anova

I have a data set where the samples are grouped by column. The following sample dataset is similar to my data's format: a = c(1,3,4,6,8) b = c(3,6,8,3,6) c = c(2,1,4,3,6) d = c(2,2,3,3,4) mydata = data.frame(cbind(a,b,c,d)) When I perform a single factor ANOVA in Excel using the above dataset, I get the following results: I know a typical format in R is as follows: group measurement a 1 a 3 a 4 . . . . . . d 4 And the command to perform ANOVA in R would be to use aov(group~measurement, data = mydata) . How do I perform single factor ANOVA in R with samples organized by column rather than by

I've been attempting to perform an ANOVA in R recently on the attached data frame. My question revolves around the setting of contrasts. My design is a 3x5 within-subjects design. There are 3 visual conditions under 'Circle1' and 5 audio under 'Beep1'. Does anyone have any idea how I should set the contrasts? This is something I'm unfamiliar with as I'm making the transition from point and click stats in SPSS to coded in R. Thanks for your time Data file: Nathaniel Payne Reiterating my answer from another stackoverflow question that was flagged as similar , since you didn't provide any code,

Suppose we have a toy data frame: x <- data.frame(x1 = gl(3, 2, labels = letters[1:3]), x2 = gl(3, 2, labels = LETTERS[1:3])) I would like to construct a model matrix # x1b x1c x2B x2C # 1 0 0 0 0 # 2 0 0 0 0 # 3 1 0 1 0 # 4 1 0 1 0 # 5 0 1 0 1 # 6 0 1 0 1 by: model.matrix(~ x1 + x2 - 1, data = x, contrasts.arg = list(x1 = contr.treatment(letters[1:3]), x2 = contr.treatment(LETTERS[1:3]))) but actually I get: # x1a x1b x1c x2B x2C # 1 1 0 0 0 0 # 2 1 0 0 0 0 # 3 0 1 0 1 0 # 4 0 1 0 1 0 # 5 0 0 1 0 1 # 6 0 0 1 0 1 # attr(,"assign") # [1] 1 1 1 2 2 # attr(,"contrasts") # attr(,"contrasts")$x1 #

This question already has an answer here: Linear Regression and group by in R 10 answers I'm trying to run anova() in R and running into some difficulty. This is what I've done up to now to help shed some light on my question. Here is the str() of my data to this point. str(mhw) 'data.frame': 500 obs. of 5 variables: $ r : int 1 2 3 4 5 6 7 8 9 10 ... $ c : int 1 1 1 1 1 1 1 1 1 1 ... $ grain: num 3.63 4.07 4.51 3.9 3.63 3.16 3.18 3.42 3.97 3.4 ... $ straw: num 6.37 6.24 7.05 6.91 5.93 5.59 5.32 5.52 6.03 5.66 ... $ Quad : Factor w/ 4 levels "NE","NW","SE",..: 2 2 2 2 2 2 2 2 2 2 ... Column r

I have created two generalised linear models as follows: glm1 <-glm(Y ~ X1 + X2 + X3, family=binomial(link=logit)) glm2 <-glm(Y ~ X1 + X2, family=binomial(link=logit)) I then use the anova function: anova(glm2,glm1) but get an error message: "Error in anova.glmlist(c(list(object),dotargs), dispersion = dispersion, : models were not all fitted to the same size of dataset" What does this mean and how can I fix this? I have attach ed the dataset at the start of my code so both models are working off of the same dataset. The main cause of that error is when there are missing values in one or more

I am looking to extract the p-value generated from an anova in R. Here is what I am running: test <- aov(asq[,9] ~ asq[,187]) summary(test) Yields: Df Sum Sq Mean Sq F value Pr(>F) asq[, 187] 1 3.02 3.01951 12.333 0.0004599 *** Residuals 1335 326.85 0.24483 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 12 observations deleted due to missingness When I look a the structure, this is what I see. I usually can work through lists to get what I need, but I am having a hard time with this one. A Google searched also seemed to reveal much simpler structures than I am getting. NOTE: