search for: fvar

...or My questions are, why doesn't this work, and how do I fix it. I'm using the formula function to create the formula that I'm sending to my function. And the mdata is a data.frame. I'm assumed that if I passed the column names as strings (response variable - rvar, fold variable - fvar) this would work. Apparently however it doesn't. Lastly, since I don't have access to MASS and there are apparently many examples of doing this kind of thing in MASS, could someone tell me if this function looks approximately correct? Thanks T -------------------------------------------...

...equal mesor, H2.x: equal amplitude and acrophase, H3: equal rhythmic components). I suppose I have to use dummy variables, but I don’t know how to do it.   I could access something similar in a solution manual of  a Weisberg book (1985), chapter 6, problem 9, as follows: m1 <- lm(Yvar~ Xvar + Fvar + Fvar:Xvar, na.action=na.omit, weights=theWeights)  # this is model 1 the most general m2 <- lm(Yvar~ Xvar + Fvar            , na.action=na.omit, weights=theWeights)  # this is model 2 parallel m3 <- lm(Yvar~ Xvar + Fvar:Xvar       , na.action=na.omit, weights=theWeights)  # this is model...

...78 386 39 66 1 204 233 39 83 1 265 360 39 87 1 270 308 39 236 1 243 323 39 174 2 244 300 39 181 2 257 366 39 241 2 305 408 39 282 2 299 599 39 125 3 153 402 39 156 3 213 384 39 183 3 227 408 39 217 3 235 360 39 159 4 205 362 39 180 4 182 314 39 189 4 224 488 39 246 4 265 468 fdays = factor(Days) fvar = factor(variety) my model is: test1 <- gam(O_EC~s(WRM)+PWM+fdays+fvar) Now, I can easily measure O_EC, PWM, fvar, and fdays, but not WRM. I can get a good estimate for s(WRM) by straight algebra and get a rough eyeball estimate of WRM from the s(WRM) vs WRM plot: s(WRM) ~ O_EC - B0 - B1*PW...

...bili) + log(protime) + log(albumin) + edema + frailty(group), na.action=na.exclude, data=Pbcseq) Then I obtain: > fitf[10] $frail [1] 0.06273372 0.16192093 0.10050877 0.37716999 -0.20853156 -0.71887977 [7] -0.10922275 And: > fitf[11] $fvar [1] 0.03631634 0.03261972 0.03750465 0.04459689 0.05113802 0.08107876 0.11482236 My understanding is that fitf$frail are the group level frailty terms. But what is fitf$fvar? Moreover, if I estimate the same model as the one above, but with a `group' variable with only 4 groups (instead of 7...

...(predict.glm(object, newdata, type='response') < 0.5, 0, 1)) return(ret) } # logerrorest takes four arguments # mdata is a data frame holding the data to be modeled # form is the output of the is.formula function # rvar is the response variable as a string, for example 'birdx' # fvar is the fold variable, for example 'recordyear' logerrorest <- function(mdata, form, rvar, fvar) { require(Hmisc) require(ipred) # determine index of variables rpos <- match(rvar, names(mdata)) fpos <- match(fvar, names(mdata)) # get fold values and count for each group vardesc &l...

...ize=4" CXXFLAGS="-march=x86-64 -mtune=generic -O2 -pipe -fstack-protector --param=ssp-buffer-size=4" LDFLAGS="-Wl,-O1,--sort-common,--as-needed,-z,relro" #-- Make Flags: change this for DistCC/SMP systems MAKEFLAGS="-j 8" #-- Debugging flags DEBUG_CFLAGS="-g -fvar-tracking-assignments" DEBUG_CXXFLAGS="-g -fvar-tracking-assignments" ------------------------------ Is there any flag which can prevent to build libguestfs? I even tried to add some more flags regarding xml2 (not sure it is worth) but it didn't change. ------------ -I/usr/inclu...

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

On Fri, Sep 25, 2020 at 5:35 PM Daniel P. Berrangé <berrange@redhat.com> wrote: > On Fri, Sep 25, 2020 at 05:31:09PM +0800, Wei Wang wrote: > > On Fri, Sep 25, 2020 at 5:24 PM Andrea Bolognani <abologna@redhat.com> > > wrote: > > > > > On Fri, 2020-09-25 at 10:16 +0100, Daniel P. Berrangé wrote: > > > > On Fri, Sep 25, 2020 at 05:03:24PM

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

...[release]), Python version (3.8.2), your OS type & version, and the command line used. [991/1250] Compiling C object tests/qemuxml2xmltest.p/qemuxml2xmltest.c.o ../tests/qemuxml2xmltest.c: In function ‘mymain’: ../tests/qemuxml2xmltest.c:131:1: note: variable tracking size limit exceeded with ‘-fvar-tracking-assignments’, retrying without 131 | mymain(void) | ^~~~~~ [992/1250] Compiling C object tests/cputest.p/cputest.c.o ninja: build stopped: subcommand failed. Thanks, Wei

On Fri, Sep 25, 2020 at 04:44:24PM +0800, Wei Wang wrote: > On Fri, Sep 25, 2020 at 4:40 PM Andrea Bolognani <abologna@redhat.com> > wrote: > > > On Fri, 2020-09-25 at 16:21 +0800, Wei Wang wrote: > > > On Fri, Sep 25, 2020 at 3:56 PM Andrea Bolognani <abologna@redhat.com> > > wrote: > > > > On Fri, 2020-09-25 at 10:33 +0800, Wei Wang wrote: