search for: iv3

...a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 - measured in years), previous performance (continuous IV2), country (categorical IV3 - six countries), the percentage of PhD graduates in each country (continuous IV4 - country level data - apparently only six different percentages since we have only six countries) and population of country (continuous IV5 - country level data - again only six numbers here, one for each country p...

...%exitcond27 = icmp eq i32 %inc10, %Size br i1 %exitcond27, label %for.cond.cleanup.loopexit, label %for.body.4.lr.ph for.body.4: ; preds = %for.body.4, %for.body.4.lr.ph %lsr.iv8 = phi i32* [ %scevgep9, %for.body.4 ], [ %lsr.iv5, %for.body.4.lr.ph ] %lsr.iv3 = phi i32* [ %scevgep4, %for.body.4 ], [ %lsr.iv1, %for.body.4.lr.ph ] %lsr.iv = phi i32 [ %lsr.iv.next, %for.body.4 ], [ %Size, %for.body.4.lr.ph ] %3 = load i32, i32* %lsr.iv8, align 4, !tbaa !1 %mul5 = mul i32 %3, %Val store i32 %mul5, i32* %lsr.iv3, align 4, !tbaa !1 %lsr.iv.next = ad...

...V) is a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 - measured in years), previous performance (continuous IV2), country (categorical IV3 - six countries), the percentage of PhD graduates in each country (continuous IV4 - country level data - apparently only six different percentages since we have only six countries) and population of country (continuous IV5 - country level data - again only six numbers here, one for each country pop...

...; + memmove(&iv1, (char *)dest + len - 8, 8); des_cbc_encrypt(dest, dest, len, ks2, iv2, DES_DECRYPT); - memcpy(iv2, &iv1, 8); /* Note how iv1 == iv2 on entry and exit. */ + memmove(iv2, &iv1, 8); /* Note how iv1 == iv2 on entry and exit. */ des_cbc_encrypt(dest, dest, len, ks3, iv3, DES_ENCRYPT); - memcpy(iv3, (char *)dest + len - 8, 8); + memmove(iv3, (char *)dest + len - 8, 8); } void @@ -66,16 +66,16 @@ { des_cblock iv1; - memcpy(&iv1, iv2, 8); + memmove(&iv1, iv2, 8); des_cbc_encrypt(src, dest, len, ks3, iv3, DES_DECRYPT); - memcpy(iv3, (char *)src +...

Does anyone know if there is a way to run multivariate linear regression in R? I tried using the lm function (e.g., lm(dv1, dv2~iv1+iv2+iv3), but got error messages. Is my syntax wrong, or do I need a particular package? Thanks, Jeff-- ________________________________________________________ Jeffrey J. Lusk, Ph.D. Postdoctoral Research Associate Department of Forestry & Natural Resources and Purdue and Climate Ch...

...telementptr i8, i8* %Dst14, i32 %lsr.iv10 %uglygep1516 = bitcast i8* %uglygep15 to i32* br label %for.body.4 for.body.4: ; preds = %for.body.4, %for.body.4.lr.ph %lsr.iv8 = phi i32* [ %scevgep9, %for.body.4 ], [ %uglygep13, %for.body.4.lr.ph ] %lsr.iv3 = phi i32* [ %scevgep4, %for.body.4 ], [ %uglygep1516, %for.body.4.lr.ph ] %lsr.iv = phi i32 [ %lsr.iv.next, %for.body.4 ], [ %Size, %for.body.4.lr.ph ] %1 = load i32, i32* %lsr.iv8, align 4, !tbaa !0 %mul5 = mul i32 %1, %Val store i32 %mul5, i32* %lsr.iv3, align 4, !tbaa !0 %lsr.iv....

..., but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1", "dv2", "dv3", "iv1", "iv2", "iv3", "iv4", "iv5", "intv1", "intv2") names(my.df) <- var.list # I have some are DVs: dvs <- c("dv1", "dv2", "dv3") # some IVs: ivs <- c("iv1", "iv2", "iv3", "iv4", "iv5&qu...

...l %for.cond.cleanup.loopexit, label % > for.body.4.lr.ph > > > > for.body.4: ; preds = > > %for.body.4, %for.body.4.lr.ph > > %lsr.iv8 = phi i32* [ %scevgep9, %for.body.4 ], [ %lsr.iv5, > > %for.body.4.lr.ph ] > > %lsr.iv3 = phi i32* [ %scevgep4, %for.body.4 ], [ %lsr.iv1, > > %for.body.4.lr.ph ] > > %lsr.iv = phi i32 [ %lsr.iv.next, %for.body.4 ], [ %Size, % > for.body.4.lr.ph ] > > %3 = load i32, i32* %lsr.iv8, align 4, !tbaa !1 > > %mul5 = mul i32 %3, %Val > > store i32 %mu...

On Mon, Aug 31, 2015 at 5:52 PM, Jake VanAdrighem <jvanadrighem at gmail.com> wrote: > Do you have some specific performance measurements? Averaging 4 runs of 10000 iterations each of Coremark on my X86_64 desktop showed: -O2 performance: +2.9% faster with the L.E.V. pass -Os size: 1.5% smaller with the L.E.V. pass In the case of Coremark, the benefit comes mainly from the matrix

...he same regression. What I am interested is to compare the coefficients of the same IVs across three regressions and across three groups.  Some basic information about my model/design: My model includes 2 fixed factors (task, group) and 5 more IVs of interest. Choice ~ Task * Group + IV1 + IV2 + IV3 + IV4 + IV5 I established that the Task * Group interaction is significant (by running two models of a regression, one comparing a model with and one without the interaction term) and I am interested to explore group differences within each task in a separate logistic regression. Task has 3 leve...

....iv)); } This doesn't use the *iv parameter. Compare with: static void des3_setiv(CipherContext *cc, const u_char *iv, u_int ivlen) { memset(cc->u.des3.iv1, 0, sizeof(cc->u.des3.iv1)); memset(cc->u.des3.iv2, 0, sizeof(cc->u.des3.iv2)); memset(cc->u.des3.iv3, 0, sizeof(cc->u.des3.iv3)); if (iv == NULL) return; memcpy(cc->u.des3.iv3, (char *)iv, 8); } which *does* use the *iv parameter. (Though, not quite how I expected it to be used.. :) I'll be the first to admit that I don't know the OpenSSH architectu...

Hello! I have data containing a large number of probabilities (about 60) of nonzero coefficients to predict 10 different independent variables (in 10 different BMA models). i've arranged these probabilities in a matrix like so: (IV1) (IV2) (IV3) ... p(b0) p(b0) p(b0) p(b1) p(b1) p(b1) p(b2) p(b2) p(b2) ... where p(b1) for independent variable 1 is p(b1 != 0) (given model uncertainty - using the BMA package). i've also set it so that if the coefficient is negative, the probability is listed as negative (to...

On Wed, Sep 23, 2015 at 12:00 PM, Hal Finkel <hfinkel at anl.gov> wrote: >> >> Should we try the patch in it's current location, namely after LSR? > > Sure; post the patch as you have it so we can look at what's going on. > http://reviews.llvm.org/D12494 One particular point: The algorithm checks that SCEV's are equal when their raw pointers are equal. Is

...,nbX) diag(sigma0)<-1 sigma1<-matrix(corrSick,nbX,nbX) diag(sigma1)<-1 dataHealthy<-mvrnorm(N*(1-propSick),c(0,0,0),sigma0) dataSick<-mvrnorm(N*propSick,c(1,1,1),sigma1) dataS<-as.data.frame(matrix(0,ncol=4,nrow=N)) dimnames(dataS)[[2]]<-c("IV1","IV2","IV3","DV") dataS$DV[1:(N*propSick)]<-1 dataS$DV<-factor(dataS$DV) dataS[1:(N*propSick),1:3]<-dataSick dataS[(N*propSick+1):N,1:3]<-dataHealthy _____________ thanks in advance for any suggestions, ************************************ Delphine Courvoisier Clinical Epidemio...

...ect syntax for doing the same thing in R. I have successfully converted Stata datasets, merged, aggregated, run lm, etc., but this simple task is evading me entirely. Here's what I have tried (and various iterations): if(merged.dataset$model1 == 1) model1.coeff <- lm(dv ~ iv1 + iv2 + iv3, data = merged.dataset) I get this: Warning message: In if (merged.dataset$model1 == 1) model1.coeff <- lm(dv ~ iv + : the condition has length > 1 and only the first element will be used What it seems to do is just run lm on the first opportunities of model1==1, but then doesn't try i...

Hello, In order to experiment with OpenCA, I have built an RPM for redhat 7.2 of a recent OpenSSL snapshot (the binary rpm on the OpenCA was built with the different target directories and libraries. Unfortunately these recent OpenSSL snapshots seems to break all OpenSSH tarballs and RPMs that I have been able to find. None of them seem to compile successfully, even the snapshots at

Dear all, I try to conduct a SEM / two stage least squares regression with the following equations: First: X ~ IV1 + IV2 * Y Second: Y ~ a + b X therein, IV1 and IV2 are the two instruments I would like to use. the structure I would like to maintain as the model is derived from economic theory. My problem here is that I have trouble solving the equations to get the reduced form so I can run