similar to: permanova on MICE object

Hello, I want to perform a permanova where the first factor called Treatment has four levels. The second factor involves sampling the same research plots for four consecutive years, hence the repeated measurements. I have been able to use the adonis function from the package vegan to run this analysis. code below: TC.perMANOVA.adonis<-adonis(TC.PerMANOVA ~ Treatment*Year,

Hello, I would like to use a permanova to analyze my repeated measures, multivariate data set using the package vegan (adonis function). I have several explanatory variables and many clinical biochemistry/hematological response variables (all continuous, non-normally distributed). My explanatory variables are: ID - 29 levels - individual subjects Treatment - 2 levels Sex - 2 levels Time -

Dear all, I am trying to get a PERMANOVA with quite large data set. I am reading a lot about this question, but I do not get the answer about it. Although I know that the R function is adonis () (vegan package), it does not work: adonis(Pha.env~SPha, data=Pha, permutations=10) The error message: Error: cannot allocate vector of size 334.2 Mb In addition: Warning messages: 1: In vegdist(lhs,

Hi! Thanks for providing great help in R-related statistics. Now, however I'm stuck. I'm not a statistics person but I was recommended to use R to perform a nmds plot and PerMANOVA of my dataset. Sample(treatment) in the columns and species (OTU) in the rows. I have 4 treatments (Ambient Temperature, Ambient temperature+Low pH, High temperature, High temperature+low pH), and I have 16

I am using -mice- for multiple imputation and would like to use the gelman diagnostic in -coda- to assess the convergence of my imputations. However, gelman.diag requires an mcmc list as input. van Buuren and Groothuis-Oudshoorn (2011) recommend running mice step-by-step to assess convergence (e.g. imp2 <- mice.mids(imp1, maxit = 3, print = FALSE) ) but this creates mids objects. How can I

Dear all, I request your help to solve a problem I've encountered in using 'mice' for multiple imputation. I want to apply a logistic regression model. I need to extract information on the fit of the model. Is there any way to calculate a likelihood ratio or the McFadden-pseudoR2 from the results of the logistic model? I mean, as it is possible to extract pooled averaging and odds

Hello all, I am trying to impute some missing data using the mice package. The data set I am working with contains 125 variables (190 observations), involving both categorical and continuous data. Some of these variables are missing up to 30% of their data. I am running into a peculiar problem which is illustrated by the following example showing both the original data (blue) and the imputed

Hello! If I understand this listserve correctly, I can email this address to get help when I am struggling with code. If this is inaccurate, please let me know, and I will unsubscribe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed

Dear R users I want to combine multiply imputed data-sets generated from mice to do classfication. However, I have various questions regarding the use of mice library. For example suppose I want to predict the class in this data.frame: data(nhanes) mydf=nhanes mydf$class="pos" mydf$class[sample(1:nrow(mydf), size=0.5*nrow(mydf))]="neg" mydf$class=factor(mydf$class) First I

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous

Hello I would like to pass a model formula as an argument to the with.mids function from the mice package. The with.mids functon fits models to multiply imputed data sets. Here's a simple example library(mice) #Create multiple imputations on the nhanes data contained in the mice package. imp <- mice(nahnes) #Fitting a linear model with each imputed data set the regular way works

Hello folks, I am a relatively new user of R and created multiply imputed data sets with the 'mice' library. This library provides two functions for complete-data analysis on multiply imputed data set objects (lm.mids and glm.mids). I am trying to estimate a series of Cox PH regression models and cannot figure out the best way to do this. Is it possible with the mitools library?

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

I'm trying to use either mice or norm to perform multiple imputation to fill in some missing values in my data. The data has some missing values because of a chemical detection limit (so they are left censored). I'd like to use MI because I have several variables that are highly correlated. In SAS's proc MI, there is an option with which you can limit the imputed values that are

Hi, I am trying to use mice() with an aov() model.I can fun mice() fine but when I try to pool the results it doesn't work. For example, I can run the following: fit=with(data=imp,exp=aov(Y~B*P*T + Error(S/(B*P*T)))) pool(fit) # doesn't work Where B, P, and T are within subject (S) factors. However, I can't go the next step to pool these results as I am guessing this hasn't been

Hi, I was wondering if someone can tell me what is the difference between "strata" argument (function "adonis" in "vegan" package) and using random effects in PERMANOVA+ add-on package to PRIMER6 when doing permutational MANOVA-s? Is the way permutations are done the same? Thank you very much in advance, Vesna -- View this message in context:

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Dear R-users, Version V2.0 of the package mice is now available on CRAN for Windows, Linux and Apple users. Multivariate Imputation by Chained Equations (MICE) is the name of software for imputing incomplete multivariate data by Fully Conditional Specifcation (FCS). MICE V1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. MICE V1.0 introduced predictor selection,

Hi all, How can I calculate the mean from several imputed data sets with the package mice? I know you can estimate regression parameters with, for example, lm and subsequently pool those parameters to get a point estimate using functions included in mice. But if I want to calculate the mean value of a variable over my multiple imputed data sets with fit <- with(data=imp, expr=mean(y)) and

Hello R-Users, I would like to check the convergence of my imputations. However, when I use the function the plot.mids(), I always obtain the following error message Error in plot.new() : figure margins too large I read the same question in thread from November 2005 (see below). I actually have the same problem. Is it now possible to plot subsets of mids.objects. If yes, how? My