search for: obese

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any o...

...0 = c(4L, 3L, 6L, 6L, 4L, 6L, 3L, 4L, > 4L, 4L, 4L, 4L, 4L), ra98 = c(1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, > 2L, 2L, 2L, 3L, 1L), CBCLAggressionAt1410 = c(NA, 0L, NA, 0L, > 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), CBCLInternalisingAt1410 = c(NA, > 0L, NA, 0L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Obese14 = c(NA, > 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Obese21 = c(NA, > 0L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Overweight14 = c(NA, > 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Overweight21 = c(NA, > 1L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 1L, 0L, NA), hibp14 = c(NA,...

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

library(glmpath) data(heart.data) # heart.data is a list, $y a vector, $x a matrix data <- data.frame(x=I(heart.data$x), y = heart.data$y) > data[1:2,] x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 y 1 160 12 5.73 23.11 1 49 25.3 97.2 52 1 2 144 0.01 4.41 28.61 0 55 28.87 2.06 63 1 > dimnames(heart.data$x)[[2]] [1] "sbp"

I am trying to figure out how to calculate p-values for the difference in prevalence of a risk factor between men and women. For example, I find that 277 out of 710 male patients and 125 out of 305 female patients have obesity, what is the p-value for their difference? If there is a package that can calculate this in bulk, I would appreciate to learn about it! Thank you [[alternative HTML

...=1,No=0) bmi: Indice de masa corporal (IMC) cuando se produce la conversión a MetS+ . Para los que permancen MetS-, esta variable indica el bmi cuando hay censura (por abandono del estudio o al finalizar el estudio en el año 25). bmi0: IMC al inicio del estudio (categórica, levels=normal/overweight/obese) apoE4: Genotipo de interés (E4, no-E4) -Mi hipótesis es que la interacción genotipo~MetS depende del IMC al principio del estudio. Concretamente, individuos 'overweight' al inicio del estudio y con el genotipo E4 hacen la conversión a MetS+ a valores de IMC mas bajos que los que tienen el...

...ummarize( Mean = mean(expend), Min = min(expend), Max = max(expend), Sigma = sd(expend), Skew = skew(expend)) # Output stature Mean Min Max Sigma Skew <fct> <dbl> <dbl> <dbl> <dbl> <dbl> 1 lean 8.07 6.13 10.9 1.24 0.907 2 obese 10.3 8.79 12.8 1.40 0.587 Why does output stats vary in decimal places even when options (digits=3) were set? All the best Thomas S. [[alternative HTML version deleted]]

I'm plotting obesity rates (y-axis) vs Public Health Unit (x-axis) for the province of Ontario and would like to have the Public Health Unit names appear vertically rather than the default, horizontally. I'm actually using the 'barplot2' function in the {gregmisc} library ... I haven't been able to find a solution in either the barplot2 options or the general plotting

Could someone kindly help me with the following question: when I analyze microarray data I need to fit multiple linear regression models between genes and clinical patameters followed by estimation of the p-values. What's the solution to implement Westfall and Young's algorithm + resampling into the scheme: lm -> stepAIC -> anova. Actually permcor works fine for me in the case of

timh at insightful.com wrote: > library(glmpath) > data(heart.data) > # heart.data is a list, $y a vector, $x a matrix > data <- data.frame(x=3DI(heart.data$x), y =3D heart.data$y) > =20 >> data[1:2,] >> =20 > x.1 x.2 x.3 x.4 x.5 x.6 x.7 x.8 x.9 y > 1 160 12 5.73 23.11 1 49 25.3 97.2 52 1 > 2 144 0.01 4.41 28.61

Dear all, How to obtain the odds ratio (OR) and 95% confidence interval (CI) with 1 standard deviation (SD) change of a continuous variable in logistic regression? for example, to investigate the risk of obesity for stroke. I choose the happening of stroke (positive) as the dependent variable, and waist circumference as an independent variable. Then I wanna to obtain the OR and 95% CI with

...lt;- mpr100 ~ time + nhb + hispanic + other + # rural + hrural + # factor(age) + factor(gender) + factor(mstat) + factor(svcpct2) + nvaclass + # a1cgrp8 + anemdef + cbd + chf + chrnlung + htn_c + # hypothy + obese + perivasc + pulmcirc + tumor + # depress + psych + # nhb*rural + hispanic*rural + other*rural + # nhb*hrural + hispanic*hrural + other*hrural + # nhb*factor(age) + hispanic*factor(age) + other*factor(a...

Hi, I''m really new to rails, so i programmed some stuff and today i read some things about skinny Controllers, fat models. My Controllers are really fat now. So i''m asking myself how can i shrink my controllers and move the code to the models, especially in fact of REST e.g. in focus on error codes? code example: # POST /tasks # POST /tasks.xml def create @authorized

...0:71089, 71400:71489, 72000:72089, 725) c1 = paste(c(2860:2869, 2871, 2873:2875), "", sep = "") coag = c(2860:2869, 2871, 2873:2875, 28600:28689, 28730:28749, 64930:64934, 28984) ob3 = paste("V", c(8530:8549, 8554), sep = "") obese = c(2780, "V854", ob3, 27800:27801, 64910:64914, 79391) wghtloss = c(260:263, 2600:2639, 26000:26389, 78321:78322) lytes = c(2760:2769, 27600:27689) bldloss = c(2800, 64820:64824) anemdef = c(2801:2819, 2859, 28010:28189, 28521:28529) alcohol = c(2910:2913, 2915, 2918,...

I've noticed that R has a number of very useful functions for obtaining descriptive statistics on groups of variables, including summary {stats}, describe {Hmisc}, and describe {psych}, but none that I have found is able to provided weighted descriptives of subsets of a data set (ex. descriptives for both males and females for age, where accurate results require use of sampling

Position: Statistician in a systems biology team focused on metabolic disorders such as diabetes and obesity. The position is part of a statistics group supporting Pfizer global research and development. Role and Responsibilities: The statistician will be an integral member of a systems biology team which develops and/or uses computational and statistical approaches to manage and derive

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...f <- mpr100 ~ time + nhb + hispanic + other + rural + hrural + factor(age) + factor(gender) + factor(mstat) + factor(svcpct2) + nvaclass + a1cgrp8 + anemdef + cbd + chf + chrnlung + htn_c + hypothy + obese + perivasc + pulmcirc + tumor + depress + psych + nhb*rural + hispanic*rural + other*rural + nhb*hrural + hispanic*hrural + other*hrural + nhb*factor(age) + hispanic*factor(age) + other*factor(age)...

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We're running Asterisk 1.8 on a 32-bit Debian machine, and it has been fine for some time now. But! We've got such a incoming call volume over the few weeks that we'll have Asterisk occasionally restart itself. My hunch is that it is in part memory pressure. I can't add RAM and have it help, because it's 32-bit. I intend to move to a 64-bit machine, but I was hoping to