By Christopher R. Bilder

"We stay in a express global! From a good or unfavourable affliction prognosis to selecting all goods that follow in a survey, results are often prepared into different types in order that humans can extra simply make feel of them. although, interpreting info from express responses calls for really expert innovations past these realized in a primary or moment path in facts. We o er this e-book to assist scholars and�Read more...

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**Additional resources for Analysis of categorical data with R**

**Sample text**

Hat1 = pi . hat1 , pi . hat2 = pi . hat2 ) > # Find joint probability for w1 and w2 > prob . w1 <- dbinom ( x = 0: n1 , size = n1 , prob = pi1 ) > prob . w2 <- dbinom ( x = 0: n2 , size = n2 , prob = pi2 ) 32 Analysis of Categorical Data with R > prob . all <- expand . grid ( prob . w1 = prob . w1 , prob . w2 = prob . w2 ) > pmf <- prob . all$prob . w1 * prob . all$prob . w2 > # P ( W1 = w1 , W2 = w2 ) > head ( data . frame ( w . 0016. Using these probabilities, we calculate the true confidence level for the interval: > var .

Thus, the true confidence level at π1 and π2 , C(π1 , π2 ), is the sum of the joint probabilities for all intervals that do contain π1 − π2 : n2 n1 I(w1 , w2 ) C(π1 , π2 ) = w2 =0 w1 =0 n1 w1 π1n1 (1 − π1 )n1 −w1 n2 w2 π2w2 (1 − π2 )n2 −w2 where the indicator function I(w1 , w2 ) is 1 if the corresponding interval contains π1 − π2 and I(w1 , w2 ) is 0 otherwise. Calculation details are given in the next example. 3. 4, n1 = 10, and n2 = 10. grid() function, which finds all possible combinations of the arguments (separated by commas) within its parentheses.

All <- expand . grid ( pi . hat1 = pi . hat1 , pi . hat2 = pi . hat2 ) > # Find joint probability for w1 and w2 > prob . w1 <- dbinom ( x = 0: n1 , size = n1 , prob = pi1 ) > prob . w2 <- dbinom ( x = 0: n2 , size = n2 , prob = pi2 ) 32 Analysis of Categorical Data with R > prob . all <- expand . grid ( prob . w1 = prob . w1 , prob . w2 = prob . w2 ) > pmf <- prob . all$prob . w1 * prob . all$prob . w2 > # P ( W1 = w1 , W2 = w2 ) > head ( data . frame ( w . 0016. Using these probabilities, we calculate the true confidence level for the interval: > var .