ben harrison ottadini

## lm_eqn.R
# Source: http://stackoverflow.com/q/7549694/857416

lm_eqn = function(m) {
  # Displays regression line equation and R^2 value on plot
  # Usage:
  # p + annotate("text", x=25, y=300, label=lm_eqn(lm(y ~ x, df)), parse=TRUE)

  l <- list(a = format(coef(m)[1], digits = 2),
            b = format(abs(coef(m)[2]), digits = 2),
            r2 = format(summary(m)$r.squared, digits = 3));

## som-prediction.R
# ===== #
library(kohonen)

# Load data, available at https://gist.github.com/ottadini/6068259
somdata <- read.csv("somdata.csv")


# Create SCALED test and training sets from data:
inTrain <- sample(nrow(somdata), nrow(somdata)*(2/3))
training <- scale(somdata[inTrain, ])

## somdata.csv

          
            MEAS_TC
            SP
            LN
            SN
            GR
            NEUT

            
              2.78
              59.1810903609045
              33.7436448559876
              19.7536103778765
              66.5766474766635
              257.0368012729

            
              1.49
              49.0477497329406
              184.145977437229
              139.079797489143
              54.7505195826885
              326.800134003894

            
              1.49
              49.1289016073479
              183.588534123696
              138.027677512105
              55.5411398043229
              327.473945839365

            
              2.201275709
              18.2403310337942
              19.2038626516248
              10.7474792741976
              62.0449186292617
              494.416136250187

            
              2.201275709
              18.2155224379773
              19.1800937852543
              10.7244616233215
              61.8744774871462
              494.740941830979

            
              1.276476312
              9.33776939549743
              14.1606094244497
              19.0690215694105
              14.9961159237259
              363.002021309839

            
              1.276476312
              9.30627807118255
              14.1655436529726
              19.0953021764089
              15.0205158738082
              363.592835920731

            
              2.4162441005
              65.9385510407827
              11.5964588279339
              13.1929031148605
              43.785062332152
              142.583172944641

            
              2.4162441005
              66.2962310687366
              11.5462659711476
              13.1725547197125
              43.607290645435
              142.672765831378
	# Source: http://stackoverflow.com/q/7549694/857416

	lm_eqn = function(m) {
	# Displays regression line equation and R^2 value on plot
	# Usage:
	# p + annotate("text", x=25, y=300, label=lm_eqn(lm(y ~ x, df)), parse=TRUE)

	l <- list(a = format(coef(m)[1], digits = 2),
	b = format(abs(coef(m)[2]), digits = 2),
	r2 = format(summary(m)$r.squared, digits = 3));
	# ===== #
	library(kohonen)

	# Load data, available at https://gist.github.com/ottadini/6068259
	somdata <- read.csv("somdata.csv")


	# Create SCALED test and training sets from data:
	inTrain <- sample(nrow(somdata), nrow(somdata)*(2/3))
	training <- scale(somdata[inTrain, ])
MEAS_TC	SP	LN	SN	GR	NEUT
2.78	59.1810903609045	33.7436448559876	19.7536103778765	66.5766474766635	257.0368012729
1.49	49.0477497329406	184.145977437229	139.079797489143	54.7505195826885	326.800134003894
1.49	49.1289016073479	183.588534123696	138.027677512105	55.5411398043229	327.473945839365
2.201275709	18.2403310337942	19.2038626516248	10.7474792741976	62.0449186292617	494.416136250187
2.201275709	18.2155224379773	19.1800937852543	10.7244616233215	61.8744774871462	494.740941830979
1.276476312	9.33776939549743	14.1606094244497	19.0690215694105	14.9961159237259	363.002021309839
1.276476312	9.30627807118255	14.1655436529726	19.0953021764089	15.0205158738082	363.592835920731
2.4162441005	65.9385510407827	11.5964588279339	13.1929031148605	43.785062332152	142.583172944641
2.4162441005	66.2962310687366	11.5462659711476	13.1725547197125	43.607290645435	142.672765831378