Displaying 3 results from an estimated 3 matches for "fullmodel".
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2009 Feb 27
1
testing two-factor anova effects using model comparison approach with lm() and anova()
...cise, I wanted to demonstrate the model comparison approach to analysis
of variance by using anova() to compare a full model that contains all
effects, to restricted models that contain all effects save for the effect
of interest.
The test of the interaction effect seems to be as I expected:
> fullmodel<-lm(DV~factorA+factorB+factorA:factorB)
> restmodel<-lm(DV~factorA+factorB)
> anova(fullmodel,restmodel)
Analysis of Variance Table
Model 1: DV ~ factorA + factorB + factorA:factorB
Model 2: DV ~ factorA + factorB
Res.Df RSS Df Sum of Sq F Pr(>F)
1 24 18.0000
2...
2011 Apr 15
1
GLM and normality of predictors
...of response variables, but I am still in doubt about that. As it is easy to understand I'm not a statistician so be patient please.
I want to estimate the possible effects of some predictors on my response variable that is nº of males and nº of females (cbind(males,females)), so, it would be:
fullmodel<-glm(cbind(males,females)~pred1+pred2+pred3, binomial)
I have n= 11 (ecological data, small sample size is a a frequent problem!).
Someone told me that I have to check for normality of the predictors (and in case transform to reach normality) but I am in doubt about the fact that a normality t...
2012 May 11
0
NLS sensitivity to start= values or poles in data range
...^3)/(1+B1*x^1+B2*x^2+B3*x^3),
from = min(K),
to = max(K),
add=TRUE,
col="blue",
lty=5, lwd=2 )
# Check the poles of the starting function
poles <- polyroot(c(1,B1,B2,B3))
cat((poles), '\n')
# Run the nls using above values as starting points
fullmodel <- {CTE ~ (A0 * K^0 + A1 * K^1 + A2 * K^2 + A3 * K^3)/
(1 + B1 * K^1 + B2 * K^2 + B3 * K^3)}
rationalfit <- nls(fullmodel,start=nlsstart)
summary(rationalfit)
rationalcoef <- as.list(coef(rationalfit))
attach(rationalcoef)
curve((A0*x^0+A1*x^1+A2*x^2+A3*x^3)/(1+B1*x^1+B2*x...