Look at the results of summary(body.pc). You will see that the first component accounts for 70% of the variability. That is very large and suggests that the variables are highly correlated with one another. You could check with cor(bodysize). None of the correlations is negative and the smallest one is .41905. That is strong evidence that body size is the main contributor to variation among the variables. Now look at the loadings for the first principal component. These are all negative and about the same magnitude. It is not the sign of the loadings, but the fact that they are all the same sign that indicates the first component measures size. The loadings provide the weights for computing the principal component scores. If we multiply the loadings by the standard deviation for each component, we get a structure matrix (also sometimes called loadings) that represents the correlation of each variable with the component:
sweep(body.pc$loadings, 2, body.pc$sdev, "*") The absolute value of the correlations for the variables with the first component are high with the lowest being .653, again suggesting that the first component reflects size. If you look at Component 2, the interpretation is not so straightforward. Only ankle is moderately large (-.533) and forearm and wrist are also negative but smaller. Chest, abdomen, hip, and thigh are all positive. The component seems to be contrasting bigger trunk development (positive direction) against limb development (negative direction). You raised a question about using identify() but you also reported using it successfully to identify outliers. The commands you posted work fine for me to identify the outliers. ------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77840-4352 -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Pavneet Arora Sent: Wednesday, March 26, 2014 4:23 AM To: John Kane Cc: r-help@r-project.org Subject: Re: [R] Principal Components Loadings Hello John As per your suggestion, I have now used the dput function. So my Rdata dataset "bodysize" looks like this. > dput(bodysize) structure(list(neck = c(36.2, 38.5, 34, 37.4, 34.4, 39, 36.4, 37.8, 38.1, 42.1, 38.5, 39.4, 38.4, 39.4, 40.5, 36.4, 38.9, 42.1, 38, 40, 39.1, 41.3, 33.9, 35.5, 34.5, 35.7, 36.2, 38.8, 36.4, 36.7, 38.7, 37.3, 38.1, 39.8, 42.1, 38.4, 38.5, 42.1, 51.2, 40.2, 43.2, 36.6, 37.3, 41.5, 31.5, 35.7, 33.6, 34.6, 32.8, 34, 34.9, 34.3, 36.5, 35.1, 37.8, 39.9, 39.1, 40.5, 40.5, 38.4, 41.4, 35.6, 38, 37.4, 40.1, 40.9, 35.6, 36.9, 37.5, 36.3, 35.5, 38.7, 36.4, 33.2, 36.5, 36, 38.7, 38.7, 37.8, 37.4, 38.4, 38.1, 39.3, 38.7, 38.5, 36.5, 37.7, 36.5, 38, 36.7, 37.2, 39.2, 37.5, 38, 37.3, 41.1, 37.5, 38.7, 35.9, 40, 40.1, 37, 36.3, 40.7, 39.6, 31.1, 38.6, 42, 38.5, 34.2, 37.2, 37.1, 40.2, 35.3, 38, 36.3, 36.8, 41, 38.3, 38, 40.8, 39.5, 36.9, 36.9, 37.7, 36.6, 38.9, 37.5, 39.8, 38.3, 35.5, 36.3, 37.8, 37.8, 36.5, 37.8, 37, 37.7, 34.3, 40.8, 37.4, 36.5, 37.5, 35.5, 38, 35.7, 39.2, 40.9, 35.2, 40.6, 35.4, 41.8, 34.1, 37.9, 38.2, 35.6, 38.5, 37, 35.9, 36.2, 35, 38.5, 40.7, 36, 39.5, 40.5, 38.5, 43.9, 40.4, 37.6, 37, 34, 38.4, 38.7, 41.5, 36, 35.3, 42.1, 38, 42.8, 40, 33.8, 35.5, 35.3, 37.7, 39.4, 41.9, 38.5, 40.8, 38, 36.4, 41.8, 40.7, 38.5, 35.4, 38.5, 35.5, 36.5, 37.6, 37.4, 37.8, 35.2, 37.9, 37.9, 40.9, 41.9, 39.1, 40.2, 36, 34.5, 35.8, 40.2, 38.3, 39, 37.4, 41.2, 34.8, 36.9, 39.4, 37.6, 38.5, 42.5, 37.4, 35.2, 41.1, 33.4, 37.2, 38.3, 38.1, 37.4, 35.2, 39.4, 38, 35.1, 40.4, 38.3, 40.6, 40.2, 37.9, 40.8, 34.7, 38.8, 41.4, 41.3, 40.7, 36.3, 40.8, 34.9, 40.9, 38.9, 38.9, 40.8), chest = c(93.1, 93.6, 95.8, 101.8, 97.3, 104.5, 105.1, 99.6, 100.9, 99.6, 101.5, 103.6, 102, 104.1, 101.3, 99.1, 101.9, 107.6, 106.8, 106.2, 103.3, 111.4, 86, 86.7, 90.2, 89.6, 88.6, 97.4, 93.5, 97.4, 100.5, 93.5, 93, 111.7, 117, 118.5, 106.5, 105.6, 136.2, 114.8, 128.3, 106, 113.3, 106.6, 85.1, 96.6, 88.2, 89.8, 92.3, 83.4, 90.2, 89.2, 89.7, 93.3, 87.6, 107.6, 100, 111.5, 115.4, 104.8, 112.3, 102.9, 107.6, 105.3, 105.3, 103, 90, 95.4, 89.3, 94.4, 97.6, 88.5, 93.6, 87.7, 93.4, 91.6, 91.6, 102, 96.4, 102.7, 97.7, 97.1, 103.1, 101.8, 101.4, 98.9, 97.5, 104.3, 97.3, 96.7, 99.7, 101.9, 97.2, 106.6, 99.6, 113.2, 99.1, 99.4, 95.1, 107.5, 106.5, 99.1, 96.7, 103.5, 104, 93.1, 105.2, 110, 110.1, 97.8, 96.3, 108, 99.7, 93.5, 100.7, 97, 96, 99.2, 95.4, 101.8, 104.3, 99.2, 99.3, 94, 98.9, 101, 98.7, 95.9, 103.9, 96.2, 97.8, 94.6, 103.6, 100.4, 98.4, 104.6, 92.9, 97.8, 98.3, 104.7, 98.6, 99.5, 102.7, 92.1, 96.6, 92.7, 102, 110.9, 92.3, 114.1, 92.9, 108.3, 88.5, 94, 101.1, 92.1, 105.6, 98.5, 88.7, 101.1, 94, 103.8, 98.9, 89.2, 111.4, 107.5, 99.1, 108.2, 114.9, 99.1, 92.2, 90.8, 100.5, 98.2, 115.3, 96.8, 92.6, 119.2, 102.7, 109.5, 108.5, 79.3, 95.5, 92.3, 98.9, 89.5, 117.5, 107.4, 109.2, 103.4, 91.4, 115.2, 104.9, 106.7, 92.2, 101.6, 97.8, 92, 94, 103.7, 102.7, 91.1, 107.2, 100.8, 121.6, 105.6, 100.6, 102.7, 99.8, 92.9, 91.2, 115.6, 98.3, 103.7, 98.7, 119.8, 92.8, 93.3, 106.8, 93.9, 99, 119.9, 94.2, 92.7, 106.9, 88.8, 101.7, 105.3, 104, 98.6, 99.6, 103.4, 100.2, 94.9, 97.2, 104.7, 104, 117.6, 95.8, 106.4, 93, 119.6, 119.7, 115.8, 118.3, 97.4, 113.7, 89.2, 108.5, 111.1, 108.3, 112.4), abdomen = c(85.2, 83, 87.9, 86.4, 100, 94.4, 90.7, 88.5, 82.5, 88.6, 83.6, 90.9, 91.6, 101.8, 96.4, 92.8, 96.4, 97.5, 89.6, 100.5, 95.9, 98.8, 76.4, 80, 76.3, 79.7, 74.6, 88.7, 73.9, 83.5, 88.7, 84.5, 79.1, 100.5, 115.6, 113.1, 100.9, 98.8, 148.1, 108.1, 126.2, 104.3, 111.2, 104.3, 76, 81.5, 73.7, 79.5, 83.4, 70.4, 86.7, 77.9, 82, 79.6, 77.6, 100, 99.8, 104.2, 105.3, 98.3, 104.8, 94.7, 102.4, 99.7, 105.5, 100.3, 83.9, 86.6, 78.4, 84.6, 91.5, 82.8, 82.9, 76, 83.3, 81.8, 78.8, 95, 95.4, 98.6, 95.8, 89, 97.8, 94.9, 99.8, 89.7, 88.1, 90.9, 86, 86.5, 95.6, 93.2, 83.1, 97.5, 88.8, 99.2, 91.6, 86.7, 88.2, 94, 95, 92, 89.2, 95.5, 98.6, 87.3, 102.8, 101.6, 88.7, 92.3, 90.6, 105, 95, 89.6, 92.4, 86.6, 90, 90, 92.4, 87.5, 99.2, 98.1, 83.3, 86.1, 84.1, 89.9, 92.1, 78, 93.5, 87, 90.1, 90.3, 99.8, 89.4, 87.2, 101.1, 86.1, 98.6, 88.5, 106.6, 93.1, 93, 91, 77.1, 85.3, 81.9, 99.1, 100.5, 76.5, 106.8, 77.6, 102.9, 72.8, 88.2, 100.1, 83.5, 105, 90.8, 76.6, 92.4, 81.2, 95.6, 92.1, 83.4, 106, 95.1, 90.4, 100.4, 115.9, 90.8, 81.9, 75, 90.3, 90.3, 108.8, 79.4, 83.2, 110.3, 92.7, 104.5, 104.6, 69.4, 83.6, 86.8, 90.4, 83.7, 109.3, 98.9, 98, 101.2, 80.6, 113.7, 94.1, 105.7, 85.6, 96.6, 86, 89.7, 78, 89.7, 89.2, 85.7, 103.1, 89.1, 113.9, 96.3, 93.9, 101.3, 83.9, 84.4, 79.4, 104, 89.7, 97.6, 87.6, 122.1, 81.1, 81.5, 100, 88.7, 91.8, 110.4, 87.6, 82.8, 95.3, 78.2, 91.1, 96.7, 89.4, 93, 86.4, 96.7, 88.1, 94.9, 93.3, 95.6, 98.2, 113.8, 82.8, 100.5, 79.7, 118, 109, 113.4, 106.1, 84.3, 107.6, 83.6, 105, 111.5, 101.3, 108.5), hip = c(94.5, 98.7, 99.2, 101.2, 101.9, 107.8, 100.3, 97.1, 99.9, 104.1, 98.2, 107.7, 103.9, 108.6, 100.1, 99.2, 105.2, 107, 102.4, 109, 104.9, 104.8, 94.6, 93.4, 95.8, 96.5, 85.3, 94.7, 88.5, 98.7, 99.8, 100.6, 94.5, 108.3, 116.1, 113.8, 106.2, 104.8, 147.7, 102.5, 125.6, 115.5, 114.1, 106, 88.2, 97.2, 88.5, 92.7, 90.4, 87.2, 98.3, 91, 89.1, 91.6, 88.6, 99.6, 102.5, 105.8, 97, 99.6, 103.1, 100.8, 99.4, 99.7, 108.3, 104.2, 93.9, 91.8, 96.1, 94.3, 98.5, 95.5, 96.3, 88.6, 93, 94.8, 94.3, 98.3, 99.3, 100.2, 97.1, 96.9, 99.6, 95, 96.2, 96.2, 96.9, 93.8, 99.3, 98.3, 102.2, 100.6, 95.4, 100.6, 101.4, 107.5, 102.4, 96.2, 92.8, 103.7, 101.7, 98.3, 98.3, 101.6, 99.5, 96.6, 103.6, 100.7, 102.1, 100.6, 99.3, 103, 98.6, 99.8, 97.5, 92.6, 99.7, 96.4, 104.3, 101, 104.1, 101.4, 97.5, 95.2, 94, 100, 98.5, 93.2, 99.5, 97.8, 95.8, 99.1, 103.2, 92.3, 98.4, 102.1, 95.6, 100.6, 98.3, 107.7, 101.6, 99.3, 98.9, 93.9, 102.5, 95.3, 110.1, 106.2, 92.1, 113.9, 93.5, 114.4, 91.1, 95.2, 105, 98.3, 106.4, 102.5, 89.8, 99.3, 91.5, 105.1, 103.5, 89.6, 108.8, 104.5, 95.6, 106.8, 111.9, 98.1, 92.8, 89.2, 98.7, 99.9, 114.4, 89.2, 96.4, 113.9, 101.9, 109.9, 109.8, 85, 91.6, 96.1, 95.5, 98.1, 108.8, 104.1, 101.8, 103.1, 92.3, 112.4, 102.7, 111.8, 96.5, 100.6, 96.2, 101, 99, 94.2, 99.2, 96.9, 105.5, 102.6, 107.1, 102, 100.1, 101.7, 91.8, 94, 89, 109, 99.1, 104.2, 96.1, 112.8, 96.3, 94.4, 105, 94.5, 96.2, 105.5, 95.6, 91.9, 98.2, 87.5, 97.1, 106.6, 98.4, 97, 90.1, 100.7, 97.8, 100.2, 94, 93.7, 101.1, 111.8, 94.5, 100.5, 87.6, 114.3, 109.1, 109.8, 101.6, 94.4, 110, 88.8, 104.5, 101.7, 97.8, 107.1), thigh = c(59, 58.7, 59.6, 60.1, 63.2, 66, 58.4, 60, 62.9, 63.1, 59.7, 66.2, 63.4, 66, 69, 63.1, 64.8, 66.9, 64.2, 65.8, 63.5, 63.4, 57.4, 54.9, 58.4, 55, 51.7, 57.5, 50.1, 58.9, 57.5, 58.5, 57.3, 67.1, 71.2, 61.9, 63.5, 66, 87.3, 61.3, 72.5, 70.6, 67.7, 65, 50, 58.4, 53.3, 52.7, 52, 50.6, 52.6, 51.4, 49.3, 52.6, 51.9, 57.2, 62.1, 61.8, 59.1, 60.6, 61.6, 60.9, 61, 60.8, 65, 64.8, 55, 54.3, 56, 51.2, 56.6, 58.9, 52.9, 50.9, 55.5, 54.5, 56.7, 55, 53.5, 56.5, 54.8, 54.8, 58.9, 56, 56.3, 54.7, 57.2, 57.8, 61, 60.4, 58.3, 58.9, 56.9, 58.9, 57.4, 61.7, 60.6, 62.1, 54.7, 62.7, 59, 59.3, 60, 59.1, 59.5, 54.7, 61.2, 55.8, 57.5, 57.5, 61.9, 63.7, 62.3, 61.5, 59.3, 55.9, 58.8, 56.8, 64.6, 58.5, 58.5, 57.1, 60.5, 58.1, 58.5, 60.7, 60.7, 53.5, 61.7, 57.4, 57, 60.3, 61.2, 56.1, 56, 58.9, 58.8, 63.6, 58.1, 66.5, 59.1, 60.4, 57.1, 56.1, 59.1, 56.4, 71.2, 68.4, 51.9, 67.6, 56.9, 72.9, 53.6, 56.8, 62.1, 57.3, 68.6, 60.8, 50.1, 59.4, 52.5, 61.4, 64, 52.4, 63.8, 64.8, 55.5, 63.3, 74.4, 60.1, 54.7, 50, 57.8, 59.2, 69.2, 50.3, 60, 69.8, 64.7, 69.5, 68.1, 47.2, 54.1, 58, 55.4, 57.3, 67.7, 63.5, 62.8, 61.5, 54.3, 68.5, 60.6, 65.3, 60.2, 61.1, 57.7, 62.3, 57.5, 58.5, 60.2, 55.5, 68.8, 60.6, 63.5, 63.3, 58.9, 60.7, 53, 56, 51.1, 63.7, 56.3, 60, 57.1, 62.5, 53.8, 54.7, 63.9, 53.7, 57.7, 64.2, 59.7, 54.4, 57.4, 50.8, 56.6, 64, 58.4, 55.4, 53, 59.3, 57.1, 56.8, 54.3, 54.4, 59.3, 63.4, 61.2, 59.2, 50.7, 61.3, 63.7, 65.6, 58.2, 54.3, 63.3, 49.6, 59.6, 60.3, 56, 59.3), knee = c(37.3, 37.3, 38.9, 37.3, 42.2, 42, 38.3, 39.4, 38.3, 41.7, 39.7, 39.2, 38.3, 41.5, 39, 38.7, 40.8, 40, 38.7, 40.6, 38, 40.6, 35.3, 36.2, 35.5, 36.7, 34.7, 36, 34.5, 35.3, 38.7, 38.8, 36.2, 44.2, 43.3, 38.3, 39.9, 41.5, 49.1, 41.1, 39.6, 42.5, 40.9, 40.2, 34.7, 38.2, 34.5, 37.5, 35.8, 34.4, 37.2, 34.9, 33.7, 37.6, 34.9, 38, 39.6, 39.8, 38, 37.7, 40.9, 38, 39.4, 40.1, 41.2, 40.2, 36.1, 35.4, 37.4, 37.4, 38.6, 37.6, 37.5, 35.4, 35.2, 37, 39.7, 38.3, 37.5, 39.3, 38.2, 38, 39, 36.5, 36.6, 37.8, 37.7, 39.5, 38.4, 39.9, 38.2, 39.7, 38.3, 40.5, 39.6, 42.3, 39.4, 39.3, 37.3, 39, 39.4, 38.4, 38.4, 39.8, 36.1, 39, 39.3, 38.7, 40, 36.8, 38, 40, 38.1, 37.8, 38.1, 36.3, 38.4, 38.8, 41.1, 39.2, 39.3, 40.5, 38.7, 36.5, 36.6, 36, 36.8, 35.8, 39, 36.9, 38.7, 38.5, 38.1, 35.6, 36.9, 37.9, 36.1, 39.2, 38.4, 42.5, 39.6, 38.2, 36.7, 36.1, 37.6, 36.5, 43.5, 40.8, 35.7, 42.7, 35.9, 43.5, 36.8, 37.4, 40, 37.8, 40, 38.5, 34.8, 39, 36.6, 40.6, 37.3, 35.6, 42, 41.3, 34.2, 41.7, 40.6, 39.1, 36.2, 34.8, 37.3, 37.7, 42.4, 34.8, 38.1, 42.6, 39.5, 43.1, 42.8, 33.5, 36.2, 39.4, 38.9, 39.7, 41.3, 39.8, 41.3, 40.4, 36.3, 45, 38.6, 43.3, 38.9, 38.4, 38.6, 38, 40, 39, 39.2, 35.7, 38.3, 39, 40.3, 39.8, 37.6, 39.4, 36.2, 38.2, 35, 40.3, 38.8, 40.9, 38.1, 36.9, 36.5, 39, 39.2, 36.2, 38.1, 42.7, 40.2, 35.2, 37.1, 33, 38.5, 42.6, 37.4, 38.8, 35, 38.6, 38.9, 35.9, 35.7, 37.1, 40.3, 41.1, 39.1, 38.1, 33.4, 42.1, 42.4, 46, 38.8, 37.5, 44, 34.8, 40.8, 37.3, 41.6, 42.2), ankle = c(21.9, 23.4, 24, 22.8, 24, 25.6, 22.9, 23.2, 23.8, 25, 25.2, 25.9, 21.5, 23.7, 23.1, 21.7, 23.1, 24.4, 22.9, 24, 22.1, 24.6, 22.2, 22.1, 22.9, 22.5, 21.4, 21, 21.3, 22.6, 33.9, 21.5, 24.5, 25.2, 26.3, 21.9, 22.6, 24.7, 29.6, 24.7, 26.6, 23.7, 25, 23, 21, 23.4, 22.5, 21.9, 20.6, 21.9, 22.4, 21, 21.4, 22.6, 22.5, 22, 22.5, 22.7, 22.5, 22.9, 23.1, 22.1, 23.6, 22.7, 24.7, 22.7, 21.7, 21.5, 22.4, 21.6, 22.4, 21.6, 23.1, 19.1, 20.9, 21.4, 24.2, 21.8, 21.5, 22.7, 23.7, 22, 23, 24.1, 22, 33.7, 21.8, 23.3, 23.8, 24.4, 22.5, 23.1, 22.1, 24.5, 24.6, 23.2, 22.9, 23.3, 21.9, 22.3, 22.3, 22.4, 23.2, 25.4, 22, 24.8, 23.5, 23.4, 24.8, 22.8, 22.3, 23.6, 23.9, 21.9, 21.8, 22.1, 22.8, 23.3, 24.8, 24.5, 24.6, 23.2, 22.6, 22.1, 23.5, 21.9, 22.2, 20.8, 21.8, 22.2, 23.2, 23, 22.6, 20.5, 23, 22.7, 22.4, 23.8, 22.5, 24.5, 21.6, 22, 22.3, 22.7, 23.2, 22, 25.2, 24.6, 22, 24.7, 20.4, 25.1, 23.8, 22.8, 24.9, 21.7, 25.2, 25, 21.8, 24.6, 21, 25, 23.5, 20.4, 23.4, 25.6, 21.9, 24.6, 24, 23.4, 22.1, 22, 22.4, 21.5, 24, 22.2, 22, 24.8, 24.7, 25.8, 24.1, 20.2, 21.8, 22.7, 22.4, 22.6, 24.7, 23.5, 24.8, 22.9, 21.8, 25.5, 24.7, 26, 22.4, 24.1, 24, 22.3, 22.5, 24.1, 23.8, 22, 23.7, 24, 21.8, 24.1, 21.4, 23.3, 22.5, 22.6, 21.7, 23.2, 23, 25.5, 21.8, 23.6, 21.5, 22.6, 22.9, 22, 23.9, 27, 23.4, 22.5, 21.8, 19.7, 22.6, 23.4, 22.5, 23.2, 21.3, 22.8, 23.6, 21, 21, 22.7, 23, 22.3, 22.3, 24, 20.1, 23.4, 24.6, 25.4, 24.1, 22.6, 22.6, 21.5, 23.2, 21.5, 22.7, 24.6), biceps = c(32, 30.5, 28.8, 32.4, 32.2, 35.7, 31.9, 30.5, 35.9, 35.6, 32.8, 37.2, 32.5, 36.9, 36.1, 31.1, 36.2, 38.2, 37.2, 37.1, 32.5, 33, 27.9, 29.8, 31.1, 29.9, 28.7, 29.2, 30.5, 30.1, 32.5, 30.1, 29, 37.5, 37.3, 32, 35.1, 33.2, 45, 34.1, 36.4, 33.6, 36.7, 35.8, 26.1, 29.7, 27.9, 28.8, 28.8, 26.8, 26, 26.7, 29.6, 38.5, 27.7, 35.9, 33.1, 37.7, 31.6, 34.5, 36.2, 32.5, 32.7, 33.6, 35.3, 34.8, 29.6, 32.8, 32.6, 27.3, 31.5, 30.3, 29.7, 29.3, 29.4, 29.3, 30.2, 30.8, 31.4, 30.3, 29.4, 29.9, 34.3, 31.2, 29.7, 32.4, 32.6, 29.2, 30.2, 28.8, 29.1, 31.4, 30.1, 33.3, 30.3, 32.9, 31.6, 30.6, 31.6, 35.3, 32.2, 27.9, 31, 31, 30.1, 31, 30.5, 35.1, 35.1, 32.1, 33.3, 33.5, 35.3, 30.7, 31.8, 29.8, 29.9, 33.4, 33.6, 32.1, 33.9, 33, 34.4, 30.6, 34.4, 35.6, 33.8, 33.9, 33.3, 31.6, 27.5, 31.2, 33.5, 33.6, 34, 30.9, 32.7, 34.3, 31.7, 35.5, 30.8, 32, 31.6, 30.5, 31.8, 33.5, 36.1, 33.3, 25.8, 36, 31.6, 38.5, 27.8, 30.6, 33.7, 32.2, 35.2, 31.6, 27, 30.1, 27, 31.3, 33.5, 28.3, 34, 36.4, 30.2, 37.2, 36.1, 32.5, 30.4, 24.8, 31, 32.4, 35.4, 31, 31.5, 34.4, 34.8, 39.1, 35.6, 27.7, 31.4, 30, 30.5, 32.9, 37.2, 36.4, 36.6, 33.4, 29.6, 37.1, 34, 33.7, 31.7, 32.9, 31.2, 30.8, 30.6, 33.8, 31.7, 29.4, 32.1, 32.9, 34.8, 37.3, 33.1, 36.7, 31.4, 29, 30.9, 36.8, 29.5, 32.7, 28.6, 34.7, 31.3, 27.5, 35.7, 28.5, 31.4, 38.4, 27.9, 29.4, 34.1, 25.3, 33.4, 33.2, 34.6, 32.4, 31.7, 31.8, 30.9, 27.8, 31.3, 30.3, 32.6, 35.1, 29.8, 35.9, 28.5, 34.9, 35.6, 35.3, 32.1, 29.2, 37.5, 25.6, 35.2, 31.3, 30.5, 33.7), forearm = c(27.4, 28.9, 25.2, 29.4, 27.7, 30.6, 27.8, 29, 31.1, 30, 29.4, 30.2, 28.6, 31.6, 30.5, 26.4, 30.8, 31.6, 30.5, 30.1, 30.3, 32.8, 25.9, 26.7, 28, 28.2, 27, 26.6, 27.9, 26.7, 27.7, 26.4, 30, 31.5, 31.7, 29.8, 30.6, 30.5, 29, 31, 32.7, 28.7, 29.8, 31.5, 23.1, 27.4, 26.2, 26.8, 25.5, 25.8, 25.8, 26.1, 26, 27.4, 27.5, 30.2, 28.3, 30.9, 28.8, 29.6, 31.8, 29.8, 29.9, 29, 31.1, 30.1, 27.4, 27.4, 28.1, 27.1, 27.3, 27.3, 27.3, 25.7, 27, 27, 29.2, 25.7, 26.8, 28.7, 27.2, 25.2, 29.6, 27.3, 26.3, 27.7, 28, 28.4, 29.3, 29.6, 27.7, 28.4, 28.2, 29.6, 27.9, 30.8, 30.1, 27.8, 27.5, 30.9, 31, 26.2, 29.2, 30.3, 27.2, 29.4, 28.5, 29.6, 30.7, 26, 28.2, 27.8, 31.1, 27.6, 27.3, 26.3, 28, 29.8, 29.5, 28.6, 31.2, 29.6, 28, 27.5, 29.2, 30.2, 30.3, 28.2, 29.6, 27.8, 26.5, 28.4, 28.6, 29.3, 29.8, 28.8, 28.3, 28.4, 27.4, 29.8, 27.9, 28.5, 27.5, 27.2, 29.7, 28.3, 30.3, 29.7, 25.2, 30.4, 29, 33.8, 26.3, 28.3, 29.2, 27.7, 30.7, 28, 34.9, 28.2, 26.3, 29.2, 30.6, 26.2, 31.2, 33.7, 28.7, 33.1, 31.8, 29.8, 27.4, 25.9, 28.7, 28.4, 21, 26.9, 26.6, 29.5, 30.3, 32.5, 29, 24.6, 28.3, 26.4, 28.9, 29.3, 31.8, 30.4, 32.4, 29.2, 27.3, 31.2, 30.1, 29.9, 27.1, 29.8, 27.3, 27.8, 30, 28.8, 28.4, 26.6, 28.9, 29.2, 30.7, 23.1, 29.5, 31.6, 27.5, 26.2, 28.8, 31, 27.9, 30, 26.7, 29.1, 26.3, 25.9, 30.4, 25.7, 29.9, 32, 27, 26.8, 31.1, 22, 29.3, 30, 30.1, 29.7, 27.3, 29.1, 29.6, 26.1, 28.7, 26.3, 28.5, 29.6, 28.9, 30.5, 24.8, 30.1, 30.7, 29.8, 29.3, 27.3, 32.6, 25.7, 28.6, 27.2, 29.4, 30), wrist = c(17.1, 18.2, 16.6, 18.2, 17.7, 18.8, 17.7, 18.8, 18.2, 19.2, 18.5, 19, 17.7, 18.8, 18.2, 16.9, 17.3, 19.3, 18.5, 18.2, 18.4, 19.9, 16.7, 17.1, 17.6, 17.7, 16.5, 17, 17.2, 17.6, 18.4, 17.9, 18.8, 18.7, 19.7, 17, 19, 19.4, 21.4, 18.3, 21.4, 17.4, 18.4, 18.8, 16.1, 18.3, 17.3, 17.9, 16.3, 16.8, 17.3, 17.2, 16.9, 18.5, 18.5, 18.9, 18.5, 19.2, 18.2, 18.5, 20.2, 18.3, 19.1, 18.8, 18.4, 18.7, 17.4, 18.7, 18.1, 17.3, 18.6, 18.3, 18.2, 16.9, 16.8, 18.3, 18.1, 18.8, 18.3, 19, 19, 17.7, 19, 19.2, 18, 18.2, 18.8, 18.1, 18.8, 18.7, 17.7, 18.8, 18.4, 19.1, 17.8, 20.4, 18.5, 18.2, 18.2, 18.3, 18.6, 17, 18.4, 19.7, 17.7, 18.8, 18.1, 19.1, 19.2, 17.3, 18.1, 17.4, 19.8, 17.4, 17.5, 17.3, 18.1, 19.5, 18.5, 18, 19.5, 18.4, 17.6, 17.6, 18, 17.6, 17.2, 17.4, 18.1, 17.7, 17.6, 17.1, 17.9, 17.3, 18.1, 17.6, 17.1, 17.7, 17.6, 18.7, 16.6, 17.8, 17.9, 18.2, 18.3, 17.3, 18.7, 18.4, 16.9, 18.4, 17.8, 19.6, 17.4, 17.9, 19.4, 17.7, 19.1, 18.6, 16.9, 18.2, 16.5, 19.1, 19.7, 16.5, 18.5, 19.4, 17.7, 19.8, 18.8, 17.4, 17.7, 16.9, 17.7, 17.8, 20.1, 16.9, 16.7, 18.4, 18.1, 19.9, 19, 16.5, 17.2, 17.4, 17.7, 18.2, 20, 19.1, 18.8, 18.5, 17.9, 19.9, 18.7, 18.5, 17.1, 18.8, 17.4, 16.9, 18.5, 18.8, 18.6, 17.4, 18.7, 18.4, 17.4, 19.4, 17.3, 18.4, 17.7, 17.6, 17.4, 18.9, 18.6, 19, 18, 18.4, 17.8, 18.6, 19.2, 17.1, 18.9, 19.6, 17.8, 17, 19.2, 15.8, 18.8, 18.4, 18.8, 19, 16.9, 19, 18, 17.6, 18.3, 18.3, 19, 18.5, 18.3, 19.1, 16.5, 19.4, 19.5, 19.5, 18.5, 18.5, 18.8, 18.5, 20.1, 18, 19.8, 20.9 )), .Names = c("neck", "chest", "abdomen", "hip", "thigh", "knee", "ankle", "biceps", "forearm", "wrist"), class = "data.frame", row.names = c(NA, -252L)) Hope this helps. From: John Kane <jrkrid...@inbox.com> To: Pavneet Arora/UK/RoyalSun@RoyalSun Date: 25/03/2014 18:34 Subject: RE: [R] Principal Components Loadings Hi Pavneet, Your data set did not come through. The R-help list is likely to strip any attachments other than things like a text file or pdf file as a security feature. You are better using the ?dput function to attached the data or perhaps use head(dput(mydata), 100) for a sample if it is a big file. John Kane Kingston ON Canada > -----Original Message----- > From: pavneet.ar...@uk.rsagroup.com > Sent: Tue, 25 Mar 2014 13:41:16 +0000 > To: r-help@r-project.org > Subject: [R] Principal Components Loadings > > Hello All, > > I have a dataset "bodysize.Rdata" from Journal of Statistics Education > Data Archive, which I have attached here. > I am trying to do principal components analysis on it using princomp, and > it seems to be working fine. However, I am really struggling in > interpretating the loadings of PCA, so I can answer questions like: > > "What features of the body sizes do the first few components reflect?" > > So for my R Analysis - following is what I have done: > body.pc <- princomp(bodysize,cor=T) > summary(body.pc) > body.pc$loadings > print(body.pc$loadings,cutoff=0.1,digits=1) > > > The book suggests that the answer to the above question should be: > it is easy to see that the first component reflects variations in overall > size > of body; the second contrasts arm size (mostly) with body and leg size; > the > third contrasts leg with rest of body, and the fourth is body against > limbs. If > we plot PC1 against PC2 (i.e., scores on first component against those on > the > second) with > > plot(body.pc$scores[,2],body.pc$scores[,1]) > > we can see one outlier at the bottom of the plot and two to the left. > Noting > the signs of the loadings on the first component (vertical axis), we can > see that > the outlier at the bottom arises from a subject with large measurements. > The > two outliers to the left are from people of average size but with > proportionately > well-developed arms by comparison with their legs. Using identify() gives > > identify(body.pc$scores[,2],body.pc$scores[,1]) > > which reveals that these outliers are observations 39 (lower) and 31 and > 86 > (rightmost). > > > But I am really struggling to see how the first component reflect > variations in overall size and the second in arm and so on forth. Please > help? > Also when I try to use the "identify" function in R, all I get is "no > point with 0.25m" or R crashes. How does one use the "identify" function > in R? > > > > Thanks in Advance > > > > > > **************************************************************** **************************************************************** **************************************************************** *********************** ________________________________________________________________ ______________________________ **************************************************************** **************************************************************** **************************************************************** *********************** MORE TH>N is a trading style of Royal & Sun Alliance Insurance plc (No. 93792). Registered in England and Wales at St. Markb ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
