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...n subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i know the predictors have an effect on DV1, and i know DV2 covaries with DV1, so it would be "cheating" to simply estimate the effect of the predictors on DV2 because those effects could be indirect (via DV1), right ? I see two solutions : *One solution would be a mixed model MANOVA (if that even exists)*. But i don't know h...

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 ne...

...0,3.400,1.499,0.351,0.082,0.518,0.471,0.036,0.721) k<-rep(1,29) x<-data.frame(k,x1,x2) freg<-function(y,x1,x2){   reg<- rlm(y ~ x1 + x2 , data=x)   return(reg) }  func <- function(x1,x2,b){       fit<-freg(y,x1,x2)         b<-c(coef(fit))       dv<-1+ b[2]*x2^b[3]         dv1<-b[2]*x2^b[3]*log(x2)       out <- ( x1/(1+ b[2]*x2^b[3]))       out1<-  c(-x1*x2^b[3]/dv^2,-x1* dv1/dv^2)         return(list( out,out1))        }> optim(par=c(b[2],b[3]), fn=out, gr =out1,         method = c("BFGS"),         lower = -Inf, upper = Inf,         control = list(...

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 Purd...

...ly so simple you won't need to. # I have some data (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 &lt...

.... If I omit it, the test fails. The only differences in the asm are stack offsets, which leads me to believe that in the failing test codegen is not accounting for all allocas properly. Before this critical instcombine the input code looks like this: ; Fails %"t$1" = alloca %DV1, align 8 ; <%DV1*> [#uses=3] %"t$2" = alloca [1 x [1 x <2 x float>]]*, i32 12, align 8 ; <[1 x [1 x <2 x float>]]**> [#uses=2] %tmpcast5 = bitcast [1 x [1 x <2 x float>]]** %"t$2" to %DV2* ; <%DV2*> [#uses=2] %"t$34" = alloca...

Dear everyone: I am a new user of R. I have a dataset with a dependent variable (DV) censored at different values. The dataset looks like, conditions .....IDV1 IDV2 DV 1 2 4 89 1 6 6 75 1 4 5 0 ( DV<=70) ...... 2 3 5 15 2 5 5 0 (DV<=11) 2 ...... 5 6 14 I do not k...

...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.Emplo...

Hi R users, I have a test dataframe ("file1," shown below) for which I am trying to create a flag for the first and last ID record (equivalent to SAS first.id and last.id variables. Dump of file1: > file1 id rx week dv1 1 1 1 1 1 2 1 1 2 1 3 1 1 3 2 4 2 1 1 3 5 2 1 2 4 6 2 1 3 1 7 3 1 1 2 8 3 1 2 3 9 3 1 3 4 10 4 1 1 2 11 4 1 2 6 12 4 1 3 5 13 5 2 1 7 14 5 2 2 8 15 5 2 3 5 16 6 2 1 2 17 6 2...

...at exists that will perform repeated measures MANOVAs? For example, let's say I have 3 DVs, one between-subjects IV, and one within-subjects IV. Based on the documentation for the manova command, a function like that below is not appropriate because it cannot take Error arguments. manova(cbind(DV1,DV2,DV3) ~ BetweenSubjectsIV * WithinSubjectsIV + Error(SubjNum/WithinSubjectsIV), data=dat) Do you know of any functions that would allow me to perform such an analysis? I realize that I could turn the DVs into levels of a new IV, and then perform a standard repeated measures ANOVA. But, I do not...

...that both methods are correct, 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 g...

...r1 <- nls(model, start = c(k=.01), data = xydata, weights = wts) #this actually performs an unweighted fit (issuing a warning): mdl <- model[[3]] r2 <- nls(y ~ eval(mdl), start = c(k=.01), data = xydata, weights = wts) #this, too, actually performs an unweighted fit (issuing a warning): dv1 <- deriv(model, "k") r3 <- nls(y ~ eval(dv1), start = c(k=.01), data = xydata, weights = wts) #weighted fit, works as expected dv2 <- deriv(model, "k", c("k", "x")) r4 <- nls(y ~ dv2(k, x), start = c(k=.01), data = xydata, weights = wts) #=====...

...r1 <- nls(model, start = c(k=.01), data = xydata, weights = wts) #this actually performs an unweighted fit (issuing a warning): mdl <- model[[3]] r2 <- nls(y ~ eval(mdl), start = c(k=.01), data = xydata, weights = wts) #this, too, actually performs an unweighted fit (issuing a warning): dv1 <- deriv(model, "k") r3 <- nls(y ~ eval(dv1), start = c(k=.01), data = xydata, weights = wts) #weighted fit, works as expected dv2 <- deriv(model, "k", c("k", "x")) r4 <- nls(y ~ dv2(k, x), start = c(k=.01), data = xydata, weights = wts) #=====...

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...Gerard Smits wrote: >Hi R users, > >I have a test dataframe ("file1," shown below) for which I am trying >to create a flag for the first and last ID record (equivalent to SAS >first.id and last.id variables. > >Dump of file1: > > > file1 > id rx week dv1 >1 1 1 1 1 >2 1 1 2 1 >3 1 1 3 2 >4 2 1 1 3 >5 2 1 2 4 >6 2 1 3 1 >7 3 1 1 2 >8 3 1 2 3 >9 3 1 3 4 >10 4 1 1 2 >11 4 1 2 6 >12 4 1 3 5 >13 5 2 1 7 >14 5 2...

...E's (a simple Lotka-Volterra predator prey model) dP/dt = beta*P*V - mu*P dV/dt = r*V - beta*P*V where P and V are the numbers of predators and prey. Now, this is easy to do, but suppose you have a system of equations like this, dP1/dt = beta1*P1*V1 - mu1*P1 dP2/dt = beta2*P2*V2 - mu2*P2 dV1/dt = r1*V1 - beta1*P1*V1 dV2/dt = r1*V2 - beta1*P2*V2 System 1 and system 2 are independent but that doesn't need to be the case. Now can you specify this system in lsoda() as: dP/dt = beta*P*V - mu*P dV/dt = r*V - beta*P*V but now the initial state variables are 1x2 vectors and the para...

...:19:12 selcuk.yazar at gmail.com selcukyazar at .................... edu.tr any advice or solution thanks in advance -- Sel?uk YAZAR http://www.selcukyazar.blogspot.com -------------- next part -------------- A non-text attachment was scrubbed... Name: dv1.png Type: image/png Size: 17791 bytes Desc: not available URL: <http://dovecot.org/pipermail/dovecot/attachments/20140516/51e30b07/attachment.png>

...0x0 0x0 0x0 0x0 0x0 <<<<<<<<<<<<<<<<< Dump Card State Ends >>>>>>>>>>>>>>>>>> (probe3:ahd0:0:3:0): inquiry data fails comparison at DV2 step (probe2:ahd0:0:2:0): inquiry data fails comparison at DV1 step (ahd0:A:3:0): refuses synchronous negotiation. Using asynchronous transfers (ahd0:A:2:0): refuses synchronous negotiation. Using asynchronous transfers da0 at ahd0 bus 0 scbus0 target 0 lun 0 da0: <FUJITSU MAW3147NC 0104> Fixed Direct Access SCSI-3 device da0: Serial Number DAA0P7A054GD...

...xists that will perform repeated measures MANOVAs? For example, let's say I have 3 DVs, one between-subjects IV, and one within-subjects IV. Based on the documentation for the manova command, a function like that below is not appropriate because it cannot take Error arguments. manova(cbind(DV1,DV2,DV3) ~ BetweenSubjectsIV * WithinSubjectsIV + Error(SubjNum/WithinSubjectsIV), data=dat) Do you know of any functions that would allow me to perform such an analysis? I realize that I could turn the DVs into levels of a new IV, and then perform a standard repeated measures ANOVA. But, I do...

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