setwd('~m/Dropbox/Documentos R/Datos')
billtips<-read.csv("MULT REGR BILL TIP LENGTH 2012")
allbt<-billtips
# str(billtips)
billtips<-subset(allbt,billtips$age.at.pic=="A")
billtips<-subset(billtips,billtips$sex=="M")
## Loading required package: plyr
sma(billtips$sideview~ billtips$Weight,method="SMA",log="xy")
## Call: sma(formula = billtips$sideview ~ billtips$Weight, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate 8.048 -10.746
## lower limit 6.054 -13.573
## upper limit 10.043 -8.508
##
## H0 : variables uncorrelated
## R-squared : 0.007886
## P-value : 0.46
sma(billtips$sideview~billtips$tarsus,method="SMA",log="xy")
## Call: sma(formula = billtips$sideview ~ billtips$tarsus, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate 5.309 -7.519
## lower limit 3.963 -9.491
## upper limit 6.656 -5.956
##
## H0 : variables uncorrelated
## R-squared : 0.01305
## P-value : 0.34
sma(billtips$sideview~billtips$Flatened,method="SMA",log="xy")
## Call: sma(formula = billtips$sideview ~ billtips$Flatened, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate -36.66 20.29
## lower limit -45.23 16.06
## upper limit -28.10 25.65
##
## H0 : variables uncorrelated
## R-squared : 0.00323
## P-value : 0.63
sma(billtips$sideview~billtips$bill.l,method="SMA",log="xy")
## Call: sma(formula = billtips$sideview ~ billtips$bill.l, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate -26.15 16.35
## lower limit -32.17 12.97
## upper limit -20.13 20.61
##
## H0 : variables uncorrelated
## R-squared : 0.02429
## P-value : 0.19
Exposed culmen allometry
Wing length
sma(billtips$bill.l~billtips$Flatened,method="SMA",log="xy")
## Call: sma(formula = billtips$bill.l ~ billtips$Flatened, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate -0.6431 1.2413
## lower limit -1.1541 0.9878
## upper limit -0.1321 1.5598
##
## H0 : variables uncorrelated
## R-squared : 0.05192
## P-value : 0.053
Tarsus
sma(billtips$bill.l~billtips$tarsus,method="SMA",log="xy")
## Call: sma(formula = billtips$bill.l ~ billtips$tarsus, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate 1.225 0.4599
## lower limit 1.142 0.3641
## upper limit 1.307 0.5810
##
## H0 : variables uncorrelated
## R-squared : 0.006995
## P-value : 0.48
Body mass
sma(billtips$bill.l~billtips$Weight,method="SMA",log="xy")
## Call: sma(formula = billtips$bill.l ~ billtips$Weight, log = "xy",
## method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate 1.0573 0.6573
## lower limit 0.9366 0.5217
## upper limit 1.1780 0.8283
##
## H0 : variables uncorrelated
## R-squared : 0.02871
## P-value : 0.15
PCA
str(billtips)
## 'data.frame': 73 obs. of 10 variables:
## $ X : int 45 48 49 50 51 53 58 63 68 69 ...
## $ sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ age.at.pic: Factor w/ 4 levels "?","A","J","J?": 2 2 2 2 2 2 2 2 2 2 ...
## $ bill.l : num 40.8 39.8 39.8 39.6 39.5 ...
## $ sideview : num 0.309 0.419 0.613 0.277 0.45 ...
## $ Weight : num 6.15 5.54 5.89 6.1 6.68 5.68 6.35 6.1 6.11 6.45 ...
## $ Flatened : num 62 61.5 63 65 60 61.5 63.5 61 61 61.5 ...
## $ unflatened: num 61 61 61.5 64 59 61 63 61 60.5 61 ...
## $ DFA : num 3.86 2.12 3.48 4.78 3.07 ...
## $ tarsus : num 5.61 5.26 5.5 5.38 6.38 5.32 6.25 5.72 5.89 5.84 ...
pca<-princomp(log(billtips[,c(5:7,10)]),cor=TRUE)
summary(pca)
## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4
## Standard deviation 1.1879 1.0278 0.9467 0.7978
## Proportion of Variance 0.3528 0.2641 0.2240 0.1591
## Cumulative Proportion 0.3528 0.6169 0.8409 1.0000
pca$loadings
##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4
## sideview 0.316 -0.573 0.756
## Weight -0.667 -0.163 0.180 -0.705
## Flatened -0.803 -0.572 0.133
## tarsus -0.668 0.263 0.696
##
## Comp.1 Comp.2 Comp.3 Comp.4
## SS loadings 1.00 1.00 1.00 1.00
## Proportion Var 0.25 0.25 0.25 0.25
## Cumulative Var 0.25 0.50 0.75 1.00
pc1<-pca$scores[,1]+abs(min(pca$scores[,1]))+0.1
sma(billtips$bill.l~pc1,method="SMA",log="xy")
## Call: sma(formula = billtips$bill.l ~ pc1, log = "xy", method = "SMA")
##
## Fit using Standardized Major Axis
##
## These variables were log-transformed before fitting: xy
##
## Confidence intervals (CI) are at 95%
##
## ------------------------------------------------------------
## Coefficients:
## elevation slope
## estimate 1.589 -0.04351
## lower limit 1.584 -0.05482
## upper limit 1.594 -0.03452
##
## H0 : variables uncorrelated
## R-squared : 0.02818
## P-value : 0.16