similar to: summary(object) not showing all values of a factor

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

Dear all, May I do multiple integration using R? I was looking adapt but it is saying it integrates a scalar function over a multidimensional rectangle. I have integrand of several variable and upper, lower limit too variable. I wanted to see the result using adapt (though it is not for this purpose, I suppose) Func<-function(x){(x[1]*x[2])} adapt(2, lo=c(0,1), up=c(1,x[1]), functn=Func) it

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Hi, I've the following kind of data Time Country Values 2010Q1 India 5 2010Q2 India 7 2010Q3 India 5 2010Q4 India 9 2010Q1 China 10 2010Q2

Dear all, I have data like 3 coulmns and many rows. Each entry is less than 10. Example x y z 1 5 3 2 2 3 7 8 3 8 9 5 4 5 4 6 -------------------------- --------------------------- I have to sum entries of each coulmn (seperately) till it be 10. This i have to start for each row. And I want to assign no. of rows needed including that row too(it to be 10 or 10+, the moment it exceeds 10, i

Hi, I am starting new on rails. Tried my first example got this error #28000Access denied for user ''root''@''localhost'' (using password: NO) Please do suggest as I am having this problem on one of my system but it works fine on the other.Really confused. Thanks Rohan -- Posted via http://www.ruby-forum.com/.

I have a very general question about what the centering option in basehaz does to factors. (basehaz computes the baseline cumulative hazard for a coxph object using the Breslow estimator). Lets say I'm interested in a survival model with two (dichotomous) factors and a continuous covariate. Variable Possible Values Factor1 0 or 1 Factor2 0 or 1

i want to make survival plots for a coxph object using survfit function. mod.phm is an object of coxph class which calculated results using columns X and Y from the DataFrame. Both X and Y are categorical. I want survival plots which shows a single line for each of the categories of X i.e. '4' and 'C'. I am getting the following error: > attach(DataFrame) >

Hi all. If I run the simple regression when x is a categorical variable ( x <- factor(x) ): > MyFit <-coxph( Surv(start, stop, event) ~ x ) How can I get the overall p-value on x other than for each dummy variable? > anova(MyFit) does NOT provide that information as previously suggested on the list. All the best, Kare [[alternative HTML version deleted]]

I?m trying to set up proportional hazard model that is stratified with respect to covariate 1 and has an interaction between covariate 1 and another variable, covariate 2. Both variables are categorical. In the following, I try to illustrate the two problems that I?ve encountered, using the lung dataset. The first problem is the warning: To me, it seems that there are too many dummies

Hi, I am trying to fit a logistic regression using glm, but my explanatory variables are of mixed type: some are numeric, some are ordinal, some are categorical, say If x1 is numeric, x2 is ordinal, x3 is categorical, is the following formula OK? *model <- glm(y~x1+x2+x3, family=binomial(link="logit"), na.action=na.pass)* * * *Thanks,* * * *-Jack* [[alternative HTML version

I have a simple linear model (fitted with lm()) with 2 independant variables : one categorical and one integer. When I run summary.lm() on this model, I get a standard linear regression summary (in which one categorical variable has to be converted into many indicator variables) which looks like : Estimate Std. Error t value Pr(>|t|) (Intercept) -3595.3 2767.1 -1.299

Hi all, I am just curious how to sort the groups in a categorical box plot chart bwplot here is the example: d<-data.frame(sample(rep(month.abb,20), 100), runif(100,1,10)); colnames(d) <- c("Month", "Value"); bwplot(d$Month ~ d$Value); as you can see Months are not sorted alphabetically. Does anybody know ho to sort those on the chart level? Thank you Jan

Hi everybody, I am a new user of R, design package. I am trying to plot the estimated hazard ratio's of my cox regression model with the confidence intervals. But I keep getting the message:Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels I have dichotomous, categorical as well as continuous

Does anyone know if the E1 cards that digium sells work in India. Also are there any distributers for those cards in India. By E1 cards I mean E100P, TE410P or TE405P -- regards Vikram (http://www.vicramresearch.com)

Hello, i'm a french student of medical oncology and i'm working on breast cancer. I have a variable with the histologic type of tumor wich is between 1 and 5. I use as.factor function to make some variable with level between 1 and 5. When i put it in the cox model i have only the level between 2 and 5. The level 1 doesn't appear. I think i have to change the number of level but i

Hi R-helpers, I was trying to put gender='Male' in newdata to create a expected survival curve for a pseudo cohort by using survfit based on Cox regression. My codes are shown below: fit<- coxph(Surv(end, status2)~gender, data=wlwsn1) Summary(fit) coef exp(coef) se(coef) z p genderMale 0.204 1.23 0.0912 2.23 0.025

Dear R users, if all my numerical variables in my datasets having the same units, may I leave them unnormalized, just do cv.glmnet directly(cv.glmnet(data,standardize=FALSE))? i know normally if there is a mixture of numerical and categorical , one has to standardize the numerical part before applying cv.glmnet with standardize=fase, but that's due to the different units in the numerical

Hi, I am attempting to evaluate the prediction error of a coxph model that was built after feature selection with glmnet. In the preprocessing stage I used na.omit (dataset) to remove NAs. I reconstructed all my factor variables into binary variables with dummies (using model.matrix) I then used glmnet lasso to fit a cox model and select the best performing features. Then I fit a coxph model

Dear list people Suppose we have a data.frame where variables are categorical and the response is categorical eg: my.df=NULL for(i in LETTERS[1:3]){my.df[[i]]=sample(letters, size=10)} my.df=data.frame(my.df) my.df$class=factor(rep(c("pos", "neg"), times=5)) my.glm=glm(class ~ ., data=my.df, family=binomial) summary(my.glm) .... Estimate Std. Error z