047 SexMale -0.11346 0.51239 -0.221 Location1B -3.97880 0.63063 -6.309 Location1C -4.04816 0.60404 -6.702 Location1D -3.36389 0.63304 -5.314 AGECAT2OLD:Location1B 0.44198 0.54751 0.807 AGECAT2YEARLING:Location1B...

...ationship between each level of this and the endogenous variables. How might I go about achieving this? Here is my model ('sex' and 'trt' are the nominal variables with 2 and 4 levels, respectively) followed by the relevant output: Path Parameter 1 sexMale->nect sexnect 2 sexMale->cono sexcono 3 sexMale->herb sexherb 4 sexMale->para sexpara 5 trtControl->nect trtContnect 6 trtControl->cono trtContcono 7 trtControl->herb...

...to do ANCOVA for these regressions without intercepts? When I do summary(lm(body length ~ sex*head length)) this will include the intercepts as below Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -6.49697 1.68497 -3.856 0.000118 *** sexMale -9.39340 1.97760 -4.750 2.14e-06 *** sexUnknown -1.33791 2.35453 -0.568 0.569927 head_length 7.12307 0.05503 129.443 < 2e-16 *** sexMale:head_length 0.31631 0.06246 5.064 4.37e-07 *** sexUnknown:head_length 0.19937 0.07022 2.8...

...efficients coef exp(coef) se(coef) z p teratmentb -0.0838877 0.9195345 0.3744511 -0.22 0.8200 treatmentb2 -0.4731922 0.6230103 0.3136199 -1.51 0.1300 treatmentn -1.0154149 0.3622521 0.4097754 -2.48 0.0130 sexmale -0.1838885 0.8320286 0.2602169 -0.71 0.4800 treatmentb:sexmale -0.3905856 0.6766605 0.2132936 -1.83 0.0670 treatmentb2:sexmale 0.6742202 1.9625020 0.3836907 1.76 0.0790 treatmentn:sexmale 1.2628977 3.5356520 0.4603589 2.74 0.0061 Random effects Group V...

...et the fact that the coefficient is negative? Assume the variale sex is treated as a factor with Female =0 and Male=1. survmodel<-survreg(survobj~sex,data=data1, dist="weibull") survsum<-summary(survmodel) survsum Value Std. Error z p SexMale -0.47830 0.3745 -1.2770 2.02e-01 -- View this message in context: http://r.789695.n4.nabble.com/Correct-Interpretation-of-survreg-coeffs-tp4608655.html Sent from the R help mailing list archive at Nabble.com.

...ctor) # 3. Develop a new 'train' dataset with a group of variables based on the significances from the logistic regression # PLEASE FEEL FREE TO SHARE FEATURE SELECTION/EXTRACTION METHODS AS I AM CLEARLY LACKING IN THAT AREA AS WELL :( > head(train) survived age sibsp pclass2 pclass3 sexmale 1 0 22 1 0 1 1 2 1 38 1 0 0 0 3 1 26 0 0 1 0 4 1 35 1 0 0 0 5 0 35 0 0 1 1 6 0 27 0 0 1 1 > sapply(train,class) su...

...om that object. Is there a way to retrieve the AIC from a coxme object? Please cc me on your response. Thanks, Chris 1> str(fit1.full) List of 20 $ coefficients :List of 2 ..$ fixed : Named num [1:5] 0.197 -1.132 -0.499 0.114 -0.329 .. ..- attr(*, "names")= chr [1:5] "sexMALES" "lifedxmCONTROL" "lifedxmMAJOR" "sexMALES:lifedxmCONTROL" ... ..$ random:List of 1 .. ..$ famid: Named num 0.964 .. .. ..- attr(*, "names")= chr "Intercept" $ frail :List of 1 ..$ famid: Named num [1:97] -0.8144 -0.6222 -0....

...quot;R" effects example, so you can show me the correct syntax. Thanks mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6)), given.values=c(sexmale=0.5)) eff.cowles plot(eff.cowles, 'neuroticism:extraversion', ylab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))) Chris Anderson Data Analyst Medical Affairs wk: 925-677-4870 cell: 707-315-8486 Fax:925-677-4670 </pre><br>This electronic message transm...

...(Intercept) 5.90543 2.60526 2.267 0.0234 * Weight -0.02521 0.01709 -1.475 0.1402 ClutchSizeTwo-eggs 1.61701 0.65699 2.461 0.0138 * ClutchSizeThree-eggs 1.07775 0.66945 1.610 0.1074 EggVolume -0.08200 0.03375 -2.430 0.0151 * SexMale 0.77882 0.35374 2.202 0.0277 * SibCompPresente 0.72333 0.40077 1.805 0.0711 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 2...

...in. But R (version 0.99.0) doesn't like this. > step(lm(ANB.DIFF~.,tink4),trace=0) Call: lm(formula = ANB.DIFF ~ SEX + AGE.YRS + OJS + S.AR1 + CD.GO1 + CD.GN1 + AR.GN1 + UAFH1 + UPFH1 + LPFH1 + AFP1 + PFP1 + OJ1 + SNA1 + SNB1 + SN.POG1, data = tink4) Coefficients: (Intercept) SEXmale AGE.YRS OJS S.AR1 -0.05001 0.47100 0.11148 -0.10458 0.11228 CD.GO1 CD.GN1 AR.GN1 UAFH1 UPFH1 0.28847 -0.38802 0.25388 0.13399 -0.31484 LPFH1 AFP1 PFP1 OJ1 S...

I'm very new to R and modeling but need some help with visualization of glms. I'd like to make a graph of my glms to visualize the different effects of different parameters. I've got a binary response variable (bird sightings) and use binomial glms. The 'main' response variable is a measure of distance to a track and the parameters I'm testing for are vegetation parameters

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

...quot;R" effects example, so you can show me the correct syntax. Thanks mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6)), given.values=c(sexmale=0.5)) eff.cowles plot(eff.cowles, 'neuroticism:extraversion', ylab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))) Chris Anderson Data Analyst Medical Affairs wk: 925-677-4870 cell: 707-315-8486 Fax:925-677-4670 </pre><br>This electronic message transm...