similar to: plotting ROC from coefficient in the model

Dear group, I have a matrix like the following: Name Sample1 sample2 sample3 sample4 ..... sample(n) nm1 10.5 13.5 30 31 nm2 8 11 34 29 nm3 9 10.3 27.8 35 nm(j) I want to be able to calculate correlation between all pairs of names. For example (nm1,nm2),

Dear R-users, I am trying to create a multipanel scatter plot which displays weighted regression lines for each subgroup. I am having diffuculty figuring out how the Trellis multipanel plotting functions work, in general. Does someone know where there are some beginners examples on how to create and use panel.xxx functions in conjunction with the xyplot functions? Back to the matter at hand

Dear all, I have found some difference of the results between multinom() function in R and multinomial logistic regression in SPSS software. The input data, model and parameters are below: choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182); sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129); case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2); result <- multinom(case ~ choles

Dear R users, I have problem with extracting coefficients from a object. Here, X (predictor)and Y (response) are two matrix , I am regressing X ( dimensions 10 x 20) on each of columns of Y[,1] (10 x 1) and want to store the coefficient values. I have performed a Elastic Net regression and I want to store the coeffcients in each iteration. I got an error message . I do not

Hi all, Can anyone suggest me how to insert rows in between data frames and also keep the ordering of row numbers correct? Estimate Std. Error t value Pr(>|t|) recmeanC2 9.275880e-17 6.322780e-17 1.467057e+00 0.14349903 recmeanC3 1.283534e-17 2.080644e-17 6.168929e-01 0.53781390

I have a simple X, Y data frame that I am trying to run regression analysis on. The linear regression looks great, but when I use lm(formula = y ~ poly(x, degree = 5)) I get the same coeffecients. So for example if I use degree =3 my formula would look like y = 4.2 x^3 + 3.2x^2 + 2.1x + 1.0 and my degree 5 would look like y = 6.5x^5+ 5.4x^4 + 4.2 x^3 + 3.2x^2 + 2.1x + 1.0, which doesn't make

Hi Folks, I'm analysing some data which, in its simplest aspect, has 3 factors A, B, C each at 2 levels. If I do lm1 <- lm(y ~ A*B) say, and then summary(lm1, corr=T) I get the correlation matrix of the estimated coeffcients with numerical values for the correlations (3 coeffs in this case). Likewise with 'glm' instead of 'lm'. However, if I do lm2 <- lm(y ~

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

Hi, I am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

Hi everyone, Can anyone tell me if its possibility to extract the coefficients from the lm() command? For instance, imagine that we have the following data set (the number of observations for each company is actually larger than the one showed...): Company Y X1 X2 1 y_1 x1_1 x2_1 1 y_2 x1_2 x2_2 1 y_3 x1_3 x2_3 (...) 2 y_4 x1_4 x2_4 2 y_5 x1_5 x2_5 2 y_6 x1_6 x2_6 (...) n y_n x1_n x2_n n

Hi All, Could someone help me decode what this error means ? > BIC(nb.80) Error in log(attr(object, "nobs")) : Non-numeric argument to mathematical function > BTW, nb.80 is a negative binomial glm model created using the MASS library with the call at the bottom of the message In the hopes of trying to figure this out I tried the following workaround but it did not work

You also need to reply-all so the mailing list stays in the loop. -- Sent from my phone. Please excuse my brevity. On December 19, 2017 4:00:29 PM PST, Timothy Axberg <axbergtimothy at gmail.com> wrote: >Sorry about that. Here is the code typed directly on the email. > >qe = (Qmax * Kl * ce) / (1 + Kl * ce) > >##The data >ce <- c(15.17, 42.15, 69.12, 237.7, 419.77)

Sorry if this is a duplicate: This is a re-post because the pdf's mentioned below did not go through. Hello, I'm new'ish to R, and very new to glm. I've read a lot about my issue: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred ...including: http://tolstoy.newcastle.edu.au/R/help/05/07/7759.html

I have googled and browsed through spec-users archives, but didn''t find anything different. I used rspec scaffold command to generate a mvc for groups, I used nested controller so I could have a separate interface for admin. the specs are the same as generated, so I won''t post the whole thing. I did put the nested routes names on the calls to the name routes: it

I tried to compare if cch() and coxph() can generate same result for same case cohort data Use the standard data in cch(): nwtco Since in cch contains the cohort size=4028, while ccoh.data size =1154 after selection, but coxph does not contain info of cohort size=4028. The rough estimate between coxph() and cch() is same, but the lower and upper CI and P-value are a little different. Can we

Hi everyone, I've been trying to find a function that outputs the Pearson and/or Spearman correlation coefficients for several variables with the associated statistics in one single table/matrix. For what I've been able to understand the Stats package is only able to compute these coeficients/statistics only in defined pairs. This becomes time consuming when we want to determine these

Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]

hi everybody I have to do a lot of Anova with R and I would like to have another type of coefficients coding.. I explain. by default if I have 2 temperatures for an experience. 100°C or 130°C and I want to see the temperature effect on the presure I want to estimate the coefficient of each temperature. I will obtain ,with the anova, juste one coefficients for example +3,56 (for 100°C), and the

Hi all I am using the caret package and having difficulty in obtaining the results using regression, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE);