search for: dv2

...wo 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 how to run a mixe...

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 an...

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...

...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 <- c("iv1&qu...

...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 [9 x [1 x <2 x float>]*], align 8 ; <[9 x [1 x <2 x float>]*]*> [#uses=3] Afterward it looks like this: ; Passes %"t$1" = alloca %DV1, align 8 ; <%DV1*> [#uses=3] %"t$26" = alloca [12...

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 know h...

...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 not fee...

...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 group,...

...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) #================================CUT=========================== as you can see the fits producing `r2' and `r3' do _not_ do what's intended. l...

...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) #================================CUT=========================== if you copy this to the R prompt you can see the fits producing `r2' and `r3...

Le lun. 9 d?c. 2019 ? 05:43, Tomas Kalibera <tomas.kalibera at gmail.com> a ?crit : > On 12/7/19 10:32 PM, Laurent Gautier wrote: > > Thanks for the quick response Tomas. > > The same error is indeed happening when trying to have a zero-length > variable name in an environment. The surprising bit is then "why is this > happening during parsing" (that is why

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...terra 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 parameters beta, mu and r are als...

...40, SCB_NEXT2 == 0x1fb CDB 12 0 0 80 88 9 STACK: 0x237 0x2 0x0 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> Fix...

...s 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...

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