From 3ae8b44a5651b98d2e8d95d42064362c86673b98 Mon Sep 17 00:00:00 2001
From: june861 <overtheriver861@gmail.com>
Date: Wed, 23 Oct 2024 09:29:36 +0800
Subject: [PATCH 1/4] add: my code

---
 mycode/test.r | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 mycode/test.r

diff --git a/mycode/test.r b/mycode/test.r
new file mode 100644
index 0000000..e69de29

From 3f8374c3b2c23fa905561713d877d5f696fe53a8 Mon Sep 17 00:00:00 2001
From: june861 <overtheriver861@gmail.com>
Date: Wed, 23 Oct 2024 16:27:53 +0800
Subject: [PATCH 2/4] add:my pkg

---
 mycode/test.r                                |  24 +++++++++++++++++++
 mypkgs/mylin2024111211/.Rbuildignore         |   2 ++
 mypkgs/mylin2024111211/.gitignore            |   1 +
 mypkgs/mylin2024111211/DESCRIPTION           |  12 ++++++++++
 mypkgs/mylin2024111211/NAMESPACE             |   6 +++++
 mypkgs/mylin2024111211/R/lin_reg.R           |  24 +++++++++++++++++++
 mypkgs/mylin2024111211/R/predict.R           |  11 +++++++++
 mypkgs/mylin2024111211/R/print.R             |  10 ++++++++
 mypkgs/mylin2024111211/man/lin_reg.Rd        |  19 +++++++++++++++
 mypkgs/mylin2024111211/man/predict.Rd        |  19 +++++++++++++++
 mypkgs/mylin2024111211/man/print.lm.Rd       |  17 +++++++++++++
 mypkgs/mylin2024111211/man/save_model.Rd     |  19 +++++++++++++++
 mypkgs/mylin2024111211/my_model_file.rda     | Bin 0 -> 185 bytes
 mypkgs/mylin2024111211/mylin2024111211.Rproj |  17 +++++++++++++
 14 files changed, 181 insertions(+)
 create mode 100644 mypkgs/mylin2024111211/.Rbuildignore
 create mode 100644 mypkgs/mylin2024111211/.gitignore
 create mode 100644 mypkgs/mylin2024111211/DESCRIPTION
 create mode 100644 mypkgs/mylin2024111211/NAMESPACE
 create mode 100644 mypkgs/mylin2024111211/R/lin_reg.R
 create mode 100644 mypkgs/mylin2024111211/R/predict.R
 create mode 100644 mypkgs/mylin2024111211/R/print.R
 create mode 100644 mypkgs/mylin2024111211/man/lin_reg.Rd
 create mode 100644 mypkgs/mylin2024111211/man/predict.Rd
 create mode 100644 mypkgs/mylin2024111211/man/print.lm.Rd
 create mode 100644 mypkgs/mylin2024111211/man/save_model.Rd
 create mode 100644 mypkgs/mylin2024111211/my_model_file.rda
 create mode 100644 mypkgs/mylin2024111211/mylin2024111211.Rproj

diff --git a/mycode/test.r b/mycode/test.r
index e69de29..a25c037 100644
--- a/mycode/test.r
+++ b/mycode/test.r
@@ -0,0 +1,24 @@
+library(mylin2024111211)
+# build a linear random datas
+x <- rnorm(100)
+y <- 20 * x + 23 + rnorm(100)
+data <- data.frame(x, y)
+# draw plot
+library(ggplot2)
+# 绘制散点图
+ggplot(data1, aes(x = x, y = y)) +
+  geom_point(color = "blue", size = 2) +  # 绘制散点
+  labs(title = "Scatter Plot", x = "X Values", y = "Y Values") +  # 添加标题和标签
+  theme_minimal()
+# fit model
+model <- lin_reg(y ~ x, data = data)
+abline(model, col = "red")
+print(model)
+model_save_dir <- getwd()
+model_save_name <- "my_linear_mode.rda"
+filename <- paste(model_save_dir, model_save_name, sep = '/')
+save_model(model = model, filename = filename)
+load(filename)
+data2 <- data.frame(x = 1:10)
+predictions <- predict(model, data2)
+print(predictions)
diff --git a/mypkgs/mylin2024111211/.Rbuildignore b/mypkgs/mylin2024111211/.Rbuildignore
new file mode 100644
index 0000000..813f03a
--- /dev/null
+++ b/mypkgs/mylin2024111211/.Rbuildignore
@@ -0,0 +1,2 @@
+^mylin2024111211\.Rproj$
+^\.Rproj\.user$
diff --git a/mypkgs/mylin2024111211/.gitignore b/mypkgs/mylin2024111211/.gitignore
new file mode 100644
index 0000000..cd67eac
--- /dev/null
+++ b/mypkgs/mylin2024111211/.gitignore
@@ -0,0 +1 @@
+.Rproj.user
diff --git a/mypkgs/mylin2024111211/DESCRIPTION b/mypkgs/mylin2024111211/DESCRIPTION
new file mode 100644
index 0000000..825fe46
--- /dev/null
+++ b/mypkgs/mylin2024111211/DESCRIPTION
@@ -0,0 +1,12 @@
+Package: mylin2024111211
+Title: What the Package Does (One Line, Title Case)
+Version: 0.0.0.9000
+Authors@R: 
+    person("First", "Last", , "first.last@example.com", role = c("aut", "cre"),
+           comment = c(ORCID = "YOUR-ORCID-ID"))
+Description: What the package does (one paragraph).
+License: `use_mit_license()`, `use_gpl3_license()` or friends to pick a
+    license
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.3.2
diff --git a/mypkgs/mylin2024111211/NAMESPACE b/mypkgs/mylin2024111211/NAMESPACE
new file mode 100644
index 0000000..daad58b
--- /dev/null
+++ b/mypkgs/mylin2024111211/NAMESPACE
@@ -0,0 +1,6 @@
+# Generated by roxygen2: do not edit by hand
+
+S3method(print,lm)
+export(lin_reg)
+export(predict)
+export(save_model)
diff --git a/mypkgs/mylin2024111211/R/lin_reg.R b/mypkgs/mylin2024111211/R/lin_reg.R
new file mode 100644
index 0000000..9c6091f
--- /dev/null
+++ b/mypkgs/mylin2024111211/R/lin_reg.R
@@ -0,0 +1,24 @@
+#' Linear Regression Model
+#'
+#' @description train a linear model
+#' @param formula describle model formula
+#' @param data data build from data.frame
+#' @return return a list consist of coefficiences and formula
+#' @export
+lin_reg <- function(formula, data) {
+  x <- model.matrix(formula, data)[, -1]
+  y <- data[[as.character(formula[[2]])]]
+  coef <- solve(t(x) %*% x) %*% t(x) %*% y
+  list(coefficients = coef, formula = formula)
+}
+
+#' Linear Regression Model
+#'
+#' @description save model
+#' @param mode the linear model you want to save
+#' @param filename the save model file name
+#' @return none
+#' @export
+save_model <- function(model, filename) {
+  save(model, file = filename)
+}
diff --git a/mypkgs/mylin2024111211/R/predict.R b/mypkgs/mylin2024111211/R/predict.R
new file mode 100644
index 0000000..377ad23
--- /dev/null
+++ b/mypkgs/mylin2024111211/R/predict.R
@@ -0,0 +1,11 @@
+#' Predict via Linear Regression Model
+#'
+#' @description train a linear model
+#' @param model describle model formula
+#' @param newdata build from data.frame
+#' @return return a list consist of coefficiences and formula
+#' @export
+predict <- function(model, newdata) {
+  x_new <- cbind(1, newdata)
+  return(x_new %*% model$coefficients)
+}
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/R/print.R b/mypkgs/mylin2024111211/R/print.R
new file mode 100644
index 0000000..4689cce
--- /dev/null
+++ b/mypkgs/mylin2024111211/R/print.R
@@ -0,0 +1,10 @@
+#' Linear Regression Model
+#'
+#' @description train a linear model
+#' @param model the model you want to display
+#' @return return a list consist of coefficiences and formula
+#' @export
+print.lm <- function(model) {
+  cat("Coefficients:\n")
+  print(model$coefficients)
+}
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/man/lin_reg.Rd b/mypkgs/mylin2024111211/man/lin_reg.Rd
new file mode 100644
index 0000000..c28f36c
--- /dev/null
+++ b/mypkgs/mylin2024111211/man/lin_reg.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lin_reg.R
+\name{lin_reg}
+\alias{lin_reg}
+\title{Linear Regression Model}
+\usage{
+lin_reg(formula, data)
+}
+\arguments{
+\item{formula}{describle model formula}
+
+\item{data}{data build from data.frame}
+}
+\value{
+return a list consist of coefficiences and formula
+}
+\description{
+train a linear model
+}
diff --git a/mypkgs/mylin2024111211/man/predict.Rd b/mypkgs/mylin2024111211/man/predict.Rd
new file mode 100644
index 0000000..718ad82
--- /dev/null
+++ b/mypkgs/mylin2024111211/man/predict.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{predict}
+\alias{predict}
+\title{Predict via Linear Regression Model}
+\usage{
+predict(model, newdata)
+}
+\arguments{
+\item{model}{describle model formula}
+
+\item{newdata}{build from data.frame}
+}
+\value{
+return a list consist of coefficiences and formula
+}
+\description{
+train a linear model
+}
diff --git a/mypkgs/mylin2024111211/man/print.lm.Rd b/mypkgs/mylin2024111211/man/print.lm.Rd
new file mode 100644
index 0000000..94e85f3
--- /dev/null
+++ b/mypkgs/mylin2024111211/man/print.lm.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/print.R
+\name{print.lm}
+\alias{print.lm}
+\title{Linear Regression Model}
+\usage{
+\method{print}{lm}(model)
+}
+\arguments{
+\item{model}{the model you want to display}
+}
+\value{
+return a list consist of coefficiences and formula
+}
+\description{
+train a linear model
+}
diff --git a/mypkgs/mylin2024111211/man/save_model.Rd b/mypkgs/mylin2024111211/man/save_model.Rd
new file mode 100644
index 0000000..61fb8bc
--- /dev/null
+++ b/mypkgs/mylin2024111211/man/save_model.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lin_reg.R
+\name{save_model}
+\alias{save_model}
+\title{Linear Regression Model}
+\usage{
+save_model(model, filename)
+}
+\arguments{
+\item{filename}{the save model file name}
+
+\item{mode}{the linear model you want to save}
+}
+\value{
+none
+}
+\description{
+save model
+}
diff --git a/mypkgs/mylin2024111211/my_model_file.rda b/mypkgs/mylin2024111211/my_model_file.rda
new file mode 100644
index 0000000000000000000000000000000000000000..15be7eeaec5ca71445b67fc294eac8534eee2516
GIT binary patch
literal 185
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diff --git a/mypkgs/mylin2024111211/mylin2024111211.Rproj b/mypkgs/mylin2024111211/mylin2024111211.Rproj
new file mode 100644
index 0000000..aaa62a5
--- /dev/null
+++ b/mypkgs/mylin2024111211/mylin2024111211.Rproj
@@ -0,0 +1,17 @@
+Version: 1.0
+
+RestoreWorkspace: No
+SaveWorkspace: No
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+Encoding: UTF-8
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+LineEndingConversion: Posix
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace

From d81c1f11f91ac9edb12edc32d1bc086c3b572917 Mon Sep 17 00:00:00 2001
From: june861 <overtheriver861@gmail.com>
Date: Wed, 23 Oct 2024 17:42:11 +0800
Subject: [PATCH 3/4] add:

---
 learn_lm.R                               |  78 +++++++++++++++++++++++
 mycode/test.r                            |  67 +++++++++++++------
 mycode/test1.R                           |  30 +++++++++
 mypkgs/mylin2024111211/R/lin_reg.R       |  16 +++--
 mypkgs/mylin2024111211/R/predict.R       |   4 +-
 mypkgs/mylin2024111211/my_model_file.rda | Bin 185 -> 0 bytes
 6 files changed, 170 insertions(+), 25 deletions(-)
 create mode 100644 learn_lm.R
 create mode 100644 mycode/test1.R
 delete mode 100644 mypkgs/mylin2024111211/my_model_file.rda

diff --git a/learn_lm.R b/learn_lm.R
new file mode 100644
index 0000000..aee9891
--- /dev/null
+++ b/learn_lm.R
@@ -0,0 +1,78 @@
+learn_ml <- function (formula, data, subset, weights, na.action, method = "qr",
+    model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
+    contrasts = NULL, offset, ...)
+{
+    ret.x <- x
+    ret.y <- y
+    cl <- match.call()
+    mf <- match.call(expand.dots = FALSE)
+    m <- match(c("formula", "data", "subset", "weights", "na.action",
+        "offset"), names(mf), 0L)
+    mf <- mf[c(1L, m)]
+    mf$drop.unused.levels <- TRUE
+    mf[[1L]] <- quote(stats::model.frame)
+    mf <- eval(mf, parent.frame())
+    if (method == "model.frame")
+        return(mf)
+    else if (method != "qr")
+        warning(gettextf("method = '%s' is not supported. Using 'qr'",
+            method), domain = NA)
+    mt <- attr(mf, "terms")
+    y <- model.response(mf, "numeric")
+    w <- as.vector(model.weights(mf))
+    if (!is.null(w) && !is.numeric(w))
+        stop("'weights' must be a numeric vector")
+    offset <- model.offset(mf)
+    mlm <- is.matrix(y)
+    ny <- if (mlm)
+        nrow(y)
+    else length(y)
+    if (!is.null(offset)) {
+        if (!mlm)
+            offset <- as.vector(offset)
+        if (NROW(offset) != ny)
+            stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
+                NROW(offset), ny), domain = NA)
+    }
+    if (is.empty.model(mt)) {
+        x <- NULL
+        z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
+            ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
+            y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
+            0) else ny)
+        if (!is.null(offset)) {
+            z$fitted.values <- offset
+            z$residuals <- y - offset
+        }
+    }
+    else {
+        x <- model.matrix(mt, mf, contrasts)
+        z <- if (is.null(w))
+            lm.fit(x, y, offset = offset, singular.ok = singular.ok,
+                ...)
+        else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
+            ...)
+    }
+    class(z) <- c(if (mlm) "mlm", "lm")
+    z$na.action <- attr(mf, "na.action")
+    z$offset <- offset
+    z$contrasts <- attr(x, "contrasts")
+    z$xlevels <- .getXlevels(mt, mf)
+    z$call <- cl
+    z$terms <- mt
+    if (model)
+        z$model <- mf
+    if (ret.x)
+        z$x <- x
+    if (ret.y)
+        z$y <- y
+    if (!qr)
+        z$qr <- NULL
+    z
+}
+
+set.seed(123) # 保证随机数可重复
+x <- rnorm(100)
+y <- 20 * x + 23 + rnorm(100)
+
+model <- learn_ml(y ~ x, data = data.frame(x = x, y = y))
\ No newline at end of file
diff --git a/mycode/test.r b/mycode/test.r
index a25c037..ff00b13 100644
--- a/mycode/test.r
+++ b/mycode/test.r
@@ -1,24 +1,51 @@
+# library(mylin2024111211)
+# # build a linear random datas
+# x <- rnorm(100)
+# y <- 20 * x + 23 + rnorm(100)
+# data <- data.frame(x, y)
+# # draw plot
+# library(ggplot2)
+# # 绘制散点图
+# ggplot(data1, aes(x = x, y = y)) +
+#   geom_point(color = "blue", size = 2) +  # 绘制散点
+#   labs(title = "Scatter Plot", x = "X Values", y = "Y Values") +  # 添加标题和标签
+#   theme_minimal()
+# # fit model
+# model <- lin_reg(y ~ x, data = data)
+# abline(model, col = "red")
+# print(model)
+# model_save_dir <- getwd()
+# model_save_name <- "my_linear_mode.rda"
+# filename <- paste(model_save_dir, model_save_name, sep = '/')
+# save_model(model = model, filename = filename)
+# load(filename)
+# data2 <- data.frame(x = 1:10)
+# predictions <- predict(model, data2)
+# print(predictions)
+
+
+
 library(mylin2024111211)
-# build a linear random datas
+
+# 创建一些数据
+set.seed(123) # 保证随机数可重复
 x <- rnorm(100)
 y <- 20 * x + 23 + rnorm(100)
-data <- data.frame(x, y)
-# draw plot
-library(ggplot2)
-# 绘制散点图
-ggplot(data1, aes(x = x, y = y)) +
-  geom_point(color = "blue", size = 2) +  # 绘制散点
-  labs(title = "Scatter Plot", x = "X Values", y = "Y Values") +  # 添加标题和标签
-  theme_minimal()
-# fit model
-model <- lin_reg(y ~ x, data = data)
-abline(model, col = "red")
+
+# 使用 lin_reg 训练模型
+model <- lin_reg(y ~ x, data = data.frame(x = x, y = y))
+
+# 输出模型信息
 print(model)
-model_save_dir <- getwd()
-model_save_name <- "my_linear_mode.rda"
-filename <- paste(model_save_dir, model_save_name, sep = '/')
-save_model(model = model, filename = filename)
-load(filename)
-data2 <- data.frame(x = 1:10)
-predictions <- predict(model, data2)
-print(predictions)
+
+# # 预测值
+predictions <- predict(model, data.frame(x = seq(min(x),
+                                                 max(x), length.out = 10)))
+
+# 绘制原始数据与预测线
+plot(x, y, pch = 19, col = "blue", main = "Linear Regression Model",
+     xlab = "x", ylab = "y")
+abline(model, col = 'orange')
+lines(seq(min(x), max(x), length.out = 10), predictions, col = "red", lwd = 2)
+legend("topleft", legend = c("Actual Data", "Fitted Line"),
+       col = c("blue", "red"), pch = c(19, NA), lty = c(NA, 1), lwd = 2)
\ No newline at end of file
diff --git a/mycode/test1.R b/mycode/test1.R
new file mode 100644
index 0000000..4a866d5
--- /dev/null
+++ b/mycode/test1.R
@@ -0,0 +1,30 @@
+if (!requireNamespace("animation")) install.packages("animation")
+library(animation)
+
+# 设置动画参数
+interval <- 20
+ani.options(interval = interval) # 设置帧间隔时间
+
+# 动态拟合线性模型
+dynamic_fit <- function(n_points_max = 100, step = 20) {
+  set.seed(123) # 设置随机种子确保可重复性
+  x <- rnorm(n_points_max)
+  y <- 20 * x + 23 + rnorm(n_points_max) # 生成带有噪声的线性数据
+  
+  saveGIF({
+    for (n in seq(step, n_points_max, by = step)) {
+      # 使用前n个点拟合模型
+      model <- lm(y ~ x, data = data.frame(x = x[1:n], y = y[1:n]))
+      # 预测整个x范围内的y值
+      predictions <- predict(model, data.frame(x = seq(min(x), max(x), length.out = 100)))
+      # 绘制当前拟合线
+      plot(x[1:n], y[1:n], pch=19, col="blue", main=paste0("Fitting with ", n, " points"),
+           xlab = "x", ylab = "y")
+      lines(seq(min(x), max(x), length.out = 100), predictions, col="red", lwd=2)
+      
+      Sys.sleep(interval) # 暂停一下，让动画效果更明显
+    }
+  }, movie.name = "dynamic_fit.gif", interval = 0.1)
+}
+
+dynamic_fit()
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/R/lin_reg.R b/mypkgs/mylin2024111211/R/lin_reg.R
index 9c6091f..7ff4514 100644
--- a/mypkgs/mylin2024111211/R/lin_reg.R
+++ b/mypkgs/mylin2024111211/R/lin_reg.R
@@ -6,10 +6,18 @@
 #' @return return a list consist of coefficiences and formula
 #' @export
 lin_reg <- function(formula, data) {
-  x <- model.matrix(formula, data)[, -1]
-  y <- data[[as.character(formula[[2]])]]
-  coef <- solve(t(x) %*% x) %*% t(x) %*% y
-  list(coefficients = coef, formula = formula)
+  m <- model.matrix(formula, data)
+  y <- model.response(model.frame(formula, data))
+  # Solve for coefficients using least squares
+  coef <- solve(crossprod(m), crossprod(m, y))
+  # Create a list that holds the model information
+  model <- list(coefficients = coef,
+                residuals = y - m %*% coef,
+                call = match.call(),
+                terms = terms(formula),
+                rank = length(coef))
+  class(model) <- "mylin"
+  return(model)
 }
 
 #' Linear Regression Model
diff --git a/mypkgs/mylin2024111211/R/predict.R b/mypkgs/mylin2024111211/R/predict.R
index 377ad23..7da5cdf 100644
--- a/mypkgs/mylin2024111211/R/predict.R
+++ b/mypkgs/mylin2024111211/R/predict.R
@@ -7,5 +7,7 @@
 #' @export
 predict <- function(model, newdata) {
   x_new <- cbind(1, newdata)
-  return(x_new %*% model$coefficients)
+  # conver x_new to numerical
+  x_new_mat <- as.matrix(x_new)
+  return(x_new_mat %*% model$coefficients)
 }
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/my_model_file.rda b/mypkgs/mylin2024111211/my_model_file.rda
deleted file mode 100644
index 15be7eeaec5ca71445b67fc294eac8534eee2516..0000000000000000000000000000000000000000
GIT binary patch
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From 61150f5accf1c548d85032ca84ce064e52da362e Mon Sep 17 00:00:00 2001
From: june861 <overtheriver861@gmail.com>
Date: Sat, 26 Oct 2024 12:51:04 +0800
Subject: [PATCH 4/4] add: modify

---
 learn_lm.R                            |  3 +-
 mycode/T.R                            | 70 ++++++++++++++++++++
 mycode/test.r                         | 51 --------------
 mypkgs/mylin2024111211/R/lin_reg.R    | 95 ++++++++++++++++++++-------
 mypkgs/mylin2024111211/R/predict.R    | 26 +++++---
 mypkgs/mylin2024111211/R/print.R      | 16 +++--
 mypkgs/mylin2024111211/man/lin_reg.Rd | 42 +++++++++++-
 7 files changed, 206 insertions(+), 97 deletions(-)
 create mode 100644 mycode/T.R
 delete mode 100644 mycode/test.r

diff --git a/learn_lm.R b/learn_lm.R
index aee9891..852456b 100644
--- a/learn_lm.R
+++ b/learn_lm.R
@@ -1,7 +1,6 @@
 learn_ml <- function (formula, data, subset, weights, na.action, method = "qr",
     model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
-    contrasts = NULL, offset, ...)
-{
+    contrasts = NULL, offset, ...) {
     ret.x <- x
     ret.y <- y
     cl <- match.call()
diff --git a/mycode/T.R b/mycode/T.R
new file mode 100644
index 0000000..972912c
--- /dev/null
+++ b/mycode/T.R
@@ -0,0 +1,70 @@
+lin_reg <- function(formula, data, subset, save_model = TRUE,
+                    weights = NULL, na.action = na.fail,
+                    model_save_path = NULL, ...) {
+
+  mf <- match.call(expand.dots = FALSE)
+  mf <- mf[c(1L, match(c("formula", "data", "subset", "save_model", "weights", "na.action", "model_save_path"), names(mf), 0L))]
+  mf$drop.unused.levels <- TRUE
+  mf[[1L]] <- as.name("model.frame")
+  mf <- eval(mf, parent.frame())
+
+  mt <- attr(mf, "terms")
+  y <- model.response(mf, "numeric")
+
+  if (!is.null(weights)) {
+    if (!is.numeric(weights))
+      stop("'weights' must be a numeric vector")
+  }
+
+  x <- model.matrix(mt, mf)
+
+  if (is.null(weights)) {
+    coef <- lm.fit(x, y)$coefficients
+  } else {
+    coef <- lm.wfit(x, y, weights)$coefficients
+  }
+
+  model <- structure(list(coefficients = coef, residuals = y - x %*% coef,
+                          call = match.call(), terms = mt, model = mf,
+                          na.action = attr(mf, "na.action"),
+                          xlevels = .getXlevels(mt, mf)),
+                     class = "lin_reg")
+
+  if (save_model) {
+    if (is.null(model_save_path)) {
+      curr_dir <- getwd()
+      model_save_path <- paste(curr_dir, "linear_model.rda", sep = "/")
+    }
+
+    # check model_save_path fairness
+    dir_path <- dirname(model_save_path)
+    if (!file.exists(dir_path)) {
+      dir.create(dir_path, recursive = TRUE)
+    }
+
+    # Check if file already exists, modify filename if needed
+    base_name <- basename(model_save_path)
+    pattern <- paste0("^", gsub("[.]", "\\.", base_name), "$")
+    dir_files <- list.files(dir_path, pattern = pattern, full.names = FALSE)
+
+    i <- 1
+    basename_omit <- strsplit(base_name, "\\.")[[1L]][[1L]]
+    while (base_name %in% dir_files) {
+      base_name <- paste0(basename_omit, i, ".rda")
+      i <- i + 1
+    }
+
+    model_save_path <- file.path(dir_path, base_name)
+    print(cat("model will be save in ", model_save_path))
+    save(model, file = model_save_path)
+  }
+
+  model
+}
+
+# 创建一些数据
+x <- rnorm(100)
+y <- 20 * x + 23 + rnorm(100)
+
+# 使用 lin_reg 训练模型
+model <- lin_reg(y ~ x, data = data.frame(x = x, y = y))
\ No newline at end of file
diff --git a/mycode/test.r b/mycode/test.r
deleted file mode 100644
index ff00b13..0000000
--- a/mycode/test.r
+++ /dev/null
@@ -1,51 +0,0 @@
-# library(mylin2024111211)
-# # build a linear random datas
-# x <- rnorm(100)
-# y <- 20 * x + 23 + rnorm(100)
-# data <- data.frame(x, y)
-# # draw plot
-# library(ggplot2)
-# # 绘制散点图
-# ggplot(data1, aes(x = x, y = y)) +
-#   geom_point(color = "blue", size = 2) +  # 绘制散点
-#   labs(title = "Scatter Plot", x = "X Values", y = "Y Values") +  # 添加标题和标签
-#   theme_minimal()
-# # fit model
-# model <- lin_reg(y ~ x, data = data)
-# abline(model, col = "red")
-# print(model)
-# model_save_dir <- getwd()
-# model_save_name <- "my_linear_mode.rda"
-# filename <- paste(model_save_dir, model_save_name, sep = '/')
-# save_model(model = model, filename = filename)
-# load(filename)
-# data2 <- data.frame(x = 1:10)
-# predictions <- predict(model, data2)
-# print(predictions)
-
-
-
-library(mylin2024111211)
-
-# 创建一些数据
-set.seed(123) # 保证随机数可重复
-x <- rnorm(100)
-y <- 20 * x + 23 + rnorm(100)
-
-# 使用 lin_reg 训练模型
-model <- lin_reg(y ~ x, data = data.frame(x = x, y = y))
-
-# 输出模型信息
-print(model)
-
-# # 预测值
-predictions <- predict(model, data.frame(x = seq(min(x),
-                                                 max(x), length.out = 10)))
-
-# 绘制原始数据与预测线
-plot(x, y, pch = 19, col = "blue", main = "Linear Regression Model",
-     xlab = "x", ylab = "y")
-abline(model, col = 'orange')
-lines(seq(min(x), max(x), length.out = 10), predictions, col = "red", lwd = 2)
-legend("topleft", legend = c("Actual Data", "Fitted Line"),
-       col = c("blue", "red"), pch = c(19, NA), lty = c(NA, 1), lwd = 2)
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/R/lin_reg.R b/mypkgs/mylin2024111211/R/lin_reg.R
index 7ff4514..79f417c 100644
--- a/mypkgs/mylin2024111211/R/lin_reg.R
+++ b/mypkgs/mylin2024111211/R/lin_reg.R
@@ -1,32 +1,77 @@
 #' Linear Regression Model
 #'
 #' @description train a linear model
-#' @param formula describle model formula
-#' @param data data build from data.frame
+#' @param formula describle model formula, such as y ~ x
+#' @param data data build from data.frame, such as data.frame(x = x, y = y)
+#' @param subset Specify a subset, such as fitting only the portion of
+#'              data greater than 0 in the dataset.
+#' @param  save_model whether save model. Default TRUE
+#' @param weights Optional vector of case weights.
+#' @param na.action Function indicating what should happen when the data contain NAs.
+#'                  default na.fail, optional is [na.omit, ]
+#' @param model_save_path User define the model save path.
 #' @return return a list consist of coefficiences and formula
 #' @export
-lin_reg <- function(formula, data) {
-  m <- model.matrix(formula, data)
-  y <- model.response(model.frame(formula, data))
-  # Solve for coefficients using least squares
-  coef <- solve(crossprod(m), crossprod(m, y))
-  # Create a list that holds the model information
-  model <- list(coefficients = coef,
-                residuals = y - m %*% coef,
-                call = match.call(),
-                terms = terms(formula),
-                rank = length(coef))
-  class(model) <- "mylin"
-  return(model)
-}
+lin_reg <- function(formula, data, subset, save_model = TRUE,
+                    weights = NULL, na.action = na.fail,
+                    model_save_path = NULL, ...) {
 
-#' Linear Regression Model
-#'
-#' @description save model
-#' @param mode the linear model you want to save
-#' @param filename the save model file name
-#' @return none
-#' @export
-save_model <- function(model, filename) {
-  save(model, file = filename)
+  mf <- match.call(expand.dots = FALSE)
+  mf <- mf[c(1L, match(c("formula", "data", "subset", "save_model", "weights", "na.action", "model_save_path"), names(mf), 0L))]
+  mf$drop.unused.levels <- TRUE
+  mf[[1L]] <- as.name("model.frame")
+  mf <- eval(mf, parent.frame())
+
+  mt <- attr(mf, "terms")
+  y <- model.response(mf, "numeric")
+
+  if (!is.null(weights)) {
+    if (!is.numeric(weights))
+      stop("'weights' must be a numeric vector")
+  }
+
+  x <- model.matrix(mt, mf)
+
+  if (is.null(weights)) {
+    coef <- lm.fit(x, y)$coefficients
+  } else {
+    coef <- lm.wfit(x, y, weights)$coefficients
+  }
+
+  model <- structure(list(coefficients = coef, residuals = y - x %*% coef,
+                          call = match.call(), terms = mt, model = mf,
+                          na.action = attr(mf, "na.action"),
+                          xlevels = .getXlevels(mt, mf)),
+                     class = "lin_reg")
+
+  if (save_model) {
+    if (is.null(model_save_path)) {
+      curr_dir <- getwd()
+      model_save_path <- paste(curr_dir, "linear_model.rda", sep = "/")
+    }
+
+    # check model_save_path fairness
+    dir_path <- dirname(model_save_path)
+    if (!file.exists(dir_path)) {
+      dir.create(dir_path, recursive = TRUE)
+    }
+
+    # Check if file already exists, modify filename if needed
+    base_name <- basename(model_save_path)
+    pattern <- paste0("^", gsub("[.]", "\\.", base_name), "$")
+    dir_files <- list.files(dir_path, pattern = pattern, full.names = FALSE)
+
+    i <- 1
+    basename_omit <- strsplit(base_name, "\\.")[[1L]][[1L]]
+    while (base_name %in% dir_files) {
+      base_name <- paste0(basename_omit, i, ".rda")
+      i <- i + 1
+    }
+
+    model_save_path <- file.path(dir_path, base_name)
+    print(cat("model will be save in ", model_save_path))
+    save(model, file = model_save_path)
+  }
+
+  model
 }
diff --git a/mypkgs/mylin2024111211/R/predict.R b/mypkgs/mylin2024111211/R/predict.R
index 7da5cdf..33af18e 100644
--- a/mypkgs/mylin2024111211/R/predict.R
+++ b/mypkgs/mylin2024111211/R/predict.R
@@ -1,13 +1,19 @@
-#' Predict via Linear Regression Model
+#' Predict from a Simple Linear Regression Model
 #'
-#' @description train a linear model
-#' @param model describle model formula
-#' @param newdata build from data.frame
-#' @return return a list consist of coefficiences and formula
+#' Predict new responses given the regression model and new input values.
+#'
+#' @param object An object of class "mylin".
+#' @param newdata A data frame or matrix of new input values.
+#' @param ... Additional arguments.
+#' @return A vector of predicted values.
 #' @export
-predict <- function(model, newdata) {
-  x_new <- cbind(1, newdata)
-  # conver x_new to numerical
-  x_new_mat <- as.matrix(x_new)
-  return(x_new_mat %*% model$coefficients)
+predict.mylin <- function(object, newdata, ...) {
+  # Extract the coefficients
+  coef <- object$coefficients
+  # Create the model matrix for the new data
+  mm <- model.frame(object$terms, newdata)
+  x <- model.matrix(object$terms, mm)
+  # Compute predictions 
+  predict <- x %*% coef
+  predict
 }
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/R/print.R b/mypkgs/mylin2024111211/R/print.R
index 4689cce..e7c3f68 100644
--- a/mypkgs/mylin2024111211/R/print.R
+++ b/mypkgs/mylin2024111211/R/print.R
@@ -1,10 +1,14 @@
-#' Linear Regression Model
+#' Print a Simple Linear Regression Model Summary
 #'
-#' @description train a linear model
-#' @param model the model you want to display
-#' @return return a list consist of coefficiences and formula
+#' Prints a summary of a simple linear regression model.
+#'
+#' @param x An object of class "mylin".
+#' @param ... Additional arguments.
 #' @export
-print.lm <- function(model) {
+print.mylin <- function(x, ...) {
+  cat("\nCall:\n", deparse(x$call), "\n\n")
   cat("Coefficients:\n")
-  print(model$coefficients)
+  print.default(format(x$coefficients), print.gap = 2L, quote = FALSE)
+  cat("\n")
+  invisible(x)
 }
\ No newline at end of file
diff --git a/mypkgs/mylin2024111211/man/lin_reg.Rd b/mypkgs/mylin2024111211/man/lin_reg.Rd
index c28f36c..06afc0d 100644
--- a/mypkgs/mylin2024111211/man/lin_reg.Rd
+++ b/mypkgs/mylin2024111211/man/lin_reg.Rd
@@ -4,12 +4,48 @@
 \alias{lin_reg}
 \title{Linear Regression Model}
 \usage{
-lin_reg(formula, data)
+lin_reg(
+  formula,
+  data,
+  subset,
+  weights,
+  na.action,
+  method = "qr",
+  model = TRUE,
+  return_x = FALSE,
+  return_y = FALSE,
+  qr = TRUE,
+  singular.ok = TRUE,
+  contrasts = NULL,
+  offset
+)
 }
 \arguments{
-\item{formula}{describle model formula}
+\item{formula}{describle model formula, such as y ~ x}
 
-\item{data}{data build from data.frame}
+\item{data}{data build from data.frame, such as data.frame(x = x, y = y)}
+
+\item{subset}{none}
+
+\item{weights}{none}
+
+\item{na.action}{none}
+
+\item{method}{which method is used to fit linear model.}
+
+\item{model}{whether return a model class.}
+
+\item{return_x}{whether need to return designer matrix. default False}
+
+\item{return_y}{whether need to return response variable . default False}
+
+\item{qr}{none}
+
+\item{singular.ok}{none}
+
+\item{contrasts}{none}
+
+\item{offset}{none}
 }
 \value{
 return a list consist of coefficiences and formula