ps2DGLM  R Documentation 
ps2DGLM
is used to smooth scattered
normal or nonnormal responses, with aniosotripic
penalization of tensor product Psplines.
ps2DGLM(
Data,
Pars = rbind(c(min(Data[, 1]), max(Data[, 1]), 10, 3, 1, 2), c(min(Data[, 2]),
max(Data[, 2]), 10, 3, 1, 2)),
ridge_adj = 0,
XYpred = Data[, 1:2],
z_predicted = NULL,
se_pred = 2,
family = "gaussian",
link = "default",
m_binomial = rep(1, nrow(Data)),
wts = rep(1, nrow(Data)),
r_gamma = rep(1, nrow(Data))
)
Data 
a matrix of 3 columns 
Pars 
a matrix of 2 rows, where the first and second row
sets the Pspline paramters for 
ridge_adj 
a ridge penalty tuning parameter, usually set to small value, e.g. 
XYpred 
a matrix with two columns 
z_predicted 
a vector of responses associated with 
se_pred 
a scalar, default 
family 

link 
the link function, one of 
m_binomial 
vector of binomial trials, default is vector of ones with 
wts 
nonnegative weights, which can be zero (default ones). 
r_gamma 
gamma scale parameter, default is vector ones with 
Support functions needed: pspline_fitter
, bbase
, and pspline_2dchecker
.
pcoef 
a vector of length 
mu 
a vector of 
dev 
the deviance of fit. 
eff_df 
the approximate effective dimension of fit. 
aic 
AIC. 
df_resid 
approximate df residual. 
cv 
leaveoneout standard error prediction, when 
cv_predicted 
standard error prediction for 
avediff_pred 
mean absolute difference prediction, when 
Pars 
the design and tuning parameters (see arguments above). 
dispersion_parm 
estimate of dispersion, 
summary_predicted 
inverse link prediction vectors, and 
eta_predicted 
estimated linear predictor of 
press_mu 
leaveoneout prediction of mean, when 
bin_percent_correct 
percent correct classification based on 0.5 cutoff (when 
Data 
a matrix of 3 columns 
Q 
the tensor product Bspline basis. 
qr 
the QR of the model. 
Paul Eilers and Brian Marx
Eilers, P.H.C. and Marx, B.D. (2021). Practical Smoothing, The Joys of Psplines. Cambridge University Press.
Eilers, P.H.C., Marx, B.D., and Durban, M. (2015). Twenty years of Psplines, SORT, 39(2): 149186.
ps2DNormal
library(fields)
library(JOPS)
# Extract data
library(rpart)
Kyphosis < kyphosis$Kyphosis
Age < kyphosis$Age
Start < kyphosis$Start
y < 1 * (Kyphosis == "present") # make y 0/1
fit < ps2DGLM(
Data = cbind(Start, Age, y),
Pars = rbind(c(1, 18, 10, 3, .1, 2), c(1, 206, 10, 3, .1, 2)),
family = "binomial", link = "logit")
plot(fit, xlab = "Start", ylab = "Age")
#title(main = "Probability of Kyphosis")
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