Discriminant spss code
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Ylab("Standardized Coefficients for Intervention Group") Scale_x_discrete(name ="Instrument",labels=c("HrQOL","Anxiety", "Dig span B", "Stroop", "BDI"))+ Geom_segment(aes(xend = Measure, yend = 0), Tibble::rownames_to_column(var = "Measure"), # Attaching package: 'gridExtra' # The following object is masked from 'package:dplyr': #With the vlim command in the second plot, I set it to the min and max of the #Right panel plots standardized coefficients for each measure. #Left panel plots canonical discriminant function boxplots for the two groups Pander("Std Coefficients") Std Coefficients Intervention Group Set_colnames(c("Intervention Group")) %>% # options are "coeffs.std", "coeffs.raw" and "structure" # This produces a nice, printable table of standardized coefficients. # CanRsq Eigenvalue Difference Percent Cumulative # Canonical Discriminant Analysis for Intervention3: # lm(formula = zBDI ~ Intervention3, data = Wk04c) # Residual standard error: 0.8591 on 162 degrees of freedom # lm(formula = zDSB ~ Intervention3, data = Wk04c) # Residual standard error: 1.003 on 162 degrees of freedom # lm(formula = zANXIETY ~ Intervention3, data = Wk04c) # Residual standard error: 0.8984 on 162 degrees of freedom
DISCRIMINANT SPSS CODE CODE
# The code below would do this, but we're skipping that here. # And then validate in another random subset (testing) # It is common to run the function in a proportion of your sample (training) # Attaching package: 'caret' # The following object is masked from 'package:purrr': Library(caret) # Loading required package: lattice # # x plyr::summarize() masks dplyr::summarize() library(MASS) # x plyr::summarise() masks dplyr::summarise() # x dplyr::filter() masks stats::filter() # x plyr::failwith() masks dplyr::failwith() # x purrr::compact() masks plyr::compact()
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# x plyr::arrange() masks dplyr::arrange() # x ggplot2::alpha() masks psych::alpha() # v readr 1.3.1 v forcats 0.5.0 # - Conflicts - tidyverse_conflicts(). Library(tidyverse) # - Attaching packages - tidyverse 1.3.0 - # v tibble 3.0.3 v purrr 0.3.4 # Two group discriminant function #install.packages("tidyverse")