similar to: Linear model - coefficients

Gente soy principiante en R , trato de hacer un logit para la escuela cuando corro las siguientes lineas me da el error: Error en fix.by(by.x, x) : 'by' must specify valid column(s) require(sqldf) require(xtable) source(file="c:/Users/Usuario/Desktop/R/libreria/.R") k<-qnorm(0.05/2, mean=0, sd=1, lower.tail = FALSE, log.p = FALSE) A1 <- factor(c(1,2,3,4)) # factor1 con 4

Hi all, I am using "lm" to fit some anova factor models with interactions. The default setting for my unordered factors is "treatment". I understand the resultant "lm" coefficients for one factors, but when it comes to the interaction term, I got confused. > options()$contrasts unordered ordered "contr.treatment"

Hi, I'd like to know if it's possible to specify different contrast matrices in lm() for a factor that is nested within another one. This is useful when we have a model where the nested factor has a different number of levels, depending on the main factor. Let me illustrate with an example to make it clearer. Consider the following data set: set.seed(1) y <-

Hello, I'm a new user to R so apologies if this is a basic question, but after scouring the web on information for summary.formula, I still am searching for an answer. I made a function to analyze my data - I have a categorical variable and three continuous variables. I am analyzing my continuous variables on the basis of my categorical variables. radioanal <- function(a) { #Educational

Hi, I run the following commands. 'A' has 3 levels and 'B' has 4 levels. Should there be totally 3+4 = 7 coefficients (A1, A2, A3, B1, B2, B3, B4)? > a=3 > b=4 > n=1000 > A = rep(sapply(1:a,function(x){rep(x,n)}),b) > B = as.vector(sapply(sapply(1:b, function(x){rep(x,n)}), function(x){rep(x,a)})) > Y = A + B + rnorm(a*b*n) > > fr =

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

I have the following data set > data A B X1 X2 Y 1 A1 B1 1.1 2.9 1.2 2 A1 B2 1.0 3.2 2.3 3 A2 B1 1.0 3.3 1.6 4 A2 B2 0.5 2.6 3.1 > sapply(data, class) A B X1 X2 Y "factor" "factor" "numeric" "numeric" "numeric" I'd like to set a specific type of contrasts to all the categorical factors

Dear List, I neeed a grouped list with two sort of categorical data. I have a data .frame like this. year cat. b c 1 2006 a1 125 212 2 2006 a2 256 212 3 2005 a1 14 12 4 2004 a3 565 123 5 2004 a2 156 789 6 2005 a1 1 456 7 2003 a2 786 123 8 2003 a1 421 569 9 2002 a2 425 245 I need a list with the sum of b and c for every year and every cat (a1, a2 or a3) in this year. I had used the tapply

Hi, I wonder if there is a possibility to avoid that R sets one level of a factor equal zero in a model fit. More precisely, I want to fit a two-way unbalanced linear model: o ~ 0 + x + y x is a factor with 10 levels, y is a factor with 9 levels. In order to get a unique solution i set the intercept =0 and impose that sum(y)=0 i.e. res <- lm(o ~ 0 + x + y,

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

Hi, I don't understand what the meaning of the following lines returned by model.matrix(). Can somebody help me understand it? What can they be used for? attr(,"assign") [1] 0 1 2 2 attr(,"contrasts") attr(,"contrasts")$A [1] "contr.treatment" attr(,"contrasts")$B [1] "contr.treatment" Regards, Peng > a=2 > b=3 > n=4

Hello, I am using the mda package and in particular the fda routine to classify/predict in terms of color to a set of 20 samples for which i don?t know the color. I preformed a flexible discriminant analysis (FDA) using a set of 147 samples for which i know all the information. My script and data follow in attachment. A total of 23 predictors were considered. 20 of the predictors are

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

Dear all, I'm running a coxph model of the form: coxph(Surv(Start, End, Death.ID) ~ x1 + x2 + a1 + a2 + a3) Within this model, I would like to compare the influence of x1 and x2 on the hazard rate. Specifically I am interested in testing whether the estimated coefficient for x1 is equal (or not) to the estimated coefficient for x2. I was thinking of using a Chow-test for this but the Chow

Dear all, I have a data.frame with a column like the x shown below myDF<-data.frame(cbind(x=c("[[1, 0, 0], [0, 1]]", "[[1, 1, 0], [0, 1]]","[[1, 0, 0], [1, 1]]", "[[0, 0, 1], [0, 1]]"))) > myDF x 1 [[1, 0, 0], [0, 1]] 2 [[1, 1, 0], [0, 1]] 3 [[1, 0, 0], [1, 1]] 4 [[0, 0, 1], [0, 1]] As you can see my x column is composed of

Hi all, This is probably going to come off as unnecessary (and show my ignorance) but I am trying to understand the parameter estimates I am getting from R when doing an ANCOVA. Basically, I am accustomed to the estimate for the categorical variable being equivalent to the respective cell means minus the grand mean. I know is the case in JMP - all other estimates from these data match the

Hi all, I have dataset with 2 independent variable, one (x1) is continuous, the other (x2) is a categorical variable with 2 levels. The dependent variable (y) is continuous. When I run linear regression y~x1*x2, I found that the p value for the continuous independent variable x1 changes when different contrasts was used (helmert vs. treatment), while the p values for the categorical x2 and

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

I would like to perform a regression like the one below: lm(x ~ 0 + a1 + a2 + a3 + b1 + b2 + b3 + c1 + c2 + c3, data=data) However, the data has the property that a1+a2+a3 = A, b1+b2+b3 = B, and c1+c2+c3 = C, where A, B, and C are positive constants. So there are two extra degrees of freedom, and R handles this by producing NA for two of the coefficients. Instead, I would prefer to remove the

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to