similar to: Repeated Measures design using lme

Hi As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test? The Data... 2 groups of patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous

Hi all , I have a dataframe (Hypertension) with following headers :- > Hypertension ID Hypertension(before drug A) Hypertension(On drug A) On drug B? Healthy diet? 1 160 90 True True 2 190

Dear All, I have a data sets as shown below A (Patient ID ), B and C are the Concentration of drug in blood on day 1 and day 4, D is the difference in conc. To do this in R I have written a code as follows, identified the number of patients who have more concentration on day 4 . Here I want to identify specifically the patient ID (is he patient 1 or 2 or 5 and 7), whose concentration is more. How

Any thoughts on the following I'd be most grateful - I'm sure there is an easy and quick way to do this but I'm having a mental block this evening. Essentially, I'm trying to replace missing data in my dataset with reference values based on age and sex. So an example dataset is set.seed(1) X = data.frame(age=rnorm(10, 50, 10), sex=rbinom(10, 1, 0.5), A=rnorm(10), B=rnorm(10))

Hi, Sorry for a basic questions on linear models. I am trying to adjust raw data for both fixed and mixed effects. The data that I output should account for these effects, so that I can use the adjusted data for further analysis. For example, if I have the blood sugar levels for 30 patients, and I know that 'weight' is a fixed effect and that 'height' is a random effect, what

Thank you Frank and all for your advices. Here I attach the raw data from the Pawinski's paper. I have obtained permission from the corresponding Author to post it here for everyone. The only condition of use is that the Authors retain ownership of the data, and any publication resulting from these data must be managed by them. The dataset is composed as follows: patient number / MMF dose in

Hi all, How does one create a matrix of values in a loop. For example, I have 46*70 matrix with columns giving the values for some blood tests performed upon the 46 patients(rows). I want to perform a simple test of normality (shapiro-wilk test) upon each of the biochemical tests for the 46 patients. Can somebody suggest the syntax for such a loop. For the data x, I wrote the following loop which

Dear R-helpers, This may sound simple to you, but I'm a beginner in this, so please be forgiving. I have a following problem: two analytes were measured in patient's blood on 4 occasions: ProteinA and ProteinB. How to correctly evaluate correlation between ProteinA and ProteinB? I tried: x <- data.frame(Patient.ID=rep(1:10, each=4), Visit=rep(c(1:4),10), ProteinA=rnorm(m=10,

Hi R-users, I'm new to R and I'm trying to make a barplot combined with two lines (refering to secondary y-axis). Bars should represent the number of transfused patients by age class and sex and lines should represent the amount of blood units given in age classes. I have now successfully made a barplot and used par(new=TRUE) to plot another empty graph at the top of the barplot.

Dear R users, Is it possible to have an overlap histogram plot? For example: stuff <- data.frame(Mode = c("Land", "Air"), AgeGroup = c("Young", "Adult", "Old"), Value = sample(1:300)) histogram( ~Value | AgeGroup * Mode, data = stuff, auto.key = TRUE) Instead of having 2 * 3 panel, I want to have just 3 panel, overlapping the "Mode"

Hi, I am trying a linear regression model where the dependent variable is the size of the heart corrected for the patient's height and weight. This is labelled as LAVI. The independent variables are race (european or non-eurpoean), age, sex (male or female) of the patient and whether they have diabetes and high blood pressure. sample size 2000 patients selected from a community. when I

I'm trying to use sqldf to query for the earliest date of a blood test when patients have had multiple tests in a given year. My query looks like this: test11 <- sqldf("select CHILD_ID, min(SAMP_DATE) from lab group by CHILD_ID having extract (year from SAMP_DATE) = 2011") SAMP_DATE has class "date." I get the error

With R 1.8.1 running in Fedora Core 1 Linux, I am having some trouble recoding and ploting some factor variables. First, can I give you some example data? Here is a column of names for age groups: agegroups <- c( "15-19", "20-24", "25-29","30-34", "35-39",

Hi all, I got a silly bug when compiling our project with the latest Clang. Here's the outputted assembly: > tst r3, #255 > strbeq r6, [r7] > ldreq r6, [r4, r6, lsl #2] > strne r6, [r7, #4] > ldr r6, [r4, r6, lsl #2] > bx r6 For the code to execute correctly, either the _ldr_ should be a _ldrne_ instruction or the _ldreq_ instruction should be removed. The error seems to

On 20 October 2017 at 09:24, Ingo Molnar <mingo at kernel.org> wrote: > > * Thomas Garnier <thgarnie at google.com> wrote: > >> Change the assembly code to use only relative references of symbols for the >> kernel to be PIE compatible. >> >> Position Independent Executable (PIE) support will allow to extended the >> KASLR randomization range below

On 20 October 2017 at 09:24, Ingo Molnar <mingo at kernel.org> wrote: > > * Thomas Garnier <thgarnie at google.com> wrote: > >> Change the assembly code to use only relative references of symbols for the >> kernel to be PIE compatible. >> >> Position Independent Executable (PIE) support will allow to extended the >> KASLR randomization range below

I am trying to build a model to aid a clinical decision. Certain patients have a blood marker measured at each visit - a rise of this may indicate recurrence of the cancer after treatment (endpoint is "clinical recurrence", censored). In a proportion (up to 30%), this rise is a false positive - hence I wish to correlate factors at the time of the rising test to clinical recurrence,

Dear all, I've tried to solve the Es. 12, cap 4 of "Introduction to GLM" by Annette Dobson. It's about the relationship between survival time of leukemia patients and blood cell count. I tried to fit a model with exponential distribution, first by glm (family gamma and then dispersion parameter fixed to 1) and then with survreg. They gave me the same point estimates but the

Hi, I have attached WIP patch for adding foldMemoryOperand to Thumb1InstrInfo. For the following case: void f(int x, int y, int z) { void bar(int, int, int); bar(x, y, z); bar(x, z, y); bar(y, x, z); bar(y, y, x); } it calls foldMemoryOperand twice, and thus converts two calls from blx to bl. callMI->dump() shows the function name "bar" correctly, however in generated

It seems to me that LLVM sub-register is not for the following hardware architecture. All instructions of a hardware are vector instructions. All registers contains 4 32-bit FP sub-registers. They are called r0.x, r0.y, r0.z, r0.w. Most instructions write more than one elements in this way: mul r0.xyw, r1, r2 add r0.z, r3, r4 sub r5, r0, r1 Notice that the four elements of r0 are written