glm

问题 I have: x = rnorm(100) # Partie b z = rbinom(100,1,0.60) # Partie c y = 1.4 + 0.7*x - 0.5*z # Partie d x1 = abs(x) y1 = abs(y) Don<-cbind(y1,x1,z) Don1 <- data.frame(Don) Reg <- glm(y1~x1+z,family=poisson(link="log"),Don1) # Partie e #Biais de beta Reg.cf <- coef(Reg) biais0 = Reg.cf[1] - 1.4 biais1 = Reg.cf[2] - 0.7 biais2 = Reg.cf[3] + 0.5 And I need to repeat all this 100 times in order to have different coefficient and calculate the bias and then put the mean of each biais in a text file.

问题 This post is somewhat related to this post. Here I have xy grouped data where y are fractions: library(dplyr) library(ggplot2) library(ggpmisc) set.seed(1) df1 <- data.frame(value = c(0.8,0.5,0.4,0.2,0.5,0.6,0.5,0.48,0.52), age = rep(c("d2","d4","d45"),3), group = c("A","A","A","B","B","B","C","C","C")) %>% dplyr::mutate(time = as.integer(age)) %>% dplyr::arrange(group,time) %>% dplyr::mutate(group_age=paste0(group,"_",age)) df1$group_age <- factor(df1$group_age,levels=unique(df1$group_age))

问题 In analysis of categorical data, we often use logistic regression to estimate relationships between binomial outcomes and one or more covariates. I understand this is a type of generalized linear model (GLM). In R, this is implemented with the glm function using the argument family=binomial . On the other hand, in categorical data analysis are multinomial models. Are these not GLMs? And can't they be estimated in R using the glm function? (In this post for Multinomial Logistic Regression. The

问题 I am trying to demonstrate via simulation the performance of different models and feature selection techniques, so I wish to pass various arguments to glm() programmatically. Under ?glm we read (italics mine): family : a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function , a family function or the result of a call to a family function. For glm.fit only the third option is supported. (See family for

问题 In R there is nice functionality for running a regression with dummy variables for each level of a categorical variable. e.g. Automatically expanding an R factor into a collection of 1/0 indicator variables for every factor level Is there an equivalent way to do this in Julia. x = randn(1000) group = repmat(1:25 , 40) groupMeans = randn(25) y = 3*x + groupMeans[group] data = DataFrame(x=x, y=y, g=group) for i in levels(group) data[parse("I$i")] = data[:g] .== i end lm(y~x+I1+I2+I3+I4+I5+I6+I7

问题 I'm trying to do a 10-fold cross validation for some glm models that I have built earlier in R. I'm a little confused about the cv.glm() function in the boot package, although I've read a lot of help files. When I provide the following formula: library(boot) cv.glm(data, glmfit, K=10) Does the "data" argument here refer to the whole dataset or only to the test set? The examples I have seen so far provide the "data" argument as the test set but that did not really make sense, such as why do 10

问题 Using stat_smooth , I can fit models to data. E.g. g=ggplot(tips,aes(x=tip,y=as.numeric(unclass(factor(tips$sex))-1))) +facet_grid(time~.) g=g+ stat_summary(fun.y=mean,geom="point") g=g+ stat_smooth(method="glm", family="binomial") I would like to know the coefficients of the glm binomial fits. I could re-do the fit with dlply and get the coefficients with ldply , but I'd like to avoid such duplication. Calling str(g) reveals the hierarchy of objects that ggplot2 creates, perhaps there's some

问题 Here are all the variables I'm working with: str(ad.train) $ Date : Factor w/ 427 levels "2012-03-24","2012-03-29",..: 4 7 12 14 19 21 24 29 31 34 ... $ Team : Factor w/ 18 levels "Adelaide","Brisbane Lions",..: 1 1 1 1 1 1 1 1 1 1 ... $ Season : int 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ... $ Round : Factor w/ 28 levels "EF","GF","PF",..: 5 16 21 22 23 24 25 26 27 6 ... $ Score : int 137 82 84 96 110 99 122 124 49 111 ... $ Margin : int 69 18 -56 46 19 5 50 69 -26 29 ... $

问题 I'm trying to run the following regression: m1=glm(y~x1+x2+x3+x4,data=df,family=binomial()) m2=glm(y~x1+x2+x3+x4+x5,data=df,family=binomial()) m3=glm(y~x1+x2+x3+x4+x5+x6,data=df,family=binomial()) m4=glm(y~x1+x2+x3+x4+x5+x6+x7,data=df,family=binomial()) and then to print them using the stargazer package: stargazer(m1,m2,m3,m4 type="html", out="models.html") Thing is, the data frame df is rather big (~600MB) and thus each glm object I create is at least ~1.5GB. This creates a memory issue

问题 Here are all the variables I'm working with: str(ad.train) $ Date : Factor w/ 427 levels "2012-03-24","2012-03-29",..: 4 7 12 14 19 21 24 29 31 34 ... $ Team : Factor w/ 18 levels "Adelaide","Brisbane Lions",..: 1 1 1 1 1 1 1 1 1 1 ... $ Season : int 2012 2012 2012 2012 2012 2012 2012 2012 2012 2012 ... $ Round : Factor w/ 28 levels "EF","GF","PF",..: 5 16 21 22 23 24 25 26 27 6 ... $ Score : int 137 82 84 96 110 99 122 124 49 111 ... $ Margin : int 69 18 -56 46 19 5 50 69 -26 29 ... $