w**a
发帖数: 1024 | 1 seems this is a very simple problem.
you can use transformation
t = ln(x)
to reduce the orignal diff. eq. to
y''+b^2 y =g(t) ,here y'' is w.r.t. variable 't'
which is a const. coeff. ode.
its solution can be obtained through Laplace transform. |
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L******k
发帖数: 2945 | 2 Using levinson-durbin recursion, you can calculate inverse of toep matrix
recursively, which at step n+1 involving calculating the reflection coeff.
and a n+1 dimension vector.
is |
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c*********t
发帖数: 340 | 5 是两个model都可以吗
另外，每个independent variable的beta value的P值怎么refer呀
谢谢
刚开始练R
不知道自己描述得对不对
具体是，
x <- glm(PHENOGROUP2 ~ Combo.new[,e[i]] + PHENOGROUP3, family="binomial",
data=Combo.new)
因为是在一个loop里面，想把每次的结果（beta[slope] 和P）输出到一个matrix里去
但是x$coeff只能有slope，P value不知道用什么来引用
谢谢啦 |
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g**********y
发帖数: 423 | 6 I am doing regression on microarray data - X.
X is the gene expression level derived from rma normalization.
Suppose there are p independent variables (genes) and n samples (
observations)
and regression by Y = X'beta. (Y is response variables, or phenotype)
Can we state that the importance of each gene is determined by its
corresponding coeff beta?
should X be standardized to mean zero variance one for each gene across all
samples? Denote it as Z. Is it better to performan regression on Z,
Y=Z' |
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F****n
发帖数: 3271 | 7 理想状态下，IVs应该是 uncorrelated, 但实际上他们或多或少的会有一些相关性，当
相关性并不强或multicollinearity在可忍受范围内时，常常直接MODEL。但在弱相关的
情况下，IVs的Coeff显然受顺序的影响。比如
Y =b1X1 + b2X2 + b0 算出来的coefficients 和
Y =b2X2 + b1X1 + b0 不一样 (OLS)。
假设X1, X2 each accounts for 40% of Y variation when modeled separately, but
when modeled together only account for 70% (as indicated by R square). So:
1) Can we say there is 10% (80%-70%) of Y variation explained jointly by X1,
X2?
2) If so can we say b1 actually is calculated to reflect 40% variability of |
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F****n
发帖数: 3271 | 8 Sorry, no PCA. I explained that multicollinearity was regarded as tolerable
(small correlation) and also PCA makes interpretation hard.
Basically I am looking for a method that can sequentially "strip away"
correlation from an IV to previous IVs. My questions actually are:
1) Whether such methods exist;
2) If no, can we interpret the coefficients as in my original post, e.g.
b1 is based on X1, b2 is based on X2 minus the correlated part with X1. My impression is probably yes, because OLS coeffs |
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o****o
发帖数: 8077 | 9 regress your X2 on the residuals from Y=b0+b1*X1
use the projected X2 from that auxilary regression to your original model
besides, I don't think the ordering matters in OLS, check the linear algebra
tolerable
impression is probably yes, because OLS coeffs and confidence intervals are
computed sequentially, but I am not sure about it at all. |
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l**********y
发帖数: 33 | 10 The REG Procedure
Model: MODEL1
Dependent Variable: score
Analysis of Variance
Sum of Mean
Source DF Squares Square F Value Pr > F
Model 7 168149 24021 11.74 <.0001
Error 96 96501 2046.88323
Corrected Total 103 364650
Root MSE 45.24249 R-Square 0.4611
Dependent Mean 81.98077 Adj R-Sq 0.4218
Coeff Var 55.18672
Parameter Estimates
Parameter Standard
Variable DF Estimate Error t Value Pr > |t|
Intercept 1 22.88491 12.67088 1.81 0.0740
pop 1 -0.17833 0.20712 -0.86 0.3914
gdp 1 -0.0000757 |
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y*********s
发帖数: 24 | 11 how to save the result of proc logistic model in a data set?
I tried the code as below in the proc logistic and print the data set out.
output out = filename;
however, it turns out it only prints out the original data set.
Is something I did wrong?
ps: I would like the model based on the training set to be assessed using
the rest of the data set. So I need to get the coeff of the model, that is
why I want to record the output of proc logistic in a data set.
Is there a much better way to do this? |
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S********a
发帖数: 359 | 12 > summary(unadj.lrm)
Error in summary.Design(unadj.lrm) :
> summary(unadj.lrm)$coeff
Error in summary.Design(unadj.lrm) :
adjustment values not defined here or with datadist for pmavg
怎么回事呀？
adjustment values not defined here or with datadist for pmavg |
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l*********s
发帖数: 5409 | 13 What is not clear is not your statement but your understanding :-)
You only need to fit lm once with the categorical variable, say D, which is a
factor of 20 lvls. lm function will give you coeff estimate for 19 lvls except the base lvl.
level |
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s****y
发帖数: 297 | 14 可是我感兴趣的是在每个factor level的A， B，C的coeff extimate 啊，呵呵
用for 循环固然可以，可是有没有更简便的办法呢？谢谢了... |
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z**********i
发帖数: 12276 | 15 我用PROC CAPABILITY来CHECK NORMALITY.
看图形很NORMAL,但PVALUE 很小.这个到底是NORMAL DISTRIBUTION吗?
多谢!
Variable: RBMI
Moments
N 37667 Sum Weights
37667
Mean 0.05291262 Sum Observations 1993.
05967
Std Deviation 0.00865415 Variance 0.
00007489
Skewness -0.3560206 Kurtosis 0.
79177021
Unc... 阅读全帖 |
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a*z
发帖数: 294 | 16 Thank you first.
I am trying to learn logistic regression and study HL's book (v2). For the
first question of Chapter 1, I plot the STA's lived pct in each age group vs
med age of each group. The plot looks awful.
But when I fit the STA vs age, coeff of age and constant are both stat
significant.
My real question is: is there any way to explore data which will lead us to
use logistic regression, or most of the time I just blindly apply logistic
to binary response problems?
Thank you again. |
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A*******s
发帖数: 3942 | 17 is there parameterization problem in the second one? i think it treats the
unknown category as the reference category. Not sure if it is valid if no
intercept in the model.
not sure if this would work--1st step, fit the model with that categorical
variable and other covariates; 2nd step, fit the model without the
categorical one and fix the coeffs of other covariates in order to find the
intercept estimate.
or, treat the categorical variable as a random effect. two methods should
have very close... 阅读全帖 |
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y*****w
发帖数: 1350 | 18 假设下面一个crossover design：
subject, random effect
renal site, fixed effect
drug treatment, fixed effect
SAS linear mixed model is as follows (no interaction effect of site and drug
):
proc mixed data=tmp cl;
class subject site drug;
model diff=site drug / ddfm=kr cl;
random subject;
run;
在crossover design的情况下，作linear mixed model后，如何计算inter-subject
and intra-subject coefficient of variation? 我看到下面这个blog提到intra-
subject coefficient of variation的计算：
http://onbiostatistics.blogspot.com/2014/03/in... 阅读全帖 |
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j*****e
发帖数: 182 | 19 what is the dimensionality of your repsonse variable?
45=10*9÷2;28=8*7÷2.
It seems that your computation of corr coeff does not conform to the
conventional approach. Please write out your parameter of interest in terms
of the distribution first.
when |
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p*********e
发帖数: 32207 | 20 简单喽,查一下coeff. of Drag也就是CD,
等效迎风面积=迎风面积*CD
也就是在你计算里面加一项而已嘛:D |
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