search for: iv1

...e first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can think of at least two possibilities: lmer(DV ~ IV1 + IV2 + (1 + IV1 + IV2 | Subject)) lmer(DV ~ IV1 + IV2 + (1 + IV1 | Subject) + (1 + IV2 | Subject)) Or maybe there is some other way to do it? Maybe the correct syntax depends on whether the random effect of subjects on the intercept and slopes are correlated or not? (If so, how do I procee...

...nting the ANCOVA. However the output confuses me; why does the covariate only appear in the first strata? Presumably it should appear in every strata in which there can be between-Ss effects or interactions with between-Ss effects, no? #generate data set.seed(1) a=rbind( expand.grid( id=1:20 ,iv1 = 1:2 ,iv2 = 1:2 ) ) a$group = factor(a$id<11) a$dv = rnorm(length(a[,1])) a$covariate = NA for(i in unique(a$id)){ a$covariate[a$id==i]= rnorm(1) } #make sure id, iv1, and iv2 are factorized a$id=factor(a$id) a$iv1=factor(a$iv1) a$iv2=factor(a$iv2) #run ANCOVA covariate_aov = aov(dv~covar...

Dear R-Users, I have a trivial problem, but extensive googling and ??'ing did not solve it: I want to obtain the sums of squares from a summary.aov() object for later use. Example: > DV <- rnorm(100) > IV1 <- as.factor(rep(c("male", "female"), times = 50)) > IV2 <- as.factor(rep(c("young", "old"), times = 50)) > > summary(aov(DV ~ IV1 * IV2)) Df Sum Sq Mean Sq F value Pr(>F) IV1 1 0.215 0.21499 0.2277 0.6343 Residuals...

...art -------------- Index: cipher.c =================================================================== RCS file: /usr/local/cvs/openssh/cipher.c,v retrieving revision 1.7 diff -u -r1.7 cipher.c --- cipher.c 2000/01/17 17:27:31 1.7 +++ cipher.c 2000/01/19 07:18:49 @@ -45,16 +45,16 @@ { des_cblock iv1; - memcpy(&iv1, iv2, 8); + memmove(&iv1, iv2, 8); des_cbc_encrypt(src, dest, len, ks1, &iv1, DES_ENCRYPT); - memcpy(&iv1, (char *)dest + len - 8, 8); + memmove(&iv1, (char *)dest + len - 8, 8); des_cbc_encrypt(dest, dest, len, ks2, iv2, DES_DECRYPT); - memcpy(iv2, &amp...

Hello everyone, I have tried to look for this everywhere and so far have no luck. I have a plotMeans(DV, IV1, IV2, error.bars="se") graph that plots my data (DV-continuous, IVs are factors, IV1 - two levels, IV2-four levels). I am trying to increase a scale of my y-axis (to be consistent with my other graphs), but unfortunately nothing works with "plotMeans" function, which is the onl...

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 the tsls function...

...: ; preds = %entry %0 = shl i32 %Size, 2 br label %for.body.4.lr.ph for.body.4.lr.ph: ; preds = %for.body.4.lr.ph.preheader, %for.cond.cleanup.3 %lsr.iv5 = phi i32* [ %Src, %for.body.4.lr.ph.preheader ], [ %2, %for.cond.cleanup.3 ] %lsr.iv1 = phi i32* [ %Dst, %for.body.4.lr.ph.preheader ], [ %1, %for.cond.cleanup.3 ] %Outer.026 = phi i32 [ %inc10, %for.cond.cleanup.3 ], [ 0, %for.body.4.lr.ph.preheader ] %lsr.iv56 = bitcast i32* %lsr.iv5 to i1* %lsr.iv12 = bitcast i32* %lsr.iv1 to i1* br label %for.body.4 for.cond.cleanup.loo...

...R just to learn R and then check them on SPSS to make sure that I am doing fine. Here is the problem that I've run into: when we use the aov function, it uses SS Type I as default (on SPSS it is Type III). Then I used the Anova function under cars package using the command: mod <- lm(DV ~ IV1*IV2, data = mydata, contrasts=list(IV1=contr.sum, IV2=contr.sum)) Anova(mod, type=”3”) Above, both of my IVs are between-SS variables. But still, results from this model do not match the results from SPSS (I have to say they are not too different either). But I was wondering if I am doing somethin...

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 Cl...

...t, but I am less familiar with R, so I was hoping someone could offer some suggestions. Oddly simple ANOVA is the same in SPSS and R. Including covariates improves the main effect (p-value) in R and diminishes it in SPSS.. The formula I have been using is: >Y = cbind(dV1, dV2, dV3) >aov(lm(Y~iV1+cV1+cV2)) The main?independent?variable is disease group and the covariates are continuous nuisance variables such as age. Both nuisance variables interact with the dependent variable but not each other. The frequency distribution of the covariates is similar for each group, but the groups are not...

...ata (there are many more variables, 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&qu...

...IVs within the 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. Tas...

...> br label %for.body.4.lr.ph > > > > for.body.4.lr.ph: ; preds = > > %for.body.4.lr.ph.preheader, %for.cond.cleanup.3 > > %lsr.iv5 = phi i32* [ %Src, %for.body.4.lr.ph.preheader ], [ %2, > > %for.cond.cleanup.3 ] > > %lsr.iv1 = phi i32* [ %Dst, %for.body.4.lr.ph.preheader ], [ %1, > > %for.cond.cleanup.3 ] > > %Outer.026 = phi i32 [ %inc10, %for.cond.cleanup.3 ], [ 0, > > %for.body.4.lr.ph.preheader ] > > %lsr.iv56 = bitcast i32* %lsr.iv5 to i1* > > %lsr.iv12 = bitcast i32* %lsr.iv1 t...

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

..., float* nocapture %c, i64 %iNumElements) { Entry: br label %L_pre_head L_pre_head: ; preds = %Entry br label %L_entry L_entry: ; preds = %L_entry, %L_pre_head %lsr.iv1 = phi i64 [ %lsr.iv.next2, %L_entry ], [ 0, %L_pre_head ] %lsr.iv = phi i64 [ %lsr.iv.next, %L_entry ], [ %iNumElements, %L_pre_head ] %idxprom = ashr exact i64 %lsr.iv1, 32 %arrayidx = getelementptr inbounds float* %a, i64 %idxprom %0 = load float* %arrayidx...

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 liste...

...// SE represent for scalar evolution pass.* *}* *}* *FP:* *bool runOnFunction(Function &F)* *{* *SE = &getAnalysis<ScalarEvolution>();* *SE->print(errs());* *}* But when processing 'getelementptr' instruction, these two ways return different result. *MP:* * %lsr.iv1 = getelementptr inbounds [1024 x i32]* %lsr.iv, i64 0, i64 0* * --> {@A,+,sizeof(i32)}<%8> Exits: (((zext i32 (0 smax %N) to i64) * sizeof(i32)) + @A)* *FP:* *%lsr.iv1 = getelementptr inbounds [1024 x i32]* %lsr.iv, i64 0, i64 0* * --> {@A,+,4}<%8> Exits: ((4 * (zext i32...

...blem when I try to call a *gls* function in loop structure. The gls function seems not able to recognize the parameters that I pass into the loop function! (But, if I use lm function, it works.) The code looks like this: ================================================= gls.lm <- function(Data, iv1, dv1) { gls.model <- gls(Data[ , dv1] ~ Data[ , iv1], correlation = corARMA(p=1), method='ML',na.action=na.omit, data = Data) print(summary(gls.model)) } regResultMatrix <- gls.lm(DR, 'Period', 'Aircraft') regResultMatrix <- gls.lm(DR, 'Period', 'Total....

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not instrumental). My current code looks like: glm(DV1~ IV1+ Control1+ Control2+ DV4+ DV5+ DV6, family="binomial", data= data) which I repeat for each of the 6 DVs. I realize that I need to run a...

...igma0<-matrix(corrHealthy,nbX,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, ************************************ Delphi...