search for: treatment3

...Intercept) 4.7039 2.1689 Residual 21.1678 4.6008 number of obs: 36, groups: Liver:(Rat:Treatment), 18; Rat:Treatment, 6; Treatment, 3 Fixed effects: Estimate Std. Error t value (Intercept) 140.500 5.184 27.104 Treatment2 10.500 7.331 1.432 Treatment3 -5.333 7.331 -0.728 Correlation of Fixed Effects: (Intr) Trtmn2 Treatment2 -0.707 Treatment3 -0.707 0.500 > (m1a<-lmer(Glycogen~Treatment+(1|Treatment)+(1|Treatment:Rat)+(1| Treatment:Rat:Liver))) Linear mixed-effects model fit by REML Formula: Glycogen ~ Treatment + (...

...Estimate Std. Error t value Pr(>|t|) (Intercept) 1.30411 0.22355 5.834 2.11e-08 *** dryweight 0.05445 0.02556 2.130 0.034358 * treatment1 -0.01390 0.10283 -0.135 0.892575 treatment2 -0.25015 0.50841 -0.492 0.623230 treatment3 -0.65174 0.50580 -1.289 0.199026 treatment4 0.35105 0.42871 0.819 0.413838 treatment5 -0.15977 0.46738 -0.342 0.732827 treatment6 -0.06789 0.85831 -0.079 0.937036 treatment7 -0.59955 0.54247 -1.105 0.270371 treat...

...,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~ outcome + treatment, family = poisson()) Coefficients: (Intercept) outcome2 outcome3 treatment2 treatment3 3.045e+00 -4.543e-01 -2.930e-01 -4.210e-16 -3.997e-16 Degrees of Freedom: 8 Total (i.e. Null); 4 Residual Null Deviance: 10.58 Residual Deviance: 5.129 AIC: 56.76 Glm> anova(f) Analysis of Deviance Table Model: poisson, link: log Response: counts Terms added sequentially...

...3 Month 0.00011693 0.010813 1.000 Number of obs: 160, groups: Block, 6 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.624030 0.175827 9.237 < 2e-16 *** Treatment2.Radiata 0.150957 0.207435 0.728 0.466777 Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009 Month -0.079955 0.022903 -3.491 0.000481 *** Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717 Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 * --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05...

...2.0609e+01 4.53976242 Residual 4.2476e+01 6.51733769 # of obs: 36, groups: Rat:Liver, 6; Rat, 2 Fixed effects: Estimate Std. Error DF t value Pr(>|t|) (Intercept) 140.5000 3.7208 33 37.7607 < 2.2e-16 *** Treatment2 10.5000 2.6607 33 3.9463 0.0003917 *** Treatment3 -5.3333 2.6607 33 -2.0045 0.0532798 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Correlation of Fixed Effects: (Intr) Trtmn2 Treatment2 -0.358 Treatment3 -0.358 0.500 > anova(model.lmer) Analysis of Variance Tabl...

Hello R users I like to extract z values for x1 and x2. I know how to extract coefficents using model$coef but I don't know how to extract z values for each of independent variable. I looked around using names(model) but I couldn't find how to extract z values. Any help would be appreciated. Thanks TM ######################################################### >summary(model) Call:

...tment, family = poisson()) Deviance Residuals: 1 2 3 4 -0.67125 0.96272 -0.16965 -0.21999 5 6 7 8 -0.95552 1.04939 0.84715 -0.09167 9 -0.96656 Coefficients: Estimate Std. Error (Intercept) 3.045e+00 1.709e-01 outcome2 -4.543e-01 2.022e-01 outcome3 -2.930e-01 1.927e-01 treatment2 8.717e-16 2.000e-01 treatment3 4.557e-16 2.000e-01 z value Pr(>|z|) (Intercept) 17.815 <2e-16 *** outcome2 -2.247 0.0246 * outcome3 -1.520 0.1285 treatment2 4.36e-15 1.0000 treatment3 2.28e-15 1.0000 The final pdf output file is mostly fine: but not all of the output of print.summary.glm obeys the width=40 command...

...mula = response ~ treatment * (site + time), whichf = "treatment", type = "Tukey") # # Tukey contrasts for factor treatment, covariables: site +time +treatment:site + treatment:time # #Coefficients: # Estimate t value Std.Err. p raw p Bonf p adj #treatment3-treatment1 -0.655 -2.004 0.327 0.050 0.149 0.120 #treatment3-treatment2 -0.581 -1.777 0.327 0.081 0.162 0.143 #treatment2-treatment1 -0.074 -0.227 0.327 0.821 0.821 0.821 ___ drs. René Eschen CABI Bioscience Switzerland Centre 1 Rue des Grillons CH-2800 Delémont Switzerland +4...

Hello, I am performing cor() of some of my data. For example, I'll do 3 corr() (many variables) operations, one for each of the three treatments. I then do the following: i <-lower.tri(treatment1.cor) cor(cbind(one = treatment1.corr[i], two = treatment2.corr[i], three = treatment3.corr[i])) Does this operation above tell me how correlated each of the three treatments is? Because this how I am interpreting it. Thanks, Michael Just [[alternative HTML version deleted]]

This is a basics beginner question. I attempted fitting a a Poisson GLM to data that is non-integer ( I believe Poisson is suitable in this case, because it is modelling counts of infections, but the data collected are all non-negative numbers with 2 decimal places). My question is, since R doesn't return an error with this glm fitting, is it important that the data is non-integer. How does

...eter.73, Diameter.85) ~ Treatment * Hormone, data = marcelo.subset) Coefficients: Diameter.38 Diameter.53 Diameter.73 Diameter.85 (Intercept) 1.24000 1.35750 1.99375 2.31000 Treatment2 -0.31625 -0.14250 0.07500 -0.13875 Treatment3 -0.19250 -0.01500 -0.20875 -0.36875 Treatment4 -0.35375 -0.08500 -0.22750 -0.27125 Treatment5 -0.29125 0.04875 -0.14375 -0.26375 Treatment6 -0.00125 -0.25750 -0.81125 -0.77750 HormoneSH -0.30875...

...ent code ...... Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.045e+00 0.000e+00 Inf <2e-16 outcome2 -4.543e-01 0.000e+00 -Inf <2e-16 outcome3 -2.930e-01 0.000e+00 -Inf <2e-16 treatment2 1.924e-08 0.000e+00 Inf <2e-16 treatment3 8.383e-09 0.000e+00 Inf <2e-16 (Dispersion parameter for poisson family taken to be 0) Null deviance: 10.5814 on 8 degrees of freedom Residual deviance: 5.1291 on 4 degrees of freedom AIC: 56.761 Number of Fisher Scoring iterations: 3 Warning message: NAs introduced by coe...

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary

...8.73096 0.26303 1249.770 < 2e-16 *** genders -37.39069 0.19661 -190.179 < 2e-16 *** condu -37.47740 0.19693 -190.308 < 2e-16 *** treatment1 0.51026 0.40084 1.273 0.203079 treatment2 -0.17333 0.23175 -0.748 0.454541 treatment3 0.07761 0.22535 0.344 0.730566 treatment4 -1.96020 0.38524 -5.088 3.73e-07 *** treatment5 NA NA NA NA ###### The TukeyHSD output (truncated) ##### Tukey multiple comparisons of means 95% family-wise confidence level Fit...

Hi, We released individual birds into a room with 2 trees. We counted the number of visits to each of the 2 tree. One of the trees is always a control tree and the other tree is either treatment 1, treatment 2 or treatment3 or treatment 4. Ind Treat ContrTree ExpTree Total visits 1 1 11 16 27 1 2 6 9 15 1 3 5 13 18 1 4 11 25 36 2 1 2 3 5 4 1 6 7 13 4 3 4 4 8 4 4 2 5 7 6 1 1 1 2 6 4 5 16 21 etc etc (as you see, not all treatments are included for all individuals) Our question is if the proportion of visits to the...

Hi, I have a simple dataset with repeated measures. one factor is treatment with 3 levels (treatment1, treatment2 and control), the other factor is time (15 time points). Each treatment group has 10 subjects with each followed up at each time points, the response variable is numeric, serum protein amount. So the between subject factor is treatment, and the within subject factor is time. I ran a

I am attempting to run a glm with a binomial model to analyze proportion data. I have been following Crawley's book closely and am wondering if there is an accepted standard for how much is too much overdispersion? (e.g. change in AIC has an accepted standard of 2). In the example, he fits several models, binomial and quasibinomial and then accepts the quasibinomial. The output for residual