search for: zval

...uot;) to work in llines(). Try, e.g., the following scaled-down example: x <- factor(c("a", "b", "a", "b")) y <- c(1,2,1,2) z <- factor(c("A", "A", "B", "B")) symbols <- 1:2 lines <- 1:2 colors <- 1:2 zvals <- levels(z) xyplot(y~x|z, panel = function(x, y, subscripts, ...) { for (i in 1:length(zvals)) { sub <- z[subscripts] == zvals[i] llines(x[sub], y[sub], lwd = 2, type = "b", col = colors[i], pch = symbols[i], lty = lines[i]) } }) Onl...

...rce_type) \\ + ZEND_FETCH_RESOURCE(rsrc, rsrc_type, &(passed_id), -1, resource_type_name, resource_type) +typedef int guestfs_string_length; +#endif + /* Convert array to list of strings. * http://marc.info/?l=pecl-dev&m=112205192100631&w=2 */ @@ -103,16 +129,28 @@ get_stringlist (zval *val) HashTable *a; int n; HashPosition p; +#if ZEND_MODULE_API_NO >= 20151012 + zval *d; +#else zval **d; +#endif size_t c = 0; a = Z_ARRVAL_P (val); n = zend_hash_num_elements (a); ret = safe_emalloc (n + 1, sizeof (char *), 0); for (zend_hash_internal_pointer_res...

...; pr " char *%s;\n" n; pr " int %s_size;\n" n + | OptString n -> + pr " char *%s = NULL;\n" n; + pr " int %s_size;\n" n | StringList n | DeviceList n -> pr " zval *z_%s;\n" n; @@ -310,7 +312,7 @@ PHP_FUNCTION (guestfs_last_error) | String n | Device n | Mountable n | Pathname n | Dev_or_Path n | Mountable_or_Path n | FileIn n | FileOut n | Key n - | OptString n | GUID n -> + | GUID n -> (* Just...

...uot;require(stats)"); r.eval("autoload(\"Normal\", \"stats\")"); r.eval("search()"); r.eval("ls(\"Autoloads\")"); System.out.println(r.eval(".Autoloaded")); r.eval("zval = .95"); System.out.println(r.eval("print(zval)")); r.eval("p3 = pnorm(3*zval)"); System.out.println(r.eval("print(p3)")); It gives as a result: [STRING "stats"] [REAL* (0.95)] null...

is there no package/function in R to calculate the conditional power or the bayesian predictive power for trials with binary endpoints? Thanks -- View this message in context: http://www.nabble.com/Conditional-power%2C-predictive-power-tf3603396.html#a10066991 Sent from the R help mailing list archive at Nabble.com.

.../usr/include/php4/Zend -I/usr/include/php4/TSRM -Wall -Wno-unused -Wno-uninitialized -g -O2 -I/usr/local/include -MT xapian_wrap.lo -MD -MP -MF .deps/xapian_wrap.Tpo -c xapian_wrap.cc -fPIC -DPIC -o .libs/xapian_wrap.o xapian_wrap.cc: In function `void _wrap_PostingIterator_get_description(int, zval*, zval*, int)': xapian_wrap.cc:2552: internal compiler error: Segmentation fault Please submit a full bug report, with preprocessed source if appropriate. See <URL:http://gcc.gnu.org/bugs.html> for instructions. For Debian GNU/Linux specific bug reporting instructions, see <URL:file://...

...nt of heterogeneity): 0.0111 (SE = 0.0095) tau (sqrt of the estimate of residual heterogeneity): 0.1054 Test for Residual Heterogeneity: QE(df = 17) = 43.0937, p-val = 0.0005 Test of Moderators (coefficient(s) 2): QM(df = 1) = 1.1069, p-val = 0.2928 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.0811 0.0606 1.3380 0.1809 -0.0377 0.2000 mods 0.0473 0.0449 1.0521 0.2928 -0.0408 0.1353 Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 ## why only one df for the 2-level factor? ## in other words, why isn't...

...owing script: MixModel<-rma(z, v.z., mods=cbind(k.animal, k.plant), intercept=FALSE, data=MData, subset=(Response=="Tolerance"))MixModel I receive the output: Test of Moderators (coefficient(s) 1,2): QM(df = 2) = 8.5758, p-val = 0.0137 Model Results: estimate se zval pval ci.lb ci.ub k.animal 0.4877 0.2703 1.8042 0.0712 -0.0421 1.0175 . k.plant 0.5095 0.2209 2.3066 0.0211 0.0766 0.9424 * Does this mean that, overall, there is a significant effect of "group" on the overall effect size (based on the QM test)? If I want to te...

...902366 0.05563833 0.07037450 0.08836045 0.09914814 0.10787837 0.11866803 0.12481970 0.12669151 0.13320179 0.12919546 ### The (lower) confidence interval printed by the summary.survfit function appears to be based on the stderr contained in the survfit object (not std.err printed in summary) > zval <- qnorm(1- (1-.95)/2, 0,1) > exp( log(s.fit$surv) - zval*s.fit$std.err) [1] 0.9318402 0.8784979 0.8144399 0.7522512 0.6616253 0.5881502 0.5159010 0.4287437 0.3519573 0.2857688 0.1932689 0.1267205 > exp( log(s.fit$surv) - zval*sum.s.fit$std.err) [1] 0.9328355 0.8818930 0.8224912 0.7665...

...trast with all I read on this issue, including R mailing lists (e.g. http://finzi.psych.upenn.edu/R/Rhelp02/archive/30588.html), e.g. if someone claims those lines would not matter and go away when printing. In my case they don't. BTW, my image() statement looks like this: image(rfc, y, -t(zVals), zlim=c(-noCols-0.5,0.5), col = heat.colors(noCols), axes=F, add=T) where z is a 127 x 3 matrix. Since I need high resolution variants for final publication I need to know whether it is possible to force image() to give up drawing these borders (I tried already several low level par paramete...

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

...ty): 0.0925 (SE = 0.0269) tau (sqrt of the estimate of residual heterogeneity): 0.3041 Test for Residual Heterogeneity: QE(df = 30) = 654.3038, p-val < .0001 *Test of Moderators (coefficient(s) 1,2,3): QM(df = 3) = 128.7528, p-val < .0001* Model Results: estimate se zval pval ci.lb ci.ub LateralWedge *0.6462* 0.0722 8.9450 <.0001 *0.5046 0.7878* *** Dome *0.6659 * 0.1471 4.5283 <.0001 *0.3777 0.9541 * *** Complex *0.6938* 0.1306 5.3136 <.0001 * 0.4379 0.9498* ***/ My suspicion is that the test of moderators...

...stimate of residual amount of heterogeneity): 0.0106 tau (sqrt of the estimate of residual heterogeneity): 0.1031 Test for Residual Heterogeneity: QE(df = 18) = 1273.9411, p-val < .0001 Test of Moderators (coefficient(s) 2,3,4): QM(df = 3) = 11.0096, p-val = 0.0117 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.4014 0.1705 2.3537 0.0186 0.0671 0.7356 * continent -0.0206 0.0184 -1.1200 0.2627 -0.0568 0.0155 approxmeanage 0.0076 0.0091 0.8354 0.4035 -0.0102 0.0254 interviewmethodcode -0.0892 0.0273 -3.2702 0.0011 -0.1426 -0.0357 ** --- Signif. codes: 0 '***' 0.001 '**...

Hello, I am having a bit of trouble with scatterplot3d(). I was able to plot a 3d cloud of points using the following code: >my.3dplot<-scatterplot3d(my.coords, pch=19, zlim=c(0,1), scale.y=0.5, angle=30, box=FALSE) where my.coords is a data frame that contains x, y, and z coordinates for grid points whose elevation we sampled. The problem occurs when I try to add points using points3d.

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I had found the author's (Wolfgang Viechtbauer) earlier meta-analytic code in R, MiMa, useful. so I have been exploring metafor using an example dataset from MiMa. metafor provides a lot more. However, MiMa provided parameter estimates, standard errors, z values, etc. for individual moderators in the meta-analysis, but I don't see how to obtain these from metafor. Have you any help

Hi I have tryed to do the meta-regression in metafor package, but I would like to get the standardized coefficients for each variable, however in command:   Ø  res<-rma.uni (yi, vi, method="REML", mods=~cota+DL+uso+gadiente+idade, data= turbidez)   I just have the coefficients no standardized (estimate) of the multiple regression. What I need to do? Thanks Fernanda Melo

Dear colleagues, I'm conducting a meta-analysis of studies evaluating adherence of HIV-positive drug users into AIDS treatment, therefore I'm looking for some advice and syntax suggestion for running the meta-regression using proportions, not the usual OR/RR frequently used on RCT studies. Have already searched already several handbooks, R-manuals, mailing lists, professors, but... not

Hello, I am performing a meta-analysis using the metafor package. My data are proportions and I used the Freeman Tukey double arcine (FT) transformation to fit the random effects model. Now I want to create a forest plot with my estimates backtransformed to the original scale of proportions. Can this be done? Regards, Patricia

https://bugzilla.redhat.com/show_bug.cgi?id=847880 https://bugzilla.redhat.com/show_bug.cgi?id=847881 This patch series adds various optional arguments to the tar-in and tar-out commands. Firstly (1/7) an optional "compress" flag is added to select compression. This makes the calls tgz-in/tgz-out/txz-in/txz-out deprecated, and expands the range of compression types available.