similar to: Multiple imputation using mice

I am having trouble using the "resize" function (in the package EBImage) with matrices containing double values. Let's say I have a matrix 'b' with these values:   > a = c(1.25,1.555,1.245,1.265,1.656,1.235,1.848,1.959,1.548,1.245454,1.989,1.24) > b = matrix(a,4,3) > b       [,1]  [,2]     [,3] [1,] 1.250 1.656 1.548000 [2,] 1.555 1.235 1.245454 [3,] 1.245 1.848

I have many text files in the format below and in certain rare instances such as below there can be nothing in one of the fields so a double comma is written but I won't know this because I am reading in many,many files sequentially. # TEXT FILE 2004-02-10 00:01:31.00000,,105.60000000 2004-02-10 00:01:32.00001,,105.60000000 2004-02-10 00:01:45.00000,,105.60000000 2004-02-10

Hi, I am analyzing a data set with greater than 1000 independent cases (collected in an unrestricted manner), where each case has 3 variables associated with it: one, a factor variable with 0/1 levels (called XX), another factor variable with 8 levels (X) and a third response variable with two levels (Y: 0/1). I am trying to see if X1 has an effect on the relationship between X2 and the

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do

Hello Again R Folks: I?m trying to clean up some code. Suppose I have an object like this: > str(test) List of 2 $ G:List of 2 ..$ cls:'data.frame': 101 obs. of 2 variables: .. ..$ V1: num [1:101] -0.0019 -0.0019 -0.00189 -0.00188 -0.00186 ... .. ..$ V2: num [1:101] 0.000206 0.000247 0.000288 0.000329 0.000371 ... ..$ rob:'data.frame': 101 obs. of 2

Dear All: In the attached file, I have 3 group patients, and there are 5 in each group (the groups are decided by the prefix of the idno). I want draw a repeat measurement comparison figure. My goal is to list 5 patients from same group on one horizontal line. But xyplot sounds pick them randomly (or I was confused?). Could you please help me modify the following code to accomplish this?

Hi everybody, this is a real dummy thing. I sorted a matrix based on a given column, and what I get is right, until it comes to columns of negative and positive values; than, "order" orders everything from max to min in the negative values, and then AGAIN from max to min in the positive values!!! Why isn't everything order from max to min, and that's it? Thank you!!! Attached