search for: resp2

...compiler exits when it find this discrepancy in \libspeex\cb_search.c : ".\libspeex\cb_search.c", line 185: cc0167: error: argument of type "spx_word16_t *" is incompatible with parameter of type "float *" compute_weighted_codebook(shape_cb, r, resp, resp2, E, shape_cb_size, subvect_size, stack); The function definition defines resp2 as a float in line 47: static void compute_weighted_codebook(const signed char *shape_cb, const spx_sig_t *r, spx_word16_t *resp, float *resp2, spx_word32_t *E, int shape_cb_size, int subvect_size, char *stack) But the...

...all, instead behaves like if it was a hig order polynomial or something similar!!!! I attach the codes below, hoping someone can point me the mistake.... I sincerely express many thanks in advance ...... Federico Bonofiglio frame3<-data.frame(id,chol,cd4,rt,sex,age, nadir,pharmac2,hbs,hcv,resp2, hivbl,switch) # I run a model specifing the function (t-t*)+ = 0 if t<=t* AND (t-t*)+ = t if t>t* with the following syntax, emulating MJ Crawley (The R book 2007, Wiley), # rt+rt*(rt>15) I run the model, is a mixed effect one....... PCWISE<-lme(chol~rt+rt*(rt>15) +se...

...et it to work. The problem seems to be passing the data argument of by() as a function argument. Is this bug, or am I missing something (or both)? > ### Generate some test data > Test <- vector("list",2) > Test[[1]] <- data.frame(Dose=rep(c(0,1),c(10,10)),Resp1=rnorm(20),Resp2=rnorm(20)) > ### The summary function > sumfun <- function(z) + { + by(data=z, + INDICES=list(factor(z[,"Dose"])), + FUN=function(y) + { + apply(as.matrix(y[,c("Resp1","Resp2")]),2, + function(x)c(Mean=mean(x),SD=sqrt(var(x...

...going with kripp.alpha(). I'm loading data from a file, like this: > library(irr) Loading required package: lpSolve > x <- read.table("foo", comment.char="#", header=TRUE, sep=",", quote="\"") > x HITId Resp1 Resp2 Resp3 1 22QI9NZW6HEZ1HYZ5BYTC69GDR5NXQ none 2 24SB6BFMFFOYTBDW8Y6DKESCJWZXKU lead lead 3 26OAUC26DG678UHYAF6K4COKLOH5CG none none none 4 28RZJMJUNU8LUZB2FVWRD8D828Y0G0 tick tick both 5 29DGULF395SW1G0WU7UN2NJBHK4QCE none none none 6 2F6CCF0CP5KXKM2IXXOYK4PMQS1VWM...

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2. Model 2 Y_ij, Y_ij2 | U_i...

I need to remove certain interactions and keep only the one between the second level of the factor and the continuous var t2 bin4 <- glm(resp2~ t*t2+c5.vrm,data=dfa,family="quasibinomial") > summary(bin4) Call: glm(formula = resp2 ~ t * t2 + c5.vrm, family = "quasibinomial", data = dfa) Deviance Residuals: Min 1Q Median 3Q Max -6.5464 -3.0720 -1.8135 0.4896 28.9207 Coefficients:...

Hello everyone, I would like to perform a 2-way manova test, but I'm having some issues. I implemented like this Y<-cbind(Resp1,Resp2,Resp3,....,Respn) model<-manova(Y "tilda" FactorA*FactorB) summary.aov(model) 1. I don't know at what level I have to do the Type I error correction. Is it on p-values returned by "summary.aov(model)? Or is it when I compare each subgroup with another subgroup? Or is it for...

...ween the response variables and consequently to specify completely parameterized covariance matrices. My current model looks like this: prior<-list(R=list(V=diag(4),nu=0.004),G=list(G1=list(V=diag(4)/4,n=4),G2=list(V=diag(4)/4,n=4),G3=list(V=diag(4)/4,n=4))) m1<-MCMCglmm(fixed=cbind(resp1,resp2,resp3) ~ trait:(expl1 + expl2+ expl3+ expl4+ expl5...)+trait-1, random=~us(trait):rand1+us(trait): rand1: rand2+us(trait): rand1: rand2: rand3, rcov=~us(trait):units,family=c("gaussian","gaussian","zapoisson"),nitt=20000,burnin=5000,thin=10,prior=prior,data=dat) Howev...

Hi all, I'm new to PCA in R, so this might be a basical thing, but I cannot find anything on the net about it. I need to make a PCA plot with two response variables (df$resp1 and df$resp2) against eight metabolites (df$met1, df$met2, ...) and I don't have a clue how to do... and I've only used the simplest PCAs before, like this: pcaObj=prcomp(t(df[idx, c(40:47)])) biplot(pcaObj) Anyone who knows how to do? Best rageds, Joel ____________________...

...repeated measures design on my advice, students randomised to four orders of a condition that can be N or E: four orders used: NEEN, NENE, ENEN, ENNE, ten students in each block. I've inherited the data (in SPSS but I can deal with that!) with variables like: ID GENDER ORDER C1 C2 C3 C4 RESP1 RESP2 RESP3 RESP4 ... ORDER is the order as a/b/c/d; C1:C4 has the N or E for each occasion, and RESP1:RESP4 the response variables. (There are a number of these but looking at each separately is justifiable theoretically). I've had a look around the R help and some S+ books I've got and I rea...

...cb_search_bfin.h @@ -33,6 +33,8 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ +#include "bfin.h" + #define OVERRIDE_COMPUTE_WEIGHTED_CODEBOOK void compute_weighted_codebook(const signed char *shape_cb, const spx_word16_t *r, spx_word16_t *resp, spx_word16_t *resp2, spx_word32_t *E, int shape_cb_size, int subvect_size, char *stack) { @@ -73,10 +75,7 @@ void compute_weighted_codebook(const signed char *shape_cb, const spx_word16_t * : : "m" (subvect_size), "m" (shape_cb), "m" (r), "m" (resp), "m&qu...