search for: polyreg

...0 0 0 > 0 0 0 0 0 0 0 0 0 0 0 0 0 > 0 0 0 > > > # en caso de que mice haga uso de números aleatorios fijo el seed para > que sea reproducible > > set.seed(123) > > imp <- mice(dataset,method="polyreg",maxit=5) > > iter imp variable > 1 1 pial4 > 1 2 pial4 > 1 3 pial4 > 1 4 pial4 > 1 5 pial4 > 2 1 pial4 > 2 2 pial4 > 2 3 pial4 > 2 4 pial4 > 2 5 pial4 > 3 1 pial4 > 3 2 pial4 > 3 3 p...

...b pial3cError en nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1068) weights -- buscando en foros encontre que debo modificar el nnet, concretamente maxNWts indicando un valor mayor al valor con problema, para modificar eso se me ocurre usar mice.impute.polyreg(dataset x=NULL, nnet.maxit = 100,nnet.trace = FALSE, nnet.maxNWts = 1500) pero el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categóricas, no me queda claro como estimarla. En si alguna ayuda para poder solucionar este problema en donde lo que...

...0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 > # en caso de que mice haga uso de números aleatorios fijo el seed para que sea reproducible > set.seed(123) > imp <- mice(dataset,method="polyreg",maxit=5) iter imp variable 1 1 pial4 1 2 pial4 1 3 pial4 1 4 pial4 1 5 pial4 2 1 pial4 2 2 pial4 2 3 pial4 2 4 pial4 2 5 pial4 3 1 pial4 3 2 pial4 3 3 pial4 3 4 pial4 3 5 pial4 4 1 pial4 4 2 pial4 4 3...

...al3b pial3cError en nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1068) weights buscando en foros encontre que debo modificar el nnet, concretamente maxNWts indicando un valor mayor al valor con problema, para modificar eso se me ocurre usar mice.impute.polyreg(dataset x=NULL, nnet.maxit = 100,nnet.trace = FALSE, nnet.maxNWts = 1500) pero el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categóricas, no me queda claro como estimarla. En si alguna ayuda para poder solucionar este problema en donde lo que...

...al3b pial3cError en nnet.default(X, Y, w, mask = mask, size = 0, skip = TRUE, softmax = TRUE, : too many (1068) weights buscando en foros encontre que debo modificar el nnet, concretamente maxNWts indicando un valor mayor al valor con problema, para modificar eso se me ocurre usar mice.impute.polyreg(dataset x=NULL, nnet.maxit = 100,nnet.trace = FALSE, nnet.maxNWts = 1500) pero el problema que me piden X que representa Matrix (n x p) of complete covariates, pero al ser las vbles categóricas, no me queda claro como estimarla. En si alguna ayuda para poder solucionar este problema en donde lo que...

R 2.7.2 PPC Mac OS X 10.4.11 library mice 1.13.1 I try to use mice for multivariate data imputation. My variables are numeric, factors, count data, ordered factors. First I created a vector for the methods to use with each variable ImpMethMice<-c(rep("logreg", 62), rep("polyreg",1), rep("norm",12), rep("polyreg",12)) next step was Test<-mice(df, im=ImpMethMice) I got the following error message: iter imp variable 1 1 variablenameFehler in impute.logreg(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, : Dimensionen [Produkt 11...

...age','sex','hepmeg','platelet', 'trt', 'trig')] d[d==-9] <- NA d[,c(4,5,7)] <- lapply(d[,c(4,5,7)], FUN=as.factor) str(d) imp <- mice(d, m=10, maxit=10, diagnostics=T, seed=500, defaultImputationMethod=c('norm', 'logreg', 'polyreg')) fit <- cox.mids(Surv(time,status) ~ age + sex + hepmeg + platelet + trt + trig, imp) pool(fit) ------------ I have looked at the "pool" function but cannot figure out what I have done wrong. Would really appreciate any help with this. Thanks, Brant Inman Mayo Clinic

Dear helpers, I am trying to use the mda package downloaded from the R website, but the data set has missing values so I got an error message. Should I manually handle these missing values? I was trying to read the documents to specify any option related to missing values, but I did not find it. Please forgive me if I ignore something obvious. Thanks, Zhu Wang Statistical Science Department

...reasonably simple to implement -- i.e. a spline surface would not be ideal in the context ;^). ) Clearly, something like fit<-lm(z~x+y+x*y+x*x+y*y, data) does not give the result I want. Is there a package somewhere that allows to do this sort of operation? Like a multidimensional version of polyreg()? Thanks in advance, RenE Bertin ______________________________________________ R-help at stat.math.ethz.ch mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help ------------------------------------------------------------------------------