62015-05-07 07:36:43mandy.vogel@googlemail.com52015-05-07 07:33:57mandy.vogel@googlemail.com42015-05-03 07:13:15mandy.vogel@googlemail.com32015-05-03 06:48:42mandy.vogel@googlemail.com22015-05-03 06:46:31mandy.vogel@googlemail.com12015-05-03 06:44:19mandy.vogel@googlemail.com

It is essential, therefore, that you can answer the following questions: Which of your variables is the response variable? Which are the explanatory variables? Are the explanatory variables continuous or categorical, or a mixture of both? What kind of response variable do you have: is it a continuous measurement, a count, a proportion, a time at death, or a category?

Explanatory Variables are all continuous Regression all categorical Analysis of variance (ANOVA) both continuous and categorical Analysis of covariance (ANCOVA)

Response Variables (a) Continuous Normal regression, ANOVA or ANCOVA(b) Proportion Logistic regression (c) Count Log-linear models (d) Binary Binary logistic analysis (e) Time at deathSurvival analysis The best model is the model that produces the least unexplained variation (the minimal residual)

It is very important to understand that there is not one model; there will be a large number of different, more or less plausible models that might be fitted to any given set of data.

We define best in terms of maximum likelihood. given the data, and given our choice of model, what values of the parameters of that model make the observed data most likely? We judge the model on the basis how likely the data would be if the model were correct.

The principle is attributed to William of Ockham, who insisted that, given a set of equally good explanations for a given phenomenon, the correct explanation is the simplest explanation. The most useful statement of the principle for scientists is when you have two competing theories which make exactly the same predictions, the one that is simpler is the better.

For statistical modelling, the principle of parsimony means that: models should have as few parameters as possible; linear models should be preferred to non-linear models; experiments relying on few assumptions should be preferred to those relying on many; models should be pared down until they are minimal adequate; simple explanations should be preferred to complex explanations.

Fitting models to data is the central function of R. There are no fixed rules and no absolutes. The object is to determine a minimal adequate model from a large set of potential models. For this reason we looking at the following types of models: the null model; the minimal adequate model; the maximal model; and the saturated model.

attachment:nullmodel.png Just one parameter, the overall mean ybar Fit: none; SSE = SSY Degrees of freedom: n-1 Explanatory power of the model: none

Minimal adequate model: attachment:minimalmodel.png model with $0 \le p' \le p$ }}} parameters Fit: less than the maximal model, but not significantly so Degrees of freedom: $n-p'-1$Explanatory power of the model: $r^2 = \frac{SSR}{SSY}$

Minimal adequate model: attachment:minimalmodel2.png model with $0 \le p' \le p$ parameters Fit: less than the maximal model, but not significantly so Degrees of freedom: $n-p'-1$Explanatory power of the model: $r^2 = \frac{SSA}{SSY}$

saturated model One parameter for every data point Fit: perfect Degrees of freedom: none Explanatory power of the model: none maximal model Contains all p factors, interactions and covariates that Degrees of freedom: $n-p-1$Explanatory power of the model: it depends

models are representations of reality that should be both accurate and convenient it is impossible to maximize a model’s realism, generality and holism simultaneously the principle of parsimony is a vital tool in helping to choose one model over another only include an explanatory variable in a model if it significantly improved the fit of the model (or if there other strong reasons) the fact that we went to the trouble of measuring something does not mean we have to have it in our model

a technique we use when all explanatory variables are categorical (factor) if there is one factor with three or more levels we use one-way ANOVA (only two levels: t-test should be preferred, would give exactly the same answer since with 2 levels $F=t^2$)for more factors there there is two-way, three-way anova central idea is to compare two or more means by comparing variances

A data frame with 14 observations on 2 variables. ozone: athmospheric ozone concentration garden: garden id 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ozone 9 7 6 8 5 11 9 11 9 6 10 8 8 12 garden a a a b a b b b b a b a a b

we plot the values in order they are measured attachment:TSS1.png

there is a lot of scatter, indicating that the variance in ozone is large to get a feel for the overall variance we plot the overall mean (8.5) and indicate each of the residuals by a vertical line attachment:TSS.png we refer to this overall variation as the total sum of squares, SSY or TSS in this case SSY = 55.5$ attachment:TSS2.png

now instead of fitting the overall mean, let us fit the individual garden means garden a b mean 7 10

{{attachment:ESS.png}} now we see that the mean ozone concentration is substantially higher in garden B the aim of ANOVA is to determine whether it is significantly higher or whether this kind of difference could come by chance alone

we define the new sum of squares as the error sum of squares (error in the sense of 'residual') in this case SSE = 24.0$ attachment:ESS2.png

then the component of the variation that is explained by the difference of the means is called the treatment sum of squares SSA analysis of variance is based on the notion that we break down the total sum of squares into useful and informative components SSA = explained variation SSE = unexplained variation

Source Sum of squares Degrees of freedom Mean square F ratio Garden 31.5 1 31.5 15.75Error 24.0 12 s^2=2.0 Total 55.5 13

now we need to test whether an F ratio of 15.75 is large or small we can use a table or software package I use here software to calculate the cumulative probability 1 - ]]> XXX noch Grafik hochladen attachment:fdens.png

in R we use the lm() or the aov() command and the formula syntax a ~ b we assign this to an variable ]]> |]]> ]]> ]]> ]]> ]]> ]]> ]]> ]]> |]]> ]]> ]]>

independed, normal distributed errors equality of variances (homogeneity)

generalization of the Welch t-test tests whether the means of the outcome variables are different across the factor levels assumes sufficiently large sample (greater than 10 times the number of groups in the calculation, groups of size one are to be excluded) sensitive to the existence of outliers (only few are allowed) the r command is oneway.test() non-parametric alternative kruskal.test() * Look at the help of the TukeyHSD function. What is its purpose? * Execute the code of the example near the end of the help page, interpret the results! * install and load the granovaGG package (a package for visualization of ANOVAs), load the arousal data frame and use the stack() command to bring the data in the long form. Do a anova analysis. Is there a difference at least 2 of the groups? If indicated do a post-hoc test. * Visualize your results

* Look at the help of the TukeyHSD function. What is its purpose? * Execute the code of the example near the end of the help page, interpret the results! * install and load the granovaGG package (a package for visualization of ANOVAs), load the arousal data frame and use the stack() command to bring the data in the long form. Do a anova analysis. Is there a difference at least 2 of the groups? If indicated do a post-hoc test. ]]> ]]> ]]> ]]> ]]> ]]> ]]> * Visualize your results\scriptsize ]]> attachment:aovgr1.png ]]> |]]> attachment:aovgr1.png