search for: glm3

...ts the dichotomized outcome. The glm1 model (see below) is the same as glm2 (2 dummy variables coding sever and mild). In both models the reference level is profound disability. My questions are: 1. How do I interpret the coefficients in the third model (the intercept is removed)? 2. Does glm3 make sense? Does anyone ever want to construct a model like glm3? If so, when? 3. What is the algebraic specification of glm3? Many thanks, -- Yuelin Li. ------------------- categ <- factor(rep(c(1, 2, 3), times=c(20, 20, 20)), labels=c("P", "S", &quo...

...irst works, for the second one it is: Error: cannot allocate vector of size 3 Kb In addition: Warning message: Reached total allocation of 255Mb: see help(memory.size) So I increase the memory > memory.limit(size=300) NULL > out2<-lapply(list.glm2, myfun) #works > out3<-lapply(list.glm3, myfun) #does not works Error: cannot allocate vector of size 31 Kb In addition: Warning message: Reached total allocation of 300Mb: see help(memory.size) Again I increase the memory.size > memory.limit(size=320) NULL > out3<-lapply(list.glm3, myfun) #works! > out4<-lapply(list.glm4...

...*' 0.95 `B' 1 I'm not worried about the symbolic representation, but should the columns be labelled this way? I can work out which is which, but it isn't immediately clear and doesn't look "nice". Is this printing intended? Because when I do: print(summary(species.glm3, correlation = TRUE), symbolic.cor = FALSE) I get a much more nicely formatted correlation matrix: Correlation of Coefficients: (Intercept) pH I(pH^2) Ca pH -0.9321 I(pH^2) 0.9233 -0.9968 Ca 0.1442 -0.4893 0.4950...

...ee my problem easily. But if I must... The appearance of the NaN seems odd, since (as I understand it) lm.influence(dep.glm2)$sigma computes sigma when each observation is removed in turn. So if I remove Observation 3 and try fitting the model, there are no problems or complaints: dep.glm3 <- glm( Counts ~ factor(Depression) + factor(SLE) + factor(Children) + factor(Depression):factor(SLE), family=poisson( link=log), subset=(-3) ) This produces: > dep.glm3 Call: glm(formula = Counts ~ factor(Depression) + factor(SLE) + factor(Children) + factor(Depression):...

...results of multiple linear model fits in formats suitable for presentation in applications contexts. An extract from the output of 'example(cremo)': cremo> data(stackloss) cremo> attach(stackloss) cremo> glm2 <- glm(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) cremo> glm3 <- glm(stack.loss ~ Air.Flow + Water.Temp) cremo> glm4 <- glm(stack.loss ~ Air.Flow * Water.Temp) cremo> try(print(cremo(list(glm2, glm3, glm4), adeqel = c("deviance", "aic")), quote = F)) var Model 1 Model 2 Model 3 [1,] (Intercept)...

...9000 Residual Deviance: 28210 AIC: 7414 > glm2 Call: glm(formula = y ~ x, weights = W) Coefficients: (Intercept) x 0.1955 7.3053 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Deviance: 1548000 Residual Deviance: 44800 AIC: 11670 > glm3 Call: glm(formula = y ~ x, weights = 1/W) Coefficients: (Intercept) x 0.03104 7.31033 Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual Null Deviance: 798900 Residual Deviance: 19900 AIC: 5285 > glm4 Call: glm(formula = ys ~ Xs - 1) Coefficients:...

This is a question regarding the 'regTermTest' function in the 'survey' package. Imagine Z as a three level factor variable, and code ZB and ZC as the two corresponding dummy variables. X is a continuous variable. In a 'glm' of Y on Z and X, say, how do the two test specifications test.terms = c("ZB:X","ZC:X") # and test.terms = ~ ZB:X + ZC:X in

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating