search for: explanatory

...rse on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into a problem because it looks like the lm model (with sqrt-transformation) fits the data best: ## model1=lm(response~explanatory,poissondata) model2=lm(sqrt(response+0.5)~explanatory,poissondata) model3=lm(log(response+1)~explanatory,poissondata...

Dear all, I'm quite sure that this is a stupid question, but I'll ask anyway. I want to perform an ANCOVA with two continuous factors and three categorical factors. Plant population growth rate (GR) = dependent variable Seed reduction due to herbivory (SR) = continuous explanatory variable Herbivore species (HS, 2 levels) = categorical explanatory variable Population (Pop, 24 levels) = categorical explanatory variable Population size (Popsize) = continuous explanatory variable Year (Year, 16 levels) = categorical explanatory variable My model is technically simple: model&l...

I am very new to R and programming and thank you in advance for your patience and help with a complete novice! I am working with a large multivariate data set that has 10 explanatory environmental variables (e.g. temp, depth) and over 60 response variables (each is a separate species). My data frame is set up like the simplified version below: JulianDay Temperature Salinity Depth Copepod Barnacle Gastropod Bivalve 222 12.1 33...

Hi R helpers! I have used a glht as a post-hoc test on an lmer with: -2 treatments (A & B) -1 categorical explanatory variable (song type) -1 response variable (latency to respond) I wanted to make comparisons between the categorical variables depending on treatment. At the moment the glht simply returns comparisons of each of the (3) categorical explanatory variables with each other overall i.e. there is no dis...

Hi all, I read in a text book, that you can examine a variable that is colinear with others, and giving different ANOVA output and explanatory power when ordered differently in the model forula, by modelling that explanatory variable, against the others colinear with it. Then, using that information to split the vector (explanatory variable) in question, into two new vectors, one should correspond to the fitted values and one the res...

How should I analysis it in R ???? all the resposes variables are ordinal from 0 to 10. and the explanatory variable is a factor ... -- View this message in context: http://old.nabble.com/Multivariate-problems-.-.-.-with-200-resposes-variables-and-1-explanatory-variable-tp26522912p26522912.html Sent from the R help mailing list archive at Nabble.com.

One of the assumptions, on which the (General) Linear Modelling is based is that the response variable is measured with some uncertainties (or weighted), but the explanatory variables are fixed. Is it possible to extend the model by assigning the weights to the explanatory variables as well? Is there a package for doing such a model fit? Thanks

.... If we repeat for each term, the variance gradually decreases, until we are left with values with relatively low variance. In the ideal case, the residuals would have zero variance. If we apply certain special conditions, then it is possible to only subtract a fitted value, where the corresponding explanatory value is valid (i.e. not missing). Where it is not valid, we just skip that subtraction operation (i.e. for that particular observation, the variance is not reduced as much). For this to work, each explanatory variable's partial residuals for each fit (not just the final fit) must be zero-cente...

Dear helpers, does anyone know a function to fit a model with: - y mean that is regressed on a set of explanatory variables - y variace behaving as a garch or as a garch in mean Thank you so much for your help, Carlo

...dependent variable) on x (independent variable) and y(-1). I have create the y(-1) variable in this way: ly<-lag(y, -1) Now if I do the following regression lm (y ~ x + ly) the results I obtain are not correct. Can someone tell me the code to use in R in order to perform a regression using as explanatory variable a lagged dependent variable? My best regards, Giacomo --------------------------------- [[alternative HTML version deleted]]

Hi All, I have run a series of random effects meta regressions on binomial outcomes using the metabin function in R. Now I would like to conduct some random effects meta regressions on the outcomes. Is there a command available which will allow for me to test the impact of a certain variable on the relative treatment effect from my meta regressions? Many Thanks, Steph -- View this message in

Hello! I want to do a non-linear regression with 2 explanatory variables (something like : length ~ a * time * exp( b* temperature)), having a data set (length, time, temperature). Which function could I use (I tried nls but I think it doesn't work) Thanks a lot! Janice

Dear all, I have a very large dataset (1712351 , 20) and would like to plot only the arrows that represent the contribution of each variables. On the sample below I woild like to plot only the explanatory variables (Murder, Assault..) and not the sites. prcomp(USArrests) # inappropriate prcomp(USArrests, scale = TRUE) prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE) plot(prcomp(USArrests)) summary(prcomp(USArrests, scale = TRUE)) biplot(prcomp(USArrests, scale = TRUE)) Thanks a lo...

...Bates and did not find anything appropriate in the archives, either ... We are preparing a short lecture on degrees of freedom and would like to show lme's as an example as we often need to work with these. I have a problem in understanding how many dfs are needed if random terms are used for explanatory variables in addition to the intercept (if I have understood correctly that ist the same as saying that interactions between random and fixed effects are considered). I tried the following code: library ('nlme') options (contrasts= c ('contr.treatment', 'contr.poly')) # cr...

Dear all, I am new to R and statistics really in general. I am hoping that someone will be able to point me in the right direction and/or suggest a technique/package/reference that will help me with the following. I have: Some input variables (integers, real) Some output variables (integers, real) and I want to find out which between the two correlate best - i.e. the salient features. I

Dear R user, I'm using qda (quadratic discriminant analysis) function (package MASS) to classify 58 explanatory variables (numeric type with different ranges) using a grouping variable (factor 2 levels "0" "1"). I'm using the qda method for class 'data.frame' (in this way I don't need to specify a formula). Using the function: result.qda<-qda(explanatory.variables, g...

Hi Folks, According to the PDF file blogit.pdf in the Zelig documentation: "Use the bivariate logistic regression model ["blogit"] if you have two binary dependent variables (Y1,Y2), and and wish to model them jointly as a function of some explanatory variables. Each pair of dependent variables (Yi1,Yi2) has four potential outcomes, (Yi1=1,Yi2=1), (Yi1=1,Yi2=0), (Yi1=0,Yi2=1), and (Yi1=0,Yi2=0). The joint probability for each of these four outcomes is modeled with three systematic components: the marginal Pr(Yi1=1) and Pr(Yi2=1),...

Hello I'm very new to R (which is excellent), so apologies if this has already been raised. In the spdep package, I'm trying to undertake an autoregressive mixed model using the lagsarlm function. This is working fine, but there does not appear to be a method of including an explanatory variable without it automatically being included as a lagged term. I'm after something along the lines of y = rho.W.y + x1 + x2 + lag(x2) but am only able to output y = rho.W.y + x1 + x2 + lag(x1) + lag(x2) Is there any way around this issue? Many thanks Roy -----------------------------...

Hello, I'm trying to find out the optimal number of splits (mtry parameter) for a randomForest classification. The classification is binary and there are 32 explanatory variables (mostly factors with each up to 4 levels but also some numeric variables) and 575 cases. I've seen that although there are only 32 explanatory variables the best classification performance is reached when choosing mtry=80. How is it possible that more variables can used than there ar...

Hi all, I am fairly new to R and I am trying to run mvpart and create a MRT using explanatory variables and covariables. I've been following the procedures in Numerical Ecoogy with R. The command (no covariables) which works fine - ABUNDTMRT <- mvpart(abundance ~ .,factors,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4) where abundance is 4th root tran...