Create a newdata frame for usage in predict methods
newdata.RdThis is a generic function. The default method covers almost all regression models.
Usage
newdata(model, predVals, n, ...)
# Default S3 method
newdata(
model = NULL,
predVals = NULL,
n = 3,
emf = NULL,
divider = "quantile",
...
)Arguments
- model
Required. Fitted regression model
- predVals
Predictor Values that deserve investigation. Previously, the argument was called "fl". This can be 1) a keyword, one of c("auto", "margins") 2) a vector of variable names, which will use default methods for all named variables and the central values for non-named variabled, 3) a named vector with predictor variables and divider algorithms, or 4) a full list that supplies variables and possible values. Please see details and examples.
- n
Optional. Default = 3. How many focal values are desired? This value is used when various divider algorithms are put to use if the user has specified keywords "default", "quantile", "std.dev." "seq", and "table".
- ...
Other arguments.
- emf
Optional. data frame used to fit model (not a model frame, which may include transformed variables like log(x1). Instead, use output from function
model.data). It is UNTRANSFORMED variables ("x" as opposed to poly(x,2).1 and poly(x,2).2).- divider
Default is "quantile". Determines the method of selection. Should be one of c("quantile", "std.dev", "seq", "table").
Value
A data frame of x values that could be used as the data = argument in the original regression model. The attribute "varNamesRHS" is a vector of the predictor variable names.
Details
It scans the fitted model, discerns the names of the predictors, and then generates a new data frame. It can guess values of the variables that might be substantively interesting, but that depends on the user-supplied value of predVals. If not supplied with a predVals argument, newdata returns a data frame with one row – the central values (means and modes) of the variables in the data frame that was used to fit the model. The user can supply a keyword "auto" or "margins". The function will try to do the "right thing."
The predVals can be a named list that supplies specific
values for particular predictors. Any legal vector of values is
allowed. For example, predVals = list(x1 = c(10, 20, 30), x2
= c(40, 50), xcat = levels(xcat))). That will create a newdata
object that has all of the "mix and match" combinations for those
values, while the other predictors are set at their central
values.
If the user declares a variable with the "default" keyword, then
the default divider algorithm is used to select focal values. The
default divider algorithm is an optional argument of this
function. If the default is not desired, the user can specify a
divider algorithm by character string, either "quantile",
"std.dev.", "seq", or "table". The user can mix and match
algorithms along with requests for specific focal values, as in
predVals = list(x1 = "quantile", x2 = "std.dev.", x3 = c(10,
20, 30), xcat1 <- levels(xcat1))
Author
Paul E. Johnson pauljohn@ku.edu
Examples
library(rockchalk)
## Replicate some R classics. The budworm.lg data from predict.glm
## will work properly after re-formatting the information as a data.frame:
## example from Venables and Ripley (2002, pp. 190-2.)
df <- data.frame(ldose = rep(0:5, 2),
sex = factor(rep(c("M", "F"), c(6, 6))),
SF.numdead = c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16))
df$SF.numalive = 20 - df$SF.numdead
budworm.lg <- glm(cbind(SF.numdead, SF.numalive) ~ sex*ldose,
data = df, family = binomial)
predictOMatic(budworm.lg)
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> $sex
#> sex ldose fit
#> 1 F 2.5 0.3255348
#> 2 M 2.5 0.5814719
#>
#> $ldose
#> sex ldose fit
#> 1 F 0.0 0.04771849
#> 2 F 1.0 0.11031718
#> 3 F 2.5 0.32553481
#> 4 F 4.0 0.65262640
#> 5 F 5.0 0.82297581
#>
predictOMatic(budworm.lg, n = 7)
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> $sex
#> sex ldose fit
#> 1 F 2.5 0.3255348
#> 2 M 2.5 0.5814719
#>
#> $ldose
#> sex ldose fit
#> 1 F 0 0.04771849
#> 2 F 1 0.11031718
#> 3 F 2 0.23478819
#> 4 F 3 0.43157393
#> 5 F 4 0.65262640
#> 6 F 5 0.82297581
#>
predictOMatic(budworm.lg, predVals = c("ldose"), n = 7)
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> sex ldose fit
#> 1 F 0 0.04771849
#> 2 F 1 0.11031718
#> 3 F 2 0.23478819
#> 4 F 3 0.43157393
#> 5 F 4 0.65262640
#> 6 F 5 0.82297581
predictOMatic(budworm.lg, predVals = c(ldose = "std.dev.", sex = "table"))
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> sex ldose fit
#> 1 F -1.06 0.01881807
#> 2 F 0.72 0.08776815
#> 3 F 2.50 0.32553481
#> 4 F 4.28 0.70771129
#> 5 F 6.06 0.92393399
#> 6 M -1.06 0.01547336
#> 7 M 0.72 0.12874384
#> 8 M 2.50 0.58147189
#> 9 M 4.28 0.92888909
#> 10 M 6.06 0.99192343
## Now make up a data frame with several numeric and categorical predictors.
set.seed(12345)
N <- 100
x1 <- rpois(N, l = 6)
x2 <- rnorm(N, m = 50, s = 10)
x3 <- rnorm(N)
xcat1 <- gl(2,50, labels = c("M","F"))
xcat2 <- cut(rnorm(N), breaks = c(-Inf, 0, 0.4, 0.9, 1, Inf),
labels = c("R", "M", "D", "P", "G"))
dat <- data.frame(x1, x2, x3, xcat1, xcat2)
rm(x1, x2, x3, xcat1, xcat2)
dat$xcat1n <- with(dat, contrasts(xcat1)[xcat1, , drop = FALSE])
dat$xcat2n <- with(dat, contrasts(xcat2)[xcat2, ])
STDE <- 15
dat$y <- with(dat,
0.03 + 0.8*x1 + 0.1*x2 + 0.7*x3 + xcat1n %*% c(2) +
xcat2n %*% c(0.1,-2,0.3, 0.1) + STDE*rnorm(N))
## Impose some random missings
dat$x1[sample(N, 5)] <- NA
dat$x2[sample(N, 5)] <- NA
dat$x3[sample(N, 5)] <- NA
dat$xcat2[sample(N, 5)] <- NA
dat$xcat1[sample(N, 5)] <- NA
dat$y[sample(N, 5)] <- NA
summarize(dat)
#> Numeric variables
#> x1 x2 x3 xcat1n xcat2n.M xcat2n.D
#> min 0 26.531 -2.290 0 0 0
#> med 6 51.387 0.036 0.500 0 0
#> max 12 76.558 2.747 1 1 1
#> mean 6.053 51.376 0.051 0.500 0.150 0.210
#> sd 2.655 11.611 0.946 0.503 0.359 0.409
#> skewness 0.106 0.115 0.077 0 1.931 1.403
#> kurtosis -0.466 -0.821 0.423 -2.020 1.747 -0.033
#> nobs 95 95 95 100 100 100
#> nmissing 5 5 5 0 0 0
#> xcat2n.P xcat2n.G y
#> min 0 0 -20.737
#> med 0 0 13.350
#> max 1 1 46.537
#> mean 0.040 0.160 12.264
#> sd 0.197 0.368 14.052
#> skewness 4.625 1.827 -0.033
#> kurtosis 19.583 1.352 -0.087
#> nobs 100 100 95
#> nmissing 0 0 5
#>
#> Nonnumeric variables
#> xcat1 xcat2
#> M: 49 R: 41
#> F: 46 M: 15
#> D: 20
#> P: 4
#> G: 15
#> nobs : 95.000 nobs : 95.000
#> nmiss : 5.000 nmiss : 5.000
#> entropy : 0.999 entropy : 2.030
#> normedEntropy: 0.999 normedEntropy: 0.874
m0 <- lm(y ~ x1 + x2 + xcat1, data = dat)
summary(m0)
#>
#> Call:
#> lm(formula = y ~ x1 + x2 + xcat1, data = dat)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -31.365 -8.259 1.769 7.314 31.049
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.3492 7.9730 0.044 0.9652
#> x1 0.9940 0.5234 1.899 0.0614 .
#> x2 0.1166 0.1265 0.922 0.3597
#> xcat1F 1.3702 2.9734 0.461 0.6462
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 13.13 on 76 degrees of freedom
#> (20 observations deleted due to missingness)
#> Multiple R-squared: 0.05301, Adjusted R-squared: 0.01563
#> F-statistic: 1.418 on 3 and 76 DF, p-value: 0.244
#>
## The model.data() function in rockchalk creates as near as possible
## the input data frame.
m0.data <- model.data(m0)
summarize(m0.data)
#> Numeric variables
#> y x1 x2
#> min -20.169 0 26.531
#> med 13.898 6 51.868
#> max 45.814 12 76.558
#> mean 13.012 6.062 51.493
#> sd 13.231 2.839 11.862
#> skewness -0.099 0.105 0.102
#> kurtosis 0.006 -0.726 -0.818
#> nobs 80 80 80
#> nmissing 0 0 0
#>
#> Nonnumeric variables
#> xcat1
#> M: 43
#> F: 37
#> nobs : 80.000
#> nmiss : 0.000
#> entropy : 0.996
#> normedEntropy: 0.996
## no predVals: analyzes each variable separately
(m0.p1 <- predictOMatic(m0))
#> $x1
#> x1 x2 xcat1 fit
#> 1 0 51.49344 M 6.352662
#> 2 4 51.49344 M 10.328492
#> 3 6 51.49344 M 12.316408
#> 4 8 51.49344 M 14.304323
#> 5 12 51.49344 M 18.280154
#>
#> $x2
#> x1 x2 xcat1 fit
#> 1 6.0625 26.53056 M 9.468186
#> 2 6.0625 42.08628 M 11.281778
#> 3 6.0625 51.86759 M 12.422150
#> 4 6.0625 59.14815 M 13.270969
#> 5 6.0625 76.55788 M 15.300715
#>
#> $xcat1
#> x1 x2 xcat1 fit
#> 1 6.0625 51.49344 M 12.37853
#> 2 6.0625 51.49344 F 13.74873
#>
## requests confidence intervals from the predict function
(m0.p2 <- predictOMatic(m0, interval = "confidence"))
#> $x1
#> x1 x2 xcat1 fit lwr upr
#> 1 0 51.49344 M 6.352662 -1.131096 13.83642
#> 2 4 51.49344 M 10.328492 5.781452 14.87553
#> 3 6 51.49344 M 12.316408 8.310004 16.32281
#> 4 8 51.49344 M 14.304323 9.818668 18.78998
#> 5 12 51.49344 M 18.280154 10.908369 25.65194
#>
#> $x2
#> x1 x2 xcat1 fit lwr upr
#> 1 6.0625 26.53056 M 9.468186 1.693545 17.24283
#> 2 6.0625 42.08628 M 11.281778 6.435471 16.12809
#> 3 6.0625 51.86759 M 12.422150 8.424236 16.42006
#> 4 6.0625 59.14815 M 13.270969 8.994808 17.54713
#> 5 6.0625 76.55788 M 15.300715 8.153417 22.44801
#>
#> $xcat1
#> x1 x2 xcat1 fit lwr upr
#> 1 6.0625 51.49344 M 12.37853 8.372678 16.38438
#> 2 6.0625 51.49344 F 13.74873 9.427021 18.07044
#>
## predVals as vector of variable names: gives "mix and match" predictions
(m0.p3 <- predictOMatic(m0, predVals = c("x1", "x2")))
#> x1 x2 xcat1 fit
#> 1 0 26.53056 M 3.442317
#> 2 4 26.53056 M 7.418148
#> 3 6 26.53056 M 9.406063
#> 4 8 26.53056 M 11.393979
#> 5 12 26.53056 M 15.369809
#> 6 0 42.08628 M 5.255910
#> 7 4 42.08628 M 9.231740
#> 8 6 42.08628 M 11.219656
#> 9 8 42.08628 M 13.207571
#> 10 12 42.08628 M 17.183402
#> 11 0 51.86759 M 6.396282
#> 12 4 51.86759 M 10.372113
#> 13 6 51.86759 M 12.360028
#> 14 8 51.86759 M 14.347943
#> 15 12 51.86759 M 18.323774
#> 16 0 59.14815 M 7.245100
#> 17 4 59.14815 M 11.220931
#> 18 6 59.14815 M 13.208846
#> 19 8 59.14815 M 15.196762
#> 20 12 59.14815 M 19.172592
#> 21 0 76.55788 M 9.274846
#> 22 4 76.55788 M 13.250677
#> 23 6 76.55788 M 15.238592
#> 24 8 76.55788 M 17.226508
#> 25 12 76.55788 M 21.202338
## predVals as vector of variable names: gives "mix and match" predictions
(m0.p3s <- predictOMatic(m0, predVals = c("x1", "x2"), divider = "std.dev."))
#> x1 x2 xcat1 fit
#> 1 0.38 27.77 M 3.964524
#> 2 3.22 27.77 M 6.787363
#> 3 6.06 27.77 M 9.610203
#> 4 8.90 27.77 M 12.433043
#> 5 11.74 27.77 M 15.255883
#> 6 0.38 39.63 M 5.347244
#> 7 3.22 39.63 M 8.170084
#> 8 6.06 39.63 M 10.992923
#> 9 8.90 39.63 M 13.815763
#> 10 11.74 39.63 M 16.638603
#> 11 0.38 51.49 M 6.729964
#> 12 3.22 51.49 M 9.552804
#> 13 6.06 51.49 M 12.375644
#> 14 8.90 51.49 M 15.198484
#> 15 11.74 51.49 M 18.021323
#> 16 0.38 63.35 M 8.112685
#> 17 3.22 63.35 M 10.935524
#> 18 6.06 63.35 M 13.758364
#> 19 8.90 63.35 M 16.581204
#> 20 11.74 63.35 M 19.404044
#> 21 0.38 75.21 M 9.495405
#> 22 3.22 75.21 M 12.318245
#> 23 6.06 75.21 M 15.141085
#> 24 8.90 75.21 M 17.963924
#> 25 11.74 75.21 M 20.786764
## "seq" is an evenly spaced sequence across the predictor.
(m0.p3q <- predictOMatic(m0, predVals = c("x1", "x2"), divider = "seq"))
#> x1 x2 xcat1 fit
#> 1 0 26.53056 M 3.442317
#> 2 3 26.53056 M 6.424190
#> 3 6 26.53056 M 9.406063
#> 4 9 26.53056 M 12.387936
#> 5 12 26.53056 M 15.369809
#> 6 0 39.03739 M 4.900450
#> 7 3 39.03739 M 7.882323
#> 8 6 39.03739 M 10.864196
#> 9 9 39.03739 M 13.846069
#> 10 12 39.03739 M 16.827942
#> 11 0 51.54422 M 6.358582
#> 12 3 51.54422 M 9.340455
#> 13 6 51.54422 M 12.322328
#> 14 9 51.54422 M 15.304201
#> 15 12 51.54422 M 18.286074
#> 16 0 64.05105 M 7.816714
#> 17 3 64.05105 M 10.798587
#> 18 6 64.05105 M 13.780460
#> 19 9 64.05105 M 16.762333
#> 20 12 64.05105 M 19.744206
#> 21 0 76.55788 M 9.274846
#> 22 3 76.55788 M 12.256719
#> 23 6 76.55788 M 15.238592
#> 24 9 76.55788 M 18.220465
#> 25 12 76.55788 M 21.202338
(m0.p3i <- predictOMatic(m0, predVals = c("x1", "x2"),
interval = "confidence", n = 3))
#> x1 x2 xcat1 fit lwr upr
#> 1 4 42.08628 M 9.23174 3.825854 14.63763
#> 2 6 42.08628 M 11.21966 6.369430 16.06988
#> 3 8 42.08628 M 13.20757 8.057822 18.35732
#> 4 4 51.86759 M 10.37211 5.836920 14.90730
#> 5 6 51.86759 M 12.36003 8.361729 16.35833
#> 6 8 51.86759 M 14.34794 9.864755 18.83113
#> 7 4 59.14815 M 11.22093 6.529180 15.91268
#> 8 6 59.14815 M 13.20885 8.935365 17.48233
#> 9 8 59.14815 M 15.19676 10.379408 20.01411
(m0.p3p <- predictOMatic(m0, predVals = c("x1", "x2"), divider = pretty))
#> x1 x2 xcat1 fit
#> 1 0 20 M 2.680940
#> 2 2 20 M 4.668855
#> 3 4 20 M 6.656770
#> 4 6 20 M 8.644686
#> 5 8 20 M 10.632601
#> 6 10 20 M 12.620516
#> 7 12 20 M 14.608432
#> 8 0 30 M 3.846808
#> 9 2 30 M 5.834724
#> 10 4 30 M 7.822639
#> 11 6 30 M 9.810554
#> 12 8 30 M 11.798470
#> 13 10 30 M 13.786385
#> 14 12 30 M 15.774300
#> 15 0 40 M 5.012677
#> 16 2 40 M 7.000592
#> 17 4 40 M 8.988508
#> 18 6 40 M 10.976423
#> 19 8 40 M 12.964338
#> 20 10 40 M 14.952254
#> 21 12 40 M 16.940169
#> 22 0 50 M 6.178546
#> 23 2 50 M 8.166461
#> 24 4 50 M 10.154377
#> 25 6 50 M 12.142292
#> 26 8 50 M 14.130207
#> 27 10 50 M 16.118123
#> 28 12 50 M 18.106038
#> 29 0 60 M 7.344415
#> 30 2 60 M 9.332330
#> 31 4 60 M 11.320245
#> 32 6 60 M 13.308161
#> 33 8 60 M 15.296076
#> 34 10 60 M 17.283991
#> 35 12 60 M 19.271907
#> 36 0 70 M 8.510283
#> 37 2 70 M 10.498199
#> 38 4 70 M 12.486114
#> 39 6 70 M 14.474029
#> 40 8 70 M 16.461945
#> 41 10 70 M 18.449860
#> 42 12 70 M 20.437775
#> 43 0 80 M 9.676152
#> 44 2 80 M 11.664068
#> 45 4 80 M 13.651983
#> 46 6 80 M 15.639898
#> 47 8 80 M 17.627814
#> 48 10 80 M 19.615729
#> 49 12 80 M 21.603644
## predVals as vector with named divider algorithms.
(m0.p3 <- predictOMatic(m0, predVals = c(x1 = "seq", x2 = "quantile")))
#> x1 x2 xcat1 fit
#> 1 0 26.53056 M 3.442317
#> 2 3 26.53056 M 6.424190
#> 3 6 26.53056 M 9.406063
#> 4 9 26.53056 M 12.387936
#> 5 12 26.53056 M 15.369809
#> 6 0 42.08628 M 5.255910
#> 7 3 42.08628 M 8.237783
#> 8 6 42.08628 M 11.219656
#> 9 9 42.08628 M 14.201529
#> 10 12 42.08628 M 17.183402
#> 11 0 51.86759 M 6.396282
#> 12 3 51.86759 M 9.378155
#> 13 6 51.86759 M 12.360028
#> 14 9 51.86759 M 15.341901
#> 15 12 51.86759 M 18.323774
#> 16 0 59.14815 M 7.245100
#> 17 3 59.14815 M 10.226973
#> 18 6 59.14815 M 13.208846
#> 19 9 59.14815 M 16.190719
#> 20 12 59.14815 M 19.172592
#> 21 0 76.55788 M 9.274846
#> 22 3 76.55788 M 12.256719
#> 23 6 76.55788 M 15.238592
#> 24 9 76.55788 M 18.220465
#> 25 12 76.55788 M 21.202338
## predVals as named vector of divider algorithms
## same idea, decided to double-check
(m0.p3 <- predictOMatic(m0, predVals = c(x1 = "quantile", x2 = "std.dev.")))
#> x1 x2 xcat1 fit
#> 1 0 27.77 M 3.586820
#> 2 4 27.77 M 7.562650
#> 3 6 27.77 M 9.550566
#> 4 8 27.77 M 11.538481
#> 5 12 27.77 M 15.514312
#> 6 0 39.63 M 4.969540
#> 7 4 39.63 M 8.945371
#> 8 6 39.63 M 10.933286
#> 9 8 39.63 M 12.921201
#> 10 12 39.63 M 16.897032
#> 11 0 51.49 M 6.352260
#> 12 4 51.49 M 10.328091
#> 13 6 51.49 M 12.316006
#> 14 8 51.49 M 14.303922
#> 15 12 51.49 M 18.279752
#> 16 0 63.35 M 7.734981
#> 17 4 63.35 M 11.710811
#> 18 6 63.35 M 13.698727
#> 19 8 63.35 M 15.686642
#> 20 12 63.35 M 19.662473
#> 21 0 75.21 M 9.117701
#> 22 4 75.21 M 13.093532
#> 23 6 75.21 M 15.081447
#> 24 8 75.21 M 17.069362
#> 25 12 75.21 M 21.045193
getFocal(m0.data$x2, xvals = "std.dev.", n = 5)
#> (m-2sd) (m-sd) (m) (m+sd) (m+2sd)
#> 27.77 39.63 51.49 63.35 75.21
## Change from quantile to standard deviation divider
(m0.p5 <- predictOMatic(m0, divider = "std.dev.", n = 5))
#> $x1
#> x1 x2 xcat1 fit
#> 1 0.38 51.49344 M 6.730366
#> 2 3.22 51.49344 M 9.553205
#> 3 6.06 51.49344 M 12.376045
#> 4 8.90 51.49344 M 15.198885
#> 5 11.74 51.49344 M 18.021725
#>
#> $x2
#> x1 x2 xcat1 fit
#> 1 6.0625 27.77 M 9.612688
#> 2 6.0625 39.63 M 10.995408
#> 3 6.0625 51.49 M 12.378129
#> 4 6.0625 63.35 M 13.760849
#> 5 6.0625 75.21 M 15.143569
#>
#> $xcat1
#> x1 x2 xcat1 fit
#> 1 6.0625 51.49344 M 12.37853
#> 2 6.0625 51.49344 F 13.74873
#>
## Still can specify particular values if desired
(m0.p6 <- predictOMatic(m0, predVals = list("x1" = c(6,7),
"xcat1" = levels(m0.data$xcat1))))
#> x1 x2 xcat1 fit
#> 1 6 51.49344 M 12.31641
#> 2 7 51.49344 M 13.31037
#> 3 6 51.49344 F 13.68661
#> 4 7 51.49344 F 14.68056
(m0.p7 <- predictOMatic(m0, predVals = c(x1 = "quantile", x2 = "std.dev.")))
#> x1 x2 xcat1 fit
#> 1 0 27.77 M 3.586820
#> 2 4 27.77 M 7.562650
#> 3 6 27.77 M 9.550566
#> 4 8 27.77 M 11.538481
#> 5 12 27.77 M 15.514312
#> 6 0 39.63 M 4.969540
#> 7 4 39.63 M 8.945371
#> 8 6 39.63 M 10.933286
#> 9 8 39.63 M 12.921201
#> 10 12 39.63 M 16.897032
#> 11 0 51.49 M 6.352260
#> 12 4 51.49 M 10.328091
#> 13 6 51.49 M 12.316006
#> 14 8 51.49 M 14.303922
#> 15 12 51.49 M 18.279752
#> 16 0 63.35 M 7.734981
#> 17 4 63.35 M 11.710811
#> 18 6 63.35 M 13.698727
#> 19 8 63.35 M 15.686642
#> 20 12 63.35 M 19.662473
#> 21 0 75.21 M 9.117701
#> 22 4 75.21 M 13.093532
#> 23 6 75.21 M 15.081447
#> 24 8 75.21 M 17.069362
#> 25 12 75.21 M 21.045193
getFocal(m0.data$x2, xvals = "std.dev.", n = 5)
#> (m-2sd) (m-sd) (m) (m+sd) (m+2sd)
#> 27.77 39.63 51.49 63.35 75.21
(m0.p8 <- predictOMatic(m0, predVals = list( x1 = quantile(m0.data$x1,
na.rm = TRUE, probs = c(0, 0.1, 0.5, 0.8,
1.0)), xcat1 = levels(m0.data$xcat1))))
#> x1 x2 xcat1 fit
#> 1 0.0 51.49344 M 6.352662
#> 2 2.0 51.49344 M 8.340577
#> 3 6.0 51.49344 M 12.316408
#> 4 8.2 51.49344 M 14.503115
#> 5 12.0 51.49344 M 18.280154
#> 6 0.0 51.49344 F 7.722860
#> 7 2.0 51.49344 F 9.710776
#> 8 6.0 51.49344 F 13.686606
#> 9 8.2 51.49344 F 15.873313
#> 10 12.0 51.49344 F 19.650352
(m0.p9 <- predictOMatic(m0, predVals = list(x1 = "seq", "xcat1" =
levels(m0.data$xcat1)), n = 8) )
#> x1 x2 xcat1 fit
#> 1 0.000000 51.49344 M 6.352662
#> 2 1.714286 51.49344 M 8.056589
#> 3 3.428571 51.49344 M 9.760517
#> 4 5.142857 51.49344 M 11.464444
#> 5 6.857143 51.49344 M 13.168372
#> 6 8.571429 51.49344 M 14.872299
#> 7 10.285714 51.49344 M 16.576226
#> 8 12.000000 51.49344 M 18.280154
#> 9 0.000000 51.49344 F 7.722860
#> 10 1.714286 51.49344 F 9.426788
#> 11 3.428571 51.49344 F 11.130715
#> 12 5.142857 51.49344 F 12.834643
#> 13 6.857143 51.49344 F 14.538570
#> 14 8.571429 51.49344 F 16.242497
#> 15 10.285714 51.49344 F 17.946425
#> 16 12.000000 51.49344 F 19.650352
(m0.p10 <- predictOMatic(m0, predVals = list(x1 = "quantile",
"xcat1" = levels(m0.data$xcat1)), n = 5) )
#> x1 x2 xcat1 fit
#> 1 0 51.49344 M 6.352662
#> 2 4 51.49344 M 10.328492
#> 3 6 51.49344 M 12.316408
#> 4 8 51.49344 M 14.304323
#> 5 12 51.49344 M 18.280154
#> 6 0 51.49344 F 7.722860
#> 7 4 51.49344 F 11.698691
#> 8 6 51.49344 F 13.686606
#> 9 8 51.49344 F 15.674522
#> 10 12 51.49344 F 19.650352
(m0.p11 <- predictOMatic(m0, predVals = c(x1 = "std.dev."), n = 10))
#> x1 x2 xcat1 fit
#> 1 -2.46 51.49344 M 3.907526
#> 2 0.38 51.49344 M 6.730366
#> 3 3.22 51.49344 M 9.553205
#> 4 6.06 51.49344 M 12.376045
#> 5 8.90 51.49344 M 15.198885
#> 6 11.74 51.49344 M 18.021725
#> 7 14.58 51.49344 M 20.844565
## Previous same as
(m0.p11 <- predictOMatic(m0, predVals = c(x1 = "default"), divider =
"std.dev.", n = 10))
#> x1 x2 xcat1 fit
#> 1 -2.46 51.49344 M 3.907526
#> 2 0.38 51.49344 M 6.730366
#> 3 3.22 51.49344 M 9.553205
#> 4 6.06 51.49344 M 12.376045
#> 5 8.90 51.49344 M 15.198885
#> 6 11.74 51.49344 M 18.021725
#> 7 14.58 51.49344 M 20.844565
## Previous also same as
(m0.p11 <- predictOMatic(m0, predVals = c("x1"), divider = "std.dev.", n = 10))
#> x1 x2 xcat1 fit
#> 1 -2.46 51.49344 M 3.907526
#> 2 0.38 51.49344 M 6.730366
#> 3 3.22 51.49344 M 9.553205
#> 4 6.06 51.49344 M 12.376045
#> 5 8.90 51.49344 M 15.198885
#> 6 11.74 51.49344 M 18.021725
#> 7 14.58 51.49344 M 20.844565
(m0.p11 <- predictOMatic(m0, predVals = list(x1 = c(0, 5, 8), x2 = "default"),
divider = "seq"))
#> x1 x2 xcat1 fit
#> 1 0 26.53056 M 3.442317
#> 2 5 26.53056 M 8.412106
#> 3 8 26.53056 M 11.393979
#> 4 0 39.03739 M 4.900450
#> 5 5 39.03739 M 9.870238
#> 6 8 39.03739 M 12.852111
#> 7 0 51.54422 M 6.358582
#> 8 5 51.54422 M 11.328370
#> 9 8 51.54422 M 14.310243
#> 10 0 64.05105 M 7.816714
#> 11 5 64.05105 M 12.786503
#> 12 8 64.05105 M 15.768375
#> 13 0 76.55788 M 9.274846
#> 14 5 76.55788 M 14.244635
#> 15 8 76.55788 M 17.226508
m1 <- lm(y ~ log(10+x1) + sin(x2) + x3, data = dat)
m1.data <- model.data(m1)
summarize(m1.data)
#> Numeric variables
#> y x1 x2 x3
#> min -20.169 0 26.531 -2.135
#> med 13.356 6 51.339 -0.006
#> max 46.537 12 76.558 2.747
#> mean 12.426 6.013 51.412 0.059
#> sd 13.556 2.786 11.838 0.938
#> skewness -0.031 0.123 0.126 0.305
#> kurtosis -0.145 -0.605 -0.804 0.446
#> nobs 80 80 80 80
#> nmissing 0 0 0 0
(newdata(m1))
#> x1 x2 x3
#> 1 6.0125 51.41176 0.05871477
(newdata(m1, predVals = list(x1 = c(6, 8, 10))))
#> x1 x2 x3
#> 1 6 51.41176 0.05871477
#> 2 8 51.41176 0.05871477
#> 3 10 51.41176 0.05871477
(newdata(m1, predVals = list(x1 = c(6, 8, 10), x3 = c(-1,0,1))))
#> x1 x2 x3
#> 1 6 51.41176 -1
#> 2 8 51.41176 -1
#> 3 10 51.41176 -1
#> 4 6 51.41176 0
#> 5 8 51.41176 0
#> 6 10 51.41176 0
#> 7 6 51.41176 1
#> 8 8 51.41176 1
#> 9 10 51.41176 1
(newdata(m1, predVals = list(x1 = c(6, 8, 10),
x2 = quantile(m1.data$x2, na.rm = TRUE), x3 = c(-1,0,1))))
#> x1 x2 x3
#> 1 6 26.53056 -1
#> 2 8 26.53056 -1
#> 3 10 26.53056 -1
#> 4 6 42.08628 -1
#> 5 8 42.08628 -1
#> 6 10 42.08628 -1
#> 7 6 51.33934 -1
#> 8 8 51.33934 -1
#> 9 10 51.33934 -1
#> 10 6 59.14815 -1
#> 11 8 59.14815 -1
#> 12 10 59.14815 -1
#> 13 6 76.55788 -1
#> 14 8 76.55788 -1
#> 15 10 76.55788 -1
#> 16 6 26.53056 0
#> 17 8 26.53056 0
#> 18 10 26.53056 0
#> 19 6 42.08628 0
#> 20 8 42.08628 0
#> 21 10 42.08628 0
#> 22 6 51.33934 0
#> 23 8 51.33934 0
#> 24 10 51.33934 0
#> 25 6 59.14815 0
#> 26 8 59.14815 0
#> 27 10 59.14815 0
#> 28 6 76.55788 0
#> 29 8 76.55788 0
#> 30 10 76.55788 0
#> 31 6 26.53056 1
#> 32 8 26.53056 1
#> 33 10 26.53056 1
#> 34 6 42.08628 1
#> 35 8 42.08628 1
#> 36 10 42.08628 1
#> 37 6 51.33934 1
#> 38 8 51.33934 1
#> 39 10 51.33934 1
#> 40 6 59.14815 1
#> 41 8 59.14815 1
#> 42 10 59.14815 1
#> 43 6 76.55788 1
#> 44 8 76.55788 1
#> 45 10 76.55788 1
(m1.p1 <- predictOMatic(m1, divider = "std.dev", n = 5))
#> $x1
#> x1 x2 x3 fit
#> 1 0.43 51.41176 0.05871477 8.010526
#> 2 3.22 51.41176 0.05871477 11.823689
#> 3 6.01 51.41176 0.05871477 14.903932
#> 4 8.80 51.41176 0.05871477 17.488084
#> 5 11.59 51.41176 0.05871477 19.713996
#>
#> $x2
#> x1 x2 x3 fit
#> 1 6.0125 27.73 0.05871477 13.73514
#> 2 6.0125 39.57 0.05871477 15.03767
#> 3 6.0125 51.41 0.05871477 14.90428
#> 4 6.0125 63.25 0.05871477 13.40232
#> 5 6.0125 75.09 0.05871477 11.29001
#>
#> $x3
#> x1 x2 x3 fit
#> 1 6.0125 51.41176 -1.82 11.83653
#> 2 6.0125 51.41176 -0.88 13.37254
#> 3 6.0125 51.41176 0.06 14.90854
#> 4 6.0125 51.41176 1.00 16.44455
#> 5 6.0125 51.41176 1.94 17.98056
#>
(m1.p2 <- predictOMatic(m1, divider = "quantile", n = 5))
#> $x1
#> x1 x2 x3 fit
#> 1 0 51.41176 0.05871477 7.333275
#> 2 4 51.41176 0.05871477 12.745859
#> 3 6 51.41176 0.05871477 14.893881
#> 4 8 51.41176 0.05871477 16.788571
#> 5 12 51.41176 0.05871477 20.016614
#>
#> $x2
#> x1 x2 x3 fit
#> 1 6.0125 26.53056 0.05871477 15.126299
#> 2 6.0125 42.08628 0.05871477 9.371888
#> 3 6.0125 51.33934 0.05871477 14.810599
#> 4 6.0125 59.14815 0.05871477 13.729476
#> 5 6.0125 76.55788 0.05871477 14.922612
#>
#> $x3
#> x1 x2 x3 fit
#> 1 6.0125 51.41176 -2.134626048 11.32242
#> 2 6.0125 51.41176 -0.491282089 14.00772
#> 3 6.0125 51.41176 -0.005748691 14.80111
#> 4 6.0125 51.41176 0.636074757 15.84988
#> 5 6.0125 51.41176 2.747403542 19.29989
#>
(m1.p3 <- predictOMatic(m1, predVals = list(x1 = c(6, 8, 10),
x2 = median(m1.data$x2, na.rm = TRUE))))
#> x1 x2 x3 fit
#> 1 6 51.33934 0.05871477 14.79804
#> 2 8 51.33934 0.05871477 16.69273
#> 3 10 51.33934 0.05871477 18.38758
(m1.p4 <- predictOMatic(m1, predVals = list(x1 = c(6, 8, 10),
x2 = quantile(m1.data$x2, na.rm = TRUE))))
#> x1 x2 x3 fit
#> 1 6 26.53056 0.05871477 15.113737
#> 2 8 26.53056 0.05871477 17.008427
#> 3 10 26.53056 0.05871477 18.703284
#> 4 6 42.08628 0.05871477 9.359326
#> 5 8 42.08628 0.05871477 11.254016
#> 6 10 42.08628 0.05871477 12.948873
#> 7 6 51.33934 0.05871477 14.798037
#> 8 8 51.33934 0.05871477 16.692726
#> 9 10 51.33934 0.05871477 18.387584
#> 10 6 59.14815 0.05871477 13.716913
#> 11 8 59.14815 0.05871477 15.611603
#> 12 10 59.14815 0.05871477 17.306461
#> 13 6 76.55788 0.05871477 14.910049
#> 14 8 76.55788 0.05871477 16.804739
#> 15 10 76.55788 0.05871477 18.499597
(m1.p5 <- predictOMatic(m1))
#> $x1
#> x1 x2 x3 fit
#> 1 0 51.41176 0.05871477 7.333275
#> 2 4 51.41176 0.05871477 12.745859
#> 3 6 51.41176 0.05871477 14.893881
#> 4 8 51.41176 0.05871477 16.788571
#> 5 12 51.41176 0.05871477 20.016614
#>
#> $x2
#> x1 x2 x3 fit
#> 1 6.0125 26.53056 0.05871477 15.126299
#> 2 6.0125 42.08628 0.05871477 9.371888
#> 3 6.0125 51.33934 0.05871477 14.810599
#> 4 6.0125 59.14815 0.05871477 13.729476
#> 5 6.0125 76.55788 0.05871477 14.922612
#>
#> $x3
#> x1 x2 x3 fit
#> 1 6.0125 51.41176 -2.134626048 11.32242
#> 2 6.0125 51.41176 -0.491282089 14.00772
#> 3 6.0125 51.41176 -0.005748691 14.80111
#> 4 6.0125 51.41176 0.636074757 15.84988
#> 5 6.0125 51.41176 2.747403542 19.29989
#>
(m1.p6 <- predictOMatic(m1, divider = "std.dev."))
#> $x1
#> x1 x2 x3 fit
#> 1 0.43 51.41176 0.05871477 8.010526
#> 2 3.22 51.41176 0.05871477 11.823689
#> 3 6.01 51.41176 0.05871477 14.903932
#> 4 8.80 51.41176 0.05871477 17.488084
#> 5 11.59 51.41176 0.05871477 19.713996
#>
#> $x2
#> x1 x2 x3 fit
#> 1 6.0125 27.73 0.05871477 13.73514
#> 2 6.0125 39.57 0.05871477 15.03767
#> 3 6.0125 51.41 0.05871477 14.90428
#> 4 6.0125 63.25 0.05871477 13.40232
#> 5 6.0125 75.09 0.05871477 11.29001
#>
#> $x3
#> x1 x2 x3 fit
#> 1 6.0125 51.41176 -1.82 11.83653
#> 2 6.0125 51.41176 -0.88 13.37254
#> 3 6.0125 51.41176 0.06 14.90854
#> 4 6.0125 51.41176 1.00 16.44455
#> 5 6.0125 51.41176 1.94 17.98056
#>
(m1.p7 <- predictOMatic(m1, divider = "std.dev.", n = 3))
#> $x1
#> x1 x2 x3 fit
#> 1 3.22 51.41176 0.05871477 11.82369
#> 2 6.01 51.41176 0.05871477 14.90393
#> 3 8.80 51.41176 0.05871477 17.48808
#>
#> $x2
#> x1 x2 x3 fit
#> 1 6.0125 39.57 0.05871477 15.03767
#> 2 6.0125 51.41 0.05871477 14.90428
#> 3 6.0125 63.25 0.05871477 13.40232
#>
#> $x3
#> x1 x2 x3 fit
#> 1 6.0125 51.41176 -0.88 13.37254
#> 2 6.0125 51.41176 0.06 14.90854
#> 3 6.0125 51.41176 1.00 16.44455
#>
(m1.p8 <- predictOMatic(m1, divider = "std.dev.", interval = "confidence"))
#> $x1
#> x1 x2 x3 fit lwr upr
#> 1 0.43 51.41176 0.05871477 8.010526 -0.1369781 16.15803
#> 2 3.22 51.41176 0.05871477 11.823689 6.4819954 17.16538
#> 3 6.01 51.41176 0.05871477 14.903932 10.2665844 19.54128
#> 4 8.80 51.41176 0.05871477 17.488084 11.8454486 23.13072
#> 5 11.59 51.41176 0.05871477 19.713996 12.4604936 26.96750
#>
#> $x2
#> x1 x2 x3 fit lwr upr
#> 1 6.0125 27.73 0.05871477 13.73514 10.302485 17.16780
#> 2 6.0125 39.57 0.05871477 15.03767 10.239298 19.83604
#> 3 6.0125 51.41 0.05871477 14.90428 10.269243 19.53932
#> 4 6.0125 63.25 0.05871477 13.40232 10.197550 16.60709
#> 5 6.0125 75.09 0.05871477 11.29001 7.605709 14.97431
#>
#> $x3
#> x1 x2 x3 fit lwr upr
#> 1 6.0125 51.41176 -1.82 11.83653 4.328724 19.34434
#> 2 6.0125 51.41176 -0.88 13.37254 7.902225 18.84285
#> 3 6.0125 51.41176 0.06 14.90854 10.270802 19.54628
#> 4 6.0125 51.41176 1.00 16.44455 10.866006 22.02309
#> 5 6.0125 51.41176 1.94 17.98056 10.315125 25.64599
#>
m2 <- lm(y ~ x1 + x2 + x3 + xcat1 + xcat2, data = dat)
## has only columns and rows used in model fit
m2.data <- model.data(m2)
summarize(m2.data)
#> Numeric variables
#> y x1 x2 x3
#> min -20.169 0 26.531 -1.816
#> med 13.420 6 51.868 -0.063
#> max 41.477 12 76.558 2.747
#> mean 12.800 5.917 51.170 0.058
#> sd 12.639 2.872 11.972 0.943
#> skewness -0.213 0.177 0.098 0.494
#> kurtosis 0.026 -0.686 -0.854 0.331
#> nobs 72 72 72 72
#> nmissing 0 0 0 0
#>
#> Nonnumeric variables
#> xcat1 xcat2
#> M: 39 R: 28
#> F: 33 M: 12
#> D: 15
#> P: 3
#> G: 14
#> nobs : 72.000 nobs : 72.000
#> nmiss : 0.000 nmiss : 0.000
#> entropy : 0.995 entropy : 2.083
#> normedEntropy: 0.995 normedEntropy: 0.897
## Check all the margins
(predictOMatic(m2, interval = "conf"))
#> $x1
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 0 51.16993 0.05760886 M R 5.64645 -2.258292 13.55119
#> 2 4 51.16993 0.05760886 M R 10.51338 5.177953 15.84880
#> 3 6 51.16993 0.05760886 M R 12.94684 7.934802 17.95888
#> 4 8 51.16993 0.05760886 M R 15.38030 9.823064 20.93754
#> 5 12 51.16993 0.05760886 M R 20.24723 11.896575 28.59788
#>
#> $x2
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 26.53056 0.05760886 M R 7.759846 0.0167103 15.50298
#> 2 5.916667 41.77574 0.05760886 M R 10.906471 5.5944483 16.21849
#> 3 5.916667 51.86759 0.05760886 M R 12.989441 7.9551258 18.02376
#> 4 5.916667 59.14815 0.05760886 M R 14.492158 8.8138824 20.17043
#> 5 5.916667 76.55788 0.05760886 M R 18.085549 9.1845127 26.98658
#>
#> $x3
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 51.16993 -1.81571235 M R 10.97593 2.990569 18.96130
#> 2 5.916667 51.16993 -0.50295611 M R 12.28602 6.893966 17.67807
#> 3 5.916667 51.16993 -0.06288919 M R 12.72519 7.686259 17.76412
#> 4 5.916667 51.16993 0.62900820 M R 13.41568 8.155673 18.67569
#> 5 5.916667 51.16993 2.74740354 M R 15.52977 5.733524 25.32601
#>
#> $xcat1
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 51.16993 0.05760886 M R 12.84544 7.837642 17.85325
#> 2 5.916667 51.16993 0.05760886 F R 17.03574 10.220653 23.85084
#>
#> $xcat2
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 51.16993 0.05760886 M R 12.845444 7.837642 17.85325
#> 2 5.916667 51.16993 0.05760886 M D 14.466524 7.004602 21.92845
#> 3 5.916667 51.16993 0.05760886 M G 3.127525 -5.171778 11.42683
#> 4 5.916667 51.16993 0.05760886 M M 11.664278 3.936787 19.39177
#> 5 5.916667 51.16993 0.05760886 M P 7.624650 -7.288038 22.53734
#>
## Lets construct predictions the "old fashioned way" for comparison
m2.new1 <- newdata(m2, predVals = list(xcat1 = levels(m2.data$xcat1),
xcat2 = levels(m2.data$xcat2)), n = 5)
predict(m2, newdata = m2.new1)
#> 1 2 3 4 5 6 7 8
#> 12.845444 17.035744 11.664278 15.854578 14.466524 18.656824 7.624650 11.814950
#> 9 10
#> 3.127525 7.317825
(m2.p1 <- predictOMatic(m2,
predVals = list(xcat1 = levels(m2.data$xcat1),
xcat2 = levels(m2.data$xcat2)),
xcat2 = c("M","D")))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5.916667 51.16993 0.05760886 M R 12.845444
#> 2 5.916667 51.16993 0.05760886 F R 17.035744
#> 3 5.916667 51.16993 0.05760886 M M 11.664278
#> 4 5.916667 51.16993 0.05760886 F M 15.854578
#> 5 5.916667 51.16993 0.05760886 M D 14.466524
#> 6 5.916667 51.16993 0.05760886 F D 18.656824
#> 7 5.916667 51.16993 0.05760886 M P 7.624650
#> 8 5.916667 51.16993 0.05760886 F P 11.814950
#> 9 5.916667 51.16993 0.05760886 M G 3.127525
#> 10 5.916667 51.16993 0.05760886 F G 7.317825
## See? same!
## Pick some particular values for focus
m2.new2 <- newdata(m2, predVals = list(x1 = c(1,2,3), xcat2 = c("M","D")))
## Ask for predictions
predict(m2, newdata = m2.new2)
#> 1 2 3 4 5 6
#> 5.682015 6.898746 8.115478 8.484261 9.700993 10.917724
## Compare: predictOMatic generates a newdata frame and predictions in one step
(m2.p2 <- predictOMatic(m2, predVals = list(x1 = c(1,2,3),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 1 51.16993 0.05760886 M M 5.682015
#> 2 2 51.16993 0.05760886 M M 6.898746
#> 3 3 51.16993 0.05760886 M M 8.115478
#> 4 1 51.16993 0.05760886 M D 8.484261
#> 5 2 51.16993 0.05760886 M D 9.700993
#> 6 3 51.16993 0.05760886 M D 10.917724
(m2.p3 <- predictOMatic(m2, predVals = list(x2 = c(0.25, 1.0),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5.916667 0.25 0.05760886 M M 1.154339
#> 2 5.916667 1.00 0.05760886 M M 1.309140
#> 3 5.916667 0.25 0.05760886 M D 3.956585
#> 4 5.916667 1.00 0.05760886 M D 4.111386
(m2.p4 <- predictOMatic(m2, predVals = list(x2 = plotSeq(m2.data$x2, 10),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5.916667 26.53056 0.05760886 M M 6.578680
#> 2 5.916667 32.08915 0.05760886 M M 7.725980
#> 3 5.916667 37.64774 0.05760886 M M 8.873280
#> 4 5.916667 43.20633 0.05760886 M M 10.020581
#> 5 5.916667 48.76493 0.05760886 M M 11.167881
#> 6 5.916667 54.32352 0.05760886 M M 12.315181
#> 7 5.916667 59.88211 0.05760886 M M 13.462481
#> 8 5.916667 65.44070 0.05760886 M M 14.609782
#> 9 5.916667 70.99929 0.05760886 M M 15.757082
#> 10 5.916667 76.55788 0.05760886 M M 16.904382
#> 11 5.916667 26.53056 0.05760886 M D 9.380926
#> 12 5.916667 32.08915 0.05760886 M D 10.528226
#> 13 5.916667 37.64774 0.05760886 M D 11.675526
#> 14 5.916667 43.20633 0.05760886 M D 12.822827
#> 15 5.916667 48.76493 0.05760886 M D 13.970127
#> 16 5.916667 54.32352 0.05760886 M D 15.117427
#> 17 5.916667 59.88211 0.05760886 M D 16.264727
#> 18 5.916667 65.44070 0.05760886 M D 17.412028
#> 19 5.916667 70.99929 0.05760886 M D 18.559328
#> 20 5.916667 76.55788 0.05760886 M D 19.706628
(m2.p5 <- predictOMatic(m2, predVals = list(x2 = c(0.25, 1.0),
xcat2 = c("M","D")), interval = "conf"))
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 0.25 0.05760886 M M 1.154339 -15.76736 18.07603
#> 2 5.916667 1.00 0.05760886 M M 1.309140 -15.43412 18.05240
#> 3 5.916667 0.25 0.05760886 M D 3.956585 -12.82800 20.74117
#> 4 5.916667 1.00 0.05760886 M D 4.111386 -12.49355 20.71632
(m2.p6 <- predictOMatic(m2, predVals = list(x2 = c(49, 51),
xcat2 = levels(m2.data$xcat2),
x1 = plotSeq(dat$x1))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 0.0000000 49 0.05760886 M R 5.19857321
#> 2 0.0000000 51 0.05760886 M R 5.61137573
#> 3 0.0000000 49 0.05760886 M M 4.01740664
#> 4 0.0000000 51 0.05760886 M M 4.43020916
#> 5 0.0000000 49 0.05760886 M D 6.81965273
#> 6 0.0000000 51 0.05760886 M D 7.23245525
#> 7 0.0000000 49 0.05760886 M P -0.02222060
#> 8 0.0000000 51 0.05760886 M P 0.39058192
#> 9 0.0000000 49 0.05760886 M G -4.51934620
#> 10 0.0000000 51 0.05760886 M G -4.10654368
#> 11 0.1212121 49 0.05760886 M R 5.34605580
#> 12 0.1212121 51 0.05760886 M R 5.75885832
#> 13 0.1212121 49 0.05760886 M M 4.16488922
#> 14 0.1212121 51 0.05760886 M M 4.57769174
#> 15 0.1212121 49 0.05760886 M D 6.96713532
#> 16 0.1212121 51 0.05760886 M D 7.37993784
#> 17 0.1212121 49 0.05760886 M P 0.12526199
#> 18 0.1212121 51 0.05760886 M P 0.53806451
#> 19 0.1212121 49 0.05760886 M G -4.37186361
#> 20 0.1212121 51 0.05760886 M G -3.95906109
#> 21 0.2424242 49 0.05760886 M R 5.49353839
#> 22 0.2424242 51 0.05760886 M R 5.90634091
#> 23 0.2424242 49 0.05760886 M M 4.31237181
#> 24 0.2424242 51 0.05760886 M M 4.72517433
#> 25 0.2424242 49 0.05760886 M D 7.11461791
#> 26 0.2424242 51 0.05760886 M D 7.52742042
#> 27 0.2424242 49 0.05760886 M P 0.27274457
#> 28 0.2424242 51 0.05760886 M P 0.68554709
#> 29 0.2424242 49 0.05760886 M G -4.22438103
#> 30 0.2424242 51 0.05760886 M G -3.81157851
#> 31 0.3636364 49 0.05760886 M R 5.64102097
#> 32 0.3636364 51 0.05760886 M R 6.05382349
#> 33 0.3636364 49 0.05760886 M M 4.45985440
#> 34 0.3636364 51 0.05760886 M M 4.87265692
#> 35 0.3636364 49 0.05760886 M D 7.26210049
#> 36 0.3636364 51 0.05760886 M D 7.67490301
#> 37 0.3636364 49 0.05760886 M P 0.42022716
#> 38 0.3636364 51 0.05760886 M P 0.83302968
#> 39 0.3636364 49 0.05760886 M G -4.07689844
#> 40 0.3636364 51 0.05760886 M G -3.66409592
#> 41 0.4848485 49 0.05760886 M R 5.78850356
#> 42 0.4848485 51 0.05760886 M R 6.20130608
#> 43 0.4848485 49 0.05760886 M M 4.60733698
#> 44 0.4848485 51 0.05760886 M M 5.02013950
#> 45 0.4848485 49 0.05760886 M D 7.40958308
#> 46 0.4848485 51 0.05760886 M D 7.82238560
#> 47 0.4848485 49 0.05760886 M P 0.56770975
#> 48 0.4848485 51 0.05760886 M P 0.98051227
#> 49 0.4848485 49 0.05760886 M G -3.92941585
#> 50 0.4848485 51 0.05760886 M G -3.51661333
#> 51 0.6060606 49 0.05760886 M R 5.93598615
#> 52 0.6060606 51 0.05760886 M R 6.34878867
#> 53 0.6060606 49 0.05760886 M M 4.75481957
#> 54 0.6060606 51 0.05760886 M M 5.16762209
#> 55 0.6060606 49 0.05760886 M D 7.55706567
#> 56 0.6060606 51 0.05760886 M D 7.96986819
#> 57 0.6060606 49 0.05760886 M P 0.71519233
#> 58 0.6060606 51 0.05760886 M P 1.12799485
#> 59 0.6060606 49 0.05760886 M G -3.78193327
#> 60 0.6060606 51 0.05760886 M G -3.36913075
#> 61 0.7272727 49 0.05760886 M R 6.08346873
#> 62 0.7272727 51 0.05760886 M R 6.49627125
#> 63 0.7272727 49 0.05760886 M M 4.90230216
#> 64 0.7272727 51 0.05760886 M M 5.31510468
#> 65 0.7272727 49 0.05760886 M D 7.70454825
#> 66 0.7272727 51 0.05760886 M D 8.11735077
#> 67 0.7272727 49 0.05760886 M P 0.86267492
#> 68 0.7272727 51 0.05760886 M P 1.27547744
#> 69 0.7272727 49 0.05760886 M G -3.63445068
#> 70 0.7272727 51 0.05760886 M G -3.22164816
#> 71 0.8484848 49 0.05760886 M R 6.23095132
#> 72 0.8484848 51 0.05760886 M R 6.64375384
#> 73 0.8484848 49 0.05760886 M M 5.04978474
#> 74 0.8484848 51 0.05760886 M M 5.46258726
#> 75 0.8484848 49 0.05760886 M D 7.85203084
#> 76 0.8484848 51 0.05760886 M D 8.26483336
#> 77 0.8484848 49 0.05760886 M P 1.01015751
#> 78 0.8484848 51 0.05760886 M P 1.42296003
#> 79 0.8484848 49 0.05760886 M G -3.48696809
#> 80 0.8484848 51 0.05760886 M G -3.07416557
#> 81 0.9696970 49 0.05760886 M R 6.37843391
#> 82 0.9696970 51 0.05760886 M R 6.79123643
#> 83 0.9696970 49 0.05760886 M M 5.19726733
#> 84 0.9696970 51 0.05760886 M M 5.61006985
#> 85 0.9696970 49 0.05760886 M D 7.99951343
#> 86 0.9696970 51 0.05760886 M D 8.41231595
#> 87 0.9696970 49 0.05760886 M P 1.15764009
#> 88 0.9696970 51 0.05760886 M P 1.57044261
#> 89 0.9696970 49 0.05760886 M G -3.33948551
#> 90 0.9696970 51 0.05760886 M G -2.92668299
#> 91 1.0909091 49 0.05760886 M R 6.52591649
#> 92 1.0909091 51 0.05760886 M R 6.93871901
#> 93 1.0909091 49 0.05760886 M M 5.34474992
#> 94 1.0909091 51 0.05760886 M M 5.75755244
#> 95 1.0909091 49 0.05760886 M D 8.14699601
#> 96 1.0909091 51 0.05760886 M D 8.55979853
#> 97 1.0909091 49 0.05760886 M P 1.30512268
#> 98 1.0909091 51 0.05760886 M P 1.71792520
#> 99 1.0909091 49 0.05760886 M G -3.19200292
#> 100 1.0909091 51 0.05760886 M G -2.77920040
#> 101 1.2121212 49 0.05760886 M R 6.67339908
#> 102 1.2121212 51 0.05760886 M R 7.08620160
#> 103 1.2121212 49 0.05760886 M M 5.49223250
#> 104 1.2121212 51 0.05760886 M M 5.90503502
#> 105 1.2121212 49 0.05760886 M D 8.29447860
#> 106 1.2121212 51 0.05760886 M D 8.70728112
#> 107 1.2121212 49 0.05760886 M P 1.45260527
#> 108 1.2121212 51 0.05760886 M P 1.86540779
#> 109 1.2121212 49 0.05760886 M G -3.04452033
#> 110 1.2121212 51 0.05760886 M G -2.63171781
#> 111 1.3333333 49 0.05760886 M R 6.82088167
#> 112 1.3333333 51 0.05760886 M R 7.23368419
#> 113 1.3333333 49 0.05760886 M M 5.63971509
#> 114 1.3333333 51 0.05760886 M M 6.05251761
#> 115 1.3333333 49 0.05760886 M D 8.44196119
#> 116 1.3333333 51 0.05760886 M D 8.85476371
#> 117 1.3333333 49 0.05760886 M P 1.60008785
#> 118 1.3333333 51 0.05760886 M P 2.01289037
#> 119 1.3333333 49 0.05760886 M G -2.89703775
#> 120 1.3333333 51 0.05760886 M G -2.48423523
#> 121 1.4545455 49 0.05760886 M R 6.96836425
#> 122 1.4545455 51 0.05760886 M R 7.38116677
#> 123 1.4545455 49 0.05760886 M M 5.78719768
#> 124 1.4545455 51 0.05760886 M M 6.20000020
#> 125 1.4545455 49 0.05760886 M D 8.58944377
#> 126 1.4545455 51 0.05760886 M D 9.00224629
#> 127 1.4545455 49 0.05760886 M P 1.74757044
#> 128 1.4545455 51 0.05760886 M P 2.16037296
#> 129 1.4545455 49 0.05760886 M G -2.74955516
#> 130 1.4545455 51 0.05760886 M G -2.33675264
#> 131 1.5757576 49 0.05760886 M R 7.11584684
#> 132 1.5757576 51 0.05760886 M R 7.52864936
#> 133 1.5757576 49 0.05760886 M M 5.93468026
#> 134 1.5757576 51 0.05760886 M M 6.34748278
#> 135 1.5757576 49 0.05760886 M D 8.73692636
#> 136 1.5757576 51 0.05760886 M D 9.14972888
#> 137 1.5757576 49 0.05760886 M P 1.89505303
#> 138 1.5757576 51 0.05760886 M P 2.30785555
#> 139 1.5757576 49 0.05760886 M G -2.60207257
#> 140 1.5757576 51 0.05760886 M G -2.18927005
#> 141 1.6969697 49 0.05760886 M R 7.26332943
#> 142 1.6969697 51 0.05760886 M R 7.67613195
#> 143 1.6969697 49 0.05760886 M M 6.08216285
#> 144 1.6969697 51 0.05760886 M M 6.49496537
#> 145 1.6969697 49 0.05760886 M D 8.88440895
#> 146 1.6969697 51 0.05760886 M D 9.29721147
#> 147 1.6969697 49 0.05760886 M P 2.04253561
#> 148 1.6969697 51 0.05760886 M P 2.45533813
#> 149 1.6969697 49 0.05760886 M G -2.45458999
#> 150 1.6969697 51 0.05760886 M G -2.04178747
#> 151 1.8181818 49 0.05760886 M R 7.41081201
#> 152 1.8181818 51 0.05760886 M R 7.82361453
#> 153 1.8181818 49 0.05760886 M M 6.22964544
#> 154 1.8181818 51 0.05760886 M M 6.64244796
#> 155 1.8181818 49 0.05760886 M D 9.03189153
#> 156 1.8181818 51 0.05760886 M D 9.44469405
#> 157 1.8181818 49 0.05760886 M P 2.19001820
#> 158 1.8181818 51 0.05760886 M P 2.60282072
#> 159 1.8181818 49 0.05760886 M G -2.30710740
#> 160 1.8181818 51 0.05760886 M G -1.89430488
#> 161 1.9393939 49 0.05760886 M R 7.55829460
#> 162 1.9393939 51 0.05760886 M R 7.97109712
#> 163 1.9393939 49 0.05760886 M M 6.37712802
#> 164 1.9393939 51 0.05760886 M M 6.78993054
#> 165 1.9393939 49 0.05760886 M D 9.17937412
#> 166 1.9393939 51 0.05760886 M D 9.59217664
#> 167 1.9393939 49 0.05760886 M P 2.33750079
#> 168 1.9393939 51 0.05760886 M P 2.75030331
#> 169 1.9393939 49 0.05760886 M G -2.15962481
#> 170 1.9393939 51 0.05760886 M G -1.74682229
#> 171 2.0606061 49 0.05760886 M R 7.70577719
#> 172 2.0606061 51 0.05760886 M R 8.11857971
#> 173 2.0606061 49 0.05760886 M M 6.52461061
#> 174 2.0606061 51 0.05760886 M M 6.93741313
#> 175 2.0606061 49 0.05760886 M D 9.32685671
#> 176 2.0606061 51 0.05760886 M D 9.73965923
#> 177 2.0606061 49 0.05760886 M P 2.48498337
#> 178 2.0606061 51 0.05760886 M P 2.89778589
#> 179 2.0606061 49 0.05760886 M G -2.01214223
#> 180 2.0606061 51 0.05760886 M G -1.59933971
#> 181 2.1818182 49 0.05760886 M R 7.85325977
#> 182 2.1818182 51 0.05760886 M R 8.26606229
#> 183 2.1818182 49 0.05760886 M M 6.67209320
#> 184 2.1818182 51 0.05760886 M M 7.08489572
#> 185 2.1818182 49 0.05760886 M D 9.47433929
#> 186 2.1818182 51 0.05760886 M D 9.88714181
#> 187 2.1818182 49 0.05760886 M P 2.63246596
#> 188 2.1818182 51 0.05760886 M P 3.04526848
#> 189 2.1818182 49 0.05760886 M G -1.86465964
#> 190 2.1818182 51 0.05760886 M G -1.45185712
#> 191 2.3030303 49 0.05760886 M R 8.00074236
#> 192 2.3030303 51 0.05760886 M R 8.41354488
#> 193 2.3030303 49 0.05760886 M M 6.81957578
#> 194 2.3030303 51 0.05760886 M M 7.23237830
#> 195 2.3030303 49 0.05760886 M D 9.62182188
#> 196 2.3030303 51 0.05760886 M D 10.03462440
#> 197 2.3030303 49 0.05760886 M P 2.77994855
#> 198 2.3030303 51 0.05760886 M P 3.19275107
#> 199 2.3030303 49 0.05760886 M G -1.71717705
#> 200 2.3030303 51 0.05760886 M G -1.30437453
#> 201 2.4242424 49 0.05760886 M R 8.14822495
#> 202 2.4242424 51 0.05760886 M R 8.56102747
#> 203 2.4242424 49 0.05760886 M M 6.96705837
#> 204 2.4242424 51 0.05760886 M M 7.37986089
#> 205 2.4242424 49 0.05760886 M D 9.76930447
#> 206 2.4242424 51 0.05760886 M D 10.18210699
#> 207 2.4242424 49 0.05760886 M P 2.92743113
#> 208 2.4242424 51 0.05760886 M P 3.34023365
#> 209 2.4242424 49 0.05760886 M G -1.56969447
#> 210 2.4242424 51 0.05760886 M G -1.15689195
#> 211 2.5454545 49 0.05760886 M R 8.29570753
#> 212 2.5454545 51 0.05760886 M R 8.70851005
#> 213 2.5454545 49 0.05760886 M M 7.11454096
#> 214 2.5454545 51 0.05760886 M M 7.52734348
#> 215 2.5454545 49 0.05760886 M D 9.91678705
#> 216 2.5454545 51 0.05760886 M D 10.32958957
#> 217 2.5454545 49 0.05760886 M P 3.07491372
#> 218 2.5454545 51 0.05760886 M P 3.48771624
#> 219 2.5454545 49 0.05760886 M G -1.42221188
#> 220 2.5454545 51 0.05760886 M G -1.00940936
#> 221 2.6666667 49 0.05760886 M R 8.44319012
#> 222 2.6666667 51 0.05760886 M R 8.85599264
#> 223 2.6666667 49 0.05760886 M M 7.26202354
#> 224 2.6666667 51 0.05760886 M M 7.67482606
#> 225 2.6666667 49 0.05760886 M D 10.06426964
#> 226 2.6666667 51 0.05760886 M D 10.47707216
#> 227 2.6666667 49 0.05760886 M P 3.22239631
#> 228 2.6666667 51 0.05760886 M P 3.63519883
#> 229 2.6666667 49 0.05760886 M G -1.27472929
#> 230 2.6666667 51 0.05760886 M G -0.86192677
#> 231 2.7878788 49 0.05760886 M R 8.59067271
#> 232 2.7878788 51 0.05760886 M R 9.00347523
#> 233 2.7878788 49 0.05760886 M M 7.40950613
#> 234 2.7878788 51 0.05760886 M M 7.82230865
#> 235 2.7878788 49 0.05760886 M D 10.21175223
#> 236 2.7878788 51 0.05760886 M D 10.62455475
#> 237 2.7878788 49 0.05760886 M P 3.36987889
#> 238 2.7878788 51 0.05760886 M P 3.78268141
#> 239 2.7878788 49 0.05760886 M G -1.12724671
#> 240 2.7878788 51 0.05760886 M G -0.71444419
#> 241 2.9090909 49 0.05760886 M R 8.73815529
#> 242 2.9090909 51 0.05760886 M R 9.15095781
#> 243 2.9090909 49 0.05760886 M M 7.55698872
#> 244 2.9090909 51 0.05760886 M M 7.96979124
#> 245 2.9090909 49 0.05760886 M D 10.35923481
#> 246 2.9090909 51 0.05760886 M D 10.77203733
#> 247 2.9090909 49 0.05760886 M P 3.51736148
#> 248 2.9090909 51 0.05760886 M P 3.93016400
#> 249 2.9090909 49 0.05760886 M G -0.97976412
#> 250 2.9090909 51 0.05760886 M G -0.56696160
#> 251 3.0303030 49 0.05760886 M R 8.88563788
#> 252 3.0303030 51 0.05760886 M R 9.29844040
#> 253 3.0303030 49 0.05760886 M M 7.70447131
#> 254 3.0303030 51 0.05760886 M M 8.11727382
#> 255 3.0303030 49 0.05760886 M D 10.50671740
#> 256 3.0303030 51 0.05760886 M D 10.91951992
#> 257 3.0303030 49 0.05760886 M P 3.66484407
#> 258 3.0303030 51 0.05760886 M P 4.07764659
#> 259 3.0303030 49 0.05760886 M G -0.83228153
#> 260 3.0303030 51 0.05760886 M G -0.41947901
#> 261 3.1515152 49 0.05760886 M R 9.03312047
#> 262 3.1515152 51 0.05760886 M R 9.44592299
#> 263 3.1515152 49 0.05760886 M M 7.85195389
#> 264 3.1515152 51 0.05760886 M M 8.26475641
#> 265 3.1515152 49 0.05760886 M D 10.65419999
#> 266 3.1515152 51 0.05760886 M D 11.06700251
#> 267 3.1515152 49 0.05760886 M P 3.81232665
#> 268 3.1515152 51 0.05760886 M P 4.22512917
#> 269 3.1515152 49 0.05760886 M G -0.68479895
#> 270 3.1515152 51 0.05760886 M G -0.27199643
#> 271 3.2727273 49 0.05760886 M R 9.18060305
#> 272 3.2727273 51 0.05760886 M R 9.59340557
#> 273 3.2727273 49 0.05760886 M M 7.99943648
#> 274 3.2727273 51 0.05760886 M M 8.41223900
#> 275 3.2727273 49 0.05760886 M D 10.80168257
#> 276 3.2727273 51 0.05760886 M D 11.21448509
#> 277 3.2727273 49 0.05760886 M P 3.95980924
#> 278 3.2727273 51 0.05760886 M P 4.37261176
#> 279 3.2727273 49 0.05760886 M G -0.53731636
#> 280 3.2727273 51 0.05760886 M G -0.12451384
#> 281 3.3939394 49 0.05760886 M R 9.32808564
#> 282 3.3939394 51 0.05760886 M R 9.74088816
#> 283 3.3939394 49 0.05760886 M M 8.14691907
#> 284 3.3939394 51 0.05760886 M M 8.55972158
#> 285 3.3939394 49 0.05760886 M D 10.94916516
#> 286 3.3939394 51 0.05760886 M D 11.36196768
#> 287 3.3939394 49 0.05760886 M P 4.10729183
#> 288 3.3939394 51 0.05760886 M P 4.52009435
#> 289 3.3939394 49 0.05760886 M G -0.38983377
#> 290 3.3939394 51 0.05760886 M G 0.02296875
#> 291 3.5151515 49 0.05760886 M R 9.47556823
#> 292 3.5151515 51 0.05760886 M R 9.88837075
#> 293 3.5151515 49 0.05760886 M M 8.29440165
#> 294 3.5151515 51 0.05760886 M M 8.70720417
#> 295 3.5151515 49 0.05760886 M D 11.09664775
#> 296 3.5151515 51 0.05760886 M D 11.50945027
#> 297 3.5151515 49 0.05760886 M P 4.25477441
#> 298 3.5151515 51 0.05760886 M P 4.66757693
#> 299 3.5151515 49 0.05760886 M G -0.24235119
#> 300 3.5151515 51 0.05760886 M G 0.17045133
#> 301 3.6363636 49 0.05760886 M R 9.62305081
#> 302 3.6363636 51 0.05760886 M R 10.03585333
#> 303 3.6363636 49 0.05760886 M M 8.44188424
#> 304 3.6363636 51 0.05760886 M M 8.85468676
#> 305 3.6363636 49 0.05760886 M D 11.24413033
#> 306 3.6363636 51 0.05760886 M D 11.65693285
#> 307 3.6363636 49 0.05760886 M P 4.40225700
#> 308 3.6363636 51 0.05760886 M P 4.81505952
#> 309 3.6363636 49 0.05760886 M G -0.09486860
#> 310 3.6363636 51 0.05760886 M G 0.31793392
#> 311 3.7575758 49 0.05760886 M R 9.77053340
#> 312 3.7575758 51 0.05760886 M R 10.18333592
#> 313 3.7575758 49 0.05760886 M M 8.58936683
#> 314 3.7575758 51 0.05760886 M M 9.00216934
#> 315 3.7575758 49 0.05760886 M D 11.39161292
#> 316 3.7575758 51 0.05760886 M D 11.80441544
#> 317 3.7575758 49 0.05760886 M P 4.54973959
#> 318 3.7575758 51 0.05760886 M P 4.96254211
#> 319 3.7575758 49 0.05760886 M G 0.05261399
#> 320 3.7575758 51 0.05760886 M G 0.46541651
#> 321 3.8787879 49 0.05760886 M R 9.91801599
#> 322 3.8787879 51 0.05760886 M R 10.33081851
#> 323 3.8787879 49 0.05760886 M M 8.73684941
#> 324 3.8787879 51 0.05760886 M M 9.14965193
#> 325 3.8787879 49 0.05760886 M D 11.53909551
#> 326 3.8787879 51 0.05760886 M D 11.95189803
#> 327 3.8787879 49 0.05760886 M P 4.69722217
#> 328 3.8787879 51 0.05760886 M P 5.11002469
#> 329 3.8787879 49 0.05760886 M G 0.20009657
#> 330 3.8787879 51 0.05760886 M G 0.61289909
#> 331 4.0000000 49 0.05760886 M R 10.06549857
#> 332 4.0000000 51 0.05760886 M R 10.47830109
#> 333 4.0000000 49 0.05760886 M M 8.88433200
#> 334 4.0000000 51 0.05760886 M M 9.29713452
#> 335 4.0000000 49 0.05760886 M D 11.68657809
#> 336 4.0000000 51 0.05760886 M D 12.09938061
#> 337 4.0000000 49 0.05760886 M P 4.84470476
#> 338 4.0000000 51 0.05760886 M P 5.25750728
#> 339 4.0000000 49 0.05760886 M G 0.34757916
#> 340 4.0000000 51 0.05760886 M G 0.76038168
#> 341 4.1212121 49 0.05760886 M R 10.21298116
#> 342 4.1212121 51 0.05760886 M R 10.62578368
#> 343 4.1212121 49 0.05760886 M M 9.03181459
#> 344 4.1212121 51 0.05760886 M M 9.44461710
#> 345 4.1212121 49 0.05760886 M D 11.83406068
#> 346 4.1212121 51 0.05760886 M D 12.24686320
#> 347 4.1212121 49 0.05760886 M P 4.99218735
#> 348 4.1212121 51 0.05760886 M P 5.40498987
#> 349 4.1212121 49 0.05760886 M G 0.49506175
#> 350 4.1212121 51 0.05760886 M G 0.90786427
#> 351 4.2424242 49 0.05760886 M R 10.36046375
#> 352 4.2424242 51 0.05760886 M R 10.77326627
#> 353 4.2424242 49 0.05760886 M M 9.17929717
#> 354 4.2424242 51 0.05760886 M M 9.59209969
#> 355 4.2424242 49 0.05760886 M D 11.98154327
#> 356 4.2424242 51 0.05760886 M D 12.39434579
#> 357 4.2424242 49 0.05760886 M P 5.13966993
#> 358 4.2424242 51 0.05760886 M P 5.55247245
#> 359 4.2424242 49 0.05760886 M G 0.64254433
#> 360 4.2424242 51 0.05760886 M G 1.05534685
#> 361 4.3636364 49 0.05760886 M R 10.50794633
#> 362 4.3636364 51 0.05760886 M R 10.92074885
#> 363 4.3636364 49 0.05760886 M M 9.32677976
#> 364 4.3636364 51 0.05760886 M M 9.73958228
#> 365 4.3636364 49 0.05760886 M D 12.12902585
#> 366 4.3636364 51 0.05760886 M D 12.54182837
#> 367 4.3636364 49 0.05760886 M P 5.28715252
#> 368 4.3636364 51 0.05760886 M P 5.69995504
#> 369 4.3636364 49 0.05760886 M G 0.79002692
#> 370 4.3636364 51 0.05760886 M G 1.20282944
#> 371 4.4848485 49 0.05760886 M R 10.65542892
#> 372 4.4848485 51 0.05760886 M R 11.06823144
#> 373 4.4848485 49 0.05760886 M M 9.47426235
#> 374 4.4848485 51 0.05760886 M M 9.88706486
#> 375 4.4848485 49 0.05760886 M D 12.27650844
#> 376 4.4848485 51 0.05760886 M D 12.68931096
#> 377 4.4848485 49 0.05760886 M P 5.43463511
#> 378 4.4848485 51 0.05760886 M P 5.84743763
#> 379 4.4848485 49 0.05760886 M G 0.93750951
#> 380 4.4848485 51 0.05760886 M G 1.35031203
#> 381 4.6060606 49 0.05760886 M R 10.80291151
#> 382 4.6060606 51 0.05760886 M R 11.21571403
#> 383 4.6060606 49 0.05760886 M M 9.62174493
#> 384 4.6060606 51 0.05760886 M M 10.03454745
#> 385 4.6060606 49 0.05760886 M D 12.42399103
#> 386 4.6060606 51 0.05760886 M D 12.83679355
#> 387 4.6060606 49 0.05760886 M P 5.58211769
#> 388 4.6060606 51 0.05760886 M P 5.99492021
#> 389 4.6060606 49 0.05760886 M G 1.08499209
#> 390 4.6060606 51 0.05760886 M G 1.49779461
#> 391 4.7272727 49 0.05760886 M R 10.95039409
#> 392 4.7272727 51 0.05760886 M R 11.36319661
#> 393 4.7272727 49 0.05760886 M M 9.76922752
#> 394 4.7272727 51 0.05760886 M M 10.18203004
#> 395 4.7272727 49 0.05760886 M D 12.57147361
#> 396 4.7272727 51 0.05760886 M D 12.98427613
#> 397 4.7272727 49 0.05760886 M P 5.72960028
#> 398 4.7272727 51 0.05760886 M P 6.14240280
#> 399 4.7272727 49 0.05760886 M G 1.23247468
#> 400 4.7272727 51 0.05760886 M G 1.64527720
#> 401 4.8484848 49 0.05760886 M R 11.09787668
#> 402 4.8484848 51 0.05760886 M R 11.51067920
#> 403 4.8484848 49 0.05760886 M M 9.91671011
#> 404 4.8484848 51 0.05760886 M M 10.32951263
#> 405 4.8484848 49 0.05760886 M D 12.71895620
#> 406 4.8484848 51 0.05760886 M D 13.13175872
#> 407 4.8484848 49 0.05760886 M P 5.87708287
#> 408 4.8484848 51 0.05760886 M P 6.28988539
#> 409 4.8484848 49 0.05760886 M G 1.37995727
#> 410 4.8484848 51 0.05760886 M G 1.79275979
#> 411 4.9696970 49 0.05760886 M R 11.24535927
#> 412 4.9696970 51 0.05760886 M R 11.65816179
#> 413 4.9696970 49 0.05760886 M M 10.06419269
#> 414 4.9696970 51 0.05760886 M M 10.47699521
#> 415 4.9696970 49 0.05760886 M D 12.86643879
#> 416 4.9696970 51 0.05760886 M D 13.27924131
#> 417 4.9696970 49 0.05760886 M P 6.02456546
#> 418 4.9696970 51 0.05760886 M P 6.43736797
#> 419 4.9696970 49 0.05760886 M G 1.52743985
#> 420 4.9696970 51 0.05760886 M G 1.94024237
#> 421 5.0909091 49 0.05760886 M R 11.39284185
#> 422 5.0909091 51 0.05760886 M R 11.80564437
#> 423 5.0909091 49 0.05760886 M M 10.21167528
#> 424 5.0909091 51 0.05760886 M M 10.62447780
#> 425 5.0909091 49 0.05760886 M D 13.01392137
#> 426 5.0909091 51 0.05760886 M D 13.42672389
#> 427 5.0909091 49 0.05760886 M P 6.17204804
#> 428 5.0909091 51 0.05760886 M P 6.58485056
#> 429 5.0909091 49 0.05760886 M G 1.67492244
#> 430 5.0909091 51 0.05760886 M G 2.08772496
#> 431 5.2121212 49 0.05760886 M R 11.54032444
#> 432 5.2121212 51 0.05760886 M R 11.95312696
#> 433 5.2121212 49 0.05760886 M M 10.35915787
#> 434 5.2121212 51 0.05760886 M M 10.77196039
#> 435 5.2121212 49 0.05760886 M D 13.16140396
#> 436 5.2121212 51 0.05760886 M D 13.57420648
#> 437 5.2121212 49 0.05760886 M P 6.31953063
#> 438 5.2121212 51 0.05760886 M P 6.73233315
#> 439 5.2121212 49 0.05760886 M G 1.82240503
#> 440 5.2121212 51 0.05760886 M G 2.23520755
#> 441 5.3333333 49 0.05760886 M R 11.68780703
#> 442 5.3333333 51 0.05760886 M R 12.10060955
#> 443 5.3333333 49 0.05760886 M M 10.50664045
#> 444 5.3333333 51 0.05760886 M M 10.91944297
#> 445 5.3333333 49 0.05760886 M D 13.30888655
#> 446 5.3333333 51 0.05760886 M D 13.72168907
#> 447 5.3333333 49 0.05760886 M P 6.46701322
#> 448 5.3333333 51 0.05760886 M P 6.87981573
#> 449 5.3333333 49 0.05760886 M G 1.96988761
#> 450 5.3333333 51 0.05760886 M G 2.38269013
#> 451 5.4545455 49 0.05760886 M R 11.83528961
#> 452 5.4545455 51 0.05760886 M R 12.24809213
#> 453 5.4545455 49 0.05760886 M M 10.65412304
#> 454 5.4545455 51 0.05760886 M M 11.06692556
#> 455 5.4545455 49 0.05760886 M D 13.45636913
#> 456 5.4545455 51 0.05760886 M D 13.86917165
#> 457 5.4545455 49 0.05760886 M P 6.61449580
#> 458 5.4545455 51 0.05760886 M P 7.02729832
#> 459 5.4545455 49 0.05760886 M G 2.11737020
#> 460 5.4545455 51 0.05760886 M G 2.53017272
#> 461 5.5757576 49 0.05760886 M R 11.98277220
#> 462 5.5757576 51 0.05760886 M R 12.39557472
#> 463 5.5757576 49 0.05760886 M M 10.80160563
#> 464 5.5757576 51 0.05760886 M M 11.21440815
#> 465 5.5757576 49 0.05760886 M D 13.60385172
#> 466 5.5757576 51 0.05760886 M D 14.01665424
#> 467 5.5757576 49 0.05760886 M P 6.76197839
#> 468 5.5757576 51 0.05760886 M P 7.17478091
#> 469 5.5757576 49 0.05760886 M G 2.26485279
#> 470 5.5757576 51 0.05760886 M G 2.67765531
#> 471 5.6969697 49 0.05760886 M R 12.13025479
#> 472 5.6969697 51 0.05760886 M R 12.54305731
#> 473 5.6969697 49 0.05760886 M M 10.94908821
#> 474 5.6969697 51 0.05760886 M M 11.36189073
#> 475 5.6969697 49 0.05760886 M D 13.75133431
#> 476 5.6969697 51 0.05760886 M D 14.16413683
#> 477 5.6969697 49 0.05760886 M P 6.90946098
#> 478 5.6969697 51 0.05760886 M P 7.32226349
#> 479 5.6969697 49 0.05760886 M G 2.41233537
#> 480 5.6969697 51 0.05760886 M G 2.82513789
#> 481 5.8181818 49 0.05760886 M R 12.27773737
#> 482 5.8181818 51 0.05760886 M R 12.69053989
#> 483 5.8181818 49 0.05760886 M M 11.09657080
#> 484 5.8181818 51 0.05760886 M M 11.50937332
#> 485 5.8181818 49 0.05760886 M D 13.89881689
#> 486 5.8181818 51 0.05760886 M D 14.31161941
#> 487 5.8181818 49 0.05760886 M P 7.05694356
#> 488 5.8181818 51 0.05760886 M P 7.46974608
#> 489 5.8181818 49 0.05760886 M G 2.55981796
#> 490 5.8181818 51 0.05760886 M G 2.97262048
#> 491 5.9393939 49 0.05760886 M R 12.42521996
#> 492 5.9393939 51 0.05760886 M R 12.83802248
#> 493 5.9393939 49 0.05760886 M M 11.24405339
#> 494 5.9393939 51 0.05760886 M M 11.65685591
#> 495 5.9393939 49 0.05760886 M D 14.04629948
#> 496 5.9393939 51 0.05760886 M D 14.45910200
#> 497 5.9393939 49 0.05760886 M P 7.20442615
#> 498 5.9393939 51 0.05760886 M P 7.61722867
#> 499 5.9393939 49 0.05760886 M G 2.70730055
#> 500 5.9393939 51 0.05760886 M G 3.12010307
#> 501 6.0606061 49 0.05760886 M R 12.57270255
#> 502 6.0606061 51 0.05760886 M R 12.98550507
#> 503 6.0606061 49 0.05760886 M M 11.39153597
#> 504 6.0606061 51 0.05760886 M M 11.80433849
#> 505 6.0606061 49 0.05760886 M D 14.19378207
#> 506 6.0606061 51 0.05760886 M D 14.60658459
#> 507 6.0606061 49 0.05760886 M P 7.35190874
#> 508 6.0606061 51 0.05760886 M P 7.76471125
#> 509 6.0606061 49 0.05760886 M G 2.85478313
#> 510 6.0606061 51 0.05760886 M G 3.26758565
#> 511 6.1818182 49 0.05760886 M R 12.72018513
#> 512 6.1818182 51 0.05760886 M R 13.13298765
#> 513 6.1818182 49 0.05760886 M M 11.53901856
#> 514 6.1818182 51 0.05760886 M M 11.95182108
#> 515 6.1818182 49 0.05760886 M D 14.34126465
#> 516 6.1818182 51 0.05760886 M D 14.75406717
#> 517 6.1818182 49 0.05760886 M P 7.49939132
#> 518 6.1818182 51 0.05760886 M P 7.91219384
#> 519 6.1818182 49 0.05760886 M G 3.00226572
#> 520 6.1818182 51 0.05760886 M G 3.41506824
#> 521 6.3030303 49 0.05760886 M R 12.86766772
#> 522 6.3030303 51 0.05760886 M R 13.28047024
#> 523 6.3030303 49 0.05760886 M M 11.68650115
#> 524 6.3030303 51 0.05760886 M M 12.09930367
#> 525 6.3030303 49 0.05760886 M D 14.48874724
#> 526 6.3030303 51 0.05760886 M D 14.90154976
#> 527 6.3030303 49 0.05760886 M P 7.64687391
#> 528 6.3030303 51 0.05760886 M P 8.05967643
#> 529 6.3030303 49 0.05760886 M G 3.14974831
#> 530 6.3030303 51 0.05760886 M G 3.56255083
#> 531 6.4242424 49 0.05760886 M R 13.01515031
#> 532 6.4242424 51 0.05760886 M R 13.42795283
#> 533 6.4242424 49 0.05760886 M M 11.83398373
#> 534 6.4242424 51 0.05760886 M M 12.24678625
#> 535 6.4242424 49 0.05760886 M D 14.63622983
#> 536 6.4242424 51 0.05760886 M D 15.04903235
#> 537 6.4242424 49 0.05760886 M P 7.79435650
#> 538 6.4242424 51 0.05760886 M P 8.20715901
#> 539 6.4242424 49 0.05760886 M G 3.29723090
#> 540 6.4242424 51 0.05760886 M G 3.71003341
#> 541 6.5454545 49 0.05760886 M R 13.16263289
#> 542 6.5454545 51 0.05760886 M R 13.57543541
#> 543 6.5454545 49 0.05760886 M M 11.98146632
#> 544 6.5454545 51 0.05760886 M M 12.39426884
#> 545 6.5454545 49 0.05760886 M D 14.78371241
#> 546 6.5454545 51 0.05760886 M D 15.19651493
#> 547 6.5454545 49 0.05760886 M P 7.94183908
#> 548 6.5454545 51 0.05760886 M P 8.35464160
#> 549 6.5454545 49 0.05760886 M G 3.44471348
#> 550 6.5454545 51 0.05760886 M G 3.85751600
#> 551 6.6666667 49 0.05760886 M R 13.31011548
#> 552 6.6666667 51 0.05760886 M R 13.72291800
#> 553 6.6666667 49 0.05760886 M M 12.12894891
#> 554 6.6666667 51 0.05760886 M M 12.54175143
#> 555 6.6666667 49 0.05760886 M D 14.93119500
#> 556 6.6666667 51 0.05760886 M D 15.34399752
#> 557 6.6666667 49 0.05760886 M P 8.08932167
#> 558 6.6666667 51 0.05760886 M P 8.50212419
#> 559 6.6666667 49 0.05760886 M G 3.59219607
#> 560 6.6666667 51 0.05760886 M G 4.00499859
#> 561 6.7878788 49 0.05760886 M R 13.45759807
#> 562 6.7878788 51 0.05760886 M R 13.87040059
#> 563 6.7878788 49 0.05760886 M M 12.27643149
#> 564 6.7878788 51 0.05760886 M M 12.68923401
#> 565 6.7878788 49 0.05760886 M D 15.07867759
#> 566 6.7878788 51 0.05760886 M D 15.49148011
#> 567 6.7878788 49 0.05760886 M P 8.23680426
#> 568 6.7878788 51 0.05760886 M P 8.64960678
#> 569 6.7878788 49 0.05760886 M G 3.73967866
#> 570 6.7878788 51 0.05760886 M G 4.15248117
#> 571 6.9090909 49 0.05760886 M R 13.60508065
#> 572 6.9090909 51 0.05760886 M R 14.01788317
#> 573 6.9090909 49 0.05760886 M M 12.42391408
#> 574 6.9090909 51 0.05760886 M M 12.83671660
#> 575 6.9090909 49 0.05760886 M D 15.22616017
#> 576 6.9090909 51 0.05760886 M D 15.63896269
#> 577 6.9090909 49 0.05760886 M P 8.38428684
#> 578 6.9090909 51 0.05760886 M P 8.79708936
#> 579 6.9090909 49 0.05760886 M G 3.88716124
#> 580 6.9090909 51 0.05760886 M G 4.29996376
#> 581 7.0303030 49 0.05760886 M R 13.75256324
#> 582 7.0303030 51 0.05760886 M R 14.16536576
#> 583 7.0303030 49 0.05760886 M M 12.57139667
#> 584 7.0303030 51 0.05760886 M M 12.98419919
#> 585 7.0303030 49 0.05760886 M D 15.37364276
#> 586 7.0303030 51 0.05760886 M D 15.78644528
#> 587 7.0303030 49 0.05760886 M P 8.53176943
#> 588 7.0303030 51 0.05760886 M P 8.94457195
#> 589 7.0303030 49 0.05760886 M G 4.03464383
#> 590 7.0303030 51 0.05760886 M G 4.44744635
#> 591 7.1515152 49 0.05760886 M R 13.90004583
#> 592 7.1515152 51 0.05760886 M R 14.31284835
#> 593 7.1515152 49 0.05760886 M M 12.71887925
#> 594 7.1515152 51 0.05760886 M M 13.13168177
#> 595 7.1515152 49 0.05760886 M D 15.52112535
#> 596 7.1515152 51 0.05760886 M D 15.93392787
#> 597 7.1515152 49 0.05760886 M P 8.67925202
#> 598 7.1515152 51 0.05760886 M P 9.09205454
#> 599 7.1515152 49 0.05760886 M G 4.18212642
#> 600 7.1515152 51 0.05760886 M G 4.59492893
#> 601 7.2727273 49 0.05760886 M R 14.04752842
#> 602 7.2727273 51 0.05760886 M R 14.46033093
#> 603 7.2727273 49 0.05760886 M M 12.86636184
#> 604 7.2727273 51 0.05760886 M M 13.27916436
#> 605 7.2727273 49 0.05760886 M D 15.66860793
#> 606 7.2727273 51 0.05760886 M D 16.08141045
#> 607 7.2727273 49 0.05760886 M P 8.82673460
#> 608 7.2727273 51 0.05760886 M P 9.23953712
#> 609 7.2727273 49 0.05760886 M G 4.32960900
#> 610 7.2727273 51 0.05760886 M G 4.74241152
#> 611 7.3939394 49 0.05760886 M R 14.19501100
#> 612 7.3939394 51 0.05760886 M R 14.60781352
#> 613 7.3939394 49 0.05760886 M M 13.01384443
#> 614 7.3939394 51 0.05760886 M M 13.42664695
#> 615 7.3939394 49 0.05760886 M D 15.81609052
#> 616 7.3939394 51 0.05760886 M D 16.22889304
#> 617 7.3939394 49 0.05760886 M P 8.97421719
#> 618 7.3939394 51 0.05760886 M P 9.38701971
#> 619 7.3939394 49 0.05760886 M G 4.47709159
#> 620 7.3939394 51 0.05760886 M G 4.88989411
#> 621 7.5151515 49 0.05760886 M R 14.34249359
#> 622 7.5151515 51 0.05760886 M R 14.75529611
#> 623 7.5151515 49 0.05760886 M M 13.16132701
#> 624 7.5151515 51 0.05760886 M M 13.57412953
#> 625 7.5151515 49 0.05760886 M D 15.96357311
#> 626 7.5151515 51 0.05760886 M D 16.37637563
#> 627 7.5151515 49 0.05760886 M P 9.12169978
#> 628 7.5151515 51 0.05760886 M P 9.53450230
#> 629 7.5151515 49 0.05760886 M G 4.62457418
#> 630 7.5151515 51 0.05760886 M G 5.03737669
#> 631 7.6363636 49 0.05760886 M R 14.48997618
#> 632 7.6363636 51 0.05760886 M R 14.90277869
#> 633 7.6363636 49 0.05760886 M M 13.30880960
#> 634 7.6363636 51 0.05760886 M M 13.72161212
#> 635 7.6363636 49 0.05760886 M D 16.11105569
#> 636 7.6363636 51 0.05760886 M D 16.52385821
#> 637 7.6363636 49 0.05760886 M P 9.26918236
#> 638 7.6363636 51 0.05760886 M P 9.68198488
#> 639 7.6363636 49 0.05760886 M G 4.77205676
#> 640 7.6363636 51 0.05760886 M G 5.18485928
#> 641 7.7575758 49 0.05760886 M R 14.63745876
#> 642 7.7575758 51 0.05760886 M R 15.05026128
#> 643 7.7575758 49 0.05760886 M M 13.45629219
#> 644 7.7575758 51 0.05760886 M M 13.86909471
#> 645 7.7575758 49 0.05760886 M D 16.25853828
#> 646 7.7575758 51 0.05760886 M D 16.67134080
#> 647 7.7575758 49 0.05760886 M P 9.41666495
#> 648 7.7575758 51 0.05760886 M P 9.82946747
#> 649 7.7575758 49 0.05760886 M G 4.91953935
#> 650 7.7575758 51 0.05760886 M G 5.33234187
#> 651 7.8787879 49 0.05760886 M R 14.78494135
#> 652 7.8787879 51 0.05760886 M R 15.19774387
#> 653 7.8787879 49 0.05760886 M M 13.60377477
#> 654 7.8787879 51 0.05760886 M M 14.01657729
#> 655 7.8787879 49 0.05760886 M D 16.40602087
#> 656 7.8787879 51 0.05760886 M D 16.81882339
#> 657 7.8787879 49 0.05760886 M P 9.56414754
#> 658 7.8787879 51 0.05760886 M P 9.97695006
#> 659 7.8787879 49 0.05760886 M G 5.06702194
#> 660 7.8787879 51 0.05760886 M G 5.47982445
#> 661 8.0000000 49 0.05760886 M R 14.93242394
#> 662 8.0000000 51 0.05760886 M R 15.34522645
#> 663 8.0000000 49 0.05760886 M M 13.75125736
#> 664 8.0000000 51 0.05760886 M M 14.16405988
#> 665 8.0000000 49 0.05760886 M D 16.55350345
#> 666 8.0000000 51 0.05760886 M D 16.96630597
#> 667 8.0000000 49 0.05760886 M P 9.71163012
#> 668 8.0000000 51 0.05760886 M P 10.12443264
#> 669 8.0000000 49 0.05760886 M G 5.21450452
#> 670 8.0000000 51 0.05760886 M G 5.62730704
#> 671 8.1212121 49 0.05760886 M R 15.07990652
#> 672 8.1212121 51 0.05760886 M R 15.49270904
#> 673 8.1212121 49 0.05760886 M M 13.89873995
#> 674 8.1212121 51 0.05760886 M M 14.31154247
#> 675 8.1212121 49 0.05760886 M D 16.70098604
#> 676 8.1212121 51 0.05760886 M D 17.11378856
#> 677 8.1212121 49 0.05760886 M P 9.85911271
#> 678 8.1212121 51 0.05760886 M P 10.27191523
#> 679 8.1212121 49 0.05760886 M G 5.36198711
#> 680 8.1212121 51 0.05760886 M G 5.77478963
#> 681 8.2424242 49 0.05760886 M R 15.22738911
#> 682 8.2424242 51 0.05760886 M R 15.64019163
#> 683 8.2424242 49 0.05760886 M M 14.04622253
#> 684 8.2424242 51 0.05760886 M M 14.45902505
#> 685 8.2424242 49 0.05760886 M D 16.84846863
#> 686 8.2424242 51 0.05760886 M D 17.26127115
#> 687 8.2424242 49 0.05760886 M P 10.00659530
#> 688 8.2424242 51 0.05760886 M P 10.41939782
#> 689 8.2424242 49 0.05760886 M G 5.50946970
#> 690 8.2424242 51 0.05760886 M G 5.92227222
#> 691 8.3636364 49 0.05760886 M R 15.37487170
#> 692 8.3636364 51 0.05760886 M R 15.78767421
#> 693 8.3636364 49 0.05760886 M M 14.19370512
#> 694 8.3636364 51 0.05760886 M M 14.60650764
#> 695 8.3636364 49 0.05760886 M D 16.99595122
#> 696 8.3636364 51 0.05760886 M D 17.40875373
#> 697 8.3636364 49 0.05760886 M P 10.15407788
#> 698 8.3636364 51 0.05760886 M P 10.56688040
#> 699 8.3636364 49 0.05760886 M G 5.65695228
#> 700 8.3636364 51 0.05760886 M G 6.06975480
#> 701 8.4848485 49 0.05760886 M R 15.52235428
#> 702 8.4848485 51 0.05760886 M R 15.93515680
#> 703 8.4848485 49 0.05760886 M M 14.34118771
#> 704 8.4848485 51 0.05760886 M M 14.75399023
#> 705 8.4848485 49 0.05760886 M D 17.14343380
#> 706 8.4848485 51 0.05760886 M D 17.55623632
#> 707 8.4848485 49 0.05760886 M P 10.30156047
#> 708 8.4848485 51 0.05760886 M P 10.71436299
#> 709 8.4848485 49 0.05760886 M G 5.80443487
#> 710 8.4848485 51 0.05760886 M G 6.21723739
#> 711 8.6060606 49 0.05760886 M R 15.66983687
#> 712 8.6060606 51 0.05760886 M R 16.08263939
#> 713 8.6060606 49 0.05760886 M M 14.48867029
#> 714 8.6060606 51 0.05760886 M M 14.90147281
#> 715 8.6060606 49 0.05760886 M D 17.29091639
#> 716 8.6060606 51 0.05760886 M D 17.70371891
#> 717 8.6060606 49 0.05760886 M P 10.44904306
#> 718 8.6060606 51 0.05760886 M P 10.86184558
#> 719 8.6060606 49 0.05760886 M G 5.95191746
#> 720 8.6060606 51 0.05760886 M G 6.36471998
#> 721 8.7272727 49 0.05760886 M R 15.81731946
#> 722 8.7272727 51 0.05760886 M R 16.23012197
#> 723 8.7272727 49 0.05760886 M M 14.63615288
#> 724 8.7272727 51 0.05760886 M M 15.04895540
#> 725 8.7272727 49 0.05760886 M D 17.43839898
#> 726 8.7272727 51 0.05760886 M D 17.85120149
#> 727 8.7272727 49 0.05760886 M P 10.59652564
#> 728 8.7272727 51 0.05760886 M P 11.00932816
#> 729 8.7272727 49 0.05760886 M G 6.09940004
#> 730 8.7272727 51 0.05760886 M G 6.51220256
#> 731 8.8484848 49 0.05760886 M R 15.96480204
#> 732 8.8484848 51 0.05760886 M R 16.37760456
#> 733 8.8484848 49 0.05760886 M M 14.78363547
#> 734 8.8484848 51 0.05760886 M M 15.19643799
#> 735 8.8484848 49 0.05760886 M D 17.58588156
#> 736 8.8484848 51 0.05760886 M D 17.99868408
#> 737 8.8484848 49 0.05760886 M P 10.74400823
#> 738 8.8484848 51 0.05760886 M P 11.15681075
#> 739 8.8484848 49 0.05760886 M G 6.24688263
#> 740 8.8484848 51 0.05760886 M G 6.65968515
#> 741 8.9696970 49 0.05760886 M R 16.11228463
#> 742 8.9696970 51 0.05760886 M R 16.52508715
#> 743 8.9696970 49 0.05760886 M M 14.93111805
#> 744 8.9696970 51 0.05760886 M M 15.34392057
#> 745 8.9696970 49 0.05760886 M D 17.73336415
#> 746 8.9696970 51 0.05760886 M D 18.14616667
#> 747 8.9696970 49 0.05760886 M P 10.89149082
#> 748 8.9696970 51 0.05760886 M P 11.30429334
#> 749 8.9696970 49 0.05760886 M G 6.39436522
#> 750 8.9696970 51 0.05760886 M G 6.80716774
#> 751 9.0909091 49 0.05760886 M R 16.25976722
#> 752 9.0909091 51 0.05760886 M R 16.67256974
#> 753 9.0909091 49 0.05760886 M M 15.07860064
#> 754 9.0909091 51 0.05760886 M M 15.49140316
#> 755 9.0909091 49 0.05760886 M D 17.88084674
#> 756 9.0909091 51 0.05760886 M D 18.29364925
#> 757 9.0909091 49 0.05760886 M P 11.03897340
#> 758 9.0909091 51 0.05760886 M P 11.45177592
#> 759 9.0909091 49 0.05760886 M G 6.54184780
#> 760 9.0909091 51 0.05760886 M G 6.95465032
#> 761 9.2121212 49 0.05760886 M R 16.40724980
#> 762 9.2121212 51 0.05760886 M R 16.82005232
#> 763 9.2121212 49 0.05760886 M M 15.22608323
#> 764 9.2121212 51 0.05760886 M M 15.63888575
#> 765 9.2121212 49 0.05760886 M D 18.02832932
#> 766 9.2121212 51 0.05760886 M D 18.44113184
#> 767 9.2121212 49 0.05760886 M P 11.18645599
#> 768 9.2121212 51 0.05760886 M P 11.59925851
#> 769 9.2121212 49 0.05760886 M G 6.68933039
#> 770 9.2121212 51 0.05760886 M G 7.10213291
#> 771 9.3333333 49 0.05760886 M R 16.55473239
#> 772 9.3333333 51 0.05760886 M R 16.96753491
#> 773 9.3333333 49 0.05760886 M M 15.37356581
#> 774 9.3333333 51 0.05760886 M M 15.78636833
#> 775 9.3333333 49 0.05760886 M D 18.17581191
#> 776 9.3333333 51 0.05760886 M D 18.58861443
#> 777 9.3333333 49 0.05760886 M P 11.33393858
#> 778 9.3333333 51 0.05760886 M P 11.74674110
#> 779 9.3333333 49 0.05760886 M G 6.83681298
#> 780 9.3333333 51 0.05760886 M G 7.24961550
#> 781 9.4545455 49 0.05760886 M R 16.70221498
#> 782 9.4545455 51 0.05760886 M R 17.11501750
#> 783 9.4545455 49 0.05760886 M M 15.52104840
#> 784 9.4545455 51 0.05760886 M M 15.93385092
#> 785 9.4545455 49 0.05760886 M D 18.32329450
#> 786 9.4545455 51 0.05760886 M D 18.73609701
#> 787 9.4545455 49 0.05760886 M P 11.48142116
#> 788 9.4545455 51 0.05760886 M P 11.89422368
#> 789 9.4545455 49 0.05760886 M G 6.98429556
#> 790 9.4545455 51 0.05760886 M G 7.39709808
#> 791 9.5757576 49 0.05760886 M R 16.84969756
#> 792 9.5757576 51 0.05760886 M R 17.26250008
#> 793 9.5757576 49 0.05760886 M M 15.66853099
#> 794 9.5757576 51 0.05760886 M M 16.08133351
#> 795 9.5757576 49 0.05760886 M D 18.47077708
#> 796 9.5757576 51 0.05760886 M D 18.88357960
#> 797 9.5757576 49 0.05760886 M P 11.62890375
#> 798 9.5757576 51 0.05760886 M P 12.04170627
#> 799 9.5757576 49 0.05760886 M G 7.13177815
#> 800 9.5757576 51 0.05760886 M G 7.54458067
#> 801 9.6969697 49 0.05760886 M R 16.99718015
#> 802 9.6969697 51 0.05760886 M R 17.40998267
#> 803 9.6969697 49 0.05760886 M M 15.81601357
#> 804 9.6969697 51 0.05760886 M M 16.22881609
#> 805 9.6969697 49 0.05760886 M D 18.61825967
#> 806 9.6969697 51 0.05760886 M D 19.03106219
#> 807 9.6969697 49 0.05760886 M P 11.77638634
#> 808 9.6969697 51 0.05760886 M P 12.18918886
#> 809 9.6969697 49 0.05760886 M G 7.27926074
#> 810 9.6969697 51 0.05760886 M G 7.69206326
#> 811 9.8181818 49 0.05760886 M R 17.14466274
#> 812 9.8181818 51 0.05760886 M R 17.55746526
#> 813 9.8181818 49 0.05760886 M M 15.96349616
#> 814 9.8181818 51 0.05760886 M M 16.37629868
#> 815 9.8181818 49 0.05760886 M D 18.76574226
#> 816 9.8181818 51 0.05760886 M D 19.17854477
#> 817 9.8181818 49 0.05760886 M P 11.92386892
#> 818 9.8181818 51 0.05760886 M P 12.33667144
#> 819 9.8181818 49 0.05760886 M G 7.42674332
#> 820 9.8181818 51 0.05760886 M G 7.83954584
#> 821 9.9393939 49 0.05760886 M R 17.29214532
#> 822 9.9393939 51 0.05760886 M R 17.70494784
#> 823 9.9393939 49 0.05760886 M M 16.11097875
#> 824 9.9393939 51 0.05760886 M M 16.52378127
#> 825 9.9393939 49 0.05760886 M D 18.91322484
#> 826 9.9393939 51 0.05760886 M D 19.32602736
#> 827 9.9393939 49 0.05760886 M P 12.07135151
#> 828 9.9393939 51 0.05760886 M P 12.48415403
#> 829 9.9393939 49 0.05760886 M G 7.57422591
#> 830 9.9393939 51 0.05760886 M G 7.98702843
#> 831 10.0606061 49 0.05760886 M R 17.43962791
#> 832 10.0606061 51 0.05760886 M R 17.85243043
#> 833 10.0606061 49 0.05760886 M M 16.25846133
#> 834 10.0606061 51 0.05760886 M M 16.67126385
#> 835 10.0606061 49 0.05760886 M D 19.06070743
#> 836 10.0606061 51 0.05760886 M D 19.47350995
#> 837 10.0606061 49 0.05760886 M P 12.21883410
#> 838 10.0606061 51 0.05760886 M P 12.63163662
#> 839 10.0606061 49 0.05760886 M G 7.72170850
#> 840 10.0606061 51 0.05760886 M G 8.13451102
#> 841 10.1818182 49 0.05760886 M R 17.58711050
#> 842 10.1818182 51 0.05760886 M R 17.99991302
#> 843 10.1818182 49 0.05760886 M M 16.40594392
#> 844 10.1818182 51 0.05760886 M M 16.81874644
#> 845 10.1818182 49 0.05760886 M D 19.20819002
#> 846 10.1818182 51 0.05760886 M D 19.62099254
#> 847 10.1818182 49 0.05760886 M P 12.36631668
#> 848 10.1818182 51 0.05760886 M P 12.77911920
#> 849 10.1818182 49 0.05760886 M G 7.86919108
#> 850 10.1818182 51 0.05760886 M G 8.28199360
#> 851 10.3030303 49 0.05760886 M R 17.73459308
#> 852 10.3030303 51 0.05760886 M R 18.14739560
#> 853 10.3030303 49 0.05760886 M M 16.55342651
#> 854 10.3030303 51 0.05760886 M M 16.96622903
#> 855 10.3030303 49 0.05760886 M D 19.35567260
#> 856 10.3030303 51 0.05760886 M D 19.76847512
#> 857 10.3030303 49 0.05760886 M P 12.51379927
#> 858 10.3030303 51 0.05760886 M P 12.92660179
#> 859 10.3030303 49 0.05760886 M G 8.01667367
#> 860 10.3030303 51 0.05760886 M G 8.42947619
#> 861 10.4242424 49 0.05760886 M R 17.88207567
#> 862 10.4242424 51 0.05760886 M R 18.29487819
#> 863 10.4242424 49 0.05760886 M M 16.70090909
#> 864 10.4242424 51 0.05760886 M M 17.11371161
#> 865 10.4242424 49 0.05760886 M D 19.50315519
#> 866 10.4242424 51 0.05760886 M D 19.91595771
#> 867 10.4242424 49 0.05760886 M P 12.66128186
#> 868 10.4242424 51 0.05760886 M P 13.07408438
#> 869 10.4242424 49 0.05760886 M G 8.16415626
#> 870 10.4242424 51 0.05760886 M G 8.57695878
#> 871 10.5454545 49 0.05760886 M R 18.02955826
#> 872 10.5454545 51 0.05760886 M R 18.44236078
#> 873 10.5454545 49 0.05760886 M M 16.84839168
#> 874 10.5454545 51 0.05760886 M M 17.26119420
#> 875 10.5454545 49 0.05760886 M D 19.65063778
#> 876 10.5454545 51 0.05760886 M D 20.06344030
#> 877 10.5454545 49 0.05760886 M P 12.80876444
#> 878 10.5454545 51 0.05760886 M P 13.22156696
#> 879 10.5454545 49 0.05760886 M G 8.31163884
#> 880 10.5454545 51 0.05760886 M G 8.72444136
#> 881 10.6666667 49 0.05760886 M R 18.17704084
#> 882 10.6666667 51 0.05760886 M R 18.58984336
#> 883 10.6666667 49 0.05760886 M M 16.99587427
#> 884 10.6666667 51 0.05760886 M M 17.40867679
#> 885 10.6666667 49 0.05760886 M D 19.79812036
#> 886 10.6666667 51 0.05760886 M D 20.21092288
#> 887 10.6666667 49 0.05760886 M P 12.95624703
#> 888 10.6666667 51 0.05760886 M P 13.36904955
#> 889 10.6666667 49 0.05760886 M G 8.45912143
#> 890 10.6666667 51 0.05760886 M G 8.87192395
#> 891 10.7878788 49 0.05760886 M R 18.32452343
#> 892 10.7878788 51 0.05760886 M R 18.73732595
#> 893 10.7878788 49 0.05760886 M M 17.14335685
#> 894 10.7878788 51 0.05760886 M M 17.55615937
#> 895 10.7878788 49 0.05760886 M D 19.94560295
#> 896 10.7878788 51 0.05760886 M D 20.35840547
#> 897 10.7878788 49 0.05760886 M P 13.10372962
#> 898 10.7878788 51 0.05760886 M P 13.51653214
#> 899 10.7878788 49 0.05760886 M G 8.60660402
#> 900 10.7878788 51 0.05760886 M G 9.01940654
#> 901 10.9090909 49 0.05760886 M R 18.47200602
#> 902 10.9090909 51 0.05760886 M R 18.88480854
#> 903 10.9090909 49 0.05760886 M M 17.29083944
#> 904 10.9090909 51 0.05760886 M M 17.70364196
#> 905 10.9090909 49 0.05760886 M D 20.09308554
#> 906 10.9090909 51 0.05760886 M D 20.50588806
#> 907 10.9090909 49 0.05760886 M P 13.25121220
#> 908 10.9090909 51 0.05760886 M P 13.66401472
#> 909 10.9090909 49 0.05760886 M G 8.75408660
#> 910 10.9090909 51 0.05760886 M G 9.16688912
#> 911 11.0303030 49 0.05760886 M R 18.61948860
#> 912 11.0303030 51 0.05760886 M R 19.03229112
#> 913 11.0303030 49 0.05760886 M M 17.43832203
#> 914 11.0303030 51 0.05760886 M M 17.85112455
#> 915 11.0303030 49 0.05760886 M D 20.24056812
#> 916 11.0303030 51 0.05760886 M D 20.65337064
#> 917 11.0303030 49 0.05760886 M P 13.39869479
#> 918 11.0303030 51 0.05760886 M P 13.81149731
#> 919 11.0303030 49 0.05760886 M G 8.90156919
#> 920 11.0303030 51 0.05760886 M G 9.31437171
#> 921 11.1515152 49 0.05760886 M R 18.76697119
#> 922 11.1515152 51 0.05760886 M R 19.17977371
#> 923 11.1515152 49 0.05760886 M M 17.58580461
#> 924 11.1515152 51 0.05760886 M M 17.99860713
#> 925 11.1515152 49 0.05760886 M D 20.38805071
#> 926 11.1515152 51 0.05760886 M D 20.80085323
#> 927 11.1515152 49 0.05760886 M P 13.54617738
#> 928 11.1515152 51 0.05760886 M P 13.95897990
#> 929 11.1515152 49 0.05760886 M G 9.04905178
#> 930 11.1515152 51 0.05760886 M G 9.46185430
#> 931 11.2727273 49 0.05760886 M R 18.91445378
#> 932 11.2727273 51 0.05760886 M R 19.32725630
#> 933 11.2727273 49 0.05760886 M M 17.73328720
#> 934 11.2727273 51 0.05760886 M M 18.14608972
#> 935 11.2727273 49 0.05760886 M D 20.53553330
#> 936 11.2727273 51 0.05760886 M D 20.94833582
#> 937 11.2727273 49 0.05760886 M P 13.69365996
#> 938 11.2727273 51 0.05760886 M P 14.10646248
#> 939 11.2727273 49 0.05760886 M G 9.19653436
#> 940 11.2727273 51 0.05760886 M G 9.60933688
#> 941 11.3939394 49 0.05760886 M R 19.06193636
#> 942 11.3939394 51 0.05760886 M R 19.47473888
#> 943 11.3939394 49 0.05760886 M M 17.88076979
#> 944 11.3939394 51 0.05760886 M M 18.29357231
#> 945 11.3939394 49 0.05760886 M D 20.68301588
#> 946 11.3939394 51 0.05760886 M D 21.09581840
#> 947 11.3939394 49 0.05760886 M P 13.84114255
#> 948 11.3939394 51 0.05760886 M P 14.25394507
#> 949 11.3939394 49 0.05760886 M G 9.34401695
#> 950 11.3939394 51 0.05760886 M G 9.75681947
#> 951 11.5151515 49 0.05760886 M R 19.20941895
#> 952 11.5151515 51 0.05760886 M R 19.62222147
#> 953 11.5151515 49 0.05760886 M M 18.02825237
#> 954 11.5151515 51 0.05760886 M M 18.44105489
#> 955 11.5151515 49 0.05760886 M D 20.83049847
#> 956 11.5151515 51 0.05760886 M D 21.24330099
#> 957 11.5151515 49 0.05760886 M P 13.98862514
#> 958 11.5151515 51 0.05760886 M P 14.40142766
#> 959 11.5151515 49 0.05760886 M G 9.49149954
#> 960 11.5151515 51 0.05760886 M G 9.90430206
#> 961 11.6363636 49 0.05760886 M R 19.35690154
#> 962 11.6363636 51 0.05760886 M R 19.76970406
#> 963 11.6363636 49 0.05760886 M M 18.17573496
#> 964 11.6363636 51 0.05760886 M M 18.58853748
#> 965 11.6363636 49 0.05760886 M D 20.97798106
#> 966 11.6363636 51 0.05760886 M D 21.39078358
#> 967 11.6363636 49 0.05760886 M P 14.13610772
#> 968 11.6363636 51 0.05760886 M P 14.54891024
#> 969 11.6363636 49 0.05760886 M G 9.63898212
#> 970 11.6363636 51 0.05760886 M G 10.05178464
#> 971 11.7575758 49 0.05760886 M R 19.50438412
#> 972 11.7575758 51 0.05760886 M R 19.91718664
#> 973 11.7575758 49 0.05760886 M M 18.32321755
#> 974 11.7575758 51 0.05760886 M M 18.73602007
#> 975 11.7575758 49 0.05760886 M D 21.12546364
#> 976 11.7575758 51 0.05760886 M D 21.53826616
#> 977 11.7575758 49 0.05760886 M P 14.28359031
#> 978 11.7575758 51 0.05760886 M P 14.69639283
#> 979 11.7575758 49 0.05760886 M G 9.78646471
#> 980 11.7575758 51 0.05760886 M G 10.19926723
#> 981 11.8787879 49 0.05760886 M R 19.65186671
#> 982 11.8787879 51 0.05760886 M R 20.06466923
#> 983 11.8787879 49 0.05760886 M M 18.47070013
#> 984 11.8787879 51 0.05760886 M M 18.88350265
#> 985 11.8787879 49 0.05760886 M D 21.27294623
#> 986 11.8787879 51 0.05760886 M D 21.68574875
#> 987 11.8787879 49 0.05760886 M P 14.43107290
#> 988 11.8787879 51 0.05760886 M P 14.84387542
#> 989 11.8787879 49 0.05760886 M G 9.93394730
#> 990 11.8787879 51 0.05760886 M G 10.34674982
#> 991 12.0000000 49 0.05760886 M R 19.79934930
#> 992 12.0000000 51 0.05760886 M R 20.21215182
#> 993 12.0000000 49 0.05760886 M M 18.61818272
#> 994 12.0000000 51 0.05760886 M M 19.03098524
#> 995 12.0000000 49 0.05760886 M D 21.42042882
#> 996 12.0000000 51 0.05760886 M D 21.83323134
#> 997 12.0000000 49 0.05760886 M P 14.57855548
#> 998 12.0000000 51 0.05760886 M P 14.99135800
#> 999 12.0000000 49 0.05760886 M G 10.08142988
#> 1000 12.0000000 51 0.05760886 M G 10.49423240
plot(y ~ x1, data = m2.data)
by(m2.p6, list(m2.p6$xcat2), function(x) {
lines(x$x1, x$fit, col = x$xcat2, lty = as.numeric(x$xcat2))
})
#> : R
#> NULL
#> ------------------------------------------------------------
#> : M
#> NULL
#> ------------------------------------------------------------
#> : D
#> NULL
#> ------------------------------------------------------------
#> : P
#> NULL
#> ------------------------------------------------------------
#> : G
#> NULL
m2.newdata <- newdata(m2, predVals = list(x2 = c(48, 50, 52),
xcat2 = c("M","D")))
predict(m2, newdata = m2.newdata)
#> 1 2 3 4 5 6
#> 11.01000 11.42280 11.83560 13.81225 14.22505 14.63785
(m2.p7 <- predictOMatic(m2, predVals = list(x2 = c(48, 50, 52),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5.916667 48 0.05760886 M M 11.01000
#> 2 5.916667 50 0.05760886 M M 11.42280
#> 3 5.916667 52 0.05760886 M M 11.83560
#> 4 5.916667 48 0.05760886 M D 13.81225
#> 5 5.916667 50 0.05760886 M D 14.22505
#> 6 5.916667 52 0.05760886 M D 14.63785
(m2.p8 <- predictOMatic(m2,
predVals = list(x2 = range(m2.data$x2, na.rm = TRUE),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5.916667 26.53056 0.05760886 M M 6.578680
#> 2 5.916667 76.55788 0.05760886 M M 16.904382
#> 3 5.916667 26.53056 0.05760886 M D 9.380926
#> 4 5.916667 76.55788 0.05760886 M D 19.706628
(m2.p9 <- predictOMatic(m2, predVals = list(x2 = plotSeq(m2.data$x2),
x1 = quantile(m2.data$x1, pr =c(0.33, 0.66), na.rm = TRUE),
xcat2 = c("M","D"))))
#> x1 x2 x3 xcat1 xcat2 fit
#> 1 5 26.53056 0.05760886 M M 5.463343
#> 2 5 27.23517 0.05760886 M M 5.608775
#> 3 5 27.93978 0.05760886 M M 5.754208
#> 4 5 28.64439 0.05760886 M M 5.899640
#> 5 5 29.34900 0.05760886 M M 6.045072
#> 6 5 30.05361 0.05760886 M M 6.190505
#> 7 5 30.75822 0.05760886 M M 6.335937
#> 8 5 31.46283 0.05760886 M M 6.481370
#> 9 5 32.16744 0.05760886 M M 6.626802
#> 10 5 32.87205 0.05760886 M M 6.772235
#> 11 5 33.57666 0.05760886 M M 6.917667
#> 12 5 34.28127 0.05760886 M M 7.063099
#> 13 5 34.98588 0.05760886 M M 7.208532
#> 14 5 35.69049 0.05760886 M M 7.353964
#> 15 5 36.39510 0.05760886 M M 7.499397
#> 16 5 37.09971 0.05760886 M M 7.644829
#> 17 5 37.80432 0.05760886 M M 7.790262
#> 18 5 38.50893 0.05760886 M M 7.935694
#> 19 5 39.21354 0.05760886 M M 8.081126
#> 20 5 39.91815 0.05760886 M M 8.226559
#> 21 5 40.62276 0.05760886 M M 8.371991
#> 22 5 41.32737 0.05760886 M M 8.517424
#> 23 5 42.03198 0.05760886 M M 8.662856
#> 24 5 42.73659 0.05760886 M M 8.808289
#> 25 5 43.44120 0.05760886 M M 8.953721
#> 26 5 44.14581 0.05760886 M M 9.099153
#> 27 5 44.85042 0.05760886 M M 9.244586
#> 28 5 45.55503 0.05760886 M M 9.390018
#> 29 5 46.25965 0.05760886 M M 9.535451
#> 30 5 46.96426 0.05760886 M M 9.680883
#> 31 5 47.66887 0.05760886 M M 9.826316
#> 32 5 48.37348 0.05760886 M M 9.971748
#> 33 5 49.07809 0.05760886 M M 10.117180
#> 34 5 49.78270 0.05760886 M M 10.262613
#> 35 5 50.48731 0.05760886 M M 10.408045
#> 36 5 51.19192 0.05760886 M M 10.553478
#> 37 5 51.89653 0.05760886 M M 10.698910
#> 38 5 52.60114 0.05760886 M M 10.844342
#> 39 5 53.30575 0.05760886 M M 10.989775
#> 40 5 54.01036 0.05760886 M M 11.135207
#> 41 5 54.71497 0.05760886 M M 11.280640
#> 42 5 55.41958 0.05760886 M M 11.426072
#> 43 5 56.12419 0.05760886 M M 11.571505
#> 44 5 56.82880 0.05760886 M M 11.716937
#> 45 5 57.53341 0.05760886 M M 11.862369
#> 46 5 58.23802 0.05760886 M M 12.007802
#> 47 5 58.94263 0.05760886 M M 12.153234
#> 48 5 59.64724 0.05760886 M M 12.298667
#> 49 5 60.35185 0.05760886 M M 12.444099
#> 50 5 61.05646 0.05760886 M M 12.589532
#> 51 5 61.76107 0.05760886 M M 12.734964
#> 52 5 62.46568 0.05760886 M M 12.880396
#> 53 5 63.17029 0.05760886 M M 13.025829
#> 54 5 63.87490 0.05760886 M M 13.171261
#> 55 5 64.57951 0.05760886 M M 13.316694
#> 56 5 65.28412 0.05760886 M M 13.462126
#> 57 5 65.98873 0.05760886 M M 13.607559
#> 58 5 66.69334 0.05760886 M M 13.752991
#> 59 5 67.39795 0.05760886 M M 13.898423
#> 60 5 68.10256 0.05760886 M M 14.043856
#> 61 5 68.80717 0.05760886 M M 14.189288
#> 62 5 69.51178 0.05760886 M M 14.334721
#> 63 5 70.21639 0.05760886 M M 14.480153
#> 64 5 70.92100 0.05760886 M M 14.625586
#> 65 5 71.62561 0.05760886 M M 14.771018
#> 66 5 72.33022 0.05760886 M M 14.916450
#> 67 5 73.03483 0.05760886 M M 15.061883
#> 68 5 73.73944 0.05760886 M M 15.207315
#> 69 5 74.44405 0.05760886 M M 15.352748
#> 70 5 75.14866 0.05760886 M M 15.498180
#> 71 5 75.85327 0.05760886 M M 15.643613
#> 72 5 76.55788 0.05760886 M M 15.789045
#> 73 7 26.53056 0.05760886 M M 7.896805
#> 74 7 27.23517 0.05760886 M M 8.042238
#> 75 7 27.93978 0.05760886 M M 8.187670
#> 76 7 28.64439 0.05760886 M M 8.333103
#> 77 7 29.34900 0.05760886 M M 8.478535
#> 78 7 30.05361 0.05760886 M M 8.623967
#> 79 7 30.75822 0.05760886 M M 8.769400
#> 80 7 31.46283 0.05760886 M M 8.914832
#> 81 7 32.16744 0.05760886 M M 9.060265
#> 82 7 32.87205 0.05760886 M M 9.205697
#> 83 7 33.57666 0.05760886 M M 9.351130
#> 84 7 34.28127 0.05760886 M M 9.496562
#> 85 7 34.98588 0.05760886 M M 9.641994
#> 86 7 35.69049 0.05760886 M M 9.787427
#> 87 7 36.39510 0.05760886 M M 9.932859
#> 88 7 37.09971 0.05760886 M M 10.078292
#> 89 7 37.80432 0.05760886 M M 10.223724
#> 90 7 38.50893 0.05760886 M M 10.369157
#> 91 7 39.21354 0.05760886 M M 10.514589
#> 92 7 39.91815 0.05760886 M M 10.660021
#> 93 7 40.62276 0.05760886 M M 10.805454
#> 94 7 41.32737 0.05760886 M M 10.950886
#> 95 7 42.03198 0.05760886 M M 11.096319
#> 96 7 42.73659 0.05760886 M M 11.241751
#> 97 7 43.44120 0.05760886 M M 11.387184
#> 98 7 44.14581 0.05760886 M M 11.532616
#> 99 7 44.85042 0.05760886 M M 11.678048
#> 100 7 45.55503 0.05760886 M M 11.823481
#> 101 7 46.25965 0.05760886 M M 11.968913
#> 102 7 46.96426 0.05760886 M M 12.114346
#> 103 7 47.66887 0.05760886 M M 12.259778
#> 104 7 48.37348 0.05760886 M M 12.405211
#> 105 7 49.07809 0.05760886 M M 12.550643
#> 106 7 49.78270 0.05760886 M M 12.696075
#> 107 7 50.48731 0.05760886 M M 12.841508
#> 108 7 51.19192 0.05760886 M M 12.986940
#> 109 7 51.89653 0.05760886 M M 13.132373
#> 110 7 52.60114 0.05760886 M M 13.277805
#> 111 7 53.30575 0.05760886 M M 13.423238
#> 112 7 54.01036 0.05760886 M M 13.568670
#> 113 7 54.71497 0.05760886 M M 13.714102
#> 114 7 55.41958 0.05760886 M M 13.859535
#> 115 7 56.12419 0.05760886 M M 14.004967
#> 116 7 56.82880 0.05760886 M M 14.150400
#> 117 7 57.53341 0.05760886 M M 14.295832
#> 118 7 58.23802 0.05760886 M M 14.441265
#> 119 7 58.94263 0.05760886 M M 14.586697
#> 120 7 59.64724 0.05760886 M M 14.732129
#> 121 7 60.35185 0.05760886 M M 14.877562
#> 122 7 61.05646 0.05760886 M M 15.022994
#> 123 7 61.76107 0.05760886 M M 15.168427
#> 124 7 62.46568 0.05760886 M M 15.313859
#> 125 7 63.17029 0.05760886 M M 15.459292
#> 126 7 63.87490 0.05760886 M M 15.604724
#> 127 7 64.57951 0.05760886 M M 15.750156
#> 128 7 65.28412 0.05760886 M M 15.895589
#> 129 7 65.98873 0.05760886 M M 16.041021
#> 130 7 66.69334 0.05760886 M M 16.186454
#> 131 7 67.39795 0.05760886 M M 16.331886
#> 132 7 68.10256 0.05760886 M M 16.477319
#> 133 7 68.80717 0.05760886 M M 16.622751
#> 134 7 69.51178 0.05760886 M M 16.768183
#> 135 7 70.21639 0.05760886 M M 16.913616
#> 136 7 70.92100 0.05760886 M M 17.059048
#> 137 7 71.62561 0.05760886 M M 17.204481
#> 138 7 72.33022 0.05760886 M M 17.349913
#> 139 7 73.03483 0.05760886 M M 17.495346
#> 140 7 73.73944 0.05760886 M M 17.640778
#> 141 7 74.44405 0.05760886 M M 17.786210
#> 142 7 75.14866 0.05760886 M M 17.931643
#> 143 7 75.85327 0.05760886 M M 18.077075
#> 144 7 76.55788 0.05760886 M M 18.222508
#> 145 5 26.53056 0.05760886 M D 8.265589
#> 146 5 27.23517 0.05760886 M D 8.411021
#> 147 5 27.93978 0.05760886 M D 8.556454
#> 148 5 28.64439 0.05760886 M D 8.701886
#> 149 5 29.34900 0.05760886 M D 8.847318
#> 150 5 30.05361 0.05760886 M D 8.992751
#> 151 5 30.75822 0.05760886 M D 9.138183
#> 152 5 31.46283 0.05760886 M D 9.283616
#> 153 5 32.16744 0.05760886 M D 9.429048
#> 154 5 32.87205 0.05760886 M D 9.574481
#> 155 5 33.57666 0.05760886 M D 9.719913
#> 156 5 34.28127 0.05760886 M D 9.865345
#> 157 5 34.98588 0.05760886 M D 10.010778
#> 158 5 35.69049 0.05760886 M D 10.156210
#> 159 5 36.39510 0.05760886 M D 10.301643
#> 160 5 37.09971 0.05760886 M D 10.447075
#> 161 5 37.80432 0.05760886 M D 10.592508
#> 162 5 38.50893 0.05760886 M D 10.737940
#> 163 5 39.21354 0.05760886 M D 10.883372
#> 164 5 39.91815 0.05760886 M D 11.028805
#> 165 5 40.62276 0.05760886 M D 11.174237
#> 166 5 41.32737 0.05760886 M D 11.319670
#> 167 5 42.03198 0.05760886 M D 11.465102
#> 168 5 42.73659 0.05760886 M D 11.610535
#> 169 5 43.44120 0.05760886 M D 11.755967
#> 170 5 44.14581 0.05760886 M D 11.901399
#> 171 5 44.85042 0.05760886 M D 12.046832
#> 172 5 45.55503 0.05760886 M D 12.192264
#> 173 5 46.25965 0.05760886 M D 12.337697
#> 174 5 46.96426 0.05760886 M D 12.483129
#> 175 5 47.66887 0.05760886 M D 12.628562
#> 176 5 48.37348 0.05760886 M D 12.773994
#> 177 5 49.07809 0.05760886 M D 12.919426
#> 178 5 49.78270 0.05760886 M D 13.064859
#> 179 5 50.48731 0.05760886 M D 13.210291
#> 180 5 51.19192 0.05760886 M D 13.355724
#> 181 5 51.89653 0.05760886 M D 13.501156
#> 182 5 52.60114 0.05760886 M D 13.646589
#> 183 5 53.30575 0.05760886 M D 13.792021
#> 184 5 54.01036 0.05760886 M D 13.937453
#> 185 5 54.71497 0.05760886 M D 14.082886
#> 186 5 55.41958 0.05760886 M D 14.228318
#> 187 5 56.12419 0.05760886 M D 14.373751
#> 188 5 56.82880 0.05760886 M D 14.519183
#> 189 5 57.53341 0.05760886 M D 14.664616
#> 190 5 58.23802 0.05760886 M D 14.810048
#> 191 5 58.94263 0.05760886 M D 14.955480
#> 192 5 59.64724 0.05760886 M D 15.100913
#> 193 5 60.35185 0.05760886 M D 15.246345
#> 194 5 61.05646 0.05760886 M D 15.391778
#> 195 5 61.76107 0.05760886 M D 15.537210
#> 196 5 62.46568 0.05760886 M D 15.682643
#> 197 5 63.17029 0.05760886 M D 15.828075
#> 198 5 63.87490 0.05760886 M D 15.973507
#> 199 5 64.57951 0.05760886 M D 16.118940
#> 200 5 65.28412 0.05760886 M D 16.264372
#> 201 5 65.98873 0.05760886 M D 16.409805
#> 202 5 66.69334 0.05760886 M D 16.555237
#> 203 5 67.39795 0.05760886 M D 16.700670
#> 204 5 68.10256 0.05760886 M D 16.846102
#> 205 5 68.80717 0.05760886 M D 16.991534
#> 206 5 69.51178 0.05760886 M D 17.136967
#> 207 5 70.21639 0.05760886 M D 17.282399
#> 208 5 70.92100 0.05760886 M D 17.427832
#> 209 5 71.62561 0.05760886 M D 17.573264
#> 210 5 72.33022 0.05760886 M D 17.718697
#> 211 5 73.03483 0.05760886 M D 17.864129
#> 212 5 73.73944 0.05760886 M D 18.009561
#> 213 5 74.44405 0.05760886 M D 18.154994
#> 214 5 75.14866 0.05760886 M D 18.300426
#> 215 5 75.85327 0.05760886 M D 18.445859
#> 216 5 76.55788 0.05760886 M D 18.591291
#> 217 7 26.53056 0.05760886 M D 10.699051
#> 218 7 27.23517 0.05760886 M D 10.844484
#> 219 7 27.93978 0.05760886 M D 10.989916
#> 220 7 28.64439 0.05760886 M D 11.135349
#> 221 7 29.34900 0.05760886 M D 11.280781
#> 222 7 30.05361 0.05760886 M D 11.426214
#> 223 7 30.75822 0.05760886 M D 11.571646
#> 224 7 31.46283 0.05760886 M D 11.717078
#> 225 7 32.16744 0.05760886 M D 11.862511
#> 226 7 32.87205 0.05760886 M D 12.007943
#> 227 7 33.57666 0.05760886 M D 12.153376
#> 228 7 34.28127 0.05760886 M D 12.298808
#> 229 7 34.98588 0.05760886 M D 12.444241
#> 230 7 35.69049 0.05760886 M D 12.589673
#> 231 7 36.39510 0.05760886 M D 12.735105
#> 232 7 37.09971 0.05760886 M D 12.880538
#> 233 7 37.80432 0.05760886 M D 13.025970
#> 234 7 38.50893 0.05760886 M D 13.171403
#> 235 7 39.21354 0.05760886 M D 13.316835
#> 236 7 39.91815 0.05760886 M D 13.462268
#> 237 7 40.62276 0.05760886 M D 13.607700
#> 238 7 41.32737 0.05760886 M D 13.753132
#> 239 7 42.03198 0.05760886 M D 13.898565
#> 240 7 42.73659 0.05760886 M D 14.043997
#> 241 7 43.44120 0.05760886 M D 14.189430
#> 242 7 44.14581 0.05760886 M D 14.334862
#> 243 7 44.85042 0.05760886 M D 14.480295
#> 244 7 45.55503 0.05760886 M D 14.625727
#> 245 7 46.25965 0.05760886 M D 14.771159
#> 246 7 46.96426 0.05760886 M D 14.916592
#> 247 7 47.66887 0.05760886 M D 15.062024
#> 248 7 48.37348 0.05760886 M D 15.207457
#> 249 7 49.07809 0.05760886 M D 15.352889
#> 250 7 49.78270 0.05760886 M D 15.498322
#> 251 7 50.48731 0.05760886 M D 15.643754
#> 252 7 51.19192 0.05760886 M D 15.789186
#> 253 7 51.89653 0.05760886 M D 15.934619
#> 254 7 52.60114 0.05760886 M D 16.080051
#> 255 7 53.30575 0.05760886 M D 16.225484
#> 256 7 54.01036 0.05760886 M D 16.370916
#> 257 7 54.71497 0.05760886 M D 16.516349
#> 258 7 55.41958 0.05760886 M D 16.661781
#> 259 7 56.12419 0.05760886 M D 16.807213
#> 260 7 56.82880 0.05760886 M D 16.952646
#> 261 7 57.53341 0.05760886 M D 17.098078
#> 262 7 58.23802 0.05760886 M D 17.243511
#> 263 7 58.94263 0.05760886 M D 17.388943
#> 264 7 59.64724 0.05760886 M D 17.534376
#> 265 7 60.35185 0.05760886 M D 17.679808
#> 266 7 61.05646 0.05760886 M D 17.825240
#> 267 7 61.76107 0.05760886 M D 17.970673
#> 268 7 62.46568 0.05760886 M D 18.116105
#> 269 7 63.17029 0.05760886 M D 18.261538
#> 270 7 63.87490 0.05760886 M D 18.406970
#> 271 7 64.57951 0.05760886 M D 18.552403
#> 272 7 65.28412 0.05760886 M D 18.697835
#> 273 7 65.98873 0.05760886 M D 18.843267
#> 274 7 66.69334 0.05760886 M D 18.988700
#> 275 7 67.39795 0.05760886 M D 19.134132
#> 276 7 68.10256 0.05760886 M D 19.279565
#> 277 7 68.80717 0.05760886 M D 19.424997
#> 278 7 69.51178 0.05760886 M D 19.570430
#> 279 7 70.21639 0.05760886 M D 19.715862
#> 280 7 70.92100 0.05760886 M D 19.861294
#> 281 7 71.62561 0.05760886 M D 20.006727
#> 282 7 72.33022 0.05760886 M D 20.152159
#> 283 7 73.03483 0.05760886 M D 20.297592
#> 284 7 73.73944 0.05760886 M D 20.443024
#> 285 7 74.44405 0.05760886 M D 20.588457
#> 286 7 75.14866 0.05760886 M D 20.733889
#> 287 7 75.85327 0.05760886 M D 20.879321
#> 288 7 76.55788 0.05760886 M D 21.024754
plot(y ~ x2 , data = m2.data)
by(m2.p9, list(m2.p9$x1, m2.p9$xcat2), function(x) {lines(x$x2, x$fit)})
#> : 5
#> : M
#> NULL
#> ------------------------------------------------------------
#> : 7
#> : M
#> NULL
#> ------------------------------------------------------------
#> : 5
#> : D
#> NULL
#> ------------------------------------------------------------
#> : 7
#> : D
#> NULL
(predictOMatic(m2, predVals = list(x2 = c(50, 60), xcat2 = c("M","D")),
interval = "conf"))
#> x1 x2 x3 xcat1 xcat2 fit lwr upr
#> 1 5.916667 50 0.05760886 M M 11.42280 3.625835 19.21977
#> 2 5.916667 60 0.05760886 M M 13.48681 5.881770 21.09186
#> 3 5.916667 50 0.05760886 M D 14.22505 6.692096 21.75800
#> 4 5.916667 60 0.05760886 M D 16.28906 8.947072 23.63105
## create a dichotomous dependent variable
y2 <- ifelse(rnorm(N) > 0.3, 1, 0)
dat <- cbind(dat, y2)
m3 <- glm(y2 ~ x1 + x2 + x3 + xcat1, data = dat, family = binomial(logit))
summary(m3)
#>
#> Call:
#> glm(formula = y2 ~ x1 + x2 + x3 + xcat1, family = binomial(logit),
#> data = dat)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.29110 1.28486 -1.005 0.315
#> x1 -0.07220 0.08954 -0.806 0.420
#> x2 0.01405 0.02059 0.683 0.495
#> x3 0.37407 0.24651 1.517 0.129
#> xcat1F 0.46421 0.49618 0.936 0.349
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 100.893 on 79 degrees of freedom
#> Residual deviance: 96.657 on 75 degrees of freedom
#> (20 observations deleted due to missingness)
#> AIC: 106.66
#>
#> Number of Fisher Scoring iterations: 4
#>
m3.data <- model.data(m3)
summarize(m3.data)
#> Numeric variables
#> y2 x1 x2 x3
#> min 0 0 26.531 -2.290
#> med 0 6 52.509 -0.063
#> max 1 12 76.558 2.747
#> mean 0.325 5.950 51.695 0.001
#> sd 0.471 2.801 12.144 1.009
#> skewness 0.733 0.163 0.051 0.193
#> kurtosis -1.480 -0.630 -0.951 0.137
#> nobs 80 80 80 80
#> nmissing 0 0 0 0
#>
#> Nonnumeric variables
#> xcat1
#> M: 42
#> F: 38
#> nobs : 80.000
#> nmiss : 0.000
#> entropy : 0.998
#> normedEntropy: 0.998
(m3.p1 <- predictOMatic(m3, divider = "std.dev."))
#> $x1
#> x1 x2 x3 xcat1 fit
#> 1 0.35 51.69472 0.001215052 M 0.3567418
#> 2 3.15 51.69472 0.001215052 M 0.3118026
#> 3 5.95 51.69472 0.001215052 M 0.2701470
#> 4 8.75 51.69472 0.001215052 M 0.2321790
#> 5 11.55 51.69472 0.001215052 M 0.1980988
#>
#> $x2
#> x1 x2 x3 xcat1 fit
#> 1 5.95 27.41 0.001215052 M 0.2083155
#> 2 5.95 39.55 0.001215052 M 0.2378458
#> 3 5.95 51.69 0.001215052 M 0.2701339
#> 4 5.95 63.83 0.001215052 M 0.3050509
#> 5 5.95 75.97 0.001215052 M 0.3423640
#>
#> $x3
#> x1 x2 x3 xcat1 fit
#> 1 5.95 51.69472 -2.02 M 0.1480542
#> 2 5.95 51.69472 -1.01 M 0.2022747
#> 3 5.95 51.69472 0.00 M 0.2700574
#> 4 5.95 51.69472 1.01 M 0.3505723
#> 5 5.95 51.69472 2.02 M 0.4406023
#>
#> $xcat1
#> x1 x2 x3 xcat1 fit
#> 1 5.95 51.69472 0.001215052 M 0.2701470
#> 2 5.95 51.69472 0.001215052 F 0.3705941
#>
(m3.p2 <- predictOMatic(m3, predVals = list(x2 = c(40, 50, 60),
xcat1 = c("M","F")),
divider = "std.dev.", interval = "conf"))
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> x1 x2 x3 xcat1 fit lwr upr
#> 1 5.95 40 0.001215052 M 0.2389940 0.1141650 0.4335160
#> 2 5.95 50 0.001215052 M 0.2654776 0.1512197 0.4230384
#> 3 5.95 60 0.001215052 M 0.2937632 0.1669658 0.4632992
#> 4 5.95 40 0.001215052 F 0.3331449 0.1824247 0.5279745
#> 5 5.95 50 0.001215052 F 0.3650569 0.2256033 0.5315435
#> 6 5.95 60 0.001215052 F 0.3982005 0.2336623 0.5894782
## Want a full accounting for each value of x2?
(m3.p3 <- predictOMatic(m3,
predVals = list(x2 = unique(m3.data$x2),
xcat1 = c("M","F")), interval = "conf"))
#> rockchalk:::predCI: model's predict method does not return an interval.
#> We will improvize with a Wald type approximation to the confidence interval
#> x1 x2 x3 xcat1 fit lwr upr
#> 1 5.95 44.59614 0.001215052 M 0.2509370 0.13239967 0.4237650
#> 2 5.95 69.47693 0.001215052 M 0.3221272 0.15791589 0.5463146
#> 3 5.95 50.53590 0.001215052 M 0.2669486 0.15278499 0.4237504
#> 4 5.95 53.51663 0.001215052 M 0.2752242 0.16012467 0.4306371
#> 5 5.95 43.29023 0.001215052 M 0.2475036 0.12730670 0.4258125
#> 6 5.95 52.77954 0.001215052 M 0.2731630 0.15853969 0.4284609
#> 7 5.95 56.91171 0.001215052 M 0.2848415 0.16530202 0.4447647
#> 8 5.95 71.45065 0.001215052 M 0.3282127 0.15428887 0.5667962
#> 9 5.95 26.53056 0.001215052 M 0.2062849 0.06584850 0.4893389
#> 10 5.95 51.49592 0.001215052 M 0.2695966 0.15541151 0.4254188
#> 11 5.95 36.57469 0.001215052 M 0.2303505 0.10060965 0.4446780
#> 12 5.95 55.53303 0.001215052 M 0.2809118 0.16362764 0.4382158
#> 13 5.95 65.89963 0.001215052 M 0.3112506 0.16328959 0.5113467
#> 14 5.95 44.13120 0.001215052 M 0.2497110 0.13060067 0.4244211
#> 15 5.95 31.67623 0.001215052 M 0.2183747 0.08249306 0.4647137
#> 16 5.95 58.88139 0.001215052 M 0.2905129 0.16668654 0.4559907
#> 17 5.95 65.93488 0.001215052 M 0.3113568 0.16324600 0.5116740
#> 18 5.95 37.04328 0.001215052 M 0.2315199 0.10243387 0.4429919
#> 19 5.95 42.15351 0.001215052 M 0.2445408 0.12279450 0.4280876
#> 20 5.95 39.50647 0.001215052 M 0.2377350 0.11218856 0.4349465
#> 21 5.95 73.30512 0.001215052 M 0.3339837 0.15057397 0.5865357
#> 22 5.95 64.02705 0.001215052 M 0.3056382 0.16528005 0.4945684
#> 23 5.95 58.26258 0.001215052 M 0.2887239 0.16637681 0.4522329
#> 24 5.95 41.88460 0.001215052 M 0.2438435 0.12172026 0.4286884
#> 25 5.95 60.21258 0.001215052 M 0.2943833 0.16697924 0.4647610
#> 26 5.95 56.45383 0.001215052 M 0.2835327 0.16481113 0.4424679
#> 27 5.95 60.43144 0.001215052 M 0.2950225 0.16698018 0.4662896
#> 28 5.95 46.95631 0.001215052 M 0.2572220 0.14118420 0.4217908
#> 29 5.95 74.77111 0.001215052 M 0.3385813 0.14747653 0.6023546
#> 30 5.95 59.71221 0.001215052 M 0.2929249 0.16692775 0.4613568
#> 31 5.95 56.72042 0.001215052 M 0.2842943 0.16510481 0.4437906
#> 32 5.95 55.36524 0.001215052 M 0.2804358 0.16338369 0.4374942
#> 33 5.95 58.24870 0.001215052 M 0.2886839 0.16636857 0.4521510
#> 34 5.95 40.36099 0.001215052 M 0.2399177 0.11561259 0.4325116
#> 35 5.95 41.44917 0.001215052 M 0.2427171 0.11997734 0.4297097
#> 36 5.95 68.86947 0.001215052 M 0.3202662 0.15894916 0.5401558
#> 37 5.95 46.08181 0.001215052 M 0.2548813 0.13800832 0.4222461
#> 38 5.95 40.19367 0.001215052 M 0.2394892 0.11494149 0.4329727
#> 39 5.95 56.87332 0.001215052 M 0.2847317 0.16526334 0.4445675
#> 40 5.95 44.94956 0.001215052 M 0.2518716 0.13375466 0.4233224
#> 41 5.95 71.57720 0.001215052 M 0.3286049 0.15404387 0.5681303
#> 42 5.95 44.00202 0.001215052 M 0.2493711 0.13009781 0.4246180
#> 43 5.95 52.23925 0.001215052 M 0.2716583 0.15727865 0.4270656
#> 44 5.95 38.43777 0.001215052 M 0.2350244 0.10792815 0.4382597
#> 45 5.95 36.75245 0.001215052 M 0.2307936 0.10130019 0.4440330
#> 46 5.95 46.88394 0.001215052 M 0.2570278 0.14092551 0.4218155
#> 47 5.95 65.56110 0.001215052 M 0.3102318 0.16369725 0.5082243
#> 48 5.95 53.21124 0.001215052 M 0.2743690 0.15948724 0.4296971
#> 49 5.95 36.75941 0.001215052 M 0.2308110 0.10132729 0.4440078
#> 50 5.95 62.61242 0.001215052 M 0.3014361 0.16631312 0.4827689
#> 51 5.95 63.19232 0.001215052 M 0.3031547 0.16594270 0.4875073
#> 52 5.95 49.19246 0.001215052 M 0.2632709 0.14873653 0.4222537
#> 53 5.95 44.94910 0.001215052 M 0.2518704 0.13375289 0.4233230
#> 54 5.95 49.47846 0.001215052 M 0.2640511 0.14963244 0.4224926
#> 55 5.95 56.28861 0.001215052 M 0.2830614 0.16461810 0.4416687
#> 56 5.95 71.80002 0.001215052 M 0.3292960 0.15360922 0.5704844
#> 57 5.95 65.44864 0.001215052 M 0.3098938 0.16382823 0.5071954
#> 58 5.95 63.21452 0.001215052 M 0.3032206 0.16592702 0.4876915
#> 59 5.95 53.22152 0.001215052 M 0.2743978 0.15950914 0.4297278
#> 60 5.95 65.30955 0.001215052 M 0.3094760 0.16398708 0.5059286
#> 61 5.95 45.78760 0.001215052 M 0.2540970 0.13691713 0.4224743
#> 62 5.95 31.54632 0.001215052 M 0.2180633 0.08204055 0.4652966
#> 63 5.95 61.57325 0.001215052 M 0.2983703 0.16677974 0.4746426
#> 64 5.95 28.76450 0.001215052 M 0.2114718 0.07274582 0.4782894
#> 65 5.95 38.03968 0.001215052 M 0.2340203 0.10635088 0.4395652
#> 66 5.95 66.42192 0.001215052 M 0.3128261 0.16262250 0.5162351
#> 67 5.95 58.83655 0.001215052 M 0.2903830 0.16666787 0.4557114
#> 68 5.95 55.24876 0.001215052 M 0.2801056 0.16320923 0.4370029
#> 69 5.95 38.15341 0.001215052 M 0.2343068 0.10680084 0.4391884
#> 70 5.95 76.55788 0.001215052 M 0.3442263 0.14355816 0.6217572
#> 71 5.95 39.52086 0.001215052 M 0.2377716 0.11224613 0.4349039
#> 72 5.95 56.68922 0.001215052 M 0.2842051 0.16507156 0.4436336
#> 73 5.95 51.29177 0.001215052 M 0.2690321 0.15487279 0.4250212
#> 74 5.95 45.77423 0.001215052 M 0.2540614 0.13686728 0.4224856
#> 75 5.95 38.59736 0.001215052 M 0.2354278 0.10856216 0.4377469
#> 76 5.95 37.06285 0.001215052 M 0.2315688 0.10251030 0.4429225
#> 77 5.95 44.05301 0.001215052 M 0.2495052 0.13029644 0.4245395
#> 78 5.95 34.99186 0.001215052 M 0.2264311 0.09455048 0.4506981
#> 79 5.95 34.52708 0.001215052 M 0.2252892 0.09280512 0.4525546
#> 80 5.95 58.96013 0.001215052 M 0.2907410 0.16671790 0.4564836
#> 81 5.95 44.59614 0.001215052 F 0.3476435 0.20544066 0.5234337
#> 82 5.95 69.47693 0.001215052 F 0.4305015 0.21491934 0.6761014
#> 83 5.95 50.53590 0.001215052 F 0.3668041 0.22703203 0.5332615
#> 84 5.95 53.51663 0.001215052 F 0.3765850 0.23285421 0.5459021
#> 85 5.95 43.29023 0.001215052 F 0.3434938 0.19928894 0.5237866
#> 86 5.95 52.77954 0.001215052 F 0.3741566 0.23175261 0.5422961
#> 87 5.95 56.91171 0.001215052 F 0.3878489 0.23514873 0.5662915
#> 88 5.95 71.45065 0.001215052 F 0.4373136 0.20943622 0.6951235
#> 89 5.95 26.53056 0.001215052 F 0.2925029 0.11169177 0.5761671
#> 90 5.95 51.49592 0.001215052 F 0.3699427 0.22930320 0.5367648
#> 91 5.95 36.57469 0.001215052 F 0.3225395 0.16379791 0.5364318
#> 92 5.95 55.53303 0.001215052 F 0.3832596 0.23475201 0.5572969
#> 93 5.95 65.89963 0.001215052 F 0.4182233 0.22381519 0.6418557
#> 94 5.95 44.13120 0.001215052 F 0.3461634 0.20329516 0.5234659
#> 95 5.95 31.67623 0.001215052 F 0.3076870 0.13733349 0.5537182
#> 96 5.95 58.88139 0.001215052 F 0.3944400 0.23455285 0.5806411
#> 97 5.95 65.93488 0.001215052 F 0.4183438 0.22373587 0.6421883
#> 98 5.95 37.04328 0.001215052 F 0.3239800 0.16636826 0.5350688
#> 99 5.95 42.15351 0.001215052 F 0.3399009 0.19364906 0.5247298
#> 100 5.95 39.50647 0.001215052 F 0.3316061 0.17978024 0.5289618
#> 101 5.95 73.30512 0.001215052 F 0.4437357 0.20402917 0.7128509
#> 102 5.95 64.02705 0.001215052 F 0.4118353 0.22772953 0.6244335
#> 103 5.95 58.26258 0.001215052 F 0.3923650 0.23487929 0.5759570
#> 104 5.95 41.88460 0.001215052 F 0.3390537 0.19228202 0.5250344
#> 105 5.95 60.21258 0.001215052 F 0.3989165 0.23345146 0.5912077
#> 106 5.95 56.45383 0.001215052 F 0.3863225 0.23509477 0.5632023
#> 107 5.95 60.43144 0.001215052 F 0.3996541 0.23322117 0.5930040
#> 108 5.95 46.95631 0.001215052 F 0.3552021 0.21543085 0.5249779
#> 109 5.95 74.77111 0.001215052 F 0.4488260 0.19962652 0.7266714
#> 110 5.95 59.71221 0.001215052 F 0.3972318 0.23392698 0.5871614
#> 111 5.95 56.72042 0.001215052 F 0.3872109 0.23513541 0.5649890
#> 112 5.95 55.36524 0.001215052 F 0.3827024 0.23465499 0.5562666
#> 113 5.95 58.24870 0.001215052 F 0.3923185 0.23488519 0.5758537
#> 114 5.95 40.36099 0.001215052 F 0.3342727 0.18434489 0.5273071
#> 115 5.95 41.44917 0.001215052 F 0.3376839 0.19004511 0.5255907
#> 116 5.95 68.86947 0.001215052 F 0.4284100 0.21653606 0.6702433
#> 117 5.95 46.08181 0.001215052 F 0.3523928 0.21191882 0.5240619
#> 118 5.95 40.19367 0.001215052 F 0.3337497 0.18345649 0.5276106
#> 119 5.95 56.87332 0.001215052 F 0.3877208 0.23514711 0.5660287
#> 120 5.95 44.94956 0.001215052 F 0.3487706 0.20703576 0.5234807
#> 121 5.95 71.57720 0.001215052 F 0.4377512 0.20907421 0.6963395
#> 122 5.95 44.00202 0.001215052 F 0.3457527 0.20268996 0.5234934
#> 123 5.95 52.23925 0.001215052 F 0.3723806 0.23080394 0.5398508
#> 124 5.95 38.43777 0.001215052 F 0.3282862 0.17399384 0.5313809
#> 125 5.95 36.75245 0.001215052 F 0.3230856 0.16477307 0.5359078
#> 126 5.95 46.88394 0.001215052 F 0.3549692 0.21514938 0.5248862
#> 127 5.95 65.56110 0.001215052 F 0.4170663 0.22456684 0.6386691
#> 128 5.95 53.21124 0.001215052 F 0.3755781 0.23242469 0.5443706
#> 129 5.95 36.75941 0.001215052 F 0.3231070 0.16481128 0.5358874
#> 130 5.95 62.61242 0.001215052 F 0.4070290 0.23024533 0.6116860
#> 131 5.95 63.19232 0.001215052 F 0.4089972 0.22926466 0.6168603
#> 132 5.95 49.19246 0.001215052 F 0.3624308 0.22323785 0.5292760
#> 133 5.95 44.94910 0.001215052 F 0.3487692 0.20703369 0.5234806
#> 134 5.95 49.47846 0.001215052 F 0.3633600 0.22410443 0.5300351
#> 135 5.95 56.28861 0.001215052 F 0.3857722 0.23505655 0.5621120
#> 136 5.95 71.80002 0.001215052 F 0.4385219 0.20843408 0.6984788
#> 137 5.95 65.44864 0.001215052 F 0.4166822 0.22481248 0.6376137
#> 138 5.95 63.21452 0.001215052 F 0.4090726 0.22922567 0.6170599
#> 139 5.95 53.22152 0.001215052 F 0.3756119 0.23243977 0.5444213
#> 140 5.95 65.30955 0.001215052 F 0.4162073 0.22511340 0.6363107
#> 141 5.95 45.78760 0.001215052 F 0.3514500 0.21068491 0.5238465
#> 142 5.95 31.54632 0.001215052 F 0.3072983 0.13665224 0.5542399
#> 143 5.95 61.57325 0.001215052 F 0.4035097 0.23181113 0.6026185
#> 144 5.95 28.76450 0.001215052 F 0.2990410 0.12245945 0.5660137
#> 145 5.95 38.03968 0.001215052 F 0.3270539 0.17182302 0.5323749
#> 146 5.95 66.42192 0.001215052 F 0.4200100 0.22262092 0.6467978
#> 147 5.95 58.83655 0.001215052 F 0.3942895 0.23458060 0.5802965
#> 148 5.95 55.24876 0.001215052 F 0.3823159 0.23458128 0.5555598
#> 149 5.95 38.15341 0.001215052 F 0.3274057 0.17244384 0.5320860
#> 150 5.95 76.55788 0.001215052 F 0.4550446 0.19415271 0.7431932
#> 151 5.95 39.52086 0.001215052 F 0.3316509 0.17985764 0.5289318
#> 152 5.95 56.68922 0.001215052 F 0.3871069 0.23513198 0.5647781
#> 153 5.95 51.29177 0.001215052 F 0.3692744 0.22885151 0.5359741
#> 154 5.95 45.77423 0.001215052 F 0.3514071 0.21062823 0.5238377
#> 155 5.95 38.59736 0.001215052 F 0.3287809 0.17486215 0.5309962
#> 156 5.95 37.06285 0.001215052 F 0.3240402 0.16647554 0.5350132
#> 157 5.95 44.05301 0.001215052 F 0.3459148 0.20292930 0.5234816
#> 158 5.95 34.99186 0.001215052 F 0.3176990 0.15513060 0.5414502
#> 159 5.95 34.52708 0.001215052 F 0.3162851 0.15259838 0.5430360
#> 160 5.95 58.96013 0.001215052 F 0.3947043 0.23450259 0.5812479
## Would like to write a more beautiful print method
## for output object, but don't want to obscure structure from user.
## for (i in names(m3.p1)){
## dns <- cbind(m3.p1[[i]][i], m3.p1[[i]]$fit)
## colnames(dns) <- c(i, "predicted")
## print(dns)
## }