principal_component_analysis
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principal_component_analysis [2019/11/15 06:00] – hkimscil | principal_component_analysis [2019/11/15 22:46] – [e.g. saq] hkimscil | ||
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====== PCA ====== | ====== PCA ====== | ||
+ | [[https:// | ||
+ | * Both are data reduction techniques — they allow you to capture the variance in variables in a smaller set. | ||
+ | * Both are usually run in stat software using the same procedure, and the output looks pretty much the same. | ||
+ | * The steps you take to run them are the same-extraction, | ||
+ | * Despite all these similarities, | ||
+ | ====== Some useful lectures ====== | ||
+ | |||
{{youtube> | {{youtube> | ||
<WRAP clear /> | <WRAP clear /> | ||
Line 153: | Line 160: | ||
ggtitle(" | ggtitle(" | ||
</ | </ | ||
+ | |||
+ | ====== e.g. saq ====== | ||
+ | SPSS Anxiety Questionnaire | ||
+ | {{: | ||
+ | |||
+ | |||
+ | |||
+ | < | ||
+ | # saq <- read.csv(" | ||
+ | saq8 <- read.csv(" | ||
+ | head(saq8) | ||
+ | saq8 <- saq8[c(-1)] | ||
+ | </ | ||
+ | |||
+ | < | ||
+ | > round(cor(saq8), | ||
+ | stat_cry afraid_spss sd_excite nmare_pearson du_stat lexp_comp comp_hate good_math | ||
+ | stat_cry | ||
+ | afraid_spss | ||
+ | sd_excite | ||
+ | nmare_pearson | ||
+ | du_stat | ||
+ | lexp_comp | ||
+ | comp_hate | ||
+ | good_math | ||
+ | > | ||
+ | </ | ||
+ | |||
+ | |||
+ |
principal_component_analysis.txt · Last modified: 2019/11/16 15:06 by hkimscil