Displaying 7 results from an estimated 7 matches for "model6".
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2013 Feb 13
2
Need Help Plotting "Line" for multiple linear regression
...ultiple regression, I used the following coding:
double=read.table("convis.txt",header=TRUE)
attach(double)
double
stem(vis)
stem(den)
stem(avoid)
stem(entrance)
plot(entrance,vis*den) *as means to see how the interaction between
visibility and density may impact entrance behaviors
model6=lm(entrance~vis*den)
model6
summary(model6)
*abline(model6) *Here is the issue as I used this for my simple linear
regression technique, but do not know what to use for a multiple regression*
If anybody can provide some feedback on this, it would be greatly
appreciated.
Kind Regards,
Craig...
2008 Nov 25
4
glm or transformation of the response?
...fits the data best:
##
model1=lm(response~explanatory,poissondata)
model2=lm(sqrt(response+0.5)~explanatory,poissondata)
model3=lm(log(response+1)~explanatory,poissondata)
model4=glm(response~explanatory,poissondata,family=poisson)
model5=glm(response~explanatory,poissondata,family=quasipoisson)
model6=glm.nb(response~explanatory,poissondata)
model7=glm(response~explanatory,quasi(variance="mu",link="identity"))
plot(explanatory,response,pch=16)
lines(explanatory,predict(model1,explanatory=explanatory))
lines(explanatory,(predict(model2,explanatory=explanatory))^2-0.5,lty=2)...
2005 Mar 01
3
packages masking other objects
...rror message when I try to use getCovariateFormula. The line I use it in, and the error message is below:
rownames(table)<-c((getCovariateFormula(model1)),(getCovariateFormula(model2)),(getCovariateFormula(model3)),(getCovariateFormula(model4)),(getCovariateFormula(model5)),(getCovariateFormula(model6)),(getCovariateFormula(modelnull)))
Error in switch(mode(x), "NULL" = structure(NULL, class = "formula"), :
invalid formula
This line works fine when I run models with different structures that are not in the lme4 package.
Does anyone have any suggestions about this...
2009 Mar 09
1
lme anova() and model simplification
...;
> model5<-lme(awsm~awpm+orien,random=~1|viney,method="ML")
> anova(model3,model5)
Model df AIC BIC logLik Test L.Ratio p-value
model3 1 6 41.47847 45.31281 -14.73924
model5 2 5 46.13124 49.32653 -18.06562 1 vs 2 6.652772 0.0099
>
> model6<-lme(awsm~awpm+orien,random=~1|viney,method="ML")
> anova(model3,model6)
Model df AIC BIC logLik Test L.Ratio p-value
model3 1 6 41.47847 45.31281 -14.73924
model6 2 5 46.13124 49.32653 -18.06562 1 vs 2 6.652772 0.0099
>
# actual data u...
2006 Mar 08
1
malloc: vm_allocate(size=381886464) failed (error code=3)
...ataset is not a sustainable solution in the long run. The data that I am
analysing is essentially big, and it would be reasonable to do the analyis
on the whole dataset without even considering to partition it. So I was
really wondering if you could give me a clue about how to handle this
problem.
model6 <-lm(logintens~ factor (slide) + factor(ind) + factor(dye) +
factor(id)*factor(l6) + factor(rep)-1, data=sample_data2)
*** malloc: vm_allocate(size=381886464) failed (error code=3)
*** malloc[387]: error: Can't allocate region
*** malloc: vm_allocate(size=381886464) failed (error code=3)
*...
2010 Sep 29
1
Understanding linear contrasts in Anova using R
...# Just as a check, I forced intercept through zero with with deviation
scores or -1 in model.
# Now force intercept to 0 by using deviation scores
devdv <- dv-mean(dv)
model5 <- lm(devdv~order.group)
summary(model5)
#Same as above except intercept = 0
# Now do it by removing the intercept
model6 <- lm(dv~order.group -1)
summary(model6)
# Weird because coeff = cell means
# Estimate Std. Error t value Pr(>|t|)
# order.group1 1.8025 0.4342 4.151 0.000116 ***
# order.group2 0.6750 0.4342 1.554 0.125824
# order.group3 -0.9125 0.4342 -2.101...
2006 Apr 16
0
[S] Problems with lme and 2 levels of nesting:Summary
...why you want to retain the Treatment
factor when you have judged it to be inert.
> model4<- lme(DeathDay ~ Treatment, random=~ 1 | Clutch/Cup,method="ML") #Full model
> model5<- lme(DeathDay ~ Treatment, random=~ 1 | Clutch,method="ML") # model minus Cup
> model6<- lme(DeathDay ~ Treatment, random=~ 1 | Cup,method="ML") # model minus Clutch
> model7<- lme(DeathDay ~ 1, random=~ 1 | Clutch/Cup,method="ML") # model minus Treatment
>
> ...and make anova(model4, modelxxx)etc to test for effects of Cup, Clutch an...