# Generalized linear models allow for a number of specific error dist...

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#1. Data exploration
# Last week, all of you noticed the highly skewed distribution of many of your
# predictor variables and decided to use a log transformation. This is still a good idea.
# Copy your code from last week, or else use the code below, to
# 1) log10+1 transform skewed predictor variables,
# 2) create a new column containing the squared values of woodebris,
# 3) log10+1 transform the response variable for visualization, and
# 4) bind all of these together with the untransformed predictor variables
#    that were not skewed into a matrix called Z. Name the columns of this matrix
#    and convert it into a data frame. Note that I have placed the response variable
#    as the last column. This makes it easier to interpret any pairplots,
#    since the response variable will appear on the y-axis, as we are used to seeing it.

# 1)
data\$L.X11_30cm <- log10(data\$X11_30cm+1)
data\$L.X31_60cm <- log10(data\$X31_60cm+1)
data\$L.totalrock <- log10(data\$totalrock+1)
data\$L.leafdebris <- log10(data\$leafdebris+1)
data\$L.seedling <- log10(data\$seedling+1)
data\$L.sapling <- log10(data\$sapling+1)
data\$L.maturetrees <- log10(data\$maturetrees+1)

# 2)
# prepare to try a quadratic regression with woodebris
data\$woodebris2 <- (data\$woodebris)^2
# 3)
# log transform the response variable for visualization only
data\$L.lizards <- log10(data\$lizards+1)

# 4)
# create a new data.frame containing these transformations

Z<-cbind(data\$location,
data\$L.X11_30cm,
data\$L.X31_60cm,
data\$L.totalrock,
data\$woodebris,
data\$woodebris2,
data\$L.leafdebris,
data\$L.seedling,
data\$L.sapling,
data\$maturetrees,
data\$L.lizards,
data\$lizards)

dim(Z)
# set names for these new variables
colnames(Z)<-c("Location",...

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